Evaluating antimalarial efficacy in single-armed and comparative drug trials using competing risk survival analysis: a simulation study

BMC Medical Research Methodology

Julie A. Simpson¹  Prabin Dahal²  Philippe J. Guerin²  Kasia Stepniewska²  Ric N. Price³ 

[1] 0000 0001 2179 088X, grid.1008.9, Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia;WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK;0000 0004 1936 8948, grid.4991.5, Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK;WorldWide Antimalarial Resistance Network (WWARN), Oxford, UK;0000 0004 1936 8948, grid.4991.5, Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK;0000 0000 8523 7955, grid.271089.5, Global and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Australia;

关键词: Malaria;    Plasmodium falciparum;    Efficacy;    Competing risk events;    Cumulative incidence function;   

DOI  :  10.1186/s12874-019-0748-2

来源: publisher

【 摘 要 】

BackgroundAntimalarial efficacy studies in patients with uncomplicated Plasmodium falciparum are confounded by a new infection (a competing risk event) since this event can potentially preclude a recrudescent event (primary endpoint of interest). The current WHO guidelines recommend censoring competing risk events when deriving antimalarial efficacy. We investigated the impact of considering a new infection as a competing risk event on the estimation of antimalarial efficacy in single-armed and comparative drug trials using two simulation studies.MethodsThe first simulation study explored differences in the estimates of treatment failure for areas of varying transmission intensities using the complement of the Kaplan-Meier (K-M) estimate and the Cumulative Incidence Function (CIF). The second simulation study extended this to a comparative drug efficacy trial for comparing the K-M curves using the log-rank test, and Gray’s k-sample test for comparing the equality of CIFs.ResultsThe complement of the K-M approach produced larger estimates of cumulative treatment failure compared to the CIF method; the magnitude of which was correlated with the observed proportion of new infection and recrudescence. When the drug efficacy was 90%, the absolute overestimation in failure was 0.3% in areas of low transmission rising to 3.1% in the high transmission settings. In a scenario which is most likely to be observed in a comparative trial of antimalarials, where a new drug regimen is associated with an increased (or decreased) rate of recrudescences and new infections compared to an existing drug, the log-rank test was found to be more powerful to detect treatment differences compared to the Gray’s k-sample test.ConclusionsThe CIF approach should be considered for deriving estimates of antimalarial efficacy, in high transmission areas or for failing drugs. For comparative studies of antimalarial treatments, researchers need to select the statistical test that is best suited to whether the rate or cumulative risk of recrudescence is the outcome of interest, and consider the potential differing prophylactic periods of the antimalarials being compared.

【 授权许可】

CC BY