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Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting.
J Eval Clin Pract. 2017 Aug; 23(4):697-702.JE

Abstract

RATIONALE, AIMS AND OBJECTIVES

When a randomized controlled trial is not feasible, health researchers typically use observational data and rely on statistical methods to adjust for confounding when estimating treatment effects. These methods generally fall into 3 categories: (1) estimators based on a model for the outcome using conventional regression adjustment; (2) weighted estimators based on the propensity score (ie, a model for the treatment assignment); and (3) "doubly robust" (DR) estimators that model both the outcome and propensity score within the same framework. In this paper, we introduce a new DR estimator that utilizes marginal mean weighting through stratification (MMWS) as the basis for weighted adjustment. This estimator may prove more accurate than treatment effect estimators because MMWS has been shown to be more accurate than other models when the propensity score is misspecified. We therefore compare the performance of this new estimator to other commonly used treatment effects estimators.

METHOD

Monte Carlo simulation is used to compare the DR-MMWS estimator to regression adjustment, 2 weighted estimators based on the propensity score and 2 other DR methods. To assess performance under varied conditions, we vary the level of misspecification of the propensity score model as well as misspecify the outcome model.

RESULTS

Overall, DR estimators generally outperform methods that model one or the other components (eg, propensity score or outcome). The DR-MMWS estimator outperforms all other estimators when both the propensity score and outcome models are misspecified and performs equally as well as other DR estimators when only the propensity score is misspecified.

CONCLUSIONS

Health researchers should consider using DR-MMWS as the principal evaluation strategy in observational studies, as this estimator appears to outperform other estimators in its class.

Authors+Show Affiliations

Linden Consulting Group, LLC, Ann Arbor, MI, USA. Division of General Medicine, Medical School, University of Michigan, Ann Arbor, MI, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28116816

Citation

Linden, Ariel. "Improving Causal Inference With a Doubly Robust Estimator That Combines Propensity Score Stratification and Weighting." Journal of Evaluation in Clinical Practice, vol. 23, no. 4, 2017, pp. 697-702.
Linden A. Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting. J Eval Clin Pract. 2017;23(4):697-702.
Linden, A. (2017). Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting. Journal of Evaluation in Clinical Practice, 23(4), 697-702. https://doi.org/10.1111/jep.12714
Linden A. Improving Causal Inference With a Doubly Robust Estimator That Combines Propensity Score Stratification and Weighting. J Eval Clin Pract. 2017;23(4):697-702. PubMed PMID: 28116816.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting. A1 - Linden,Ariel, Y1 - 2017/01/24/ PY - 2017/01/01/received PY - 2017/01/03/accepted PY - 2017/1/25/pubmed PY - 2018/5/8/medline PY - 2017/1/25/entrez KW - causal inference KW - doubly robust KW - inverse probability of treatment weights KW - marginal mean weighting through stratification KW - propensity score KW - stratification KW - treatment effects SP - 697 EP - 702 JF - Journal of evaluation in clinical practice JO - J Eval Clin Pract VL - 23 IS - 4 N2 - RATIONALE, AIMS AND OBJECTIVES: When a randomized controlled trial is not feasible, health researchers typically use observational data and rely on statistical methods to adjust for confounding when estimating treatment effects. These methods generally fall into 3 categories: (1) estimators based on a model for the outcome using conventional regression adjustment; (2) weighted estimators based on the propensity score (ie, a model for the treatment assignment); and (3) "doubly robust" (DR) estimators that model both the outcome and propensity score within the same framework. In this paper, we introduce a new DR estimator that utilizes marginal mean weighting through stratification (MMWS) as the basis for weighted adjustment. This estimator may prove more accurate than treatment effect estimators because MMWS has been shown to be more accurate than other models when the propensity score is misspecified. We therefore compare the performance of this new estimator to other commonly used treatment effects estimators. METHOD: Monte Carlo simulation is used to compare the DR-MMWS estimator to regression adjustment, 2 weighted estimators based on the propensity score and 2 other DR methods. To assess performance under varied conditions, we vary the level of misspecification of the propensity score model as well as misspecify the outcome model. RESULTS: Overall, DR estimators generally outperform methods that model one or the other components (eg, propensity score or outcome). The DR-MMWS estimator outperforms all other estimators when both the propensity score and outcome models are misspecified and performs equally as well as other DR estimators when only the propensity score is misspecified. CONCLUSIONS: Health researchers should consider using DR-MMWS as the principal evaluation strategy in observational studies, as this estimator appears to outperform other estimators in its class. SN - 1365-2753 UR - https://www.unboundmedicine.com/medline/citation/28116816/Improving_causal_inference_with_a_doubly_robust_estimator_that_combines_propensity_score_stratification_and_weighting_ L2 - https://doi.org/10.1111/jep.12714 DB - PRIME DP - Unbound Medicine ER -