Review of Statistical Considerations and Data Imputation Methodologies in Psoriasis Clinical Trials.

Abstract

Numerous clinical trials have established that various biologic and oral small-molecule therapies are efficacious in patients with psoriasis. However, as there are limited head-to-head trials, healthcare providers may compare results across multiple trials when providing treatment recommendations. Direct comparisons among agents are challenging because psoriasis trials differ in terms of study design, patient population, and data analysis methodologies. Long-term clinical trials present additional challenges because the number of patients enrolled generally declines over time. The missing patient data that might occur, coupled with the specific approach used to substitute or impute that missing data, might introduce bias and skew efficacy results. In this review, we discuss how variations in study design and analytical methodologies affect efficacy outcomes in clinical trials. We also review published trials of biologic and oral small-molecule therapies for psoriasis to illustrate how issues related to missing data and choices in data imputation methodologies can affect the interpretation of efficacy outcomes. Imputation methodologies discussed include nonresponder imputation, modified nonresponder imputation, treatment failure rules, last observation carried forward, modified baseline observation carried forward, and multiple imputation. This review provides a foundation for the healthcare provider's critical evaluation of the psoriasis literature and emphasizes the importance of considering the level of evidence provided in a clinical trial when making treatment decisions.