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Optimal Dynamic Treatment Regime Estimation in the Presence of Nonadherence (2402.12555v1)

Published 19 Feb 2024 in stat.ME

Abstract: Dynamic treatment regimes (DTRs) are sequences of functions that formalize the process of precision medicine. DTRs take as input patient information and output treatment recommendations. A major focus of the DTR literature has been on the estimation of optimal DTRs, the sequences of decision rules that result in the best outcome in expectation, across the complete population were they to be applied. While there is a rich literature on optimal DTR estimation, to date there has been minimal consideration of the impacts of nonadherence on these estimation procedures. Nonadherence refers to any process through that an individual's prescribed treatment does not match their true treatment. We explore the impacts of nonadherence and demonstrate that generally, when nonadherence is ignored, suboptimal regimes will be estimated. In light of these findings we propose a method for estimating optimal DTRs in the presence of nonadherence. The resulting estimators are consistent and asymptotically normal, with a double robustness property. Using simulations we demonstrate the reliability of these results, and illustrate comparable performance between the proposed estimation procedure adjusting for the impacts of nonadherence and estimators that are computed on data without nonadherence.

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References (35)
  1. Statistical Methods for Dynamic Treatment Regimes. Springer New York, 2013. doi: 10.1007/978-1-4614-7428-9. URL https://doi.org/10.1007/978-1-4614-7428-9.
  2. Dynamic Treatment Regimes. Chapman and Hall/CRC, dec 2019. doi: 10.1201/9780429192692. URL https://doi.org/10.1201/9780429192692.
  3. Christopher J. C. H. Watkins and Peter Dayan. Q-learning. Mach Learn, 8(3-4):279–292, May 1992. doi: 10.1007/bf00992698. URL https://doi.org/10.1007%2Fbf00992698.
  4. S. A. Murphy. Optimal dynamic treatment regimes. Journal of the Royal Statistical Society, Series B, 65(2):331–355, may 2003. doi: 10.1111/1467-9868.00389. URL https://doi.org/10.1111\%2F1467-9868.00389.
  5. A robust method for estimating optimal treatment regimes. Biometrics, 68(4):1010–1018, May 2012. doi: 10.1111/j.1541-0420.2012.01763.x. URL https://doi.org/10.1111/j.1541-0420.2012.01763.x.
  6. Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association, 107(499):1106–1118, June 2012. doi: 10.1080/01621459.2012.695674. URL https://doi.org/10.1080/01621459.2012.695674.
  7. Michael P. Wallace and Erica E M Moodie. Doubly-robust dynamic treatment regimen estimation via weighted least squares. Biometrics, 71(3):636–644, 2015. ISSN 15410420. doi: 10.1111/biom.12306.
  8. Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens. Statistics in Medicine, 37(26):3776–3788, June 2018. doi: 10.1002/sim.7844. URL https://doi.org/10.1002/sim.7844.
  9. James M. Robins. Optimal structural nested models for optimal sequential decisions, pages 189–326. Springer New York, New York, NY, 2004. ISBN 978-1-4419-9076-1. doi: 10.1007/978-1-4419-9076-1“˙11. URL https://doi.org/10.1007/978-1-4419-9076-1\_11.
  10. Measurement error and precision medicine: Error-prone tailoring covariates in dynamic treatment regimes. Statistics in Medicine, 39(26):3732–3755, August 2020. doi: 10.1002/sim.8690. URL https://doi.org/10.1002%2Fsim.8690.
  11. M. Robin DiMatteo. Variations in patients’ adherence to medical recommendations. Medical Care, 42(3):200–209, mar 2004. doi: 10.1097/01.mlr.0000114908.90348.f9. URL https://doi.org/10.1097%2F01.mlr.0000114908.90348.f9.
  12. Adherence: Comparison of methods to assess medication adherence and classify nonadherence. Annals of Pharmacotherapy, 43(3):413–422, mar 2009. doi: 10.1345/aph.1l496. URL https://doi.org/10.1345%2Faph.1l496.
  13. Kelly B. Haskard Zolnierek and M Robin DiMatteo. Physician communication and patient adherence to treatment. Medical Care, 47(8):826–834, aug 2009. doi: 10.1097/mlr.0b013e31819a5acc. URL https://doi.org/10.1097%2Fmlr.0b013e31819a5acc.
  14. Depression and HIV/AIDS treatment nonadherence: A review and meta-analysis. JAIDS Journal of Acquired Immune Deficiency Syndromes, 58(2):181–187, oct 2011. doi: 10.1097/qai.0b013e31822d490a. URL https://doi.org/10.1097%2Fqai.0b013e31822d490a.
  15. SandeepK Gupta. Intention-to-treat concept: A review. Perspectives in Clinical Research, 2(3):109, 2011. doi: 10.4103/2229-3485.83221. URL https://doi.org/10.4103%2F2229-3485.83221.
