Addressing Bias in Responder Analysis of Patient-Reported Outcomes

Joseph C Cappelleri, Richard Chambers, Joseph C Cappelleri, Richard Chambers

Abstract

Introduction: Quantitative patient-reported outcome (PRO) measures ideally are analyzed on their original scales and responder analyses are used to aid the interpretation of those primary analyses. As stated in the FDA PRO Guidance for Medical Product Development (2009), one way to lend meaning and interpretation to such a PRO measure is to dichotomize between values where within-patient changes are considered clinically important and those that are not. But even a PRO scale with a cutoff score that discriminates well between responder and non-responders is fraught with some misclassification.

Methods: Using estimates of sensitivity and specificity on classification of responder status from a PRO instrument, formulas are provided to correct for such responder misclassification under the assumption of no treatment misclassification. Two case studies from sexual medicine illustrate the methodology.

Results: Adjustment formulas on cell counts for responder misclassification are a direct extension of correction formulas for misclassification on disease from a two-way cross-classification table of disease (yes, no) and exposure (yes, no). Unadjusted and adjusted estimates of treatment effect are compared in terms of odds ratio, response ratio, and response difference. In the two case studies, there was considerable underestimation of treatment effect.

Discussion and conclusions: The methodology can be applied to different therapeutic areas. Limitations of the methodology, such as when adjusted cell estimates become negative, are highlighted. The role of anchor-based methodology is discussed for obtaining estimates of sensitivity and specificity on responder classification. Correction for treatment effect bias from misclassification of responder status on PRO measures can lead to more trustworthy interpretation and effective decision-making. Clinicaltrials.gov: NCT00343200.

Keywords: Information bias; Measurement error; Misclassification; Patient-reported outcomes; Responder analysis; Treatment effect.

Conflict of interest statement

The authors are employees and stockholders of Pfizer Inc. This study was sponsored by Pfizer.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
a Response Ratio vs. Specificity (Sensitivity = 1). b. Response Ratio vs. Sensitivity (Specificity = 1; True and Observed Response Ratio = 2)
Fig. 2
Fig. 2
a Response Difference vs. Specificity (Sensitivity = 1, True Response Difference = 0.40). b Response Difference vs. Sensitivity (Specificity = 1, True Response Difference = 0.40)
Fig. 3
Fig. 3
a. Odds Ratio vs. Specificity (Sensitivity = 1). b. Odds Ratio vs. Sensitivity (Specificity = 1)

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