Science method

Cohort Studies - Science method

Cohort Studies are studies in which subsets of a defined population are identified. These groups may or may not be exposed to factors hypothesized to influence the probability of the occurrence of a particular disease or other outcome. Cohorts are defined populations which, as a whole, are followed in an attempt to determine distinguishing subgroup characteristics.
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I want to do a comparative study between two places for duration of 5 years? what is the name of study design? Is it comparative prospective cohort study?
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Hi,
Your question's last statement itself is the answer. You can refer to STROBE guidelines for the components of such studies:
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How to calculate sample size for cohort study for the first time? if there is no previous cohort study?
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Pilot study needs to be done to get Mean and SD
And then you can use the below link
Statulator – Simple | Free |
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I want to know if there are alternative design to longitudinal cohort to study changes(ex.in air pollution and type 2 diabetes incidence)?
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We know classical prospective cohort study is an analytical study . But can a cohort study be there without any comparison group and what would be the strength of association in this case?
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Good afternoon..No cohort with no comparison group
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I'm looking for studies and experiments done on human subjects to measure their mental stress and how that can connect to the development of musculoskeletal disorders.
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Studies on fibromyalgia involve the cognitive aspects
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I want a brief explanation for the new castle Ottawa scale for cohort studies. I cant understand the comparability part of this scale
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The Ottawa Hospital has resources that provide concise explanation on using the Newcastle-Ottawa Scale (http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp). In here, I think that the coding manual they shared (http://www.ohri.ca/programs/clinical_epidemiology/nos_manual.pdf) addresses your question succintly. These can be found easily through Google searching.
If you find the explanation wanting, please read on confounders/confounding in research from any epidemiology/research design textbook.
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A case-control study has been used to construct a prediction model and there is no cohort study to calibrate it.
What solution do you suggest for calibration?
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Have you used the regression model? For example, logistic regression model?
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Greetings,
We have been conducting a retrospective cohort study. The variables we are examining are assumed to be very age-dependent and the exposed population is very small (~40 patients), therefore we have considered matching for age and sex at a 1:2 or 1:3 ratio to increase statistical power and limit confounding.
Which statistical test would be most appropriate for calculating risk ratios for dichotomous categorical variables?
This article ( https://academic.oup.com/epirev/article/25/1/43/718675 ) suggests conditional Poisson regression, which I have attempted in Stata, but it appears to work only for 1:1 matched pairs.
It also suggests an adjustment of Cox regression so as to yield the same results as conditional Poisson regression (" if the time to death or censoring is set to some arbitrary constant value and if the Breslow or Efron methods are used to account for tied survival times, the results will be the same as those from conditional Poisson regression, as the likelihoods for these methods are identical when the data come only from matched pairs ").
I have recently attempted a similar adjustment (as described here: https://www.ibm.com/support/pages/conditional-logistic-regression-using-coxreg ) to yield the same results as conditional logistic regression (odds ratio) for a 1:N matched case-control study using Cox regression.
If such an adjustment is possible, how exactly could it be implemented in SPSS? If not, what other alternatives are available to us in this juncture?
Thank you in advance.
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Maybe not useful for the original question anymore, but still good to know: a very detailed exposition on how to perform a matched cohort analysis is present in Kleinbaum's Logistic Regression 3rd edition. It gives the specifics on how to do it in SPSS using the Cox regression module in the Appendix. I used it some years ago, and it works (obviously). Hope this helps.
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Hello everyone. I and my team are in the process of designing a retrospective cohort study. I am wondering if there is a need to register a retrospective cohort study (for transparency), and if so, are there any appropriate free databases (analogous to ClinicalTrials.gov) that would accept such a study. Thank you all!
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you can share your study protocol via your Researchgate or other academic and scientific pages or try the FDA system for registry of cross-sectional, case-control, and retrospective study designs (the system gives you a code like the RCT registry code but it's acceptable/valid for all journals and reviewers). However, as you know, there is no obligation for early protocol registration of such studies (before running the study). At this time, clinical trials and systematic reviews/meta-analyses are the most important studies there is sensitivity and obligation on their prior protocol registration. So, if you don't register your retrospective study protocol (in prior) it's ok! But if you will register it, it's a valuable point for your study. And the last but not least; if you will register or share your protocol before running the study, you should determine your primary and secondary aims and statistical analysis. So you should follow the pre-determined rules and aims (as you have registered in your protocol) in your manuscript/article. Also, if you want to change your way different from your registered protocol, you should justify it for reviewers of your manuscript.
Good luck!
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How can I improve the detection bias in these studies?
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How can I use NOS for assessing a nested case-control study? Should I assess all the primary cohort studies?
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Dear All
I am looking for a review which is interested in publishing protocol design. However, my study is not a random clinical study but a cohort study.
thanks in advanced for the help
Best regards
Karim
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Plos One, BMJ and trial
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Dear Sir/Madam, my question is that as we know we use PICO model in Systematic Literature Review (SLR) mostly for Randomize Clinical Studies. so what model or approach should we use for Observational cohort studies, or case studies or in qualitative research or any other study design? Thank you in advance for kind guidance
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Thanks all of you for your kind answers. I got it because of your support. Thank you guys ,have a blessed life
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A salam,
Dear SIr/Madam
I have a question , as we know that mostly we use PICO model or approach for clinical trials in conducting systematic Literature review, so what model or approach we use in observational cohort studies or case studies, or in qualitative research or any other study design. thank you very much for advance for your kind answer.
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You can modify the PICO model into PECO model (E stands for exposure) while developing your research question for observational cohort design. Here C is considered as the 'unexposed' group. For example, we want to check whether 'attending a health education session on HPV vaccination' is linked to the 'outcome (eg correct knowledge about need and timing of HPV vaccination)' among a 'population of mothers of 8 year old girls'. Here we use PECO in following manner to develop our research question:
P - mothers of 8 year old girls
E - 'attending a health education session on HPV vaccination'
C - not attending this health education session (though invited)
O - correct knowledge about need and timing of HPV vaccination (captured by using a HPV vaccination knowledge scale)
Research Question: Does attendance in a health education session on HPV vaccination improve the correct knowledge about need and timing of HPV vaccination among mothers of 8 year old girls (as compared to not attending this health education session)?
