Skip to main content
SpringerLink
Log in

Estimation of DTRs for Alternative Outcome Types

  • Chapter
  • First Online:
Statistical Methods for Dynamic Treatment Regimes

Part of the book series: Statistics for Biology and Health ((SBH))

  • 4721 Accesses

Abstract

In this chapter, we consider the estimation of dynamic treatment regimes for a variety of outcome types, including multi-dimensional continuous outcomes, time-to-event outcomes in the presence of censoring, and discrete outcomes. Methods discussed include Q-learning, marginal structural models, and a fully parametric, likelihood-based approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Almirall, D., Lizotte, D., & Murphy, S. A. (2012b). SMART design issues and the consideration of opposing outcomes, Discussion of “Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer” by Wang et al. Journal of the American Statistical Association, 107, 509–512.

    Article  MathSciNet  MATH  Google Scholar 

  • Carlin, B. P., Kadane, J. B., & Gelfand, A. E. (1998). Approaches for optimal sequential decision analysis in clinical trials. Biometrics, 54, 964–975.

    Article  MATH  Google Scholar 

  • D’Agostino, R. B., Jr. (1998). Tutorial in biostatistics: Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265–2281.

    Article  Google Scholar 

  • Ernst, D., Stan, G. B., Goncalves, J., & Wehenkel, L. (2006). Clinical data based optimal STI strategies for HIV: A reinforcement learning approach. In Proceedings of the machine learning conference of Belgium and The Netherlands (Benelearn), Ghent (pp. 65–72).

    Google Scholar 

  • Ferguson, T. S. (1996). A course in large sample theory. London: Chapman & Hall/CRC.

    MATH  Google Scholar 

  • Greenland, S. (2003). Quantifying biases in causal models: Classical confounding vs collider-stratification bias. Epidemiology, 14, 300–306.

    Google Scholar 

  • Gunter, L., Zhu, J., & Murphy, S. A. (2007). Variable selection for optimal decision making. In Proceedings of the 11th conference on artificial intelligence in medicine, Amsterdam.

    Google Scholar 

  • Henderson, R., Ansell, P., & Alshibani, D. (2010). Regret-regression for optimal dynamic treatment regimes. Biometrics, 66, 1192–1201.

    Article  MathSciNet  MATH  Google Scholar 

  • Hernán, M. A., Lanoy, E., Costagliola, D., & Robins, J. M. (2006). Comparison of dynamic treatment regimes via inverse probability weighting. Basic & Clinical Pharmacology & Toxicology, 98, 237–242.

    Article  Google Scholar 

  • Hirano, K., & Porter, J. (2009). Asymptotics for statistical treatment rules. Econometrica, 77, 1683–1701.

    Article  MathSciNet  MATH  Google Scholar 

  • Huber, P. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 53, 73–101.

    Article  Google Scholar 

  • Joffe, M. M. (2000). Confounding by indication: The case of calcium channel blockers. Pharamcoepidemiology and Drug Safety, 9, 37–41.

    Article  Google Scholar 

  • Lu, W., Zhang, H. H., & Zeng, D. (2013). Variable selection for optimal treatment decision. Statistical Methods in Medical Research (in press). doi:10.1177/0962280211428383.

    Google Scholar 

  • Lusted, L. B. (1968). Introduction to medical decision making. Springfield: Thomas.

    Google Scholar 

  • Mason, J., Freemantle, N., & Eccles, M. (2000). Fatal toxicity associated with antidepressant use in primary care. British Journal of General Practice, 50, 366–370.

    Google Scholar 

  • Mortimer, K. M., Neugebauer, R., Van der Laan, M. J., & Tager, I. B. (2005). An application of model-fitting procedures for marginal structural models. American Journal of Epidemiology, 162, 382–388.

    Article  Google Scholar 

  • Oslin, D. (2005). Managing alcoholism in people who do not respond to naltrexone (ExTENd). Bethesda: National Institutes of Health. http://clinicaltrials.gov/ct2/show/NCT00115037?term=oslin\&rank=8.

  • Rosenbaum, P. R. (1991). Discussing hidden bias in observational studies. Annals of Internal Medicine, 115, 901–905.

    Article  Google Scholar 

  • Shivaswamy, P., Chu, W., & Jansche, M. (2007). A support vector approach to censored targets. In Proceedings of the seventh IEEE international conference on data mining, Omaha (pp. 655–660).

    Google Scholar 

  • Shortreed, S. M., & Moodie, E. E. M. (2012). Estimating the optimal dynamic antipsychotic treatment regime: Evidence from the sequential-multiple assignment randomized CATIE Schizophrenia Study. Journal of the Royal Statistical Society, Series B, 61, 577–599.

    MathSciNet  Google Scholar 

  • Song, R., Wang, W., Zeng, D., & Kosorok, M. R. (2011). Penalized Q-learning for dynamic treatment regimes arXiv:1108.5338v1 [stat.ME].

    Google Scholar 

  • Thall, P. F., Sung, H. G., & Estey, E. H. (2002). Selecting therapeutic strategies based on efficacy and death in multicourse clinical trials. Journal of the American Statistical Association, 97, 29–39.

    Article  MathSciNet  MATH  Google Scholar 

  • Thall, P. F., Wooten, L. H., Logothetis, C. J., Millikan, R. E., & Tannir, N. M. (2007a). Bayesian and frequentist two-stage treatment strategies based on sequential failure times subject to interval censoring. Statistics in Medicine, 26, 4687–4702.

    Article  MathSciNet  Google Scholar 

  • Vogt, W. P. (1993). Dictionary of statistics and methodology: A nontechnical guide for the social sciences. Newbury Park: Sage Publications.

    Google Scholar 

  • Wahed, A. S., & Tsiatis, A. A. (2006). Semiparametric efficient estimation of survival distributions in two-stage randomisation designs in clinical trials with censored data. Biometrika, 93, 163–177.

    Article  MathSciNet  MATH  Google Scholar 

  • Wald, A. (1949). Statistical decision functions. New York: Wiley.

    Google Scholar 

  • Wathen, J. K., & Thall, P. F. (2008). Bayesian adaptive model selection for optimizing group sequential clinical trials. Statistics in Medicine, 27, 5586–5604.

    Article  MathSciNet  Google Scholar 

  • Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society, Series B, 73, 3–36.

    Article  Google Scholar 

  • Xiao, Y., Abrahamowicz, M., Moodie, E. E. M., Weber, R., and Young, J. (2013) Flexible marginal structural models for estimating cumulative effect of time-dependent treatment on the hazard: Reassessing the cardiovascular risks of didanosine treatment in the Swiss HIV Cohort. (In revision.)

    Google Scholar 

  • Zhao, Y., Zeng, D., Rush, A. J., & Kosorok, M. R. (2012). Estimating individual treatment rules using outcome weighted learning. Journal of the American Statistical Association, 107, 1106–1118.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biostatistics, Columbia University, New York, USA

    Bibhas Chakraborty

  2. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada

    Erica E. M. Moodie

Authors
  1. Bibhas Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erica E. M. Moodie
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media New York

About this chapter

Cite this chapter

Chakraborty, B., Moodie, E.E.M. (2013). Estimation of DTRs for Alternative Outcome Types. In: Statistical Methods for Dynamic Treatment Regimes. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7428-9_7

Download citation

Publish with us

Policies and ethics

Search

Navigation