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The Kaplan-Meier (K-M) method is currently the preferred approach to derive an efficacy estimate from anti-malarial trial data. In this approach event times are assumed to be continuous and estimates are generated on the assumption that there is only one cause of failure. In reality, failures are captured at pre-scheduled time points and patients can fail treatment due to a variety of causes other than the primary endpoint, commonly termed competing risk events. Ignoring these underlying assumptions can potentially distort the derived efficacy estimates and result in misleading conclusions. This review details the evolution of statistical methods used to derive anti-malarial efficacy for uncomplicated Plasmodium falciparum malaria and assesses the limitations of the current practices. Alternative approaches are explored and their implementation is discussed using example data from a large multi-site study.

Original publication

DOI

10.1186/s12936-017-2074-7

Type

Journal article

Journal

Malar J

Publication Date

26/10/2017

Volume

16

Keywords

Comparative studies, Competing risks, Cumulative incidence function, Kaplan–Meier, Plasmodium falciparum, Antimalarials, Clinical Trials as Topic, Data Interpretation, Statistical, Humans, Kaplan-Meier Estimate, Malaria, Falciparum, Treatment Failure