ABSTRACT
This study aims to survey the current practice in Japan for the prevention and treatment of missing data in clinical trials since the publication of regulatory guidelines on missing data issues. A web-based questionnaire was conducted among 65 member companies of the Japan Pharmaceutical Manufacturers Association in 2013. Responses were obtained on 187 clinical trials from 55 companies, including 42 based in Japan and 13 based in other countries. Missing data were most frequent in trials involving the central nervous system (65.2% had ≥10% missing data). Overall, last observation carried forward (LOCF) was the most popular method for handling missing data (45.0%), followed by mixed-effect models for repeated measures (15.5%), although this was used as frequently as LOCF imputation in central nervous system trials. Even after the publication of regulatory guidelines discouraging use of LOCF, LOCF imputation remains the most popular method for treating missing data among pharmaceutical manufacturers in Japan.
ABSTRACT
Medical diagnostic tests must enjoy appropriate validity and high reliability in order to qualify as adequate assessment tools. Without a gold standard test, available medical diagnostic tests are not perfect; hence, the reliability of such tests must be evaluated precisely. Kappa coefficient statistics are often utilized to assess reliability of tests when there are two or more medical diagnostic tests. However, the statistics are imprecise for a typical case when the prevalence rate of a target disease is unknown. Although latent class models could be used to assess reliability, the models cannot estimate reliability in the case of two tests, due to unidentifiability or the lack of degrees of freedom. An alternative approach to assess reliability for the case of two tests is stratifying a two-by-two contingency table under the assumption that sensitivities and specificities between the two tests be equal over all strata and that prevalence rates in the strata be different from each other. Because stratification is basically a multi-sample analysis, it should not be applied to the situation where subsamples (i.e., centers) are randomly selected from a larger population. In this article, a type of mixed-effect model is proposed to evaluate the reliability of two tests for trials in randomly selected multiple centers. Several types of distributions for prevalence rates over subpopulations are considered. Simulation studies show that our proposed method performs nicely. Analysis of real data is also reported.
