Classification of outcomes in antimalarial therapeutic efficacy studies with Aster

Abstract

Reliable assessment of antimalarial drug efficacy is crucial for effective response to emerging drug resistance, and therapeutic efficacy studies (TES) are the primary means of estimating in vivo efficacy. Accuracy of such estimates rests on correctly classifying recurrent infections developed during follow-up as recrudescences or new infections. Genotyping is used to guide classification, but polyclonal infections and alleles matching by chance make classification challenging, especially in high transmission settings. Match-counting algorithms currently recommended by World Health Organization are unreliable and produce biased results, necessitating development of principled statistical approaches. Modern genotyping methods such as multiplexed amplicon sequencing hold great potential for resolving recurrences and motivate the need for corresponding statistical methods able to utilize the rich data they provide. We propose an Adaptive Statistical framework for Therapeutic Efficacy and Recrudescence (Aster) that delivers accurate and consistent results by explicitly incorporating complexity of infection (COI), population allele frequencies, and imperfect detection of alleles in minority strains. Using an identity by descent approach, Aster accounts for alleles matching by chance and for a background infection relatedness structure that can otherwise lead to misclassification. The flexible framework can also use external information, such as parasite density and performance characteristics of a genotyping panel. Using simulations, we show that Aster dramatically outperforms match-counting algorithms in a wide variety of transmission settings and demonstrates consistently balanced performance that improves with more informative genotyping panels. Aster is implemented in a fast, fully scalable, and user-friendly R software package asterTES and provides accurate estimates of treatment failure for TES with any type of genotyping data, facilitating reliable evaluation of drug efficacy and effective management of malaria.