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In my project, I have many variables but a very small sample (non-parametric). I'm trying to prove a link between two variables while correcting for covariates. In short, we are looking at white matter tracts and their links to visuospatial function and quality of life (QoL). We are analyzing 3 tracts, which can either be normal, displaced or ruptured (ordinal) and we have 8 scores for visuospatial abilities. Let's define variables:
  • Y1 = tract 1 integrity (3 categories)
  • Y2 = tract 2 integrity (3 categories)
  • Y3 = tract 3 integrity (3 categories)
  • X1 to X8 = visuospatial test scores (quantitative discret, could be dichotomous with pass/fail)
Others:
  • X9 = QoL score (quantitative discrete)
  • X10 ... = age, gender, etc. (these covariates are not important for right now, I'll had them in my study latter)
1) The thing is that while we are looking the links between the integrity of the first tract and the visuospatial function (Y1 and X1-X9) for example, there is a possibility that the second tract (Y2 and/or Y3) is also affected and thus, Y2 might be the one responsible of the deficit and not Y1 (I need to correct for Y2 and Y3 to prove that Y1 is responsible alone). I was thinking of logistic regression, but I'm not sure how to treat Y2 and Y3.
2)There is a second part to the project. Each patient undergo a surgery and we want to compare if the change in the integrity of the tract (Y) correlates with the change in visuospatial capabilities (X) pre and post surgery (so if a white matter tract is repaired, does visuospatial function return and conversely, if we disrupt a tract in surgery, does the visuospatial function decrease?). So it's like a repeated mesures (2 times), but I still have the same issue with Y2 and Y3 vs Y1 (if Y1 is repaired, but Y2 is not and Y3 is ruptured during surgery for example)... I was thinking about a generalized estimating equation (mixed model), but still not sure how to treat my variables.
3) We also want to do 1) and 2) with QoL instead of visuospatial tests (I guess I'll use the same test)
I had the idea of categorized differently, but my sample will probably be too small to do this:
Y1 = tract 1 only
Y2 = tract 2 only
Y3 = tract 3 only
Y4 = tract 1 and 2 (but how can I capture the difference between 1 displace and 2 rupture vs both rupture or displaced?)
Y5 = tract 2 and 3 (id.)
Y6 = tract 1 and 3 (id.)
Y7 = all tracts
I looked into non parametric test (such as Mann-Whitney, Kruskal-Wallis, Pearson, tau, etc.) but none can be applied to my research (many variables interact with each other...)
Thanks for your help!
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sorry for the vague term, I used simple term to explain to someone and forgot to edit it in this post (and English is not my first language, sorry!). I want to find the correlation (or prove there is a relationship) between my variables. My hypothesis is that when Y1 is normal, visuospatial tests are normal and as we go to displaced and ruptured, the visuospatial tests are getting worse (probably linearly). After, if we find a correlation between the integrity of the tract and visuospatial skills, I want to show that if we ´´repair’´ the tract during surgery, the visuospatial skills are reversible and the patient will score better on the test. I have some background in stats, but not in biostats and the statistician I contacted isn’t either…
exactly, in the litterature those 3 tracks have some unclear link to visuospatial abilities, so that’s why we chose them. They should be independent (meaning that when only y1 is rupture for example, there is a decrease in visuospatial score, same for y2 and y3 taken alone). But, if y1 and y2 are ruptured, I want to show that there is even worse score
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Hello fellow researchers,
i’m writing my PhD thesis and i need a little help with the statistic part. Without getting into great detail my study is as follows: during a 6 month period an expert was giving his advice on therapy options on our ward (all patients, lets say group A). The next 6 months the expert is gone, but the ward staff continued to make the therapy options accordingly to what they learnt from the expert (all patients, group B). I want to show that the the primary endpoint was unchanged, basically there was no difference between outcomes, thus proving the sustainability of a temporary expert supervision. I would like then to compare these 2 groups as a whole with a historical group were the old therapy was in place (before the expert was even there). How can i make that?
Thank you in advanced!
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It is confounded by time. The patients coming in may have been systematically different (particularly if done during COVID). So, short answer is NONE.
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The study aim investigated the relationship between meal frequency and timing with changes in BMI. Based on the cohort study data of meal frequency obtain during last follow up and changes in bmi comparing bmi at baseline and last follow up.
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retrospective cohort study,as you went back to study relationship between exposure and outcome
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I plan to conduct prospective cohort study but there have not been similar studies with my proposed title.
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In the equations to estimate sample size, you are required to have prevalence of the outcome in exposed and unexposed group; which you can get from any related study from similar setting else where. Or you assume OR of 2. But in either case you need to have an estimate of the prevalence of the exposure in the population to decide on unexposed:exposed ratio.
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I am writting a metanalysis, the relation of My exposition and outcome variable is present in Cross sectional, case - control and cohort studies, also in clinical trials. These studies have diferents ways to calculate association measures
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Thanks to Proloy, Robert and Vasileios for your answers!
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Hi all, thank you for your help previously. My study currently narrowed the article list to two RCT and three open-label cohort study. However, one of the cohort study is an extension study of the other cohort study with terms of including patients from the original trial and new participants. Similar outcomes are measured, can I include it in my meta analysis as the ideal number for meta-analysis would be at least five studies.
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You could include the extended cohort as it is the result of your article screening process. However, you should properly mention it in your result, as you explaining the characteristics of each included study. It won't (supposedly) affect the desired outcome nor giving another bias, as long as you are certain all the samples are subjected to similar conditions or interventions (similar starting point). If it were indeed different, you might want to try subset analysis.
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I am doing a retrospective quantitative cohort study. Need assistance in calculation a sample size.
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Una muestra es representativa si representa más del 10% de la población estudiada.. Operó para ser confiable en un 95% y a un nivel de significación de 0.05 debes hallar la proporción del evento. P=1915/8592
P= 22%
Por tanto necesitas una muestra igual o mayor a 256 para tener una muestra en el rango del nivel de confianza necesitas
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The impact of an intervention to an outcome of interest was sought to be assessed using retrospective data from two groups of patients - one group exposed to the intervention, the other was not. The catch is that the complete data from the unexposed group were obtained from 2016 to 2017, while those from the exposed group obtained from 2019 to 2020. The study group recognizes the limitations such circumstance has on the quality/validity of the evidence presented.
All of these being said, would you still consider the design being retrospective cohort, when there is temporal difference between the data obtained for the two groups?
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Provided there was no temporal effect on the confounders and no significant environmental change between the two time periods (eg Covid, heatwave,fire, flood ,war) I would say you could regard it as retrospective cohort study.
.There is a one year follow up period for the exposed and unexposed group but it is a different calendar year for each.
