Dr. Peter Austin is a Senior Scientist at the Institute for Clinical Evaluative Sciences (ICES) in Toronto, Canada, and a Professor in the department of Health Policy, Management and Evaluation at the University of Toronto. His research interests include statistical methods for the analysis of large health care databases, propensity-score methods for causal inference, predictive models for cardiovascular outcomes, and statistical methods for provider profiling.
Propensity score methods
Using Ensemble-Based Methods for Directly Estimating Causal Effects: An Investigation of Tree-Based G-Computation, Multivariate Behavioral Research (2012)
Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to...
An introduction to propensity-score methods for reducing confounding in observational studies, Multivariate Behavioral Research (2011)
The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The...
A Tutorial and Case Study in Propensity Score Analysis: An Application to Estimating the Effect of In-Hospital Smoking Cessation Counseling on Mortality, Multivariate Behavioral Research (2011)
Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data....
A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review (with Andreas Laupacis), The International Journal of Biostatistics (2011)
In randomized controlled trials (RCTs), treatment assignment is unconfounded with baseline covariates, allowing outcomes to...
Comparing paired vs. non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples., Statistics in Medicine (2011)
Propensity-score matching allows one to reduce the effects of treatment-selection bias or confounding when estimating...
Predictive models
Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes, Journal of Clinical Epidemiology (2013)
OBJECTIVE: Physicians classify patients into those with or without a specific disease. Furthermore, there is...
Comparative ability of comorbidity classification methods for administrative data to predict outcomes in patients with chronic obstructive pulmonary disease, Annals of Epidemiology (2012)
PURPOSE: Administrative healthcare databases are used for health services research, comparative effectiveness studies, and measuring...
Interpreting the concordance statistic of a logistic regression model: relation to the variance and odds ratio of a continuous explanatory variable, BMC Medical Research Methodology (2012)
Background: When outcomes are binary, the c-statistic (equivalent to the area under the Receiver Operating...
Regression trees for predicting mortality in patients with cardiovascular disease: what improvement is achieved by using ensemble-based methods?, Biometrical Journal (2012)
In biomedical research, the logistic regression model is the most commonly used method for predicting...
Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a population-based cohort of subjects with schizophrenia in Ontario, Canada, Psychiatry Research (2012)
Administrative health care databases are increasingly used for health services and comparative effectiveness research. When...
Statistical methods for provider profiling
Estimating Multilevel Logistic Regression Models When the Number of Clusters is Low: A Comparison of Different Statistical Software Procedures, The International Journal of Biostatistics (2010)
Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public...
Are (the log-odds of) Hospital Mortality Rates Normally Distributed in Ontario? Implications for Studying Variations in Outcomes of Medical Care, Journal of Evaluation in Clinical Practice (2009)
Objective: Hierarchical regression models are used to examine variations in outcomes following the provision of...
Bayes Rules for Optimally using Hierarchical Regression Models to Identify High-mortality Hospitals, BMC Health Research Methodology (2008)
Background
There is a growing trend towards the production of "hospital report-cards" in which hospitals...
Optimal Bayesian Probability Levels for Hospital Report Cards, Health Services and Outcomes Research Methodology (2008)
here is a growing trend towards the production of “hospital report-cards” in which hospitals with...
Comparing Clinical Data with Administrative Data for Producing AMI Report Cards, Journal of the Royal Statistical Society – Series A (Statistics in Society). (2006)
We compared measures of hospital performance by using both administrative and clinical data sources. Hospital-specific...
Multilevel models for health services research
Estimating Multilevel Logistic Regression Models When the Number of Clusters is Low: A Comparison of Different Statistical Software Procedures, The International Journal of Biostatistics (2010)
Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public...
Are (the log-odds of) Hospital Mortality Rates Normally Distributed in Ontario? Implications for Studying Variations in Outcomes of Medical Care, Journal of Evaluation in Clinical Practice (2009)
Objective: Hierarchical regression models are used to examine variations in outcomes following the provision of...
Bias in Penalized Quasi-Likelihood Estimation in Random Effects Logistic Regression Models when the Random Effects are not Normally Distributed, Communications in Statistics – Simulation and Computation (2005)
Regression models incorporating random effects are being used with increasing frequency to examine variations in...
Comparing hierarchical modeling with traditional logistic regression analysis among patients hospitalized with acute myocardial infarction: Should we be analyzing cardiovascular outcomes data differently?, American Heart Journal (2003)
Background: Data in health research are frequently structured hierarchically. For example, data may consist of...
