Statistical approaches to analyse patient-reported outcomes as response variables: An application to health-related quality of life

Abstract

Patient-reported outcomes (PRO) are used as primary endpoints in medical research and their statistical analysis is an important methodological issue. Theoretical assumptions of the selected methodology and interpretation of its results are issues to take into account when selecting an appropriate statistical technique to analyse data. We present eight methods of analysis of a popular PRO tool under different assumptions that lead to different interpretations of the results. All methods were applied to responses obtained from two of the health dimensions of the SF–36 Health Survey. The proposed methods are: multiple linear regression (MLR), with least square and bootstrap estimations, tobit regression, ordinal logistic and probit regressions, beta-binomial regression (BBR), binomial-logit-normal regression (BLNR) and coarsening. Selection of an appropriate model depends not only on its distributional assumptions but also on the continuous or ordinal features of the response and the fact that they are constrained to a bounded interval. The BBR approach renders satisfactory results in a broad number of situations. MLR is not recommended, especially with skewed outcomes. Ordinal methods are only appropriate for outcomes with a few number of categories. Tobit regression is an acceptable option under normality assumptions and in the presence of moderate ceiling or floor effect. The BLNR and coarsening proposals are also acceptable, but only under certain distributional assumptions that are difficult to test a priori. Interpretation of the results is more convenient when using the BBR, BLNR and ordinal logistic regression approaches.