Questions related to Medical Statistics
We're conducting a research design as follow:
- An observational longitudinal study
- Time period: 5 years
- Myocardial infarction (MI) patients without prior heart failure are recruited (we'll name this number of people after 5 years of conducting our study A)
- Exclusion criteria: Death during MI hospitalization or no data for following up for 3-6 months after discharge.
- Outcome/endpoint: heart failure post MI (confirmed by an ejection fraction (EF) < 40%)
- These patients will then be followed up for a period of 3 to maximum 6 months. If their EF during this 3-6 months after discharge is <40% -> they are considered to have heart failure post MI. (we'll name this number of people after 5 years of conducting our study B)
- Otherwise they are not considered to have the aforementioned outcome/endpoint.
My question is as follow:
- What is the A/B best called? Is it cumulative incidence? We're well-aware of similar studies to ours but the one main different is they did not limit the follow up time (i.e: a patient can be considered to have heart failure post MI even 4 years after they were recruited). I wonder if this factor limits the ability to calculate cumulative incidence in our study?
- Is there a more appropriate measure to describe what we're looking to measure? How can we calculate incidence in this study?
- We also wanted to find associated factors (risk factor?) with heart failure post-MI. We collected some data about the MI's characteristics, the patients' comorbidities during the MI hospitalization (when they were first recruited). Can we use Cox proportional hazards model to calculate the HR of these factors?
Hi We are comparing mortality of two therapies in COVID patients. We have identified 74 patients in our hospital records (details in the attached image). We also have data of vital signs and lab data for these patients taken at different intervals.
Our idea is using logistic regression with mortality/discharge as endpoint, adjusted by patient status on admission.
Is this sample size enough for this kind of analysis? If not, how do you suggest we analyze this data?
The specific queries are;
1. The baseline value in a study is 47.9±13.6 (mean ±SD) and the percentage reduction following intervention is 69.1±14.9 (mean ±SD). How to get the final post interventional SD value by calculating the percentage reduction from baseline SD?
2. The baseline value is 1702.7±876 (mean ± SD) and the average decrease after intervention is 38.27% (95% CI for percent change is -58.59 to -17.94; p=0.0047). How to calculate the final SD value post intervention?
Can anybody please guide us whether it is possible to calculate SD using any formula, with the above available details?
I have two data sets -
1) 10 years of data on what percentage of the total patients in a hospital emergency room are diabetic
2) 10 years of data on what percentage of the total patients admitted in the same hospital are diabetic.
While I can easily compare the above data in the form of two linear trend lines (X-axis - years, Y-axis - Percentage of total patients being diabetic ), I wanted to ask how to statistically compare the two trend lines?
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!
when i search the articles to know the correlation coefficient between two variables, i got only regression equations showing relationship between two variables.
for example: total length of femur= 32.19+0.16 (segment 1)
from this equation can i calculate value of correlation coefficient between total length of femur and segment 1.
I have an enquiry on statistical analysis. I was looking for many forum and it's still cannot solve my problem.
I want to compare means of two groups of data but only with two measurements. For example:
For the case, I would like to compare is it have any significance between two groups. But I couldn't use One-way ANOVA since it only have two measurement.
Hope anyone can help me! Thank you vey much!!!
We wish to test for both true positive instances and true negative instances. As tests are done separately for each diseases with the device, should we consider it as 3 separate tests or should it be considered as single test for the purpose of sample size calculation?
I'm trying to assess severity of symptoms among GI patients pre and post intervention over time (post 12+ months). Dataset provided collected 1000 patients at 7 time-points (pre surgery, 0 - 1 mo., 2 -3 mo., 4 - 5 mo., 6 - 8 mo., 9 - 11 mo, 12 + mo.) with 767 unique patients (who responded only once). Total N for each time point vary between 80 - 185. Data collected used a survey which total score ranged between 0 - 50. scores were then categorized in 3 groups: none, moderate, and severe. Was asked to conduct a group x time interaction. But unsure how do it with unequal time intervals, different participants for each time point, and data not normally distributed. When assessing proportions per group results for moderate and severe show a "U" shape. Only additional data was given was gender and age.
Currently, I've only been able to do chi-square, or fisher as needed. But would like to do additional stats tests and would like ask what would be the best stats to do?
I have limited stats experience and have assess to Prism GraphPad. Any recommendations how to best review the data would be extremely helpful.
I want to teach about Bayes conditional probability.
