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.
Questions related to Cohort Studies
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.
I want a brief explanation for the new castle Ottawa scale for cohort studies. I cant understand the comparability part of this scale
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.
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!
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
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
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.
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)
- 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!
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!
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.
I plan to conduct prospective cohort study but there have not been similar studies with my proposed title.
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
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.
I am doing a retrospective quantitative cohort study. Need assistance in calculation a sample size.
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?
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?
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?
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 !
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.
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?
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?
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?
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!
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?
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?
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?
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?
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?
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.
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?
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?
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?
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.
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.
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
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!
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)?
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?
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).
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).
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!!!!!
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
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.
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:
- Whether a data differences (x'-x) could be directly used in logistic regression?
- 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.
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)?
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.
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.
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.
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?
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.
in research, we need to know the limit number of enrolled participants for successful analysis of data especially in a cohort study
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
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.
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:
Thibault Fioletbut 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?
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?
The answer will help me in understanding how to critically use evidence in public health
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.
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?
example prolonged second stage of labor and and demographics factors with fetal and maternal outcome.
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
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.
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
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
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.
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
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.
Note: bold and Italicized words/statements are modified items
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!
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.
What assessment tool(s) do you recommend to assess prospective cohort studies and retrospective studies for a systematic review Thanks
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.
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?
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.