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Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

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Listed:
  • Noémi Kreif
  • Oleg Sofrygin
  • Julie A. Schmittdiel
  • Alyce S. Adams
  • Richard W. Grant
  • Zheng Zhu
  • Mark J. van der Laan
  • Romain Neugebauer

Abstract

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR‐based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR‐based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment‐monitoring interventions, due to a large decrease in data support and concerns over finite‐sample bias from near‐violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.

Suggested Citation

  • Noémi Kreif & Oleg Sofrygin & Julie A. Schmittdiel & Alyce S. Adams & Richard W. Grant & Zheng Zhu & Mark J. van der Laan & Romain Neugebauer, 2021. "Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies," Biometrics, The International Biometric Society, vol. 77(1), pages 329-342, March.
    • Handle: RePEc:bla:biomet:v:77:y:2021:i:1:p:329-342
    DOI: 10.1111/biom.13271
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    References listed on IDEAS

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    1. Cain Lauren E. & Robins James M. & Lanoy Emilie & Logan Roger & Costagliola Dominique & Hernán Miguel A., 2010. "When to Start Treatment? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-26, April.
    2. van der Laan Mark J. & Petersen Maya L, 2007. "Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-55, March.
    3. Neugebauer Romain & Schmittdiel Julie A. & van der Laan Mark J., 2016. "A Case Study of the Impact of Data-Adaptive Versus Model-Based Estimation of the Propensity Scores on Causal Inferences from Three Inverse Probability Weighting Estimators," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 131-155, May.
    4. Neugebauer Romain & Schmittdiel Julie A. & Adams Alyce S. & Grant Richard W. & van der Laan Mark J., 2017. "Identification of the Joint Effect of a Dynamic Treatment Intervention and a Stochastic Monitoring Intervention Under the No Direct Effect Assumption," Journal of Causal Inference, De Gruyter, vol. 5(1), pages 1-44, March.
    5. Iván Díaz Muñoz & Mark van der Laan, 2012. "Population Intervention Causal Effects Based on Stochastic Interventions," Biometrics, The International Biometric Society, vol. 68(2), pages 541-549, June.
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