Speaker
Description
Patient reported outcomes (PROs) are routinely used in randomized clinical trials (RCTs) to capture patients’ health status. Symptom-related PROs represent patients’ subjective perception of their health and are often collected multiple times during a clinical trial. For instance, in COPD, breathlessness or cough scores are captured using a small-range ordinal scale (0-4), representing breathing difficulty and coughing severity. With the possibility of collecting PROs on a daily basis with e-diaries in RCTs, it is important to evaluate the statistical methods used for analysis of treatment effects on PRO-related endpoints. The standard approach using linear mixed models for change from baseline in weekly averages of symptom scores neglects the ordinal structure of the scores and does not consider more sophisticated dynamics and heterogeneity between patients in short and long-term fluctuations that are usually observed in these data. The ordinal structure of the scores can be accounted for using a latent process model allowing for a nonlinear link function between the scores and their underlying process. The heterogeneity in the residual variance allowing for variability in patients’ short-term fluctuations can be analyzed using a location-scale latent process model in which the variance is expressed as a linear structure of a treatment covariate and a patient-specific random intercept. In this work, we compare various statistical methods for the analysis of ordinal scores using a simulation study and clinical trial data. We consider different assumptions regarding the variance of the residual errors, distribution of the scores, as well as using univariate scores or composite scores versus multivariate scores. The goal of this work is to contribute to the discussion on the trade-off between simplicity of the analysis used for the scores and accuracy as well as completeness in evaluating the treatment effect on the PROs from a longitudinal perspective.
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