Clinical trials : journal of the Society for Clinical Trials
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There has been tremendous progress over the last decade in the development of health products--drugs, vaccines, and diagnostics--for neglected diseases. There are now dozens of candidate products in the pipeline. ⋯ Realizing the promise of the neglected disease product pipeline will require not only increased funding for large-scale clinical trials and capacity building, but also greater attention to how these trials and their regulatory pathways can be improved to reduce unnecessary costs, delays, and risks to trial subjects. We propose a two-prong strategy: (1) adaptation and adoption of emerging research on 'sensible guidelines' for reducing large-scale, randomized clinical trial costs to the demands of the neglected disease product pipeline and (2) regional approaches to regulation and ethical review of clinical trials for health products for neglected diseases.
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The current practice for seeking genomically favorable patients in randomized controlled clinical trials using genomic convenience samples. ⋯ Complete ascertainment of genomic samples in a randomized controlled trial should be the first step to explore if a favorable genomic patient subgroup suggests a treatment effect when there is no clear prior knowledge and understanding about how the mechanism of a drug target affects the clinical outcome of interest. When stratified randomization based on genomic biomarker status cannot be implemented in designing a pharmacogenomics confirmatory clinical trial, if there is one genomic biomarker prognostic for clinical response, as a general rule of thumb, a sample size of at least 100 patients may be needed to be considered for the lower prevalence genomic subgroup to minimize the chance of an imbalance of 20% or more difference in the prevalence of the genomic marker. The sample size may need to be at least 150, 350, and 1350, respectively, if an imbalance of 15%, 10% and 5% difference is of concern.
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The aim was to compare simple imputation, multiple imputation, and modeling approaches to deal with 'missing' quality of life data. Data were obtained from five clinical trials, which employed a reminder system for follow-up questionnaires. Previous studies have compared imputation strategies by artificially removing data according to prespecified mechanisms. Our approach differs from previous study as actual collected data are utilized. ⋯ Multiple imputation is recommended for missing quality of life data as it makes the assumption of missing at random which in the quality of life setting is more plausible than the assumption of missing completely at random for which most simple imputation methods are based. Pattern mixture models can be complex and did not perform well in this setting.