The International Journal of Biostatistics CAUSAL INFERENCE When to Start Treatment ? A Systematic Approach to the Comparison of Dynamic Regimes Using Observational Data

Dynamic treatment regimes are the type of regime most commonly used in clinical practice. For example, physicians may initiate combined antiretroviral therapy the first time an individual's recorded CD4 cell count drops below either 500 cells/mm3 or 350 cells/mm3. This paper describes an approach for using observational data to emulate randomized clinical trials that compare dynamic regimes of the form “initiate treatment within a certain time period of some time-varying covariate first crossing a particular threshold." We applied this method to data from the French Hospital database on HIV (FHDH-ANRS CO4), an observational study of HIV-infected patients, in order to compare dynamic regimes of the form “initiate treatment within m months after the recorded CD4 cell count first drops below x cells/mm3" where x takes values from 200 to 500 in increments of 10 and m takes values 0 or 3. We describe the method in the context of this example and discuss some complications that arise in emulating a randomized experiment using observational data.

[1]  M A Fischl,et al.  A controlled trial of two nucleoside analogues plus indinavir in persons with human immunodeficiency virus infection and CD4 cell counts of 200 per cubic millimeter or less. AIDS Clinical Trials Group 320 Study Team. , 1997, The New England journal of medicine.

[2]  M. Robins James,et al.  Estimation of the causal effects of time-varying exposures , 2008 .

[3]  M. Hernán,et al.  Does obesity shorten life? The importance of well-defined interventions to answer causal questions , 2008, International Journal of Obesity.

[4]  J. Robins,et al.  Estimation and extrapolation of optimal treatment and testing strategies , 2008, Statistics in medicine.

[5]  M. T. Paixão,et al.  Expanded European AIDS case definition , 1993, The Lancet.

[6]  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 .

[7]  J. Robins,et al.  Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments , 2001 .

[8]  Stephen R Cole,et al.  Constructing inverse probability weights for marginal structural models. , 2008, American journal of epidemiology.

[9]  John W. Ward,et al.  1993 revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. , 1993, MMWR. Recommendations and reports : Morbidity and mortality weekly report. Recommendations and reports.

[10]  S. Cole,et al.  Accounting for leadtime in cohort studies: evaluating when to initiate HIV therapies , 2004, Statistics in medicine.

[11]  Calvin Cohen,et al.  Randomised placebo-controlled trial of ritonavir in advanced HIV-1 disease , 1998, The Lancet.

[12]  Jonathan AC Sterne,et al.  Long-term effectiveness of potent antiretroviral therapy in preventing AIDS and death: a prospective cohort study , 2005, The Lancet.

[13]  J. Robins,et al.  The International Journal of Biostatistics CAUSAL INFERENCE Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes , Part I : Main Content , 2011 .

[14]  C Maurath,et al.  Randomised placebo-controlled trial of ritonavir in advanced HIV-1 disease. The Advanced HIV Disease Ritonavir Study Group. , 1998, Lancet.

[15]  Tyler J. VanderWeele,et al.  Concerning the consistency assumption in causal inference. , 2009, Epidemiology.

[16]  J. Robins,et al.  Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures , 2002, Statistics in medicine.

[17]  D. Costagliola,et al.  Marked increase in the incidence of invasive anal cancer among HIV-infected patients despite treatment with combination antiretroviral therapy , 2008, AIDS.

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

[19]  Glenn Shafer,et al.  Comments on "Causal Inference without Counterfactuals" by A.P. Dawid , 1999 .

[20]  M. J. van der Laan,et al.  Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules , 2007, The international journal of biostatistics.

[21]  James M Robins,et al.  The effect of combined antiretroviral therapy on the overall mortality of HIV-infected individuals , 2010, AIDS.

[22]  J. Robins,et al.  Effect of highly active antiretroviral therapy on time to acquired immunodeficiency syndrome or death using marginal structural models. , 2003, American journal of epidemiology.

[23]  J. Robins,et al.  Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers , 1992 .

[24]  James M Robins,et al.  Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part II: Proofs of Results , 2010, The international journal of biostatistics.

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