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BackgroundThe susceptibility of microbes such as Plasmodium falciparum to drugs is measured in vitro as the concentration of the drug achieving 50% of maximum effect (IC50); values from a population are summarized as geometric means. For antimalarial drugs, as well as for antibiotics, assessing changes in microbe susceptibility over time under drug pressure would help inform treatment policy decisions, but no standard statistical method exists as yet.MethodsA mixed model was generated on loge-transformed IC50 values and calculated geometric least squares means (GLSM) with 90% confidence intervals (CIs). In order to compare IC50s between years, GLSM ratios (GLSMR) with 90%CIs were calculated and, when both limits of the 90%CIs were below or above 100%, the difference was considered statistically significant. Results were compared to those obtained from ANOVA and a generalized linear model (GLM).ResultsGLSMRs were more conservative than ANOVA and resulted in lower levels of statistical significance. The GLSMRs approach allowed for random effect and adjustment for multiple comparisons. GLM was limited in the number of year-to-year comparisons by the need for a single reference year. The three analyses yielded generally consistent results.ConclusionA robust analytical method can palliate inherent limitations of in vitro sensitivity testing. The random effects GLSMRs with adjustment for multiple comparisons and 90%CIs require only assumptions on the mixed model to be applied. Results are easy to display graphically and to interpret. The GLMSRs should be considered as an option for monitoring changes in drug susceptibility of P. falciparum malaria and other microbes.

Original publication




Journal article


Malaria journal

Publication Date





Clinical Epidemiology and Public Health Unit, Centre for Health Studies, Centre de Recherche Publique (CRP)-Santé, Luxembourg.


Animals, Humans, Plasmodium falciparum, Antimalarials, Analysis of Variance, Linear Models, Least-Squares Analysis, Parasitic Sensitivity Tests, Inhibitory Concentration 50, Models, Biological