I would like to use data about the probability of having a number of symptoms for a number of common diseases.
I am surprised that this is not easily found!
Where can I get some data (symptoms probabilities by disease) to use on my class?
I've been reading up on parametric survival analysis, especially on accelerated failure time models, and I am having trouble wrapping my head around the family of distributions.
I am aware that there are the exponential/weibull/log-normal/log-logistic distributions. But what I can't seem to find a clear and consistent answer on is which of the following is the one that is actually assumed to follow one of those distributions? Is it the survival time T, the log survival time ln(T), the hazard function h(t), the survival function S(t), or the residuals ε?
Thanks in advance.
I am a bit stuck with the following problem. I am currently performing a meta analysis on observational studies using CMA where my outcome variable is the performance on a cognitive test as a function of adherence to an exposure variable (on a scale of 0 to 9). Normally, the results are presented either as means differences between tertiles according to the level of adherence (low, middle or high) or as a regression coefficient per additional unit of the exposure variable.
My main question is, how can I pool together both types of studies into the same meta analysis? I have found a similar question on risk estimates that suggest to estimate the linear trend of the categorical results. I don't have access to raw data and I only know sample sizes, mean differences and confidence interval. Is it possible to do the same in this case? If so, how should I do it?
I was thinking of just including each tertile comparison as a subgroup of the same study, and leave the continuous variables as they are. But I am not sure if this lousy approach is acceptable.
I have a theoretical problem with a statistical analysis. I was looking a lot at different fora but I could not find an easy explanation for my problem.
I want to compare means of two groups of data. In a simple case, I would use "t-test". However, in each group, I have few measurements for each individual. First, I wanted to measure a mean for every individual in a group, then compare the means of groups, but I know that it is not a good idea (mean of means/average of averages...).
For example how to compare this data sets:
Individual 1: 5, 6, 7
Individual 2: 5, 7, 7, 6
Individual 3: 6, 7, 7
Individual 1: 4, 5, 4
Individual 2: 7, 8, 6
Individual 3: 4, 3, 4, 2
You can imagine two groups of people. A - treated, B - untreated. In each group there are 3 people and some variable were measured with 3-4 repeats.
As you can see there are two groups made of few individuals for which few repeated measurements were made. I would like to compare two groups using means calculated for individuals, not measure simple mean for the whole group.
I have read a lot about pooled data, weighed means etc. but I still do not know how to perform t-test in that case (or another).
I hope you can help me!
If correlation coefficient (r) is 0.6 then is it considered as low/moderate/high relationship with variable specially in medical research?
I run a network meta-analysis. I try to run egger's and begg's test for different subgroups using these commands in stata:
metabias logrr selogrr, egger by(treatment)
metabias logrr selogrr, begg by(treatment)
but I get this response:
option by(treatment) not allowed
Can anyone please help me with this issue?
Dear fellow researchers,
Does anyone hold any useful information (or possibly a reasonable estimate) on the incidence rates (by age groups, if possible) of lactose intolerance and/or lactase deficiency in US?
Very frequently in papers, devoted to parallel clinical trials, I face a situation, when a calculated SD for an effect in each group is approximately equal to an SD of effect difference between the two groups.
An example may be found, e.g. in the following paper (Table 2):
Pelubiprofen achieved an efficacy in VAS scale of 26.2 with SD = 19.5. Celecoxib achieved efficacy of 21.2 with SD = 20.8. However, a difference is 5.0 with SD = 20.1! I was expecting SD ~sqrt(2) more, since the samples are independent and has approximately equal size.
I am looking into factors affecting grip strength into sonographers and am taking it further but struggling to work out which statistical test to use.
I have compared factors affecting grip strength and as am considering age, BMI, gender, years scanning, past injury and self reported estimates of percentage of obese patients scanned I am now looking at some of those in relation to each other e.g. Injury and estimate of number of obese cases, years of scanning, age.
I am really struggling with how do decide which statistical test is correct looking at these factors against each other, any suggestions appreciated.
I have noted contradictory advice from statisticians on how to model time-varying covariates in a repeated measures mixed effect model. For instance, you may have BMI measured every month as the exposure and a blood biomarker measured at the same time (or maybe different times) every month as the outcome. If you wanted to determine the effect of BMI throughout the follow-up period then how would you do this?
Some statisticians say you don't do anything differently (at least not in STATA coding) because in long format STATA can determine which variables are level 1 and level 2 by whether or not they vary by time-point. Other statisticans state that you need to create new variables that equate to the between- and within-person effect on the outcome.
