Estimating causal effects for survival (time-to-event) outcomes by combining classification tree analysis and propensity score weighting.
Journal:
Journal of evaluation in clinical practice
Published Date:
Dec 12, 2017
Abstract
RATIONALE, AIMS AND OBJECTIVES: A common approach to assessing treatment effects in nonrandomized studies with time-to-event outcomes is to estimate propensity scores and compute weights using logistic regression, test for covariate balance, and then estimate treatment effects using Cox regression. A machine-learning alternative-classification tree analysis (CTA)-used to generate propensity scores and to estimate treatment effects in time-to-event data may identify complex relationships between covariates not found using conventional regression-based approaches.
Authors
Keywords
Age Factors
Comorbidity
Decision Trees
Female
Health Services
Heart Failure
Humans
Machine Learning
Male
Middle Aged
Models, Statistical
Monte Carlo Method
Patient Readmission
Propensity Score
Proportional Hazards Models
Pulmonary Disease, Chronic Obstructive
Sex Factors
Socioeconomic Factors
Survival Analysis
Time Factors