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- W84201711 abstract "Recently meta analysis has been widely utilized to combine information across comparative clinical studies for evaluating drug safety profile. The standard meta analysis procedures, based on large sample approximations, may give misleading or invalid results when the sample sizes of individual studies are not large or the total number of studies is small, or when the event rates are low. Moreover, when dealing with rather rare events, a substantial proportion of studies may not have any events of interest. Conventional methods either exclude such studies or add arbitrary positive values to the corresponding 2× 2 tables in the analysis. In this article, we present a simple, effective procedure to make valid inferences about the parameter of interest with all available data without continuity corrections. We then use the procedure to analyze the data from 48 comparative trials involving Rosigliazone, a type 2 diabetes drug, with respect to its possible cardiovascular toxicity. The results are markedly different from those of the meta analysis reported in Nissen and Wolski1. For example, based on the data from entire 48 studies, the 95% confidence interval for the risk difference with respect to MI is (−0.08, 0.38)% (p-value=0.27) and the interval estimate with respect to CVD related death is (−0.13, 0.23)% (p-value=0.83). On the other hand, excluding studies which do not have any events of interest, Nissen and Wolski reported that for the odds ratio the corresponding intervals are (1.03, 1.98) (p-value=0.03) for MI and (0.98, 2.74) (p-value=0.06) for CVD death." @default.
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- W84201711 date "2007-01-01" @default.
- W84201711 modified "2023-09-24" @default.
- W84201711 title "Effectively Combining Independent 2 x 2 Tables for Valid Inferences in Meta Analysis with all Available Data but no Artificial Continuity Corrections for Studies with Zero Events and its Application to the Analysis of Rosiglitazone's Cardiovascular Disease Related Event Data" @default.
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