Latest research explores new statistical approaches for estimating antimalarial drug efficacy

In clinical studies looking at the efficacy of antimalarial treatments for uncomplicated Plasmodium falciparum malaria, the primary measurement in determining efficacy is the recurrence of parasites which are genetically identical to parasites in the initial infection i.e. recrudescence. Recurrent infection can however occur as a result of a new infection with P. falciparum or another species of malaria parasite. Recrudescent infection can be potentially pre-empted when the number of parasites of the newly acquired infection outnumbers those of the existing infection, or if the new infection is due to a more resistant parasite strain. This gives rise to a scenario where the new infection masks or outcompetes the recrudescent infection. Such events are considered as competing risk events in statistical literature.

The Kaplan-Meier (K-M) method is currently recommended by the World Health Organization (WHO) for estimating the efficacy of antimalarial treatments but does not account for competing risk events. The Cumulative Incidence Function (CIF) provides an alternative approach for estimating efficacy by accounting for the competing risk events.

Authors set out to comprehensively investigate how the choice of analytical method can impact the derived estimates and interpretation of drug efficacy data in antimalarial clinical studies. Simulation studies were carried out for different scenarios of existing drug efficacy in the areas of high and low malaria transmission intensity.

The simulation study showed substantial differences in the derived estimates of drug efficacy between the two methods in the areas of high transmission settings. The Kaplan-Meier approach overestimated failure (underestimated the efficacy) and the degree of overestimation in treatment failure reached as high as 3% when the drug efficacy fell to 90%.

First author on the study, Prabin Dahal, comments: “Competing risk analysis has not gathered much attention in antimalarial literature. We undertook this simulation study and explored when and where does competing risk approach can prove to be a useful alternative to the currently used Kaplan-Meier approach for measuring antimalarial drug efficacy. We were able to demonstrate that the choice of analytical approach can have implications on the derived estimate, especially in the areas of high transmission settings. Hence, it is important to utilise the current statistical tools at our disposal to maximise the information obtained from the clinical trials. It is also equally important to take into consideration the biological plausibility of a new infection outcompeting an existing recrudescent infection, which is very difficult to discern with data collected during a clinical trial. We suggest interpreting the results of competing risk analysis by taking this limitation into account”.

These results have important clinical consequences, as for new drugs to be eligible as first line treatments their efficacy estimates should exceed certain thresholds (e.g. 95% desired efficacy for introducing new drug as a first line therapy and 90% efficacy demanded for an existing drug). The simulation study demonstrates that such a threshold based approach for judging the current status of antimalarial efficacy is vulnerable to the choice of analytical approach used, especially when the derived estimates are at the cusp of these thresholds. The study found that in high transmission settings, the choice of statistical method led to the derived estimates being just under or over this threshold. Authors suggest that in high transmission settings competing risk survival analysis provides an alternative approach for estimating drug efficacy.

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Read the paper: Evaluating antimalarial efficacy in single-armed and comparative drug trials using competing risk survival analysis: a simulation study. BMC Medical Research Methodology. 2019; 19:107.