Statistical methods
parasitaemia
REGRESSION, SECONDARY
For the primary efficacy analysis, data processing, PK parameter estimation and logistic regression analyses were conducted within R (3.5.1) combined with MONOLIX (MLX2019R1) and the IQR package (v1.1.1) developed by IntiQuan (Basel, Switzerland) to support the entire workflow of a PK and logistic regression analysis from estimations to simulations. For simulations, IQR uses the library SUNDIALS (v2.9.0) from Computation (USA) (Additional files The contribution of artefenomel exposure to the Day 28 PCR-adjusted ACPR of the combination was evaluated using logistic regression evaluating the exposure to artefenomel (AUCThe base model included ferroquine AUCFor other secondary efficacy outcomes and safety outcomes, statistical analysis was done using SAS (version 9.4). Day 28 PCR-adjusted and crude ACPR were summarized for the PP and mITT populations, with Clopper–Pearson 95% CI. All other secondary efficacy outcomes were evaluated in the mITT population. Parasite clearance parameters were calculated using the WorldWide Antimalarial Resistance Network parasite clearance estimator (WWARN PCE) based on the linear portion of the individual natural logarithm parasitaemia–time profiles [Pharmacokinetic analysis was performed using non-linear mixed effect modelling as implemented in Monolix (version 2019R1), applying previously developed population PK models (see Additional file 