G-estimation for Accelerated Failure Time Models

In this chapter we examine the problem of time-varying confounding, and one method (structural nested accelerated failure time models, estimated using and also known as g-estimation) which may be used to overcome it. A practical example is given, and the methodology demonstrated. Cautions as to the use of g-estimation are provided, and alternative methods suggested. Much of the material in this chapter has been published as an application to analysis of a longitudinal study (Tilling et al. Am J Epidemiol 155:710–718, 2002) and as a description of the implementation of these methods in standard statistical software (Sterne and Tilling, Stata J 2:164–182, 2002). The material is used here with permission from the Stata Journal and the American Journal of Epidemiology (Oxford University Press and the Society for Epidemiologic Research).

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

[2]  D R Hoover,et al.  Effect of treatment with zidovudine on subsequent incidence of Kaposi's sarcoma. , 1997, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[3]  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.

[4]  Yoav Ben-Shlomo,et al.  Rising to the challenges and opportunities of life course epidemiology. , 2007, International journal of epidemiology.

[5]  A. Philip Dawid,et al.  Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview , 2010, ArXiv.

[6]  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 .

[7]  A. W. van der Vaart,et al.  Estimating the causal effect of a time‐varying treatment on time‐to‐event using structural nested failure time models , 2004, math/0409165.

[8]  S. Vansteelandt,et al.  On the adjustment for covariates in genetic association analysis: a novel, simple principle to infer direct causal effects , 2009, Genetic epidemiology.

[9]  M. Pearce,et al.  Lifecourse predictors of adult respiratory function: results from the Newcastle Thousand Families Study , 2008, Thorax.

[10]  H. Farrar,et al.  Cocaine: clinical pharmacology and toxicology. , 1989, The Journal of pediatrics.

[11]  Sengwee Toh,et al.  Causal Inference from Longitudinal Studies with Baseline Randomization , 2008, The international journal of biostatistics.

[12]  Sherri Rose,et al.  Implementation of G-computation on a simulated data set: demonstration of a causal inference technique. , 2011, American journal of epidemiology.

[13]  Y. Ohashi,et al.  Adjusting for differential proportions of second‐line treatment in cancer clinical trials. Part I: Structural nested models and marginal structural models to test and estimate treatment arm effects , 2004, Statistics in medicine.

[14]  Shah Ebrahim,et al.  Influence of area and individual lifecourse deprivation on health behaviours: findings from the British Women's Heart and Health Study , 2009, European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology.

[15]  J. Robins,et al.  A Structural Approach to Selection Bias , 2004, Epidemiology.

[16]  Y. Ohashi,et al.  Estimation of treatment effect adjusting for treatment changes using the intensity score method: Application to a large primary prevention study for coronary events (MEGA study) , 2008, Statistics in medicine.

[17]  N Keiding,et al.  The Graft Versus Leukemia Effect after Bone Marrow Transplantation: A Case Study Using Structural Nested Failure Time Models , 1999, Biometrics.

[18]  J. Robins,et al.  Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/pds.1064 , 2022 .

[19]  Els Goetghebeur,et al.  Estimation of controlled direct effects , 2008 .

[20]  J M Robins,et al.  Causal models for estimating the effects of weight gain on mortality , 2008, International Journal of Obesity.

[21]  Jessica G. Young,et al.  Relation between three classes of structural models for the effect of a time-varying exposure on survival , 2010, Lifetime data analysis.

[22]  Roderick J. A. Little,et al.  Statistical Analysis with Missing Data: Little/Statistical Analysis with Missing Data , 2002 .

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

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

[25]  J. Robins.  Estimation of the time-dependent accelerated failure time model in the presence of confounding factors , 1992 .

[26]  J. Sterne,et al.  G-estimation of Causal Effects, Allowing for Time-varying Confounding , 2002 .

[27]  Mary Beth Terry,et al.  Life course socioeconomic conditions, passive tobacco exposures and cigarette smoking in a multiethnic birth cohort of U.S. women , 2009, Cancer Causes & Control.

[28]  J. Robins,et al.  Estimation of the Causal Effect of a Time-Varying Exposure on the Marginal Mean of a Repeated Binary Outcome , 1999 .

[29]  J. Robins,et al.  G-estimation of causal effects: isolated systolic hypertension and cardiovascular death in the Framingham Heart Study. , 1998, American journal of epidemiology.

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

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

[32]  N M Laird,et al.  Correcting for non-compliance in randomized trials: an application to the ATBC Study. , 1999, Statistics in medicine.

[33]  J. Robins,et al.  Estimating exposure effects by modelling the expectation of exposure conditional on confounders. , 1992, Biometrics.

[34]  Y. Ben-Shlomo,et al.  A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives , 2002 .

[35]  Glenn D. Davis.  College Station, Texas , 2011 .

[36]  J. Robins,et al.  Estimating the effect of zidovudine on Kaposi's sarcoma from observational data using a rank preserving structural failure-time model. , 1998, Statistics in medicine.

[37]  James M. Robins,et al.  Invited Commentary Invited Commentary: Effect Modification by Time-varying Covariates American Journal of Epidemiology Advance Access Standard versus History-adjusted Marginal Structural Models Model Incompatibility in History-adjusted Marginal Structural Models Structural Nested Models versus Histo , 2006 .

[38]  J. Robins,et al.  Estimating the causal effect of smoking cessation in the presence of confounding factors using a rank preserving structural failure time model. , 1993, Statistics in medicine.

[39]  J. Robins,et al.  Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. , 2009, International journal of epidemiology.

[40]  Moyses Szklo,et al.  Estimating the effect of cardiovascular risk factors on all-cause mortality and incidence of coronary heart disease using G-estimation: the atherosclerosis risk in communities study. , 2002, American journal of epidemiology.

[41]  M. Glymour,et al.  Lifecourse social conditions and racial disparities in incidence of first stroke. , 2008, Annals of epidemiology.

[42]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[43]  J. Robins,et al.  G-Estimation of the Effect of Prophylaxis Therapy for Pneumocystis carinii Pneumonia on the Survival of AIDS Patients , 1992, Epidemiology.

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