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I'm currently doing a meta-analysis and I came across a study in which there's a cohort with 1201 patients, 107 were on a particular drug and 1094 were not on that drug. Then they further did 2, 1:1 case-control analysis matching. In total, we have 3 datasets one from the original cohort and 2 from the 1:1 matches so can we use all these 3 datasets separately in our analysis?
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By using all the datasets from the single cohort study haphazardly, you will problematically duplicate or perhaps triplicate the statistical weight contribution of that study in your systematic review. It is your responsibility to assess which of the three analyses - the cohort, case-control #1 or case-control #2 - is uniquely relevant for your review. It cannot be all three. The more important component of the term "meta-analysis" is "analysis" - don't just pool data just for the sake of it.
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I am currently writing a manuscript of a cohort study. In order to strengthen the discussion and better provide the evidence, I would like to incorporate current cohort study data into a meta-analysis. This would be possible because the demographic of my present study subjects is different from any published studies. However, I wonder whether I should publish the cohort study first, then make a meta-analysis or I can directly combine both designs into one article?
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You can, and I notice that this practice was quite prevalent in the past. Consider the following points:
1. Making a single publication (your cohort study together with a systematic review & meta-analysis [SRMA]) will make your work more loaded with content. However, I foresee that aiming for a single publication will take more time compared to publishing first the cohort study.
2. An SRMA is not simply about pooling common outcomes and data across included studies - it is a distinct endeavor in itself, with rationale, methods, results, discussions (with limitations) and conclusions. I fear that in your attempt to generate a single work from the cohort study and the SRMA, you will inadvertently put more weight in the cohort study and "relegate" the SRMA as if it is a minor part of the discussion. This is problematic. You have to be as detailed in your cohort study reporting as you did your subsequent SRMA. In doing so, it may be difficult for future readers to digest/understand your work as they are basically dealing with two studies in one paper.
3. If you are faculty member, it would be more advantageous for your promotion/tenure if you publish the cohort study and the SRMA separately - consider the weight of 2 publications versus 1 publication only. While in a lot of cases this will be considered "devious," I think that this will be acceptable in this context as an SRMA is a legitimate, standalone research endeavor on its own.
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I am looking for the right statistical test to use for a matched cohort (individual matching 1:2 or 1:3) with continuous outcomes.
All the articles I find on this subject recommend the use of conditional logistic regression but they all concern case-control studies with binary outcomes. In my case, I have 2 groups of participants and I match each participant of the experimental group with 2 or 3 participants of the control group. I choose this design because my N is really small and I want to increase the statistical power. I want to see the effect of group membership on a continuous outcome (an amplitude in microvolt). What type of test should I use?
Thanks in advance !
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Do you think only matching for a few covariates can eliminate confounding bias? If not, besides the exact matching, you can consider propensity score matching. Once you achieve the covariate balance between the treated and control group, then run the outcome model (e.g., linear regression for your case). Since there are multiple matched controls for each treated subject, you should account for it. You can use the robust standard error, but the better approach could be bootstrapping.
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I am trying to analyze some questions for infants. I want to find some questionnairs used by birth cohort study or some commonly uesd questionnaires of infants.
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Hi,
Bayley Scales of Infant and Toddler Development, however, requires country-specific standardization.
Probably you can look at the cross-references from the references given below
Wu W, Qu G, Wang L, Tang X, Sun YH. Meta-analysis of the mental health status of left-behind children in China. J Paediatr Child Health. 2019 Mar;55(3):260-270. doi: 10.1111/jpc.14349
Cook F, Conway LJ, Giallo R, Gartland D, Sciberras E, Brown S. Infant sleep and child mental health: a longitudinal investigation. Arch Dis Child. 2020 Jul;105(7):655-660. doi: 10.1136/archdischild-2019-318014
Wozney L, Radomski AD, Newton AS. The Gobbledygook in Online Parent-Focused Information about Child and Adolescent Mental Health. Health Commun. 2018 Jun;33(6):710-715. doi: 10.1080/10410236.2017.1306475.
Stewart SL, Hamza CA. The Child and Youth Mental Health Assessment (ChYMH): An examination of the psychometric properties of an integrated assessment developed for clinically referred children and youth. BMC Health Serv Res. 2017 Jan 26;17(1):82. doi: 10.1186/s12913-016
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I have calculate annual event rates for a longitudinal population. Each year my study cohort characteristics are changing and I re-capture the changes at the beginning of the year, all these changes contribute to a single risk score which is also re-calculated at the start of each year. The distribution of cases over risk scores are changing on yearly basis.
I was wondering what is the best way to adjust the annual event rates for population risk?
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Fatemeh Torabi Prevalence and cumulative incidence to measure risk ratio
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I'm conducting a meta-analysis of the prevalence and risk factors of pancreatic exocirne insufficieny (PEI) after pancreaticoduodenectomy.
All of the studies that I've included that report the prevalence of PEI are cohort studys. So, I've assessed their risk of bias using the Newcastle Ottawa scale.
Some of them, in the same study, perform a nested case-control study to analyse risk factors of PEI after pancreatoduodenectomy.
My question is, do I have to assess the risk of bias of the nested case-control study that is included in the original cohort study? This meaning, a unique article would have to different assessments of bias, as it includes two different types of study designs.
Or should I assess the risk of bias of the global study? In this case, how do I do it, as it has two different designs?
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Best to use a tool for prevalence studies as you are extracting prevalence information and the design is not really relevant
A tool of interest would be
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I was wondering how do you provide feedback to participants (wich support: letter email, acces to website) and especially how do you provide feedback for participants of a longitudinal cohort study, if studies part of the cohort have been published but you are still recruiting for the big study?
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Hi Elisabeth. Take a look at EUPATI guidance (other valuable resources available at the website): https://eupati.eu/resources/patient-engagement-roadmap/
Hope this helps.
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I am currently analyzing lipidomic profiles in a cohort study. I have seen some papers adjusting for total HDL and LDL concentrations, but others do not. I am wondering if there are any particularly strong reasons to either adjust or not adjust for them?
When adjusting for it, does this mean you find the effect of unbound lipids or is it more similar to a clustering correction?
I am relatively new to this field, so I would appreciate to hear your opinion about this!
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Another point to consider is if the two groups are matched for HDL-C and LDL-C levels (these being significantly different or not). If they are matched, I would be much less worried about further normalisations. However, it also depends upon the question you are trying to address.
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A few days ago a colleague of mine made me think about the impact the COVID19 crisis will have on cohort studies. Especially those focused on causes of mortality and the elderly will be deeply impacted by the number of deaths due to the pandemic.
Is this something manageable?
How big is this matter in your mind?
How can this be handled?
