Comparing the Ability of Physician-Reported Versus Patient-Reported Performance Status to Predict Survival in a Population-Based Cohort of Newly Diagnosed Cancer Patients.

AIMS: Performance status is an important prognostic tool in cancer. In oncology, the Eastern Cooperative Oncology Group (ECOG) measure is commonly used. Patient-reported functional status (PRFS) is an emerging method that allows patients to provide an estimate of their function; however, there is limited information about its prognostic significance. The aim of this study was to compare the predictive validity of functional status as reported by patients and physicians in relation to the observed survival after a new cancer diagnosis. MATERIALS AND METHODS: This was a retrospective, population-based study using observational data of newly diagnosed patients in Ontario, Canada. We included patients who had both PRFS and ECOG recorded on the same day during an outpatient cancer clinic visit between March 2013 and March 2018. The dataset was randomly divided into 60% training and 40% validation cohorts. One-year survival was estimated by modelling clinical characteristics with PRFS, with ECOG, and alone. RESULTS: In total, 13 045 patients met the inclusion criteria. Covariates were similar at baseline for both training and validation datasets. PRFS and ECOG scores were statistically significant predictors of overall survival. Higher PRFS and ECOG scores were both associated with inferior survival, hazard ratio = 1.71 (P < 0.0001) and hazard ratio = 1.90 (P < 0.0001), respectively. Models that included either PRFS or ECOG scores outperformed the model with clinical characteristics only. C statistics were 0.836, 0.839 and 0.811, respectively. CONCLUSIONS: PRFS adds to survival modelling and is equally predictive as the ECOG scale. PRFS may be used instead of ECOG in clinical or research settings for survival estimation.

Estimation of state occupancy probabilities in multistate models with dependent intermittent observation, with application to HIV viral rebounds.

In follow-up studies on chronic disease cohorts, individuals are often observed at irregular visit times that may be related to their previous disease history and other factors. This can produce bias in standard methods of estimation. Working in the context of multistate models, we consider a method of nonparametric estimation for state occupancy probabilities that adjusts for dependent follow-up through the use of inverse-intensity-of-visit weighted estimating functions and smoothing. The methodology is applied to the estimation of viral rebound probabilities in the Canadian Observational Cohort on HIV-positive persons. Copyright © 2016 John Wiley & Sons, Ltd.

Estimation and assessment of markov multistate models with intermittent observations on individuals.

Multistate models provide important methods of analysis for many life history processes, and this is an area where John Klein made numerous contributions. When individuals in a study group are observed continuously so that all transitions between states, and their times, are known, estimation and model checking is fairly straightforward. However, individuals in many studies are observed intermittently, and only the states occupied at the observation times are known. We review methods of estimation and assessment for Markov models in this situation. Numerical studies that show the effects of inter-observation times are provided, and new methods for assessing fit are given. An illustration involving viral load dynamics for HIV-positive persons is presented.