• Bibhas Chakraborty
  • Erica E. M. Moodie
Part of the Statistics for Biology and Health book series (SBH)


This book was written to summarize and describe the state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. In the first chapter, we introduce the concepts and motivation underpinning the pursuit of evidence-based personalized medicine, highlight the need for dynamic treatment regimes in multi-stage treatment settings, and provide an overview of some recent examples of dynamic regimes in the health domain.


Cognitive Behavioral Therapy Decision Rule International Normalize Ratio Personalized Medicine Chronic Care Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Carlin, B. P., Kadane, J. B., & Gelfand, A. E. (1998). Approaches for optimal sequential decision analysis in clinical trials. Biometrics54, 964–975.zbMATHCrossRefGoogle Scholar
  2. Chakraborty, B., & Moodie, E. E. M. (2013). Estimating optimal dynamic treatment regimes with shared decision rules across stages: An extension of Q-learning (under revision).Google Scholar
  3. Chen, Y. K. (2011). Dose finding by the continual reassessment method. Boca Raton: Chapman & Hall/CRC.Google Scholar
  4. Coffey, C. S., Levin, B., Clark, C., Timmerman, C., Wittes, J., Gilbert, P., & Harris, S. (2012). Overview, hurdles, and future work in adaptive designs: Perspectives from an NIH-funded workshop. Clinical Trials, 9, 671–680.CrossRefGoogle Scholar
  5. Collins, L. M., Murphy, S. A., Nair, V. N., & Strecher, V. J. (2005). A strategy for optimizing and evaluating behavioral interventions. Annals of Behavioral Medicine30, 65–73.CrossRefGoogle Scholar
  6. Dawson, R., & Lavori, P. W. (2010). Sample size calculations for evaluating treatment policies in multi-stage designs. Clinical Trials7, 643–652.CrossRefGoogle Scholar
  7. Diggle, P. J., Heagerty, P., Liang, K.-Y., Zeger, S. L. (2002). Analysis of longitudinal data (2nd ed.). Oxford: Oxford University Press.Google Scholar
  8. Gail, M. H., & Benichou, J. (Eds.). (2000). Encyclopedia of epidemiologic methods. Chichester/New York: Wiley.Google Scholar
  9. Holland, P. (1986). Statistics and causal inference. Journal of the American Statistical Association81, 945–970.MathSciNetzbMATHCrossRefGoogle Scholar
  10. Lavori, P. W., & Dawson, R. (2004). Dynamic treatment regimes: Practical design considerations. Clinical Trials1, 9–20.CrossRefGoogle Scholar
  11. Lavori, P. W., & Dawson, R. (2008). Adaptive treatment strategies in chronic disease. Annual Review of Medicine59, 443–453.CrossRefGoogle Scholar
  12. Lavori, P. W., Rush, A. J., Wisniewski, S. R., Alpert, J., Fava, M., Kupfer, D. J., Nierenberg, A., Quitkin, F. M., Sackeim, H. M., Thase, M. E., & Trivedi, M. (2001). Strengthening clinical effectiveness trials: Equipoise-stratified randomization. Biological Psychiatry48, 605–614.CrossRefGoogle Scholar
  13. Levin, B., Thompson, J. L. P., Chakraborty, R. B., Levy, G., MacArthur, R., & Haley, E. C. (2011). Statistical aspects of the TNK-S2B trial of tenecteplase versus alteplase in acute ischemic stroke: An efficient, dose-adaptive, seamless phase II/III design. Clinical Trials8, 398–407.CrossRefGoogle Scholar
  14. Lindley, D. V. (2002). Seeing and doing: The concept of causation. International Statistical Review70, 191–214.zbMATHCrossRefGoogle Scholar
  15. Lusted, L. B. (1968). Introduction to medical decision making. Springfield: Thomas.Google Scholar
  16. Manski, C. F. (2000). Identification problems and decisions under ambiguity: Empirical analysis of treatment response and normative analysis of treatment choice. Journal of Econometrics95, 415–442.zbMATHCrossRefGoogle Scholar
  17. Manski, C. F. (2002). Treatment choice under ambiguity induced by inferential problems. Journal of Statistical Planning and Inference105, 67–82.MathSciNetzbMATHCrossRefGoogle Scholar
  18. Manski, C. F. (2004). Statistical treatment rules for heterogeneous populations. Econometica72, 1221–1246.MathSciNetzbMATHCrossRefGoogle Scholar
  19. Mark, S. D., & Robins, J. M. (1993). Estimating the causal effect of smoking cessation in the presence of confounding factors using a rank preserving structural failure time model. Statistics in Medicine12, 1605–1628.CrossRefGoogle Scholar
  20. Murphy, S. A. (2005a). An experimental design for the development of adaptive treatment strategies. Statistics in Medicine24, 1455–1481.MathSciNetCrossRefGoogle Scholar
