Journal of Biometrics & Biostatistics

Responses in the form of counts arise in many clinical trials and epidemiological studies, and are usually extradispersed. When one wishes to estimate the treatment effect in comparison with a placebo in clinical trials, confidence intervals are frequently used. It is of common interest in many clinical trials and epidemiological studies, to obtain the confidence interval for one of the two quantities, mean difference and mean ratio. The preference of one measure over the other depends on the design of the study. In many situations, the mean ratio is more relevant than the difference of means. Confidence interval procedures for the mean difference between treatment and control groups in the analysis of such extra-dispersed counts have been studied recently, but no attention has been paid to investigating the problem of confidence interval construction for the mean ratio. In this article, we develop several asymptotic confidence interval procedures for the mean ratio, by using the delta method, to extend the variance of a single mean estimate to the variance of the mean ratio estimate. The simulation studies indicate that all procedures perform reasonably well in terms of coverage. However, the interval based on the generalized estimating equation approach, using the logarithmic transformation, performs uniformly best in terms of coverage, expected width and location, and is preferable to the other intervals, in most of the situations considered here. Finally, three real-life examples from clinical trials are analyzed to illustrate the proposed confidence interval procedures.

 In an attempt to discover and unravel genetic predisposition to complex traits, new statistical methods have emerged that utilize multiple sources of data. This appeal to data aggregation is seen on various levels: across genetic variants, across genomic/biological/environmental measures and across different studies, often with fundamentally differing designs. While combining data can increase power to detect genetic variants associated with disease phenotypes, care must be taken in the design, analysis, and interpretation of such studies. Here, we explore methodologies employed to combine sources of genetic data and discuss the prospects for novel advances in the fields of statistical genetics and genetic epidemiology.

Many clinical investigators find the Number Needed to Treat (NNT) an appealing measure of treatment effect and use it routinely in reporting the results of randomized trials. It is most easily computed and interpreted for trials with binary responses, but attempts have been made to compute NNT-like measures for recurrent event outcomes. We discuss methodological issues concerning the construction of NNT-like measures of treatment effect based on recurrent event outcomes. Rate and mean functions are used to develop nonparametric estimates of NNT-like measures of treatment effect for recurrent events in terms of the number of individuals to be treated to expect to prevent a k and simply to prevent any event. Parametric analyses facilitate the derivation of alternative measures and associated estimates. Applications to a trial of patients with cystic fibrosis are given for illustration. In settings where mortality rates are non-negligible, joint NNT-like measures for the recurrent event and survival processes are required and these are discussed.