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. 2025 Jan-Feb;24(1):e2436.
doi: 10.1002/pst.2436. Epub 2024 Sep 5.

Propensity Score Analysis With Baseline and Follow-Up Measurements of the Outcome Variable

Peter C Austin  1   2   3
Affiliations

Propensity Score Analysis With Baseline and Follow-Up Measurements of the Outcome Variable

Peter C Austin. Pharm Stat. 2025 Jan-Feb.

Abstract

A common feature in cohort studies is when there is a baseline measurement of the continuous follow-up or outcome variable. Common examples include baseline measurements of physiological characteristics such as blood pressure or heart rate in studies where the outcome is post-baseline measurement of the same variable. Methods incorporating the propensity score are increasingly being used to estimate the effects of treatments using observational studies. We examined six methods for incorporating the baseline value of the follow-up variable when using propensity score matching or weighting. These methods differed according to whether the baseline value of the follow-up variable was included or excluded from the propensity score model, whether subsequent regression adjustment was conducted in the matched or weighted sample to adjust for the baseline value of the follow-up variable, and whether the analysis estimated the effect of treatment on the follow-up variable or on the change from baseline. We used Monte Carlo simulations with 750 scenarios. While no analytic method had uniformly superior performance, we provide the following recommendations: first, when using weighting and the ATE is the target estimand, use an augmented inverse probability weighted estimator or include the baseline value of the follow-up variable in the propensity score model and subsequently adjust for the baseline value of the follow-up variable in a regression model. Second, when the ATT is the target estimand, regardless of whether using weighting or matching, analyze change from baseline using a propensity score that excludes the baseline value of the follow-up variable.

Keywords: Monte Carlo simulations; baseline covariates; inverse probability of treatment weighting; propensity score analysis; propensity score matching.

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Conflict of interest statement

The author declares no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Characteristics of different simulation scenarios.
FIGURE 2
FIGURE 2
Balance of baseline covariates between treated and control subjects in the super–population.
FIGURE 3
FIGURE 3
Relative bias: ATE (Weighting).
FIGURE 4
FIGURE 4
Coverage of 95% confidence intervals: ATE (Weighting).
FIGURE 5
FIGURE 5
Empirical standard errors: ATE (Weighting).
FIGURE 6
FIGURE 6
Relative percent increase in precision compared to change from baseline: ATE (Weighting).
FIGURE 7
FIGURE 7
Relative percent error in estimated standard error: ATE (Weighting).
FIGURE 8
FIGURE 8
Relative bias: ATT (Weighting).
FIGURE 9
FIGURE 9
Coverage of 95% confidence intervals: ATT (Weighting).
FIGURE 10
FIGURE 10
Empirical standard errors: ATT (Weighting).
FIGURE 11
FIGURE 11
Relative percent increase in precision compared to change from baseline: ATT (Weighting).
FIGURE 12
FIGURE 12
Relative percent error in estimated standard error: ATT (Weighting).
FIGURE 13
FIGURE 13
Relative bias: ATT (Matching).
FIGURE 14
FIGURE 14
Coverage of 95% confidence intervals: ATT (Matching).
FIGURE 15
FIGURE 15
Empirical standard errors: ATT (Matching).
FIGURE 16
FIGURE 16
Relative percent increase in precision compared to change from baseline: ATT (Matching).
FIGURE 17
FIGURE 17
Relative percent error in estimated standard error: ATT (Matching).
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