Just wondering what you all thought of this.
Suppose I have a Questionnaire for a Stress assessment that contains 30 questions, each question has 5 answers (0- no stress, 1-mild stress, 2- moderate , 3-High stress, 4- Severe stress). The Total score of the 30 question varies from 0 - 120.
How we can categories the Total score (the range of total score is 0-120) into mild , moderate and severe? Which cut off s should l take for mild, moderate and severe?
I have a survey with dichotomous variables and need to do a factor analysis. I am working with SPSS and not very familirar with how to write syntax for that. I seen some examples but it is too complicated for me.
Does anyone know of a step-by-step procedures how to do tetrachoric factor analysis?
Or, can anyone help me with that process?
Thank you very much.
I was wondering if anyone had any resources on how to do a pooled prevalence in R? Is it possible to have a forest plot as a result? Any help would be greatly appreciated.
Let's say I want to compare the trend of CRP and the trend of WBC over a period of time of the same patient.
As I understand correlation coefficient compares two data points at single time point, and what I want is a trend of CRP compared to a trend of WBC.
Which test would be appropriate for that?
I am after some suggestions on what statistical analysis I can perform to show a before-and-after effect in a longitudinal electronic healthcare record (EHR). I have N number of EHRs, of varying sizes/time-spans. Each record has a history of recurrent disease records (for the one disease). To see whether a particular drug has had an effect on the disease outcome (duration before the next relapse), I have used time-gap recurrent cox regression.
However, I would now like to see whether the disease outcome (a series of remissions into relapses, good = long durations in between, bad = short durations in between) is immediately clear from the first prescription of a particular drug. In my head I imagine, taking all of the records (of vary time-span sizes -- very important to remember), and adjusting so every record overlaps when the drug of interest is first prescribed. Y axis is disease prevalence or risk, and x axis is time. From before the initial drug prescription event, disease prevalence/risk should be high, then after crossing the initial prescription time, disease prevalence/risk should drop. This would help demonstrate the efficacy of the drug.
Some points to remember: 1) Each medical record maybe unique in timespan. 2) The first prescription event of a particular drug will happen at different times across the record set. 3) Some records may have no medical events before the drug was prescribed (as all the diseases of interest feel after the drug prescription of interest). 4) The number of medical events either before or after the first prescription of the drug may be sparsely populated (making binning by time very difficult) or richly populated.
Is there a name for this kind of analysis? I am using R. Any suggestions are very welcome.
I am looking to find a method to compare the likelihood ratio for two non-binary diagnostic tests, performed on the one group of patients.
Specifically, I have the results of two different antibody staining results that have 4 possible scores (e.g. 0, 1+, 2+, 3+). I then compared the results to the gold standard, which is a binary result (e.g. FISH +ve, or FISH -ve). Using this information, I was able to generate a likelihood ratio and 95% CI for each score.
I found a paper on how to calculate interactions between two estimates (e.g. risk ratios or LRs) by analyzing the values on a log scale to generate a z-score [Altman and Bland (2003). Interaction revisited: the difference between two estimates]. Would this be valid on my sample? I think there was a mention that the test only works for independent measurements, and should not be used on two estimates from the same patients. The two antibodies are independent tests, but does it make the test invalid if I compared their results on the same set of patients?
For disease X there exists no real, modern gold standard. The disease can be diagnosed with 4 diagnostic tests (A,B,C,D), all leading to a yes or no answer (binary).
I have a data set with results of those 4 tests of 100 persons. Not every test has been run in every person. Now I want to calculate the sensitivity of test A.
In order to receive a relative sensitivity of test A (relative to B,C,D), could I
- summ up the number of all the positive test results of B,C and D of those cases (I believe all positive, "BP"), in which test A has been run
- and divide this number by all positive results of test A?
Is that legit?
Furthermore I want to to calculate the sensitivity of test B (summ up the number of all the positive test results of A,C and D of those cases, in which test B has been run and divide this number by all positive results of test B), C (same procedure) and D (same procedure).
Is that a legit way to compare relative sensitivities in my data set? Is there any literature confirming / strengthening this procedure?
Looking forward to your answers!
Thank you very much!
I am comparing between two popular shoulder scores ( different values for high possible scores) and I would like to produce conversion formula by using simple linear regression model but I have found heteroskedasticity between two shoulder scores ( Unstandardized residuals Vs Independent variables ) ?
