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Background: Use of different methods for assessing the efficacy of artemisinin-based combination antimalarial treatments (ACTs) will result in different estimates being reported, with implications for changes in treatment policy. Methods: Data from different in vivo studies of ACT treatment of uncomplicated falciparum malaria were combined in a single database. Efficacy at day 28 corrected by PCR genotyping was estimated using four methods. In the first two methods, failure rates were calculated as proportions with either (1a) reinfections excluded from the analysis (standard WHO per-protocol analysis) or (1b) reinfections considered as treatment successes. In the second two methods, failure rates were estimated using the Kaplan-Meier product limit formula using either (2a) WHO (2001) definitions of failure, or (2b) failure defined using parasitological criteria only. Results: Data analysed represented 2926 patients from 17 studies in nine African countries. Three ACTs were studied: artesunate-amodiaquine (AS+AQ, N = 1702), artesunate-sulphadoxinepyrimethamine (AS+SP, N = 706) and artemether-lumefantrine (AL, N = 518). Using method (1a), the day 28 failure rates ranged from 0% to 39.3% for AS+AQ treatment, from 1.0% to 33.3% for AS+SP treatment and from 0% to 3.3% for AL treatment. The median [range] difference in point estimates between method 1a (reference) and the others were: (i) method 1b = 1.3% [0 to24.8], (ii) method 2a = 1.1% [0 to21.5], and (iii) method 2b = 0% [-38 to19.3]. The standard per-protocol method (1a) tended to overestimate the risk of failure when compared to alternative methods using the same endpoint definitions (methods 1b and 2a). It either overestimated or underestimated the risk when endpoints based on parasitological rather than clinical criteria were applied. The standard method was also associated with a 34% reduction in the number of patients evaluated compared to the number of patients enrolled. Only 2% of the sample size was lost when failures were classified on the first day of parasite recurrence and survival analytical methods were used. Conclusion: The primary purpose of an in vivo study should be to provide a precise estimate of the risk of antimalarial treatment failure due to drug resistance. Use of survival analysis is the most appropriate way to estimate failure rates with parasitological recurrence classified as treatment failure on the day it occurs. [ABSTRACT FROM AUTHOR]

The objectives of these analyses were to assess the feasibility of the latest WHO recommendations (28-day follow-up with PCR genotyping) for the assessment of antimalarial drug efficacy in vivo and to examine how different statistical approaches affect results. We used individual-patient data from 13 studies of uncomplicated pediatric falciparum malaria conducted in sub-Saharan Africa, using chloroquine (CQ), sulfadoxine/pyrimethamine (SP), or amodiaquine (AQ). We assessed the use effectiveness and test performance of PCR genotyping in distinguishing recurrent infections. In analyzing data, we compared (i) the risk of failure on target days (days 14 and 28) by using Kaplan-Meier and per-protocol evaluable patient analyses, (ii) PCR-corrected results allowing (method 1) or excluding (method 2) new infections, (iii) and day 14 versus day 28 results. Of the 2,576 patients treated, 2,287 (89%) were evaluable on day 28. Of the 695 recurrences occurring post-day 14, 650 could be processed and 584 were resolved (PCR use effectiveness, 84%; test performance, 90%). The risks of failure on day 28 with Kaplan-Meier and evaluable-patient analyses tended to be generally close (except in smaller studies) because the numbers of dropouts were minimal, but attrition rates on day 28 were higher with the latter method. Method 2 yielded higher risks of failure than method 1. Extending observation to 28 days produced higher estimated risks of failure for SP and AQ but not for CQ (high failure rates by day 14). Results support the implementation of the current WHO protocol and favor analyzing PCR-corrected outcomes by Kaplan-Meier analysis (which allows for dropouts) and retaining new infections (which minimizes losses).