On Corrected Score Approach for Proportional Hazards Model with Covariate Measurement Error

In the presence of covariate measurement error with the proportional hazards model, several functional modeling methods have been proposed. These include the conditional score estimator (Tsiatis and Davidian, 2001), the parametric correction estimator (Nakamura, 1992) and the nonparametric correction estimator (Huang and Wang, 2000, 2003) in the order of weaker assumptions on the error. Although they are all consistent, each suffers from potential difficulties with small samples and substantial measurement error. In this article, upon noting that the conditional score and parametric correction estimators are asymptotically equivalent in the case of normal error, we investigate their relative finite sample performance and discover that the former is superior, which may be explained by the unbiasedness of its estimating equation. This finding motivates a general refinement approach to parametric and nonparametric correction methods. The refined correction estimators are asymptotically equivalent to their standard counterparts, but have improved numerical properties. Simulation results and application to an HIV clinical trial are presented

A Corrected Pseudo-score Approach for Additive Hazards Model With Longitudinal Covariates Measured With Error

In medical studies, it is often of interest to characterize the relationship between a time-to-event and covariates, not only time-independent but also time-dependent. Time-dependent covariates are generally measured intermittently and with error. Recent interests focus on the proportional hazards framework, with longitudinal data jointly modeled through a mixed effects model. However, approaches under this framework depend on the normality assumption of the error, and might encounter intractable numerical difficulties in practice. This motivates us to consider an alternative framework, that is, the additive hazards model, under which little has been done when time-dependent covariates are measured with error. We propose a simple corrected pseudo-score approach for the regression parameters with no assumptions on the distribution of the random effects and the error beyond those for the variance structure of the latter. The estimator has an explicit form and is shown to be consistent and asymptotically normal. We illustrate the method via simulations and by application to data from an HIV clinical trial

Lifetime economic burden of prostate cancer

<p>Abstract</p> <p>Background</p> <p>Prostate cancer (PCa) is the most common cancer affecting men in the United States. The initial treatment and subsequent monitoring of PCa patients places a large burden on U.S. health care systems. The objectives of this study were to estimate the total and disease-related per-patient lifetime costs using a phase-based model of cancer care for PCa patients enrolled in Medicare.</p> <p>Methods</p> <p>A model was developed to estimate life-time costs for patients diagnosed with PCa. Patients ≥ 65 years old and diagnosed with PCa between calendar years 1991-2002 were selected from the SEER database. Using SEER, we estimated survival times for PCa patients from diagnosis until death. The period of time patients contributed to treatment phases was determined using an algorithm designed to model the natural history of PCa. Costs were obtained from the US SEER-Medicare database and estimated during specific phases of care. Cost estimates were then combined with survival data to yield total and PCa-related life-time costs.</p> <p>Results</p> <p>Overall, the model estimated life-time costs of $110,520 (95$34,432).</p> <p>Conclusions</p> <p>Prostate cancer places a significant burden on U.S. health-care systems with average life-time PCa-related costs in excess of $30,000.</p