Biostatistics Commons^™

Graphical Assessment Of Internal And External Calibration Of Logistic Regression Models Using Loess Smoothers, Peter C. Austin

Peter Austin

Predicting the probability of the occurrence of a binary outcome or condition is important in biomedical research. While assessing discrimination is an essential issue in developing and validating binary prediction models, less attention has been paid to methods for assessing model calibration. Calibration refers to the degree of agreement between observed and predicted probabilities, and is often assessed by testing lack-of-fit. The objective of our study was to examine the ability of graphical methods to assess calibration of logistic regression models. We examined lack of internal calibration, which was related to mis-specification of the logistic regression model, and lack of …

An Introduction To Propensity-Score Methods For Reducing Confounding In Observational Studies, Peter C. Austin

Peter Austin

The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (non-randomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. We describe four different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the …

Are (The Log-Odds Of) Hospital Mortality Rates Normally Distributed In Ontario? Implications For Studying Variations In Outcomes Of Medical Care, Peter C. Austin

Peter Austin

Objective: Hierarchical regression models are used to examine variations in outcomes following the provision of medical care across providers. These models frequently assume a normal distribution for the provider-specific random effects. Poincaré said, “Everyone believes in the normal law, the experimenters because they imagine it a mathematical theorem, and the mathematicians because they think it an experimental fact”. Our objective was to examine the appropriateness of this assumption when examining variations in mortality.

Study design and setting: We used Bayesian model selection methods to compare hierarchical regression models in which the provider-specific random effects were either a normal distribution or …