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Well, the cohort study is highly suggested in covid pendamic. however, the covid-19 is no more a cohort situation it is done for observation and per and post-test by training and other medication.
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The RoB 2 tool assesses the risk of bias in randomized clinical trials and the ROBINS 1 tool in non-randomized studies of interventions, such as cohort studies, case-controls and non-randomized clinical trials. But my question is, if there are clinical trials and cohort studies that do not have a control group, can the ROBINS 1 tool be applied? Or is there a more suitable tool?
Thank you.
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can we do cohort study for rare exposure only? if no why
and how do we calculate sample size for prospective cohort study if there is previous similar study done on the topic? is there a rule of thumb to take the %of outcome in exposed and un-exposed group?
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no. a cohort study can be done can be done for all exposures.
a simple power /sample size calculator is given at
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I noticed from several articles saying that case-controlled study is more bias prone than cohort study (in the context of cancer early detection using molecular marker). I spent sometime to try to rationalize the idea (I guess it has something to do with case-controlled study having more risk of carrying confounder?) and could not find the logics. Can someone some comments on this?
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Cases and controls are often not matched for life status. In mortality studies controls tend to be live whole cases have died. Live controls have more information about past exposures than relatives of people who have died. For example if work with asbestos brakes is an exposure of interest, relative are far less likely to know about occ changes than are actual cases or controls.
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If I understand correctly, retrospective cohort study collect data by recall, just as case-controlled study. Both of them suffer from recall bias. What is the pros of retrospective cohort study over case-controlled study?
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In short, retrospective cohort studies allow researchers to examine the risk associated with the exposures and express them as relative risk (RR), while case-control studies only allow researchers to express the association between exposures and outcomes using odds ratio (OR). Unlike RR, OR cannot be interpreted directly and thus yield lower quality to explain causations underlying the observed effects. I agree with Babak Saravi for the rest of the explanations.
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Hi! Me and my team mates are currently doing a meta analysis on the prevalence of cytokine elevation and its correlation to the severity of a disease. Considering that we will be using cohort studies, I would like to ask what stats can you suggest for this study and how will we assess the heterogeneity in the study.
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As Babak Saravi and Shuaibu Suleiman have already pointed pout: Usually Q and I2 are used when assessing the heterogeneity. However, one has to be cautious when intepreting them. I'll address the problems below.
The problem with Q is that it depends on the number of studies so it's hard to interpret it without having additional information. Researchers often report a p-value for the Q statistic. A small p-value could mean that there is heterogeneity. However, it is well known that p-values depend on the sample size (in this case number of studies). Thus, when analyzing many studies one can get a small p-value even if there is only marginal heterogeneity. In contrast, when only a few studies are available one could get a large p-value even if there is a considerable amount of heterogeneity.
The problem with I2 is that it only indicates the RELATIVE amount of true heterogeneity (as compared to the error variance). Babak Saravi cited the threshold of 75%, which is often interpreted as high heterogeneity. However, it is not recommended to evaluate the heterogeneity solely using the I2 value. Why? I'll illustrate it with a short example.
  • Consider a meta-analysis where the true heterogeneity (tau) equals .01 and the error heterogeneity equals .005. In this case I2 = 80%. [.012/(.012+.0052)]
  • Now consider a meta-analysis with tau = .10 and error = .05. We get the same I value I2 = 80%. [.102/(.102+.052)]
  • Can we really say that we have a high amount of true heterogeneity in both scenarios? No. We have a high RELATIVE amount (relative to the error heterogeneity). In order to assess the absolute amount of heterogeneity it's good to interpret the tau value (true heterogeneity) on it's own.
  • You're estimating the prevalence in your meta-analysis, so a tau value of .01 would mean that it's normal that the prevalence estimates vary by 1% between studies or within studies. In many cases it's not a big deal. A prevalence of 74%, 75% or 76% is huge and a difference of 1% doesn't seem to be noteworthy so many people would argue that there is no considerable heterogeneity (it depends on the context!). However, consider the 2nd scenario. If the prevalence estimates vary by 10% (e.g., 75%, 65%, 85%) then most people would agree that there is a lot of heterogeneity.
  • Some statistical packages report the true heterogeneity as variance (tau2). In this case it is recommended to take the square root in order to interpret the heterogeneity on the scale of the chosen effect size (e.g., prevalence).
  • One of the researchers that popularized I2 (Julian Higgins) has coauthored an article dealing with the wrong interpretation of I2: DOI: 10.1002/jrsm.1230
Summing up, please do not rely solely on Q and I2 when assessing heterogeneity. It can lead to wrong conclusions. Consider interpreting the value of true heterogeneity (tau) too.
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As cohort study usually has exposed and unexposed subjects with specific outcome of interest which can be incidence of a particular disease, is it suitable to study disease prevalence using cohort study and how?
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Thank you @ Gulam
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What exactly is the main difference between longitudinal and cohort studies? I'm going to examine the effect of some variables during pregnancy on birth outcomes and then child's growth. Can someone help me decide whether my study is a prospective longitudinal or cohort study?
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Longitudinal and cohort studies follow the same group of individuals over time. They allow researchers to compare outcomes according to exposure to suspected risk factors by creating a hypothesis to study. The strength of a cohort study is that it is the best way to determine the causes of a disease, condition or issue because it allows researchers to follow one group of people over time and allows researchers to study multiple outcomes at one time.
Numerous limitations exist in conducting longitudinal/cohort studies. They are costly to perform, a large sample is needed for rare outcomes (e.g. mortality), and loss to follow-up is common. Given the inherent time frame required of this approach, it can be very challenging to employ such a study in conflict and post-conflict settings where conditions can change rapidly.
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In my research, several variables consist of 2 items. For example, one is named as "screen time". the items are:
1) "On a normal weekday during term time, how many hours do you spend watching television programmes or films?"
2) "On a normal weekday during term time, how many hours do you spend playing electronic games on a computer or games systems, such as Wii, Nintendo D-S, X-Box or PlayStation?"
I have data from 4 different samples (cohort study) and the inter-item correlation scores are ranged from .18 to .28. The Cronbach alpha scores are lower than .5.
Do you think it is enough to use the inter-item correlation for the reliability or what else can I do?
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Cronbach's alpha score above 0.7 is recommended.
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>500 in the sample
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Why you don't try Matlab
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Dear researchers,
I would like to get your response on how to estimate the minimal sample required for a longitudinal study.