  21. Murphy, S. A. (2005b). A generalization error for Q-learning. Journal of Machine Learning Research6, 1073–1097.zbMATHGoogle Scholar
  22. Murphy, S. A., Lynch, K. G., Oslin, D., Mckay, J. R., & TenHave, T. (2007a). Developing adaptive treatment strategies in substance abuse research. Drug and Alcohol Dependence88, s24–s30.CrossRefGoogle Scholar
  23. Pearl, J. (2009). Causality (2nd ed.). New York: Cambridge University Press.zbMATHGoogle Scholar
  24. Pineau, J., Bellernare, M. G., Rush, A. J., Ghizaru, A., & Murphy, S. A. (2007). Constructing evidence-based treatment strategies using methods from computer science. Drug and Alcohol Dependence88, S52–S60.CrossRefGoogle Scholar
  25. Pocock, S. J. (1977). Group sequential methods in the design and analysis of clinical trials. Biometrika64, 191–199.CrossRefGoogle Scholar
  26. Rich, B., Moodie, E. E. M., Stephens, D. A., & Platt, R. W. (2010). Model checking with residuals for g-estimation of optimal dynamic treatment regimes. The International Journal of Biostatistics6.Google Scholar
  27. Robins, J. M., & Hernán, M. A. (2009). Estimation of the causal effects of time-varying exposures. In G. Fitzmaurice, M. Davidian, G. Verbeke, & G. Molenberghs (Eds.), Longitudinal data analysis. Boca Raton: Chapman & Hall/CRC.Google Scholar
  28. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology66, 688–701.CrossRefGoogle Scholar
  29. Sterne, J. A. C., May, M., Costagliola, D., de Wolf, F., Phillips, A. N., Harris, R., Funk, M. J., Geskus, R. B., Gill, J., Dabis, F., Miró, J. M., Justice, A. C., Ledergerber, B., Fätkenheuer, G., Hogg, R. S., D’Arminio Monforte, A., Saag, M., Smith, C., Staszewski, S., Egger, M., Cole, S. R., & The When To Start Consortium (2009). Timing of initiation of antiretroviral therapy in AIDS-free HIV-1-infected patients: A collaborative analysis of 18 HIV cohort studies. Lancet373, 1352–1363.CrossRefGoogle Scholar
  30. Stewart, C. E., Fielder, A. R., Stephens, D. A., & Moseley, M. J. (2002). Design of the Monitored Occlusion Treatment of Amblyopia Study (MOTAS). British Journal of Ophthalmology86, 915–919.CrossRefGoogle Scholar
  31. 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 Association97, 29–39.MathSciNetzbMATHCrossRefGoogle Scholar
  32. 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 Medicine26, 4687–4702.MathSciNetCrossRefGoogle Scholar
  33. Thall, P. F., Logothetis, C., Pagliaro, L. C., Wen, S., Brown, M. A., Williams, D., & Millikan, R. E. (2007b). Adaptive therapy for androgen-independent prostate cancer: A randomized selection trial of four regimens. Journal of the National Cancer Institute99, 1613–1622.CrossRefGoogle Scholar
  34. Tsiatis, A. A. (2006). Semiparametric theory and missing data. New York: Springer.zbMATHGoogle Scholar
  35. Wahed, A. S., & Tsiatis, A. A. (2004). Optimal estimator for the survival distribution and related quantities for treatment policies in two-stage randomized designs in clinical trials. Biometrics60, 124–133.MathSciNetzbMATHCrossRefGoogle Scholar
  36. Wahed, A. S., & Tsiatis, A. A. (2006). Semiparametric efficient estimation of survival distributions in two-stage randomisation designs in clinical trials with censored data. Biometrika93, 163–177.MathSciNetzbMATHCrossRefGoogle Scholar
  37. Wald, A. (1949). Statistical decision functions. New York: Wiley.Google Scholar
  38. Wang, Y., Petersen, M. L., Bangsberg, D., & Van der Laan, M. J. (2006). Diagnosing bias in the inverse probability of treatment weighted estimator resulting from violation of experimental treatment assignment. UC Berkeley Division of Biostatistics Working Paper Series.Google Scholar
  39. Westreich, D., Cole, S. R., Young, J. G., Palella, F., Tien, P. C., Kingsley, L., Gange, S. J., & Hernán, M. A. (2012). The parametric g-formula to estimate the effect of highly active antiretroviral therapy on incident AIDS or death. Statistics in Medicine31, 2000–2009.MathSciNetCrossRefGoogle Scholar
  40. Wood, S. N. (2006). Generalized additive models: An introduction with R. Boca Raton: Chapman & Hall/CRC.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Bibhas Chakraborty
    • 1
  • Erica E. M. Moodie
    • 2
  1. 1.Department of BiostatisticsColumbia UniversityNew YorkUSA
  2. 2.Department of Epidemiology, Biostatistics, and Occupational HealthMcGill UniversityMontrealCanada

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