Time After Time: Difference-in-Differences and Interrupted Time Series Models in SAS

Health care and other epidemiological researchers are increasingly turning to difference-in-differences (DID) and interrupted time series models (ITS) to analyze pre- and post-changes in outcomes around an intervention or exposure. These models are often used in quasi-experimental studies with non-randomized exposures using retrospective observational data, and they enable a causal interpretation and adjustment for secular trends in the outcome of interest. DID models compare the rate of change of an outcome measure before and after an exposure in exposed and control groups based on a single measure in each period. DID data can be modeled with repeated-measures generalized linear models with an interaction term between time period and the exposure variable. The ITS—an extension of the DID design—compares trends in an outcome over multiple pre- and post-time period measures, allowing for a discontinuity (an “interruption”) in both control and exposure average rates during the study period. Models of this type account for situations where rates of an outcome of interest shift in both exposed and control groups at a specific time point, such as the change from ICD9 to ICD10 diagnosis codes during 2015. We discuss the statistical basis of these methods and illustrate data structure, modeling methods, power calculations, and interpretation of model estimates for both models with data from studies performed in a large integrated health care system.

Presenter: E. Margaret Warton, Senior Data Consultant, Kaiser Permanente
Session ID: 4674
Topic: Analytics
Industry: Non-Industry Specific
Audience: Analyst, Programmer, Statistician, Biostatistician
Skill Level: All

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