How I can correct heteroskedasticity if I would not use any transformation approaches because it will affect on final linear regression equation.
- Reviewers and Editors operate at the cutting edge of science, at a frontier where fact and fancy/fiction intermix, at the border of the measurable and the immeasurable, on the slippery slope of insight where a nebulous cloud stubbornly refuses to lift sometimes for decades or centuries, and at times battle with conflict of interest if they themselves are active researchers in that field.
- What are the qualities of an ideal reviewer? What is the role of instinct in review? Can any two or three reviewers have the same mental horizon, the same willingness to consider new proposals with equanimity, the same ability to understand the complex mathematical game of medical statistics, the same ability to see through the written lines and the hedging terms used and the claims of originality or being the first to present a view or an investigation, and to judge with impartiality the ultimate objective of the authors who in general fervently wish to place a stake in the field as if buying a piece of real estate?
- Has any editor ever recused herself/himself on the grounds of conflict of interest? Should they?
- How can journals compensate reviewers for their time and effort?
- Since the reviewer-editor combine forms the most significant gate-keeper function for science, this column should produce lively discussion and contribute to a better general understanding for all stakeholders, both authors and reviewers.
- I have been a reviewer also for around 2 decades now for several high profile medical journals (see file). I will also participate in the discussion that will surely follow to hopefully usher in a better future for medical/scientific publishing.
We are testing a new diagnostic tool and comparing it to the actual gold standard for this diagnosis.
Briefly, we examined 25 patients with the new diagnostic tool (test A) and the gold standard diagnostic tool (test B). Test A gives a positive result or a negative result (no variability or range in numbers, just "positive" or "negative" as outcome). We then performed test B which also gives a "positive" or "negative" results and which is considered the true result since this is the gold standard diagnostic tool.
All patients having a positive result on test A (n=18), had a positive result on test B (n=18).
Of all patients having a negative result on test A (n=7), 5 were negative on test B but 2 were positive on test B.
Overall, 23 patients had the same outcome on test A and test B, 2 were different, which means that our new diagnostic test has a sensitivity of 92% (if we consider test B to have 100% sensitivity).
Can you recommend me any more statistics on this data, to draw conclusions? Any idea to look at this data from another perspective? Any help or insight is appreciated.
This is my first ever medical statistics/epidemiology questions, so please be patient if I come across as naive, I normally focus on drug and protein chemistry.
I have a huge medical dataset. From this set I have divided up the population by certain characteristics, a particular disease, an age group, and gender. I have four disease to consider over seven age groups of gender, thus 56 sub populations. Per sub-population I determine the prevalence within that population of reported comorbidities. This will leave me with 56 lists of "disease (a name) - prevalence ((0-1])". That is a lot of data. Putting aside a particular hypothesis or particular descriptive question, how would you go about displaying this amount of data in a report or a publication?
Best medical statistical software ?, I used spss but want to try easier software. A friend mentioned medcalc but lisence cost 450$, Does it worth ?
In modeling time to event data using a proportional hazards regression approach for repeated events, in which some patients have multiple events, the situation is often conditional since a patient can only have a subsequent event if they had a previous event. For example, a cardiac patient having one or more arrhythmias after heart surgery or a metastatic breast cancer patient having multiple recurrences or progressions of their disease after chemotherapy treatment. Are there useful ways of estimating the hazard ratio with reliable standard errors in these kind of recurrent event processes? It would seem that the correlation between the events within each patient or subject should be accounted for in the model.
Let's say recruitment period is X months after which randomization to exercise intervention groups occurs. The outcome is changes in depression. What are some methodological and ethical issues in choosing to defer treatment until after the recruitment period. The other option would be randomization and start of intervention as participants are recruited, but I can think of many reasons why this would be tough to do.
I would like to use the propensity score matching in measuring the effect of treatment between the control and treated group
doing it by spss 22 after the R plug is easy but I would like to understand the output and measure the effect
Effect size is reported in literature in multiple ways. One common form is risk ratio. Using this risk ratio of a paper as reference for sample size calculations in a new study, is difficult for me as it doesn't give me enough inputs to put into the sample size calculations.
knowing that the outcome y has two options only
the treatment [0,1]
I'm conducting a meta-analysis on hypoglycemic risk associated with diabetic drugs. Some studies report only the incidence rate of hypoglicemic events and the number of patients. Data are in the following format: 3.2 hypo/patient/year with 100 patients on drug A versus 2.1 hypo/patient/year with 100 patients on drug B; the event can occur more than once in each patient. Is there any way of estimating standard error when nor confidence intervals or standard deviations are given? Thanks
I conduct a study with a group of 44 students on the effectiveness of some tasks on their writing quality. However, I do not assign control group and treatment group but instead compare the difference between those who follow the treatment and those do not.
