Simulating from marginal structural models with time-dependent confounding

Statistics in Medicine

Volumn 31, Issue 30, 2012, Pages 4190-4206

  Havercroft, W G ^a   Didelez, V ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Causal inference; Inverse probability weights; Longitudinal data; Marginal structural models; Simulation; Survival analysis; Time dependent confounding

Indexed keywords

ALGORITHM; ARTICLE; CALIBRATION; CD4 LYMPHOCYTE COUNT; CONFOUNDING VARIABLE; HIGHLY ACTIVE ANTIRETROVIRAL THERAPY; HUMAN IMMUNODEFICIENCY VIRUS INFECTED PATIENT; LONGITUDINAL STUDY; MARGINAL STRUCTURAL VARIABLE; MONTE CARLO METHOD; PROBABILITY; REGRESSION ANALYSIS; SAMPLING ERROR; SIMULATION; STATISTICAL ANALYSIS; SURVIVAL;

ANTIRETROVIRAL THERAPY, HIGHLY ACTIVE; CAUSALITY; COMPUTER SIMULATION; CONFOUNDING FACTORS (EPIDEMIOLOGY); HIV INFECTIONS; HUMANS; LOGISTIC MODELS; LONGITUDINAL STUDIES; MODELS, STATISTICAL; OUTCOME ASSESSMENT (HEALTH CARE); PROBABILITY; SURVIVAL ANALYSIS; SWITZERLAND; TIME FACTORS;

EID: 84870954553     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5472     Document Type: Article

References (40)

