Addressing the statistical analysis dilemma that exists when analyzing clinical trial results with full efficacy using the Kaplan Meier survival analysis method

Journal article

Abstract The use of a Kaplan–Meier (K–M) survival time approach is generally considered appropriate to report antimalarial efficacy trials. However, when a treatment arm has 100% efficacy, confidence intervals may not be computed. Furthermore, methods that use probability rules to handle missing data for instance by multiple imputation, encounter perfect prediction problem when a treatment arm has full efficacy, in which case all imputed values are either treatment success or all imputed values are failures. The use of a survival K–M method addresses this imputation problem in estimating the efficacy estimates also referred to as cure rates. We discuss the statistical challenges and propose a potential way forward. The proposed approach includes the use of K–M estimates as the main measure of efficacy. Confidence intervals could be computed using the binomial exact method. p-Values for comparison of difference in efficacy between treatments can be estimated using Fisher’s exact test. We emphasize that when efficacy rates are not 100% in both groups, the K–M approach remains the main strategy of analysis considering its statistical robustness in handling missing data and confidence intervals can be computed under such scenarios.

Authors: Peerawaranun, P; van der Pluijm, RW; Mukaka, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Cambridge University Press (CUP)
• Journal: Experimental Results
• Volume: 2
• Article number: e32
• Publication date: 2021-11-04
• DOI: 10.1017/exp.2021.21
• ISSN: 2516-712X
• Language: English
• Keywords: Missing data; Confidence interval; Survival analysis; Computer science; Mathematics; Imputation (statistics); Econometrics; Statistics; Robustness (evolution)