Individualized treatment rules: Generating candidate clinical trials

Individualized treatment rules, or rules for altering treatments over time in response to changes in individual covariates, are of primary importance in the practice of clinical medicine. Several statistical methods aim to estimate the rule, termed an optimal dynamic treatment regime, which will result in the best expected outcome in a population. In this article, we discuss estimation of an alternative type of dynamic regime-the statically optimal treatment rule. History-adjusted marginal structural models (HA-MSM) estimate individualized treatment rules that assign, at each time point, the first action of the future static treatment plan that optimizes expected outcome given a patient's covariates. However, as we discuss here, HA-MSM-derived rules can depend on the way in which treatment was assigned in the data from which the rules were derived. We discuss the conditions sufficient for treatment rules identified by HA-MSM to be statically optimal, or in other words, to select the optimal future static treatment plan at each time point, regardless of the way in which past treatment was assigned. The resulting treatment rules form appropriate candidates for evaluation using randomized controlled trials. We demonstrate that a history-adjusted individualized treatment rule is statically optimal if it depends on a set of covariates that are sufficient to control for confounding of the effect of past treatment history on outcome. Methods and results are illustrated using an example drawn from the antiretroviral treatment of patients infected with HIV. Specifically, we focus on rules for deciding when to modify the treatment of patients infected with resistant virus.

[1]  J. Robins.  Addendum to “a new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect” , 1987 .

[2]  James M. Robins,et al.  Causal diagrams for epidemiologic research. , 1999 .

[3]  J. Robins,et al.  Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. , 2000, Epidemiology.

[4]  Ree Dawson,et al.  Dynamic treatment regimes: practical design considerations , 2004, Clinical trials.

[5]  J M Robins,et al.  Marginal Mean Models for Dynamic Regimes , 2001, Journal of the American Statistical Association.

[6]  Mark J van der Laan,et al.  Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics , 2004, Statistical applications in genetics and molecular biology.

[7]  James M. Robins,et al.  Unified Methods for Censored Longitudinal Data and Causality , 2003 .

[8]  James M. Robins,et al.  Causal Inference from Complex Longitudinal Data , 1997 .

[9]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[10]  J. Robins.  Correcting for non-compliance in randomized trials using structural nested mean models , 1994 .

[11]  Philip W. Lavori,et al.  A design for testing clinical strategies: biased adaptive within‐subject randomization , 2000 .

[12]  T. Speed,et al.  On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 , 1990 .

[13]  M. J. van der Laan,et al.  The International Journal of Biostatistics Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules , 2011 .

[14]  J. Robins,et al.  Comparison of dynamic treatment regimes via inverse probability weighting. , 2006, Basic & clinical pharmacology & toxicology.

[15]  Mark J. van der Laan,et al.  History-Adjusted Marginal Structural Models: Time-Varying Effect Modification , 2005 .

[16]  Mark J van der Laan,et al.  Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment Rules , 2007, The international journal of biostatistics.

[17]  G. Shaw,et al.  Maternal pesticide exposure from multiple sources and selected congenital anomalies. , 1999 .

[18]  P. Rosenbaum,et al.  Invited commentary: propensity scores. , 1999, American journal of epidemiology.

[19]  James M. Robins,et al.  Marginal Structural Models versus Structural nested Models as Tools for Causal inference , 2000 .

[20]  Donald B. Rubin,et al.  Bayesian Inference for Causal Effects: The Role of Randomization , 1978 .

[21]  M B Keller,et al.  A dynamic adaptation of the propensity score adjustment for effectiveness analyses of ordinal doses of treatment , 2001, Statistics in medicine.

[22]  J. Robins.  A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .

[23]  S. Murphy,et al.  Optimal dynamic treatment regimes , 2003 .

[24]  J. Robins,et al.  Marginal Structural Models and Causal Inference in Epidemiology , 2000, Epidemiology.

[25]  Marshall M Joffe,et al.  History-Adjusted Marginal Structural Models and Statically-Optimal Dynamic Treatment Regimens , 2005 .

[26]  S. Murphy,et al.  An experimental design for the development of adaptive treatment strategies , 2005, Statistics in medicine.

[27]  Erica E M Moodie,et al.  Demystifying Optimal Dynamic Treatment Regimes , 2007, Biometrics.

[28]  A Muñoz,et al.  Effectiveness of potent antiretroviral therapy on time to AIDS and death in men with known HIV infection duration. Multicenter AIDS Cohort Study Investigators. , 1998, JAMA.

[29]  R. D'Agostino.  Adjustment Methods: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non‐Randomized Control Group , 2005 .

[30]  James M. Robins,et al.  Optimal Structural Nested Models for Optimal Sequential Decisions , 2004 .

[31]  D. Rubin.  [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies , 1990 .

[32]  S. Deeks,et al.  Treatment of antiretroviral-drug-resistant HIV-1 infection , 2003, The Lancet.