1. Robins J. 1999. Marginal structural models versus structural nested models as tools for causal inference, In Statistical Models in Epidemiology, the Environment and Clinical Trials, Halloran E, Berry D (eds), Springer, New York, NY.
2. Robins J, Hernán M, Brumback B.Marginal structural models and causal inference in epidemiology. Epidemiology 2000 11(5): 550.
3. Sterne J, Hernán M, Ledergerber B, Tilling K, Weber R, Sendi P, Rickenbach M, Robins J, Egger M.Long-term effectiveness of potent antiretroviral therapy in preventing AIDS and death: a prospective cohort study. The Lancet 2005 366(9483): 378-384.
4. Robins J, Orellana L, Rotnitzky A.Estimation and extrapolation of optimal treatment and testing strategies. Statistics in Medicine 2008 27(23): 4678-4721.
5. Sterne J, May M, Costagliola D, de Wolf F, Phillips A, Harris R, Funk M, Geskus R, Gill J, Dabis F et al. Timing of initiation of antiretroviral therapy in AIDS-free HIV-1-infected patients: a collaborative analysis of 18 HIV cohort studies. The Lancet 2009 373(9672): 1352-1363.
6. Rosthøj S, Fullwood C, Henderson R, Stewart S.Estimation of optimal dynamic anticoagulation regimes from observational data: a regret-based approach. Statistics in Medicine 2006 25(24): 4197-4215.
7. Henderson R, Ansell P, Alshibani D.Regret-regression for optimal dynamic treatment regimes. Biometrics 2009 66(4): 1192-1201.
8. Robins J.A new approach to causal inference in mortality studies with a sustained exposure period-application to control of the healthy worker survivor effect. Mathematical Modelling 1986 7(9-12): 1393-1512.
9. Daniel R, Cousens S, De Stavola B, Kenward M, Sterne J.Tutorial in biostatistics: methods for dealing with time-varying confounding. 2011. (submitted).
10. Bryan J, Yu Z, Van Der Laan M.Analysis of longitudinal marginal structural models. Biostatistics 2004 5(3): 361.
11. Young J, Hernán M, Picciotto S, Robins J. Simulation from structural survival models under complex time-varying data structures, JSM Proceedings, Section on Statistics in Epidemiology: American Statistical Association, Denver, CO., 2008.
12. Young J, Hernán M, Picciotto S, Robins J. Relation between three classes of structural models for the effect of a time-varying exposure on survival. Lifetime data analysis 2010 16(1): 71-84.
13. Pearl J.Causality: Models, Reasoning and Inference, 2nd ed., Cambridge University Press: New York, NY, 2000.
14. Rubin D.Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 1974 66: 688-701.
15. Rubin D.Bayesian inference for causal effects: the role of randomization. Annals of Statistics 1978 6(1): 34-68.
16. Dawid A.Causal inference without counterfactuals (with Discussion). Journal of the American Statistical Association 2000 95: 407-448.
17. Dawid A. 2007. Counterfactuals, hypotheticals and potential responses: a philosophical examination of statistical causality, In Causality and Probability in the Sciences, Russo F, Williamson J (eds), College Publications, Texts In Philosophy Series Vol. 5, London.
18. Lauritzen S.Graphical Models, Oxford University Press: New York, NY, 1996.
19. Dawid A.Conditional independence in statistical theory (with Discussion). Journal of the Royal Statistical Society 1979 41: 1-31.
20. Lauritzen S. 2000. Causal inference from graphical models, In Complex Stochastic Systems, Barndorff-Nielsen OE, Cox DR, Klüppelberg C (eds), CRC Press, London.
21. Dawid A, Didelez V.Identifying the consequences of dynamic treatment strategies: a decision-theoretic overview. Statistics Surveys 2010 4: 184-231.
22. Pearl J. Graphical models, causality and interventions. Statistical Science 1993 8: 266-269.
23. Dawid A.Influence diagrams for causal modelling and inference. International Statistical Review 2002 70: 161-189. Corrigenda, ibid., 437.
24. Spirtes P, Glymour C, Scheines R.Causation, Prediction and Search, Springer: New York, 1993.
25. Pearl J.Causal diagrams for empirical research. Biometrika 1995 82(4): 669-710.
26. Robins J. 1997. Causal inference from complex longitudinal data, In Latent Variable Modeling and Applications to Causality, vol. 120, Berkane M (ed), Lecture Notes in Statistics, Springer, New York.
27. Pearl J, Robins J. 1995. Probabilistic evaluation of sequential plans from causal models with hidden variables, In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Besnard P, Hanks S (eds), Morgan Kaufmann, San Francisco.
28. Hernán M, Hernández-Díaz S, Robins J. A structural approach to selection bias. Epidemiology 2004 15(5): 615.
29. Cole S, Platt R, Schisterman E, Chu H, Westreich D, Richardson D, Poole C. Illustrating bias due to conditioning on a collider. International Journal of Epidemiology 2009 39(2): 417-420.
30. Greenland S.Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology 2003 14(3): 300.
31. Robins J, Blevins D, Ritter G, Wulfsohn M.G-estimation of the effect of prophylaxis therapy for pneumocystis carinii pneumonia on the survival of aids patients. Epidemiology 1992 3(4): 319-336.
32. Xiao Y, Abrahamowicz M, Moodie E. Accuracy of conventional and marginal structural Cox model estimators: A simulation study. The International Journal of Biostatistics 2010 6(2): 13.
33. Naimi A, Cole S, Westreich D, Richardson D.A comparison of methods to estimate the hazard ratio under conditions of time-varying confounding and nonpositivity. Epidemiology 2011 22(5): 718.
34. Westreich D, Cole S, Tien P, Chmiel J, Kingsley L, Funk M, Anastos K, Jacobson L.Time scale and adjusted survival curves for marginal structural cox models. American journal of epidemiology 2010 171(6): 691-700.
35. Røysland K.A martingale approach to continuous-time marginal structural models. Bernoulli 2011 17(3): 895-915.
36. D'Agostino R, Lee M, Belanger A, Cupples L, Anderson K, Kannel W. Relation of pooled logistic regression to time dependent Cox regression analysis: the Framingham Heart Study. Statistics in Medicine 1990 9(12): 1501-1515.
37. Daniel R, De Stavola B, Cousens S. gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computation formula. The Stata Journal 2011 11(4): 479.
38. Hernán M, Lanoy E, Costagliola D, Robins J.Comparison of dynamic treatment regimes via inverse probability weighting. Basic & Clinical Pharmacology & Toxicology 2006 98(3): 237-242.
39. Orellana L, Rotnitzky A, Robins J.Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: main content. The International Journal of Biostatistics 2010 6(2).
40. Cain L, Robins J, Lanoy E, Logan R, Costagliola D, Hernán MA.When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data. The International Journal of Biostatistics 2010 6(2).