There's a possibility that the sample size of one group might be much larger than the other. But I still wonder whether is it possible to conduct the study this way and what kind of test should I use to the calculation of differences?
It is said that for retrospective studies in hospital setup does not required a sample size calculation as limited number of samples are available for research. If appropriate numbers are not there then how can one justify the p-value at the time of interpretation.
I think an optimum number of sample size is required for every research whether it is clinical trial, prospective or retrospective study...
I have 31 patients with arthritis and 10 coders for ultrasound from different countries. Every coder got 3-4 patients for rating (ordinal rate 1-3). How can I determine inter-rater reliability? What type of kappa coefficient should I use in this situation? Should I use ICC or just Fliess kappa?
Is it applicable to calculate PABAK and prevalence index for more than 2 coders and ordinal data (1-3)?
Does it even make sense to study other outcomes in a matched nested case-control study? In these type of studies, do we always have to use case /control as our outcome?
I've been analyzing a few studies, but was struggling to classify these two as case controlled studies or RCTs or something else. The studies are:
It seems like a case-controlled study because it is comparing those with colorectal against those who do not, with the aim of identifying the level of microRNAs (which I assume is the risk factor) to determine the relationship between microRNA expression and colorectal cancer. There's also no intervention introduced. However, my understanding was that case-controlled trials are typically retrospective, but the two studies above seem to have prospectively enrolled subjects.
Could someone let me know whether the two linked studies are Case Controlled Studies or not? Many thanks!
for G power software users: when to use chi-square and when to use Z test as test family in a priori sample size calculation.
If my primary outcome is nominal data (clinical cure), I count the number of patients cured and then I get the % of patients cured in every group, and I am having two groups (test and standard therapy), also previous trials in this discipline used non parametric analysis for their primary endpoint (which is clinical cure also), can I use chi-square in Priori sample size calculation, and use it in post hoc power analysis ?? (the primary outcome was non normally distributed
Thanks in advance
I performed association study of SNPs with type 2 diabetes and diabetic retinopathy. I am confused whether I need to apply Bonferroni correction after multivariate logistic analysis. Even, for haplotype analysis is it necessary to consider Bonferroni correction? Because I am getting p value less than 0.05 but greater than threshold p value after correction in both the cases. So how to interpret? Any suggestion?
If I do a meta analysis of incidence rate of observational studies, can I also include an incidence rate of a control group of RCT? Could the control group of RCT be treated as a comparable cohort? ( I am not interested in effect size.)
I want to conduct a randomized controlled trial to examine the effect of hypnosis in women with breast cancer during chemotherapy in their health-related quality of life, fatigue, anxiety, depression, and insomnia. I did not find any previous similar studies. Therefore, is it ok to conduct this study even though I don't have evidence? Also, I do not have effect size to calculate sample size too. How can I estimate sample in this situation? Also, I want to know that whether is it ok to divide the group into hypnosis and control group (I mean need to adopt other method)? I want your valuable suggestions.
Thank you so much.
The 2 year overall survival rate and p value of log rank test between two groups are available. Is it possible to calculate the hazard ratio(HR)?
My research is medical research (retrospective review) of the effect of having a novel medical procedure versus none, on the outcome parameter(death/heart attack) over a one year period. I performed Cox regression analysis to look for predictors of the outcome which included various explanatory variables such as age, gender etc and also having the procedure. But SPSS output gave hazard ratio of not having the procedure as 1.8 with Confidence intervals. However for publishing, I need to express it as the hazard ratio of having the procedure with CI. Can I calculate using inverse of HR and CI (that is 1/HR and 1/CI). Is there any other method?
I'm conducting a case-control study from the analysis of 5 SNPs in differents genes and I have 62 patients with the disease and 68 from healthy individuals* (dependent variable).
Now I need to get the adjusted odds ratio by age and gender* (independent variables) for the genotypes and alleles for each SNP.
With this sample size, can I do a logistic regression? If not, what are the alternatives?
