Estimation of the effect of interventions that modify the received treatment

Statistics in Medicine

Volumn 32, Issue 30, 2013, Pages 5260-5277

  Haneuse, S ^a   Rotnitzky, A ^b  

^a HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSIDAD TORCUATO DI TELLA   (Argentina)

Author keywords

Causal inference; Double robustness; Marginal structural mean model; Observational study

Indexed keywords

ARTICLE; DATA ANALYSIS; HEALTH CARE POLICY; INTERVENTION STUDY; LIMIT OF QUANTITATION; LUNG CANCER; MAGNITUDE ESTIMATION METHOD; MODIFIED TREATMENT POLICY; OBSERVATIONAL STUDY; POSTOPERATIVE PERIOD; PROBABILITY;

CAUSAL INFERENCE; DOUBLE ROBUSTNESS; MARGINAL STRUCTURAL MEAN MODEL; OBSERVATIONAL STUDY;

ADULT; AGED; AGED, 80 AND OVER; CARCINOMA, NON-SMALL-CELL LUNG; HUMANS; LUNG NEOPLASMS; MIDDLE AGED; MODELS, STATISTICAL; OPERATIVE TIME; POSTOPERATIVE COMPLICATIONS; REGRESSION ANALYSIS; TREATMENT OUTCOME;

EID: 84897077322     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.5907     Document Type: Article

References (27)

1. Rubin D. Estimating causal effects of treatment in randomized and nonrandomized studies. Journal of Educational Psychology 1974; 66:688-701.
2. Gill R, Robins J. Causal inference for complex longitudinal data: the continuous case. Annals of Statistics 2001; 29:1785-1811.
3. Hirano K, Imbens G. The propensity score with continuous treatments. In Applied Bayesian Modeling and Causal Inference from Incomplete-data Perspectives, Gelman A, Meng X (eds). John Wiley & Sons: Chichester, UK, 2004; 73-84.
4. DiazMunoz I, vander Laan M. Population intervention causal effects based on stochastic interventions. Biometrics 2012; 68:541-549.
5. Robins J. A new approach to causal inference in mortality studies with sustained exposure periods: applications to control of the healthy worker survivor effect. Mathematical Modeling 1986; 7:1393-1512.
6. Robins J. Optimal structured nested models for optimal sequential decisions. In Proceedings of the Second Seattle Symposium on Biostatistics, Lin DY, Heagerty P (eds). Springer: New York, 2004; 189-326.
7. Murphy S. Optimal dynamic treatment regimes (with discussion). Journal of the Royal Statistical Society, Series, B 2003; 58:331-366.
8. Moodie E, Richardson T, Stephens D. Demystifying optimal dynamic treatment regimes. Biometrics 2007; 63:447-455.
9. Robins J, Hernan M, Seibert U. Estimation of the effects of multiple interventions. In Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors, Vol.1, Ezzati M, Lopez A, Rodgers A, Murray C (eds). World Health Organization: Geneva, Switzerland, 2004; 2191-2230.
10. Taubman S, Robins J, Mittleman M, Hernan M. Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. International Journal of Epidemiology 2009; 38:1599-1611.
11. Shpitser I, Pearl J. Effects of treatment on the treated: identification and generalization. Uncertainty in Artificial Intelligence, Montreal, QC, Canada, 2009; 514-521.
12. Rubin D. Which ifs have causal answers? Journal of the American Statistical Association 1986; 81:961-962.
13. Cox D. Planning of Experiments. John Wiley & Sons: New York, New York, 1958.
14. VanderWeele T. Concerning the consistency assumption in causal inference. Epidemiology 2009; 20:880-833.
15. Rotnitzky A, Lei Q, Sued M, Robins JM. Improved double-robust estimation in missing data and causal inference models. Biometrika 2012; 99:439-456.
16. Robins J, Rotnitzky A. Comment on the article 'Inference for semiparametric models: Some questions and an answer'. Statistica Sinica 2001; 11:920-936.
17. Hahn J. On the role of the propensity score in efficient semi parametric estimation of average treatment effects. Econometrica 1998; 66:315-331.
18. Hirano K, Imbens G, Ridder G. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 2003; 71:1161-1189.
19. Lei Q. Improved double-robust estimation of missing data and causal inference models and efficient estimation of the average treatment effect on the treated. Ph.D. Thesis, Department of Biostatistics, Harvard School of Public Health, 2011.
20. Vander Laan M, Robins J. Unified Methods for Censored Longitudinal Data and Causality. Springer: New York, 2003.
21. Robins J, Rotnitzky A, Zhao L-P. Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association 1994; 89:846-866.
22. Pierce D. The asymptotic effect of substituting estimators for parameters in certain type of statistics. Annals of Statistics 1982; 10:475-478.
23. Henmi M, Eguchi S. A paradox concerning nuisance parameters and projected estimating functions. Biometrika 2004; 91:929-941.
24. vander Laan M, Peterson M. Causal effect models for realistic individualized treatment and intention to treat rules. International Journal of Biostatistics 2007; 3.
25. Robins J, Orellana L, Rotnitzky A. Estimation and extrapolation of optimal treatment and testing strategies. Statistics in Medicine 2008; 27: 4678-4721.
26. Orellana L, Rotnitzky A, Robins J. Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, part I: main content. International Journal of Biostatistics 2010; 2.
27. