Epidemiology and Infection


Uncertain outcomes: adjusting for misclassification in antimalarial efficacy studies

K. A. PORTERa1 c1, C. L. BURCHa2, C. POOLEa1, J. J. JULIANOa3, S. R. COLEa1 and S. R. MESHNICKa1

a1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, NC, USA

a2 Department of Biology, University of North Carolina at Chapel Hill, NC, USA

a3 Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, NC, USA


Evaluation of antimalarial efficacy is difficult because recurrent parasitaemia can be due to recrudescence or re-infection. PCR is used to differentiate between recrudescences and re-infections by comparing parasite allelic variants before and after treatment. However, PCR-corrected results are susceptible to misclassification: false positives, due to re-infection by the same variant present in the patient before treatment; and false negatives, due to variants that are present but too infrequent to be detected in the pre-treatment PCR, but are then detectable post-treatment. This paper aimed to explore factors affecting the probability of false positives and proposes a Monte Carlo uncertainty analysis to account for both types of misclassification. Higher levels of transmission intensity, increased multiplicity of infection, and limited allelic variation resulted in more false recrudescences. The uncertainty analysis exploits characteristics of study data to minimize bias in the estimate of efficacy and can be applied to areas of different transmission intensity.

(Accepted June 15 2010)

(Online publication July 12 2010)


c1 Author for correspondence: Ms. K. A. Porter, Department of Epidemiology, Campus Box 7435, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. (Email: kporter@email.unc.edu)