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I need help in knowing which test to perform with this analysis.

I have two groups (lets say group A and B). A study was performed with initial rates of heart failures in group A and B (i.e, baseline rates) were measured. After 6 months of follow up the heart failure rates in group A and B (i.e, re measurement rates) were measured again. By simple mathematics, I know that the improvement rate is better in group A as compared to group B.

  • How do I test whether the difference in rates between the groups is statistically significant?

I am using t-test for this but not sure how to go about it.

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    $\begingroup$ It seems like you have a binary (failure/no failure) dependant variable? If so, you would rather need logistic regression to get the most out of your data. $\endgroup$ – Dominic Comtois Jul 25 '11 at 5:40
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T-tests are for normal or close-to-normal distributions. You need a nonparametric test. Since you have the same two groups being assessed twice, I don't think you can use the McNemar test for dependent proportions. However, maybe you could do a chi-square test on the increases within each group as fractions of the sample sizes. E.g., if group A had 5/100 develop the illness and group B had 12/100, you would run the chi-square test using the numbers 5, 95, 12, and 88.

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  • $\begingroup$ Thanks for your reply. Can I not do a paired t-test as the baseline and re measurement was performed at the same institution? $\endgroup$ – Nupur Jul 25 '11 at 1:19
  • $\begingroup$ To be more explicit. I have the baseline and remeasurement rates for both groups (each group has 10 hospitals in it). I calculated relative improvement rate, RIR (which is [baseline - remeasurement] / baseline) for each hospital in both groups. With t-test, I analysed if the mean RIR for both groups are statistically significant? Is this a right way of doing it? for confirming the results, I also categorized each hospital as improvement/noimprovement based on RIR value and then did a chi-sq. $\endgroup$ – Nupur Jul 25 '11 at 20:10
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    $\begingroup$ It is seldom an optimum way to analyze ratios. If relative changes are relevant and you've verified the Bland-Altman conditions, then analysis of log ratios is more appropriate. But even more frequently, analysis of covariance of the final measurement, adjusted for the initial measurement as a covariate, works better. One example of this is when the baseline is measured with much error or is poorly correlated with the follow-up measurement; in that case you lose power by analyzing change instead of using analysis of covariance. $\endgroup$ – Frank Harrell Aug 24 '11 at 2:40
  • $\begingroup$ I don't see how ancova can apply when the data consist of group-level aggregate rates, pre and post, or else of binary variables describing each individual hospital, again pre and post. $\endgroup$ – rolando2 Sep 26 '11 at 18:05

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