… included might be regarded as unlikely outcomes for a cluster randomized trial, we believe
that the relationships quantified here are likely to hold for other types of binary data. It is to be …

… Binary outcomes are often encountered in pharmaceutical … Binary response variables may
be natural binary traits such … single step, simultaneous confidence intervals can be used. …

… For binary outcomes, the simulations do not suggest an important difference in terms of
bias and coverage of confidence intervals for the summary treatment effect when using REML …

… The pooled ratio of odds ratios was 0.64 (95% confidence interval 0.43 to 0.96) with … though
sensitivity and subgroup analyses in general had wide confidence intervals (table 3⇓). In the …

… missing; simple imputation, where a chosen outcome is … data in meta-analyses with binary
outcomes, we conducted a survey of … We have used confidence intervals at the 95% level, but …

… , which gives the effect estimates and confidence intervals. Contrary to the randomization-…
time-to-response data [3] or a binary outcome [4]. These methods were the extension of the …

… After we run metamiss2, the “adjusted” study-specific relative effects along with their 95%
confidence intervals are given in the output. The same results are obtained when we run the …

… reported (ie 7.43 with 95% confidence interval of 4.79 to 11.51) based on the random effects
model by DerSimonian and Laird. The reason might be the fact that an ad hoc continuity …

… the risk of a binary outcome varies widely between subgroups. … results often have wide
confidence intervals and lack statistical … When a trial outcome is binary, a number of different effect …

… , both for prediction models for binary outcomes as well as for time-to-event outcomes. The
c-statistic is … standard error or confidence interval. We can derive the logit c-statistic as follows …