Gerlovina, I.; Berube, S.; Briggs, J.; Murie, K.; Murphy, M.; Wesolowski, A.; Greenhouse, B.

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 allele chance matching still make classification challenging, especially in high transmission settings. Established methods for analyzing genotyping data are biased or difficult to use; few take full advantage of data from modern genotyping methods such as multiplexed amplicon sequencing. We propose an Adaptable 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 background relatedness that can otherwise lead to misclassification. The extensible framework can also utilize external information, such as parasite density and performance characteristics of a genotyping panel. Aster employs efficient combinatorial algorithms to process unphased polyclonal data, making it fast and fully scalable. Using simulations, we show that Aster dramatically outperforms match-counting algorithms currently recommended by WHO in a wide variety of settings and demonstrates consistently balanced performance measures that improve with more informative genotyping panels. Aster provides accurate study-level estimates of treatment failure for TES with any type of genotyping data, facilitating reliable evaluation of drug efficacy and effective management of malaria.