Health Systems, Economic Evaluations, Quality of Life

EuroQol - a new facility for the measurement of health-related quality of life

A Proportional Hazards Model for the Subdistribution of a Competing Risk

With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In recent years many clinicians have begun using the cumulative incidence function, the marginal failure probabilities for a particular cause, which is intuitively appealing and more easily explained to the nonstatistician. The cumulative incidence is especially relevant in cost-effectiveness analyses in which the survival probabilities are needed to determine treatment utility. Previously, authors have considered methods for combining estimates of the cause-specific hazard functions under the proportional hazards formulation. However, these methods do not allow the analyst to directly assess the effect of a covariate on the marginal probability function. In this article we propose a novel semiparametric proportional hazards model for the subdistribution. Using the partial likelihood principle and weighting techniques, we derive estimation and inference procedures for the finite-dimensional regression parameter under a variety of censoring scenarios. We give a uniformly consistent estimator for the predicted cumulative incidence for an individual with certain covariates; confidence intervals and bands can be obtained analytically or with an easy-to-implement simulation technique. To contrast the two approaches, we analyze a dataset from a breast cancer clinical trial under both models.

Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010

Cost-Effectiveness in Health and Medicine

Abstract This is a unique, in-depth discussion of the uses and conduct of cost-effectiveness analyses (CEA) as decision-making aids in the health and medical fields. The product of over two years of deiberation by a multi-disciplinary Public Health Service appointed panel that included economists, ethicists, psychometricians, and clinicians, it explores cost-effectiveness in the context of societal decision-making for resource allocation purposes. It proposes that analysts include a “reference-case” analysis in all CEA’s designed to inform resource allocation and puts forth the most expicit set of guidelines (together with their rationale) ever outlined of the conduct of CEAs. Important theoretical and practical issues encountered in measuring costs and effectiveness, valuing outcomes, discounting, and dealing with uncertainty are examined in separate chapters. These discussions are complemented by additional chapters on framing and reporting of CEAs that aim to clarify the purpose of the analysis and the effective communication of its findings. Primarily intended for analysts in medicine and public health who wish to improve practice and comparability of CEAs, this book will also be of interest to decision-makers in government, managed care, and industry who wish to consider the roles and limitations of CEA and become familiar with criteria for evaluating these studies.

Assessing the Performance of Prediction Models

The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.