  16. Eric McCoy. Understanding the intention-to-treat principle in randomized controlled trials. Western Journal of Emergency Medicine, 18(6):1075–1078, oct 2017. doi: 10.5811/westjem.2017.8.35985. URL https://doi.org/10.5811%2Fwestjem.2017.8.35985.
  17. Common pitfalls in statistical analysis: Intention-to-treat versus per-protocol analysis. Perspectives in Clinical Research, 7(3):144, 2016. doi: 10.4103/2229-3485.184823. URL https://doi.org/10.4103%2F2229-3485.184823.
  18. Intention-to-treat analysis and the goals of clinical trials. Clinical Pharmacology & Therapeutics, 57(1):6–15, jan 1995. doi: 10.1016/0009-9236(95)90260-0. URL https://doi.org/10.1016%2F0009-9236%2895%2990260-0.
  19. The multcenter AIDS cohort study: Rationale, organization, and selected characteristics of the participants. American Journal of Epidemiology, 126(2):310–318, aug 1987. doi: 10.1093/aje/126.2.310. URL https://doi.org/10.1093%2Faje%2F126.2.310.
  20. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology, 11(5):561–570, sep 2000. doi: 10.1097/00001648-200009000-00012. URL https://doi.org/10.1097%2F00001648-200009000-00012.
  21. Model assessment in dynamic treatment regimen estimation via double robustness. Biometrics, 72(3):855–864, jan 2016. doi: 10.1111/biom.12468. URL https://doi.org/10.1111%2Fbiom.12468.
  22. Determinants of heterogeneous adherence to HIV-antiretroviral therapies in the multicenter AIDS cohort study. JAIDS Journal of Acquired Immune Deficiency Syndromes, 26(1):82–92, jan 2001. doi: 10.1097/00126334-200101010-00012. URL https://doi.org/10.1097%2F00126334-200101010-00012.
  23. Donald B Rubin. Causal inference using potential outcomes. Journal of the American Statistical Association, 100(469):322–331, March 2005. doi: 10.1198/016214504000001880. URL https://doi.org/10.1198%2F016214504000001880.
  24. James M. Robins. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12):1393–1512, 1986. doi: 10.1016/0270-0255(86)90088-6. URL https://doi.org/10.1016%2F0270-0255%2886%2990088-6.
  25. Donald B. Rubin. Bias reduction using mahalanobis-metric matching. Biometrics, 36(2):293, June 1980. doi: 10.2307/2529981. URL https://doi.org/10.2307%2F2529981.
  26. Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5):550–560, September 2000. doi: 10.1097/00001648-200009000-00011. URL https://doi.org/10.1097/00001648-200009000-00011.
  27. Comparison of dynamic treatment regimes via inverse probability weighting. Basic & Clinical Pharmacology & Toxicology, 98(3):237–242, mar 2006. doi: 10.1111/j.1742-7843.2006.pto˙329.x. URL https://doi.org/10.1111%2Fj.1742-7843.2006.pto_329.x.
  28. A data augmentation method for estimating the causal effect of adherence to treatment regimens targeting control of an intermediate measure. Statistics in Biosciences, 3(1):28–44, jul 2011. doi: 10.1007/s12561-011-9038-1. URL https://doi.org/10.1007%2Fs12561-011-9038-1.
  29. Sukjin Han. Identification in nonparametric models for dynamic treatment effects. Journal of Econometrics, 225(2):132–147, dec 2021. doi: 10.1016/j.jeconom.2019.08.014. URL https://doi.org/10.1016%2Fj.jeconom.2019.08.014.
  30. R. Morton. Efficiency of estimating equations and the use of pivots. Biometrika, 68(1):227–233, 1981. doi: 10.1093/biomet/68.1.227. URL https://doi.org/10.1093%2Fbiomet%2F68.1.227.
  31. John P. Buonaccorsi. Measurement Error: Models, Methods, and Applications. Chapman and Hall/CRC, 2010. URL https://www.amazon.com/Measurement-Error-Applications-Interdisciplinary-Statistics-ebook/dp/B008KZUGWE?SubscriptionId=0JYN1NVW651KCA56C102\&tag=techkie-20\&linkCode=xm2\&camp=2025\&creative=165953\&creativeASIN=B008KZUGWE.
  32. S Walter. Estimation of test error rates, disease prevalence and relative risk from misclassified data: a review. Journal of Clinical Epidemiology, 41(9):923–937, 1988. doi: 10.1016/0895-4356(88)90110-2. URL https://doi.org/10.1016%2F0895-4356%2888%2990110-2.
  33. Inference for non-regular parameters in optimal dynamic treatment regimes. Statistical Methods in Medical Research, 19(3):317–343, jul 2009. doi: 10.1177/0962280209105013. URL https://doi.org/10.1177%2F0962280209105013.
  34. Erica E. M. Moodie and Thomas S. Richardson. Estimating optimal dynamic regimes: Correcting bias under the null. Scandinavian Journal of Statistics, 37(1):126–146, mar 2010. doi: 10.1111/j.1467-9469.2009.00661.x. URL https://doi.org/10.1111%2Fj.1467-9469.2009.00661.x.
  35. Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme. Biometrics, 69(3):714–723, 2013. ISSN 0006341X, 15410420. URL http://www.jstor.org/stable/24538137.

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