Quasi-Likelihood Analysis of Patient Satisfaction with Medical Care

In health services research studies, a score for satisfaction with care is often determined for a patient by summing several items, each measured on a Likert scale with ordered response options indicating satisfaction with some aspect of medical care (e.g., five categories ordered 1-poor to 5-excellent). A common goal is to determine patient and physician level predictors of patient satisfaction using regression analysis. The large number of categories in the ordinal summed response variable may present obstacles to traditional analytic methods for ordinal data such as the proportional odds model for cumulative logits. Further, linear regression is generally known to be inappropriate. Quasi-likelihood methods provide a flexible and tractable alternative modelling procedure. Weak assumptions about the measurement scale may be made by estimating parameters that define a family of link functions. A quasi-likelihood analysis of data from a study of elderly patients' satisfaction with communication with their primary care physician is presented. Although several factors are significantly related to satisfaction, a diagnostic plot based upon cumulative deviances reveals inadequacy of fit for the patients with the lowest observed satisfaction.