An Introduction to Multilevel Regression Models, Canadian Journal of Public Health (2001)
Data in health research are frequently structured hierarchically. For example, data may consist of patients...
Randomized controlled trials
A Comparison of the Statistical Power of Different Methods for the Analysis of Repeated Cross-Sectional Cluster Randomization Trials with Binary Outcomes, The International Journal of Biostatistics (2010)
Repeated cross-sectional cluster randomization trials are cluster randomization trials in which the response variable is...
A substantial and confusing variation exists in handling of baseline covariates in randomized controlled trials: a review of trials published in leading medical journals., Journal of Clinical Epidemiology (2010)
Objective: Statisticians have criticized the use of significance testing to compare the distribution of baseline...
A comparison of the statistical power of different methods for the analysis of cluster randomization trials with binary outcomes, Statistics in Medicine (2007)
Cluster randomization trials are randomized controlled trials (RCTs) in which intact clusters of subjects are...
Impact of the Pravastatin or Atorvastatin Evaluation and Infection Therapy-Thrombolysis In Myocardial Infarction 22/Reversal of Atherosclerosis With Aggressive Lipid Lowering Trials on Trends in Intensive Versus Moderate Statin Therapy in Ontario, Canada, Circulation (2005)
Background— In March 2004, the Reversal of Atherosclerosis With Aggressive Lipid Lowering (REVERSAL) trial demonstrated...
Changes in Prescribing Patterns Following Publication of the ALLHAT Trial, Journal of the American Medical Association (2004)
Miscellaneous
The Mortality Risk Score and the ADG Score: two points-based scoring systems for the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada, Medical Care (2011)
Background: Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins’...
Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada, Medical Care (2011)
Background: Administrative health care databases are increasingly used for health services and comparative effectiveness research....
A data-generation process for data with specified risk differences or numbers needed to treat, Communications in Statistics - Simulation and Computation (2010)
Monte Carlo simulation methods are increasingly being used to evaluate the performance of statistical methods...
Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research, Communications in Statistics – Simulation and Computation (2009)
Researchers are increasingly using the standardized difference to compare the distribution of baseline covariates between...
Comparing clinical and administrative data for profiling hospitals on post-discharge medication use by AMI patients, American Heart Journal (2008)
Background
Postdischarge medication use is frequently used as a measure of hospital performance, with many...
Bayesian methods
Bayesian Modeling of Missing Data in Clinical Research, Computational Statistics & Data Analysis (2005)
The issue of missing data frequently confronts researchers using data derived from patient medical records....
A Comparison of Bayesian Methods for Profiling Hospital Performance, Medical Decision Making (2002)
There is a growing interest in the use of Bayesian methods for profiling institutional performance....
Bayesian Extensions of the Tobit Model for Analyzing Measures of Health Status, Medical Decision Making (2002)
Self-reported health status is often measured using utility indices that provide a score intended to...
Bayeswatch: An Overview of Bayesian Statistics, Journal of Evaluation in Clinical Practice (2002)
Increasingly, clinical research is evaluated on the quality of its statistical analysis. Traditionally, statistical analyses...
A Comparison of a Bayesian versus a Frequentist Method for Profiling Hospital Performance, Journal of Evaluation in Clinical Practice (2001)
The objective of this study was to compare the classification of hospitals as outcomes outliers...
Regression methods
Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes, Journal of Clinical Epidemiology (2013)
OBJECTIVE: Physicians classify patients into those with or without a specific disease. Furthermore, there is...
Comparative ability of comorbidity classification methods for administrative data to predict outcomes in patients with chronic obstructive pulmonary disease, Annals of Epidemiology (2012)
PURPOSE: Administrative healthcare databases are used for health services research, comparative effectiveness studies, and measuring...
Regression trees for predicting mortality in patients with cardiovascular disease: what improvement is achieved by using ensemble-based methods?, Biometrical Journal (2012)
In biomedical research, the logistic regression model is the most commonly used method for predicting...
Using Ensemble-Based Methods for Directly Estimating Causal Effects: An Investigation of Tree-Based G-Computation, Multivariate Behavioral Research (2012)
Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to...
A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review (with Andreas Laupacis), The International Journal of Biostatistics (2011)
In randomized controlled trials (RCTs), treatment assignment is unconfounded with baseline covariates, allowing outcomes to...
