Abstract : BACKGROUND: Malaria remains a burden in Sub-Saharan Countries. The strategy proposed by the World Health Organization (WHO) is to systematically compare the therapeutic efficacy of antimalarial drugs using as primary outcome for efficacy, a four-category ordered criterion. The objective of the present work was to analyze the treatment effects on this primary outcome taking into account both a center-effect and individual covariates. A three-arm, three-centre trial of Amodiaquine (AQ), sulfadoxine-pyrimethamine (SP) and their combination (AQ + SP), conducted by OCEAC-IRD in 2003, in 538 children with uncomplicated Plasmodium falciparum malaria, is used as an illustration. METHODS: Analyses were based on ordinal regression methods, assuming an underlying continuous latent variable, using either the proportional odds (PO) or the proportional hazards (PH) models. Different algorithms, corresponding to both frequentist- and bayesian-approaches, were implemented using the freely available softwares R and Winbugs, respectively. The performances of the different methods were evaluated on a simulated data set, and then they were applied on the trial data set. RESULTS: Good coverage probability and type-1 error for the treatment effect were achieved. When the methods were applied on the trial data set, results highlighted a significance decrease of SP efficacy when compared to AQ (PO, odds ratio [OR] 0.14, 95% confidence interval [CI] 0.04-0.57; hazard ratio [HR] 0.605, 95% CI 0.42-0.82), and an equal effectiveness between AQ + SP and AQ (PO, odds ratio [OR] 1.70, 95% confidence interval [CI] 0.25-11.44; hazard ratio [HR] 1.40, 95% CI 0.88-2.18). The body temperature was significantly related to the responses. The patient weights were marginally associated to the clinical response. CONCLUSION: The proposed analyses, based on usual statistical packages, appeared adapted to take into account the full information contained in the four categorical outcome in malaria trials, as defined by WHO, with the possibility of adjusting on individual and global covariates.