Introduction to special section: quantitative methods.

Abstract

For the past 40 years, quantitative methods and psychometric analyses have been an integral component for developing and evaluating the measurement properties of patient-reported and health-related quality of life (HRQL) outcome measures. Advances in the development of new quantitative methods and in the application of these new methods have increased our understanding of the relationship among physiologic, clinical, and HRQL outcomes and improved the development and evaluation of new health outcome instruments. In this section, we are pleased to provide a good cross section of the quantitative methods applied to HRQL and other patient-reported outcome measures in this issue of Quality of Life Research. The twelve papers included in this section cover a range in topics from various approaches to evaluating longitudinal HRQL data, evaluating theoretical models, and applying advanced psychometric methods to understanding conceptual equivalence across countries or in analyzing PRO item-level data. First, there are several articles summarizing advanced quantitative methods of handling longitudinal data. For example, Anota et al. [1] summarize the issues and definitions associated with time-to-deterioration type analyses, with illustrations from early breast cancer and metastatic pancreatic cancer samples. A number of different definitions for deterioration in HRQL outcomes can be specified, and these decisions have implications for the results of the time-to-deterioration analyses. The authors provide some guidance on the use of time-to-deterioration versus time-todefinitive-deterioration. In the article by de Bock et al. [2], Rasch analysis is used to handle informative intermittent missing data for longitudinal comparisons of PRO data. They developed several simulations with varying amounts of informative and non-informative missing data and applied longitudinal Rasch mixed models and linear mixed models. The two analysis methods were comparable when there was little missing data (\15 %), but the longitudinal Rasch mixed models performed better when there was greater missing data ([15 %). Terrin et al. [3] evaluated prediction models for transplant-related mortality based on HRQL data in a study pediatric hematopoietic stem cell transplant patients. Joint models were used to analyze the longitudinal HRQL data and the time-to-mortality data within the same statistical analysis using a single likelihood function. They found that trajectories of HRQL outcomes predicted transplant-related mortality in pediatric hematopoietic stem cell transplant patients, even after adjusting survival for baseline demographic and clinical characteristics. The next set of several articles evaluate theoretical models for understanding the relationship between clinical and HRQL measures. Mayo et al. [4] evaluated the Wilson– Cleary model [5] in patients recovering from a recent stroke. They apply structural equation models (SEM) to examine the relationship among biological variables, symptoms, functional outcomes, and health perceptions in 533 patients D. A. Revicki (&) Outcomes Research, Evidera, 7101 Wisconsin Ave., Suite 1400, Bethesda, MD 201814, USA e-mail: dennis.revicki@evidera.com