Systematic review of clinical trials assessing the therapeutic efficacy of visceral leishmaniasis treatments: A first step to assess the feasibility of establishing an individual patient data sharing platform. - Jacob T Bush; Monique Wasunna; Fabiana Alves; Jorge Alvar; Piero L Olliaro; Michael Otieno; Carol Hopkins Sibley; Nathalie Strub Wourgaft; Philippe J Guerin (Public Library of Science (PLoS), 2017)
There are an estimated 200,000 to 400,000 cases of visceral leishmaniasis (VL) annually. A variety of factors are taken into account when considering the best therapeutic options to cure a patient and reduce the risk of resistance, including geographical area, malnourishment and HIV coinfection. Pooled analyses combine data from many studies to answer specific scientific questions that cannot be answered with individual studies alone. However, the heterogeneity of study design, data collection, and analysis often makes direct comparison difficult. Individual Participant Data (IPD) files can be standardised and analysed, allowing detailed analysis of this merged larger pool, but only a small fraction of systematic reviews and meta-analyses currently employ pooled analysis of IPD. We conducted a systematic literature review to identify published studies and studies reported in clinical trial registries to assess the feasibility of developing a VL data sharing platform to facilitate an IPD-based analysis of clinical trial data. Studies conducted between 1983 to 2015 that reported treatment outcome were eligible.From the 2,271 documents screened, 145 published VL clinical trials were identified, with data from 26,986 patients. Methodologies varied for diagnosis and treatment outcomes, but overall the volume of data potentially available on different drugs and dose regimens identified hundreds or possibly thousands of patients per arm suitable for IPD pooled meta-analyses.A VL data sharing platform would provide an opportunity to maximise scientific use of available data to enable assessment of treatment efficacy, contribute to evidence-based clinical management and guide optimal prospective data collection.