We are currently working on mental health status of medical students in a medical college before COVID-19 outbreak and in the present scenario (longitudinal study). The total number of students participated in the before covid-19 survey was 276 (out of the total 300). We have epi info (statcalc). If i choose cross-sectional/cohort design in statcalc, what should be the exposed and unexposed group? My understanding is that depression in medical students will be the exposed group and that in the non medical (college) students will be the non exposed group. Am I right or should i opt for other options(designs) in statcalc like population survey?
Your suggestions and answers are highly welcome.
Thank you
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Dear Saikarthik Jayakumar,
another usefull online tools are OpenEpi and powerandsamplesize.com. Both provide specific calculation each study design and provide basic knownledge about power and sample size calculation itselfes and about how to use the tools.
You can reach them via the following links:
Good luck and kind regards!
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Hi friends and colleagues, 
Does any of you know of functional directories that synthesize cohort studies from around the world? I am looking for a source to find past and ongoing international cohort studies - both for children and adults.
Your help is greatly appreciated.
Thanks, 
Haneen.
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Hi Haneen,
Have a look here - perhaps helpful in your search:
Good luck :)
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I am conducting a comparison cohort study between the hysterectomy group and non-hysterectomy group among patients with placenta accreta spectrum. The main outcome is the health-related quality of life scores, using SF-36 questionnaire and one another obstetrics-specific HQoL scale. But I'm not sure about how to calculate the right sample size for the two groups. can anyone help me solve this situation?
Thank you a lot for reading and sharing
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I still confused with the answer , if we tried to do cohort case control study .... witch equation we use to calculate sample number and controls number ?
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I have a dataset comprising clinical characteristics and glycomics profiles of disease samples (n=100+) and matched healthy controls. So far, I have been able to do classification modeling to discriminate between disease vs healthy using the biomolecular profiles.
On top of that, I have glycomics profiles collected across 2 more time points (total 3 time points - > timepoint 1 = time of discharge, timepoint 2 = 1 month follow-up, timepoint 3 = 6 months follow-up) from the disease patients. However, the healthy controls only have their glycan profiles measured at 1 time point (this is because no follow-up assessments and blood taking were done for the healthy controls).
My question is this: What kind of statistical analysis can I perform to draw meaningful insights on how the glycan profiles change across the 3 timepoints? I was originally thinking of survival analysis, but only a handful of patients out of the 100+ samples had adverse outcomes. So I question the applicability of that. Other than that, are univariate or multivariate tests to determine significant differences in the biomolecular profiles between each time point the only thing I can do?
I apologise for the lengthy question and appreciate any advice given!
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As the samples are acquired from the same patient across time, the ideal thing would be to perform a paired analysis (repeated measures ANOVA or a non-parametric equivalent). However, the levels of the factor time (the timepoints) are required for every sample group, which are absent in controls). Therefore, I'd move to a combination of pairwise comparisons (t test or Mann-Whitney, depending on data normality), at the expense of sacrifizing the pvalue of interactions. However, post-hoc corrections as FDR may penalize less the false positives as variables in your glicomics data are considered in individual pairwise comparisons, while the correction initially should correct for all variables tested in the study.
Hope this helps!!!
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For quality assessment of observational studies, as done in a systematic review, a number of scales exist. The major ones are ROBINS-I, Newcastle Ottawa Scale, and Appraisal Tool for Cross-Sectional Studies (AXIS).
1. Which of these is best validated and the most widely used?
2. Should there be a field-specific preference for either of these (for instance, medicine vs nursing vs psychology)?
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Any validated tool can be used but I personally believe that Newcastle Ottawa Scale is quite handy for observational studies.
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My colleagues and I have applied for a project using the comprehensive cohort design or "Patient Preference Trial". A reviewer has now asked how high the percentage of study participants who agree to randomisation must be in order for the study to have a sufficiently high proportion of randomly distributed participants. Can this question be answered?
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Many thanks for the helpful feedback!!!
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I have seen from some review articles which used the EPHPP or the Newcastle Ottawa Scale (NOS), but I can imagine that these are mainly used for clinical studies (cohort studies, case-control studies, etc.). I did a check on the keys used for the assessment to confirm it.
My systematic review however is not related to clinical studies. I am working on a review in the field of environmental pollution, particularly PAH pollution (which reports on the analytical techniques and the PAH concentrations in the different media), and I need to assess the quality of the already selected list of papers (after using PRISMA guidelines or checklist).
Thank you!
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Dear Samuel,
Hello,
First of all, I would like to thank you for the sake of this interesting discussion!
Although many of important criteria have been addressed by the friends participated in this discussion, I would like to add that novelty of the idea, good review of the literature, accurate research method, clarity and presentation of the work, and usefulness of the article contribution can also taken into account.
All the best,
Ali
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After performing a systematic review we are looking to assign strength of evidence score using an existing system for cohort studies. Any suggestions for acceptable tools (such as GRADE) would be welcomed - particularly one that a practitioner audience would understand (something simple and straight forward).
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The tool you use will depend on the study designs. Different study designs require different tools. Some existing tools (i.e. GRADE) gain popularity not because they are better than others. "Today" it is GRADE, "tomorrow" it will be another popular tool. Some tools are more subjective than others. I prefer more objective tools. One easy and recommended tool for longitudinal designs is NOS: Newcastle-Ottawa Quality Assessment Form for Cohort Studies. Another tool is the RoB (Cochrane risk of bias tool) which is more for randomised or controlled studies.
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Can any one tell how to determine minimum sample size in a prospective cohort study when the cohort is not divided into exposure and non exposure groups? Thank you!!!!!
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Dear Behailu I have the same problem you had. I can't find any formula or software to calculate the sample size for a retrospective single-arm cohort study (not divided into exposed and unexposed cohort) in which the population is finite. Would you be so kind as to tell me how you solved this problem? I would greatly appreciate it.
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for e.g. lets say, out of 14 question, if we give 1 point to 3 question , 0 point to 1 study, other 10 questions are "not applicable" to this study. what is total score and what is conclusion
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It's not logic to have 10 not applicable questions,
Anyway,
Yes>>1
No and NA>> 0
0-4 points= poor
5-9= fair
10-14= good
QA is summarised as fair, good, fair as a column in characteristics table
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I want to conduct a retrospective cohort study using an health exam database.
- Each participants had 2 data in the study
(Baseline: y, x1, x2...)
(Follow-up: y', x1', x2', x3'...).
- Outcome(y, y') is categorical (in my case, "hematuria(+) or hematuria(-)").
- Independent factors (x1, x2,...) could be continuous or categorical.
(ex: BP, weight, ALT, Cre, Hypertension,...)
