Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets

Statistics in Medicine

Volumn 34, Issue 1, 2015, Pages 106-117

  Gruber, Susan ^a   Logan, Roger W ^a   Jarrín, Inmaculada ^b   Monge, Susana ^b   Hernán, Miguel A ^a,c  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b CARLOS III INSTITUTE OF HEALTH   (Spain)

^c HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

Author keywords

Data adaptive; Ensemble learning; Inverse probability weighting; Longitudinal data; Marginal structural model; Super learning

Indexed keywords

COMPUTER SIMULATION; CONFIDENCE INTERVAL; EPIDEMIOLOGY; HIGHLY ACTIVE ANTIRETROVIRAL THERAPY; HIV INFECTIONS; HUMAN; MACHINE LEARNING; MORTALITY; PROBABILITY; SPAIN; STATISTICAL ANALYSIS; STATISTICAL BIAS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TRENDS;

ANTIRETROVIRAL THERAPY, HIGHLY ACTIVE; BIAS (EPIDEMIOLOGY); COMPUTER SIMULATION; CONFIDENCE INTERVALS; CONFOUNDING FACTORS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; HIV INFECTIONS; HUMANS; LOGISTIC MODELS; MACHINE LEARNING; MODELS, STATISTICAL; MORTALITY; PROBABILITY; SPAIN;

EID: 84914814777     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.6322     Document Type: Article

References (33)

1. Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 2000; 11(5):561-570.
2. Neugebauer R, Fireman B, Roy J, Raebel M, Nichols G, O'Connor P. Super learning to hedge against incorrect inference from arbitrary parametric assumptions in marginal structural modeling. Journal of Clinical Epidemiology 2013; 66:S99-S109.
3. Neugebauer R, Chandra M, Paredes A, Graham D, McCloskey C, Go A. A marginal structural modeling approach with super learning for a study on oral bisphosphonate therapy and atrial fibrillation. Journal of Causal Inference 2013; 1:21-50.
4. van der Laan M, Polley E, Hubbard A. Super learner. Statistical Applications in Genetics and Molecular Biology 2007; 6(25):1-25.
5. Polley E, van der Laan M. Super learner in prediction, Technical Report, Division of Biostatistics, University of California Berkeley, 2010.
6. Caro-Murillo AM, Castilla J, Pérez-Hoyos S, Miró JM, Podzamczer D, Rubio R, Riera M, Viciana P, López Aldeguer J, Iribarren JA, de los Santos-Gil I, Gómez-Sirvent JL, Berenguer J, F Gutiérrez, Saumoy M, Segura F, Soriano V, Peña A, Pulido F, Oteo JA, Leal M, Casabona J, del Amo J, Moreno S, Grupo de trabajo de la Cohorte de la Red de Investigación en Sida (CoRIS) . Spanish cohort of naive HIV-infected patients (CoRIS): rationale, organization and initial results. Enfermedades Infecciosas Y Microbiologia Clinica 2007; 25:23:31.
7. The HIV-CAUSAL Collaboration . The effect of combined antiretroviral therapy on the overall mortality of HIV-infected individuals. AIDS 2010; 24(1):123-137.
8. 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; 12:1501-1515.
9. Robins JM, Hernán MA. Estimation of the causal effects of time-varying exposures. In Advances in Longitudinal Data Analysis. Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G (eds). Chapman and Hall/CRC Press: Boca Raton, 2009; 553-599.
10. Lee B, Lessler J, Stuart E. Improved propensity score weighting using machine learning. Statistics in Medicine 2009; 29:337-346.
11. McCaffrey D, Ridgeway G, Morral A. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods 2004; 9(4):403-425.
12. Polikar R. Ensemble learning. Scholarpedia 2009; 4(1):2776.
13. Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Statistic Surveys 2010; 4:40-79.
14. Ting K, Witten IH. Issues in stacked generalization. Journal of Artificial Intelligence Research 1999; 10:271-289.
15. Wolpert D. Stacked generalization. Neural Networks 1992; 5:241-259.
16. Breiman L. Stacked regression. Machine Learning 1996; 24:49-64.
17. Venables WN, Ripley BD. Modern Applied Statistics with S 4th edn. Springer: New York, 2002. http://www.stats.ox.ac.uk/pub/MASS4 [Accessed on 15 October 2013].
18. Breiman L, Friedman JH, Olshen R, Stone CJ. Classification and Regression Trees, The Wadsworth statistics/probability series. Wadsworth International Group: Chapman and Hall, New York, 1984.
19. Ridgeway G, McCaffrey D, Morral A, Burgette L, Griffin B. Toolkit for Weighting and Analysis of Nonequivalent Groups: A Tutorial for the Twang Package. 2013. URL http://CRAN.R-project.org/package=gbm, R package. [Accessed on 15 October 2013].
20. McCaffrey D, Burgette L, Griffin B, Martin C . Toolkit for Weighting and Analysis of Nonequivalent Groups: A Tutorial for the Twang SAS Macro, 2014. URL http://www.rand.org/pubs/tools/TL136.html [Accessed on 6 October 2014].
21. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning, Springer Series in Statistics. Springer: New York 2002.
22. van der Vaart A, Dudoit S, van der Laan M. Oracle inequalities for multi-fold cross-validation. Statistics and Decisions 2006; 24(3):351-371.
23. Development Core Team, R. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing: Vienna, Austria, 2012. URL http://www.R-project.org [Accessed on 15 October 2013].
24. Polley E. SuperLearner: super learner prediction, 2012. URL http://CRAN.R-project.org/package=SuperLearner, R package version 2.0-9. [Accessed on 15 October 2013].
25. Arlot S, Lerasle M. V-fold cross-validation and V-fold penalization in least-squares density estimation, 2012. eprint arXiv:1210.5830.
26. Mocroft A, Ledergerber B, Katlama C, Kirk O, Reiss P, d'Arminio Monforte A, Knysz B, M Dietrich, Phillips AN, Lundgren JD, EuroSIDA study group . Decline in the AIDS and death rates in the EuroSIDA study: an observational study. Lancet 2003; 362:22-29.
27. Babiker A, Bhaskaran K, Darbyshire J, Pezzotti P, Porter K, Walker A. Determinants of survival following HIV-1 seroconversion after the introduction of HAART. Lancet 2003; 362:1267-1274.
28. van der Laan M, Dudoit S. Unified cross-validation methodology for selection among estimators and a general cross-validated adaptive epsilon-net estimator: Finite sample oracle inequalities and examples, Division of Biostatistics, University of California, Berkeley, November 2003.
29. Imai K, Ratkovic M. Robust estimation of inverse probability weights for marginal structural models, Princeton University, URL http://imai.princeton.edu/research/MSM.html [Accessed on 15 October 2013] May 2013.
30. Vermeulen K, Vansteelandt S. Bias-reduced doubly robust estimation. Journal of the American Statistical Association 2014. (under revision).
31. Zigler CM, Watts K, Yeh RW, Wang Y, Coulli BA, Dominici F. Model feedback in bayesian propensity score estimation. Biometrics 2013.
32. van der Laan M, Gruber S. Collaborative double robust penalized targeted maximum likelihood estimation. The International Journal of Biostatistics 2010; 6(1):1-70.
33. Gruber S, van der Laan M. An application of collaborative targeted maximum likelihood estimation in causal inference and genomics. The International Journal of Biostatistics 2010; 6(1):1-30.