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Related Experiment Video

Updated: Jun 2, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Potential for bias in case-crossover studies with shared exposures analyzed using SAS.

Shirley V Wang¹, Brent A Coull, Joel Schwartz

¹Center for Environmental Health and Technology, Brown University, Providence, Rhode Island, USA.

American Journal of Epidemiology

|May 5, 2011

Summary

Case-crossover studies can yield biased results when using SAS software with shared exposures. Using the "Breslow" option in stratified Cox models ensures unbiased health-effect estimates in these environmental epidemiology analyses.

Related Experiment Videos

Last Updated: Jun 2, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Epidemiology
Biostatistics
Environmental Health

Background:

Case-crossover studies efficiently assess transient exposures and acute events.
Aggregate time-series data are common in environmental epidemiology.
Standard analysis methods may introduce bias with shared exposures in SAS.

Purpose of the Study:

To identify potential biases in case-crossover analyses using SAS.
To recommend appropriate statistical methods for unbiased estimation.

Main Methods:

Simulations were conducted to compare different statistical models.
Conditional logistic regression and stratified Cox models (ties=discrete, ties=Breslow) were evaluated.
Analysis focused on case-crossover studies with shared exposures and multiple cases.

Main Results:

Stratified Cox models with the "Breslow" option provided unbiased health-effect estimates.
Conditional logistic regression and stratified Cox models (ties=discrete) showed bias (22%-39%) away from the null.
Bias was not observed in R or Stata software.

Conclusions:

The "Breslow" option in stratified Cox models is crucial for unbiased results in SAS for case-crossover studies with shared exposures.
Researchers using SAS should carefully select statistical options to avoid biased health-effect estimates.
Alternative software like R or Stata do not exhibit this specific bias.