- n= 8000.
Our hypothesis:
For some factor x, "change" matters more rather than the cross-section data
(x_change (x'-x) predicts y').
===========================================
Since there is only 2 timepoints in my study, I simply calculated the differences (x'-x) to make it like a paired-t test and tried to use Multiple Logistic Regression model to find which factors matter. (y' ~ x1_change + x2_change+....).
My question is:
  1. Whether a data differences (x'-x) could be directly used in logistic regression?
  2. Since it is a retrospective cohort study, we would like to corrected bias as possible. As in other cross-sectional study, some factors had been shown related to the outcome, (that is x ~ y or x'~y'), which should be considered in our model. However, if I use x, x', (x'-x) at the same time, it might not fit the basic assumption of regression model (all the variable are random and independent to each other).
Is there any solution to this kind of problem? I've consider other model like GEE but not quitely sure about that.
Thank you all in advance.
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Thank you both for your immediate reply and providing possible ways for my analysis. Your opinions really helps.
I might need some time to have a thorough understanding of these methods, but will try to figure out which fit the study best.
I will update here when I completed it.
Much thanks again!
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We are comparing the incidence rate of two age groups in a cohort study; those under and over 50 years of age. As patients age, they will pass from one group to the next, so their count for some person-years in the first group, and than the second. Is there a way to adjust for the time since they are in the cohort?
In this particular case, we are dealing with HIV patients, so as patients become infected, they will be included in the cohort, and during their follow-up they might have some sort of event due to the infection. What I'm wondering is, if a patient has an event at 52, and counts as 0.5 person-years for the >50, is there a way to adjust for time depending on when this person was infected at 58 or at 30 (so, in essence, to adjust for time since infection)?
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if you take this approach, alternative methods would be to restict study to comparable exposure durations or to use regression modelling with length of exposure as a covariate.
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In prospective cohort study design, in acute setting, where days count, how I decide how often studied laboratory parameter should be measured? The study measuring diagnostic and prognostic value of given parameter.
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It depends on the outcome/disease of the study.
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In this article, we have 2 groups, patients with clefts and patients without clefts. Our outcome is teeth with developmental defects "enamel defects". The authors report this as a "cross-sectional study".
In this article, we have 2 groups, patients with clefts and patients without clefts. Our outcome is teeth with developmental defects or dental anomalies. The authors report this as a "retrospective study" (case-control?)
In this article, we have 2 groups, patients with clefts and patients without clefts. Our outcome is teeth with dental anomalies or developmental defects. The authors report this as a "cohort study"
My question is why is the study design different, if in all cases we recruit patients and examine clinically / with radiography to assess the outcome.
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In the past (and sometimes still) clinical researchers sometimes used the term "case control study" to mean comparing outcome in an exposed group to that in a "control" (ie un exposed group) ,cross sectionally. That is why in modern epidemiology (ie since about 1985) the term case-referent study is now preferred .
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CER , can be applied both in pragmaticRCTs (pRCT) and comparative cohort studies. but how to allocate the patients when there are two arms who can take either adjuvant verum or adjuvant placebo with the base of standard care, when there are no studies undertaken on a particular subject. keeping in view the time, money, manpower, rigour, validity involved in RCTs, it is not wise to conduct an double blind RCT at first instance. the first step may be CER-cohort study to generate hypothesis and determine sample size.
should we straight away jump to pilot RCT or begin with CER-cohort study. if the later, what things to be kept in mind while allocating hte patients to two groups with methodological rigour.
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I am writing a proposal for my PhD study. My research is an epidemiological study, which needs large number participants (nearly 3000 subjects) and temporal data collection for the outcome and exposure variables. I planned to use prospective cohort study. However, cohort study by nature is expensive and time taking. While I was reading alternative design for cohort study, I found "CASE-COHORT" study design. But, the design, sample size determination and analysis of the data seems complicated. After several days effort, I am clear of the design and sample determination from the full cohort. But, although several authors say case-cohort design produces similar information as cohort study, I am really in doubt. My major concern is " how the design gives the same information with out 'control' group. I talked to several researchers and colleagues, but no one know about this design. Even after they read articles, they are confused how the design with out control and with such smaller sample size be considered as valid as full cohort study?
Would you please highlight me this issue?
1. Is the design valid and acceptable for academic purposes?
2. Does it work for diarrhea incidence estimation? I couldn't any article on diarrhea done by this design.
3. should the data generated by this design be extrapolated back to the full-cohort?
4. Is there any other financially affordable and less time-consuming design which can give similar findings as cohort study other than case-cohort?
5. If this doesn't work for me, and I decide to use the prospective study, what is the smallest acceptable and valid sample size for a cohort study in my case?
Thank you
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Hello dr Adane,,,
Thanks for this good question. I recommend to check the following brief pdf
Difference between case cohort and nested case control
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Hello,
I need to assess the quality of studies included in a systematic review of the literature based on the tool developed by the National Heart, Lung, and Blood Institute (NIH): Quality Assessement Tool for Observational cohort and cross-sectional studies.
I'm having difficulty answering question #5: "Was a (...) power description, or variance and effect estimates provides?"
In the cohort studies included in my systematic review (N= between 564 and 50,000 participants), how do I know if a description of power or variance and effect estimates was provided? How is this information usually formulated?
Many thanks in advance.
Murielle
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The quality assessment is usually based on the reported data. So, if the author reported that they calculated the sample size or they mentioned the CI for OR, RR, or MD, you can consider that as they calculated the sample size. you can find that mentioned in the study methodology
If you did not find any evidence, so it is unclear and you cannot consider it as 1
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in research, we need to know the limit number of enrolled participants for successful analysis of data especially in a cohort study
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According to the global publishing standard, the least statistically possible number is 100, less than this number is not possible as a result
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i want to observe an outcome difference between the two hand of the same patient. one hand is exposed to a routine intra-operative intervention while the other hand is not. i expect this outcome intra- operatively under anesthesia, and will a prospective cohort study be suitable to study the difference in outcome between the two hands during this short period of time, and if not, what is the more suitable type of observational study
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Would this be more accurately termed a 'case study' if there is just one participant?
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What kind of bias is introduced from consent in cohort studies.
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The informed consent process may over select individuals who are depressed and for some reasons are at high risk for dementia (they may be more eager to participate and to continue follow up) and thus would potentially inflate the association; sort of selection bias. Also if outcome data collectors are not blinded to exposure status, there may be information bias.
On the other hand, depressed individuals may die earlier and thus would not survive long enough to get dementia, which would reduce the association.
