title: Do repeated assessments of performance status improve predictions for risk of death among patients with cancer? A population-based cohort study.
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.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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: Institute for Clinical Evaluative Sciences, Toronto, ON, Canada.
authors: Jiandong
authors: J
authors: Su
authors: 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.
authors: Lisa
authors: L
authors: Barbera
authors: 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.
authors: Rinku
authors: R
authors: Sutradhar
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003643:Death; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D009369:Neoplasms; D010166:Palliative Care; D011237:Predictive Value of Tests; D018570:Risk Assessment; D055815:Young Adult
keywords: Cox model; Performance status scores; concordance probability; longitudinal data; time-varying covariates