Health services research
Comparative ability of comorbidity classification methods for administrative data to predict outcomes in patients with chronic obstructive pulmonary disease, Annals of Epidemiology (2012)
PURPOSE: Administrative healthcare databases are used for health services research, comparative effectiveness studies, and measuring...
Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a population-based cohort of subjects with schizophrenia in Ontario, Canada, Psychiatry Research (2012)
Administrative health care databases are increasingly used for health services and comparative effectiveness research. When...
A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review (with Andreas Laupacis), The International Journal of Biostatistics (2011)
In randomized controlled trials (RCTs), treatment assignment is unconfounded with baseline covariates, allowing outcomes to...
Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies., Pharmaceutical Statistics (2011)
In a study comparing the effects of two treatments, the propensity score is the probability...
The Mortality Risk Score and the ADG Score: two points-based scoring systems for the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada, Medical Care (2011)
Background: Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins’...
Health related quality of life
A Comparison of Methods for Analyzing Health Related Quality of Life Measures, Value in Health (2002)
Objectives: Self-reported health status is often measured using psychometric or utility indices that provide a...
Bayesian Extensions of the Tobit Model for Analyzing Measures of Health Status, Medical Decision Making (2002)
Self-reported health status is often measured using utility indices that provide a score intended to...
Health Services Research
Comparative ability of comorbidity classification methods for administrative data to predict outcomes in patients with chronic obstructive pulmonary disease, Annals of Epidemiology (2012)
PURPOSE: Administrative healthcare databases are used for health services research, comparative effectiveness studies, and measuring...
Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a population-based cohort of subjects with schizophrenia in Ontario, Canada, Psychiatry Research (2012)
Administrative health care databases are increasingly used for health services and comparative effectiveness research. When...
A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review (with Andreas Laupacis), The International Journal of Biostatistics (2011)
In randomized controlled trials (RCTs), treatment assignment is unconfounded with baseline covariates, allowing outcomes to...
Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies., Pharmaceutical Statistics (2011)
In a study comparing the effects of two treatments, the propensity score is the probability...
The Mortality Risk Score and the ADG Score: two points-based scoring systems for the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada, Medical Care (2011)
Background: Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins’...
Computation
Generating survival times to simulate Cox proportional hazards models with time-varying covariates., Statistics in Medicine (2012)
Simulations and Monte Carlo methods serve an important role in modern statistical research. They allow...
Estimating Multilevel Logistic Regression Models When the Number of Clusters is Low: A Comparison of Different Statistical Software Procedures, The International Journal of Biostatistics (2010)
Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public...
General Biostatistics
Predictive accuracy of risk factors and markers: a simulation study of the effect of novel markers on different performance measures for logistic regression models, Statistics in Medicine (2013)
The change in c-statistic is frequently used to summarize the change in predictive accuracy when...
Comparing the cohort design and the nested case-control design in the presence of both time-invariant and time-dependent treatment and competing risks: bias and precision, Pharmacoepidemiology and Drug Safety (2012)
Purpose: Observational studies using electronic administrative health care databases are often used to estimate the...
Generating survival times to simulate Cox proportional hazards models with time-varying covariates., Statistics in Medicine (2012)
Simulations and Monte Carlo methods serve an important role in modern statistical research. They allow...
Using Ensemble-Based Methods for Directly Estimating Causal Effects: An Investigation of Tree-Based G-Computation, Multivariate Behavioral Research (2012)
Researchers are increasingly using observational or nonrandomized data to estimate causal treatment effects. Essential to...
A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review (with Andreas Laupacis), The International Journal of Biostatistics (2011)
In randomized controlled trials (RCTs), treatment assignment is unconfounded with baseline covariates, allowing outcomes to...
Epidemiology
Comparative ability of comorbidity classification methods for administrative data to predict outcomes in patients with chronic obstructive pulmonary disease, Annals of Epidemiology (2012)
PURPOSE: Administrative healthcare databases are used for health services research, comparative effectiveness studies, and measuring...
Comparing the cohort design and the nested case-control design in the presence of both time-invariant and time-dependent treatment and competing risks: bias and precision, Pharmacoepidemiology and Drug Safety (2012)
Purpose: Observational studies using electronic administrative health care databases are often used to estimate the...
A Tutorial on Methods to Estimating Clinically and Policy-Meaningful Measures of Treatment Effects in Prospective Observational Studies: A Review (with Andreas Laupacis), The International Journal of Biostatistics (2011)
In randomized controlled trials (RCTs), treatment assignment is unconfounded with baseline covariates, allowing outcomes to...