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Dear RG community,
I found it a bit confusing on few terms and aspect of study design.
1. From my understanding, longitudinal study is an observational study consist of cohort (prospective or retrospective) and panel study.
How about diary study, is it considered panel study?
How about repeated cross sectional studies that also often referred as longitudinal study?
2. Experimental study consist of before-after (or known as pre-post) or repeated measure interventional study, either randomized or non-randomized. Am i right?
3. For longitudinal study, especially prospective cohort, most references highlight on two group (exposed vs unexposed) with similar baseline and categorical outcome measure.
How about single group cohort with different baseline and outcome continous measure?
4. For longitudinal study especially cohort study, most references talk about months to years of followup duration.
But, for some psychlogical measure such as stress and fatigue, there should be no problem to conduct shorter duration of cohort study e.g. within 8 hours, am i right? For example, we measure baseline stress level prework, and followup the outcome stress messure postwork, i.e. after 8 hours of continous work. Is it appropriate?
.
Thank you for kind assistance.
Your response is highly appreciated.
Regards,
Fadhli
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I have been very interested in the recent paper - Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé)
BMJ 2019; 365 doi: https://doi.org/10.1136/bmj.l1451 (Published 29 May 2019) Cite this as: BMJ 2019;365:l1451
The paper is authored by Bernard Srour and colleagues. I then found:
authored by
Thibault Fiolet
but Bernard Srour was also in the team.
These excellent papers warn about the foods that so many people consume. How can the message be passed down to those who can be helped and how should people be assisted to make potentially life changing changes in their diet and fluid intake?
Mary Wilson
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Unfortunately most of the victims of unhealthy food environment are deprived people who do not have many healthy options. The most plausible approach could be changing the food environment. The systems approach is suggested to address this issue whereby everyone involved i.e. food industry, policy makers, consumers, practitioners etc. are brought together to improve food environment. It is a hard approach but promising. Health education alone is not sufficient to make a dietary change for instance. unhealthy food environment is a wicked problem and challenging.
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on Spss, for a cohort study, should i use logistic regression to assess the association between two categorical variables? or i still can use the linear regression? or chi-square test? which one is the best?
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Slight adjustment to Ms. Cowdhuri's reply. First where she says multiple logistic regression she means multinomial logistic regression. Second if the DV is ordinal you want ordinal logistic regression. Useful set of notes is attached. Best, David Booth
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The answer will help me in understanding how to critically use evidence in public health
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This an old list of criteria to judge the quality of research. The list might be useful to you
Research Evaluation Outline: Criteria for the judging of research work.
Problem
1. Is the problem stated clearly?
2. Does the problem have a solution the way it is formulated?
3. Is it clear whether this is a normative, or a survey or a study to test hypothesis?
4. Is the literature of previous studies adequately reviewed or taken into account?
5. Are the essential concepts necessary to understanding the problem defined?
6. Is the context of the problem described so that what is included or excluded is readily apparent
7. Is the importance of the problem sufficiently developed so that the judgment can be about the suitability of the method?
8. Are the consequences of the possible findings pointed out?
Design
1. Was the design of the study planned and evaluated beforehand?
2. Does the design take into account all the pertinent aspects of study subjects, materials, environment, variables, measurements, and observations "statistical methods"?
3. Is the design clearly presented?
4. Were alternative designs considered and reasons for their rejection given?
5. Are the compromises made with an ideal design described?
6. Is it possible from the design to answer the questions posed?
7. Can the design efficiently solve the problem in terms of money, subjects and time?
Sampling of subjects or materials
1. Is the sample adequately described?
2. Of what population is the sample representative?
3. Is the sample an appropriate one for the purpose of the study?
4. Were the subjects selected according to the design of the study and with regard to the statistical method to be used?
5. Is the size of the sample adequate?
6. Was the sample collected for the purpose of this study or for other purpose?
7. Are the methods of sampling described?
Controls
1. What controls were exercised through sampling?
2. What controls were exercised by selection of setting? By natural habitat?
3. What controls were exercised by experimental manipulation?
4. Were the conditions the same for all subjects or were adjustments made?
5. If controls were changed, were results analyzed with respect to the altered conditions?
6. Are there any important controls missing from the study?
7. Were the usual controls for a study of this type absent, and if so, was the absence justified?
8. Could any additional controls have been included that would have increased the efficiency of the study?
Measurements
1. Are the techniques of measurement adequately described?
2. From the information presented, could the measurement be repeated by another investigator?
3. Are the measurements suitable for the problem?
Data treatment
1. Are the methods of recording and the treatment of data described?
2. Are the statistical procedures described and are they suitable?
3. Are the significant tests described and are they suitable?
Results
1. Are the results or data adequately presented so the reader may verify the author's statements about them?
2. Are estimates of error provided?
3. Have the essential relationships posed by the problem been analyzed and tested for significance?
4. Are the results clearly reported in tables and graphs so that others may use the data or reproduce the results?
Conclusions
1. Does the author draw conclusions about the major problem of the study?
2. Are the conclusions clearly supported by the data?
3. Are important reservations or qualifications pointed out?
4. Are artifacts or spurious relations pointed out?
5. Has the author overlooked important aspects of the results?
6. Are necessary modifications of theory, current interpretations of data or practice pointed out?
7. Are the results interpreted in relation to other published information and is their significance for related fields pointed out?
8. Are the methods used in the study critically reviewed in the light of the obtained results?
9. Are the interpretations, implications for future research, and development of new methods appropriate for the present study, or do they reflect an overestimation or underestimation of the significance of the study?
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Hi,
Currently I am screening articles for my systematic review. Most of the articles are human retrospective cohorts studies and couple of articles have animal (mice) studies. If mice experimental studies meets our interventional criteria, can we include those studies in systematic review?
Your comments will help me a lot.
Thanks.
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If you decide to include both types of studies because they address your research question (as mentioned previously), it may be a good idea to stratify the results by study type. How you will judge the quality of research and interpret effect sizes etc is likely to vary between animal and human studies.
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An observational cohort study involves measuring procalcitonin PCT on a daily basis in a cohort of patients with infection who are on antibiotics. When PCT concentration reaches a level of <0.25 ng/ml it can be taken that on this day that infection has been erradicated and antibiotics could have been potentially stopped. I want to compare the potential stop day against the actual stop day on antibiotics to determine if PCT can be used to reduce duration of antibiotics.
Therfore what statistical analysis do i use? Is it an independent t-test comparing potential stop against actual stop in a patient receiving antibiotics?
