Jiandong Su1, Lisa Barbera2, Rinku Sutradhar3. 1. Institute for Clinical Evaluative Sciences, Toronto, ON, Canada. 2. Institute for Clinical Evaluative Sciences, Toronto, ON, Canada Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. 3. Institute for Clinical Evaluative Sciences, Toronto, ON, Canada Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada rinku.sutradhar@ices.on.ca.
Abstract
BACKGROUND: Prior work has utilized longitudinal information on performance status to demonstrate its association with risk of death among cancer patients; however, no study has assessed whether such longitudinal information improves the predictions for risk of death. AIM: To examine whether the use of repeated performance status assessments improve predictions for risk of death compared to using only performance status assessment at the time of cancer diagnosis. DESIGN: This was a population-based longitudinal study of adult outpatients who had a cancer diagnosis and had at least one assessment of performance status. To account for each patient's changing performance status over time, we implemented a Cox model with a time-varying covariate for performance status. This model was compared to a Cox model using only a time-fixed (baseline) covariate for performance status. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive ability of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. RESULTS: Our study consisted of 15,487 cancer patients with over 53,000 performance status assessments. The utilization of repeated performance status assessments improved predictions for risk of death compared to using only the performance status assessment taken at diagnosis. CONCLUSION: When studying the hazard of death among patients with cancer, if available, researchers should incorporate changing information on performance status scores, instead of simply baseline information on performance status.
BACKGROUND: Prior work has utilized longitudinal information on performance status to demonstrate its association with risk of death among cancerpatients; however, no study has assessed whether such longitudinal information improves the predictions for risk of death. AIM: To examine whether the use of repeated performance status assessments improve predictions for risk of death compared to using only performance status assessment at the time of cancer diagnosis. DESIGN: This was a population-based longitudinal study of adult outpatients who had a cancer diagnosis and had at least one assessment of performance status. To account for each patient's changing performance status over time, we implemented a Cox model with a time-varying covariate for performance status. This model was compared to a Cox model using only a time-fixed (baseline) covariate for performance status. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive ability of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. RESULTS: Our study consisted of 15,487 cancerpatients with over 53,000 performance status assessments. The utilization of repeated performance status assessments improved predictions for risk of death compared to using only the performance status assessment taken at diagnosis. CONCLUSION: When studying the hazard of death among patients with cancer, if available, researchers should incorporate changing information on performance status scores, instead of simply baseline information on performance status.
Authors: Amy Hawarden; Bryn Russell; Mary Ellen Gee; Fatima Kayali; Andrew Clamp; Emma Jayne Crosbie; Richard John Edmondson Journal: BMC Cancer Date: 2021-04-08 Impact factor: 4.430