Methods for Imputing Missing Efficacy Data in Clinical Trials of Biologic Psoriasis Therapies: Implications for Interpretations of Trial Results
August 2017 | Volume 16 | Issue 8 | Original Article | 734 | Copyright © August 2017
Richard G.B. Langley MD FRCPC,a Kristian Reich MD PhD,b Charis Papavassilis MD PhD,c Todd Fox PharmD ACPR,c Yankun Gong PhD,d and Achim Güttner PhDc
aDalhousie University, Halifax, Canada bDermatologikum Hamburg and Georg-August-University Göttingen, Germany cNovartis Pharma AG, Basel, Switzerland dBeijing Novartis Pharma Co. Ltd., Shanghai, China
BACKGROUND: An issue in long-term clinical trials of biologics in psoriasis is how to handle missing efficacy data. This methodological challenge may not be understood by clinicians, yet can have a significant effect on the interpretation of clinical trials.
OBJECTIVE Evaluate the effects of different data imputation methods on apparent secukinumab response rates.
METHODS: Post hoc analyses were conducted on efficacy data from 2 phase III, multicenter, randomized, double-blind trials (FIXTURE and ERASURE) of secukinumab in moderate to severe plaque psoriasis. Per study protocols, missing data were imputed using strict non-response imputation (NRI), a highly conservative method that assumes non-response for all missing data. Alternative imputation methods (observed data, last observation carried forward [LOCF], modified NRI, and multiple imputation [MI]) were applied in this analysis and the resultant response rates compared.
RESULTS: Response rates obtained with each imputation method diverged increasingly over 52-weeks of follow-up. Strict NRI response estimates were consistently lower than those using the other methods. At week 52, Psoriasis Area and Severity Index (PASI) 90 rates for secukinumab 300 mg based on strict NRI were 9.2% (FIXTURE) and 8.7% (ERASURE) lower than estimates obtained using the least conservative method (observed data). Estimates obtained through LOCF and modified NRI were closest to those produced by MI, currently regarded as the most methodologically sophisticated approach available.
CONCLUSION: Awareness of differences in assumptions and limitations among imputation methods is necessary for well-informed interpretation of trial data.
J Drugs Dermatol. 2017;16(8):734-742.