Obs: Gender is matched across the groups
I want to conduct an impact assessment study from a randomised sample and no baseline data. Having read literature, I became confused as I realised that some authors tend to combine the two approaches (ESR and PSM). What is the best model to use?
Your comments will be useful please.
I have a considerable amount of multi variate data which includes clinical parameters (Age, gender, obesity) as well as non clinical data (Cytokine expression data, Seroconversion data etc) for two groups (Severe and non severe) who were infected with a specific Virus.
I wanted to associate certain factors with disease severity (For example, over expression of a certain cytokine in obese individuals who succumbed to severe disease). I am familiar with R programming and made heatmaps for the cytokine data. But I wanted to know if its possible to make something such as a circos plot which would enable me to make correlations?
Thank you for you time!
While the estimate and 95% Confidence Interval are available, it is unclear what the degrees of freedom would be. For example, with completely made up data, one might want to compare the association between sleep disturbance and depression (e.g., OR=1.2 [1.1, 1.3] k=10) as well as sleep disturbance and anxiety (e.g., OR=1.25 [1.15, 1.35], k=15).
208 subjects were randomly assigned into control group and intervention group on average, medical costs were counted at 3- ,6- ,12-, 24- month, If I want to know the differences between 2 groups at different time points, what kind of statistical methods should I use? By the way, medical costs are not normal distribution data. Really need some help on Statistical analysis!
I am looking for a way of how to compare Brown and Hauenstein's (2005) agreement index awg (independent awgs). Ideally, this method should also allow to compare the mean awgs: I got several states and two groups (e.g., women and men) in each state. I now wanna test whether people in one group agree on average more across all states than people in the other group.
The study is to explore the exposures (risk factors) that related to the effect. Effect itself defined as positive response to a treatment.
Exposures: some risk factors (demographic, family and past history of diseases, clinical conditions, intensity and hemodynamics response during event)
Main effects: cTn elevation
Treatment/intervention: event of long distance cycling
Population study: a sample group of participants join the event
observe risk factors before treatment
measure some marker before and after treatment
measure intensity and hemodynamics response during event
What is the suitable design for this research; observational study? (what kind of observational: cohort or cross sectional) Or One group pre test - post test design?
small pilot RCT, looking at an intervention relative to usual care. n<40.
2 x 2 between within design, 45 x 3 trials per condition
When prevalence is not known and difficult to get mean and standard deviation in that cases how to calculate sample size. Does it matter for a descriptive, Analytical and empirical studies.
From what I can work out criterion 10 is looking at statistical significance e.g. p values.
Is criterion 11 looking at standard mean difference and confidence intervals?
Most of the RCT's I have looked at reported the mean and SD before and after treatment for the two groups and reported a significance level comparing groups but this seems to be answering criterion 10 not 11?
I'm struggling to work out what criterion 11 is asking for?
Any advice would be much appreciated.
For publication bias how can we interpret Begg-Mazumdar: Kendall's tau value, Egger bias and Harbord bias on basis of results?
Begg-Mazumdar: Kendall's tau = 0.047 P > 0.9999 (low power)
Egger: bias = -26.99 (95% CI = -85.69 to 31.69) P = 0.29
Harbord: bias = 8.73 (92.5% CI = -4.86 to 22.33) P = 0.20
How we can know any publication bias exist or not?
I am working on a meta-analysis of RCTs, and I have to calculate .metabias (several tests, including Egger's) for continuous data (variables such as means and standard deviations). What is the process? Which are the commands used?
Thank you so much.
I have 43 questions for my research. Now i want to covert these questions into 12 variables through SPSS. If you provide further guidelines for calculating mean and standard deviation, i would be grateful to you. thanks in advance.
do you know how to run chi square in non parametric data ,using 2 descriptive variables like for e.g. sex and age >60 age <=60 on SPSS ?
Thanks a lot in advance
In medical and surgical research the hazard function is often used to estimate risk of an event across time. However the assumption of constant hazard is often not met and is not ideal when analyzing repeated events such are valve replacements, reoperations, reinterventions, multiple episodes of infection or repeated transplant rejections. I have read some information on modulated renewal theory and the Nelson estimator which involve calculating interfailure event times and segments/gaps between events within the same patient. Can anyone suggest a useful and relatively simple way of approaching this in SAS, Stata or R? Thank you!
when age distribution in category like 19-24, 25-30 so on , prevalence of chd age wise and crude age prevalence age wise and standardized who prevalence chart also given.