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Hi Cahal,
You would be interested in comparing the mean number of days to PCT < 0.25 to the mean number of days of antibiotic use, trying to prove that there is a statistically significant difference between the two.
While this is the same sample, you have two different measures an it seems ideal to make an independent sample comparison, assuming normality.
Regards,
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example prolonged second stage of labor and and demographics factors with fetal and maternal outcome.
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thank you for your answer
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Dear colleagues,
I am currently considering the Newcastle-Ottawa Scale (NOS) to appraise risk of bias or methodological quality of non-randomized studies (e.g., cohort and case-control) for my systematic review. This tool is based on three broad perspectives: 1) the selection of study participants; 2) the comparability of the study groups; and 3) the identification of either the outcome of interest or exposure for cohort or case-control studies, respectively. However, I noticed that the original tool is grounded on population- or community-based evidence appraisal. Since my subjects are all hospital-based, the following ‘Selection’ subcategories (item number 2, Selection of Non-exposed Cohorts; item number 3, Selection of Controls) for cohort-type and case-control studies were modified:
Newcastle–Ottawa Quality Assessment Scale for Cohort Studies
Category: Selection
Item No.: 2, Selection of Non-Exposed Cohort.
Item Purpose: This item assesses whether the control series used in the study is derived from the same population as the cases and essentially would have been cases had the outcome been present.
Original Criteria:
a. Drawn from the same community as the exposed cohort
b. Drawn from a different source
c. No description of the derivation of the non-exposed cohort
Modified Criteria:
a. Drawn from the sameICU/hospital as the exposed cohort (e.g. exposed and unexposed drawn from the same database or group of patients presenting at same points of care from same hospital over the same or different time frame)
b. Drawn from different source (e.g. exposed and unexposed drawn from the same database or group of patients presenting two different points of care from another hospital over a same or different time frame)
c. No description of the derivation of the non-exposed cohort
Newcastle–Ottawa Quality Assessment Scale for Case–Control Studies
Category: Selection
Item No.: 3, Selection of Controls
Item Purpose: This item assesses whether the control series used in the study is derived from the same population as the cases and essentially would have been cases had the outcome been present.
Original Criteria:
a. Community controls (e.g., same community as cases and would be cases if had outcome)
b. Hospital controls, within same community as cases (i.e. not another city) but derived from a hospitalized population
c. No description
Modified criteria:
a. ICU-based controls (e.g. same hospital/ICU as cases and would be cases if had outcome) 

b. Hospital controls, within same or similar ICU-type settings as cases (i.e. not another different ICU type) but derived from another hospital
c. No description
I would like to seek opinions from a randomly select group of international experts with extensive experience in using NOS to validate whether the modified items are appropriate for an hospital-based review. Hope to hear from you.
Thank you,
John
Note: bold and Italicized words/statements are modified items
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Hello Godfrey,
Thank you for your suggestions. I appreciate it. I will take note of this.
Regards,
John
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Dose-repose meta analysis is restricted to three types of studies: cohort studies with cumulative incidence rate, cohort study with person-year incidence rate, and case-control studies. But, is it possible to combine these three types of studies into one dose-response meta-analysis using STATA software? Waiting for answers, thanks a lot!
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Epidemiologically, cohort study and case-control study are different types of observational study. While you may be tempted to combine the outcome (because they were measured similarly), it's will be better to stratify your meta-analysis by study type i.e. separate meta-analysis for cohort and separate meta-analysis for case-control. In doing so you will still have the overall estimates which may probably the same as just combining both cohort and case-control together.
Cheers
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It is a prospective study with pre-post design . First observational data were collected, then, after an application of intervention, post- intervention observation datas were collected and compared.
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It is definitely not a quasi experimental designs since there are no comparison group (control group) but simply a pre-post study designs. For quality assessment tool you can go to this link
There is a specific tool for such study designs.
Best Regards.
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Hello!
What assessment tool(s) do you recommend to assess prospective cohort studies and retrospective studies for a systematic review Thanks
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Currently, I am doing a systematic review and I have few questions regarding that.
1. Can I combine results of cross-sectional and cohort studies if the outcome is given as Odds ratio for both of them? If yes then please give me some suggestions about that.
2. Can I combine the results of case-control studies results if some of them are in OR and some in HR?
I will be really thankful if someone can help me out.
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No OR and RR cannot be combined since they are different ratios.
Yes this is appropriate
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Dealing with cohort study data, I have done logistic regression to find risk factors associated with acquiring a particular disease. I have expressed it as adjusted Odds Ratio and 95% confidence interval. However I am confused as to what should be considered as valid or invalid data?
1. For Odds ratio: I know that below 1 is protective and above 1 is risk. But how much below or how much above? Is 0.5 or 0.8 considered preventive; is 1.2 or 1.5 considered a risk?
2. For confidence interval: What range is considered too wide? If the difference between means is greater than 0.5, does it become insignificant? I have found 95% CI range from 0 -14, which is obviously too large. However, is a 95% CI of 0.5 - 2 considered too large also?
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Thank you your quarries. The odd ratio is greater than 1 and it (1) doesn't lies in 95% CI, this condition it has risk factors. The odd ratio is less than 1 and it doesn't lies in CI,It has protective. It has no specific cut-off point. Second one the length of CI is larger if your sample size is small. If sample size is large then it becomes small. If you want to calculate the small size of confidence interval, you can data handling in Bayesian approach. I attach a article, read it
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Hello All,
I'm looking for a quality assessment tool for case series. The Newcastle Ottawa Scale doesn't seem appropriate, as it was designed for case control and cohort studies, and the ROBIN-1 seems best used for non-randomized controlled trials, not observational studies with no comparison groups. Any suggestions? Thank you for your help.
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Samuel,
Methodological quality and synthesis of case series and case reports
Mohammad Hassan Murad1, Shahnaz Sultan2, Samir Haffar3, Fateh Bazerbachi4
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Can any one tell me what and how general cohort study is conducted?
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A study is classified as a cohort when one or more samples (called cohorts) are followed prospectively and evaluated with regards to the outcome of interest to assess whether the initial exposures (risk factors) of the individuals are associated with the occurrence of the outcome.
You can find distinction online regarding "general cohort" or "special exposure cohorts", in which you follow members of the population in general (think of the Framingham study, or a study that follow residents in a given neighborhood in a city) or a specific population with a very clear characteristic that make them markedly different than the general population: a cohort of soldiers that went to the Iraq war, for example. The distinction between "general" or "special exposure" cohorts is, at least for me, much less important than grasping the concept of what a cohort study is.
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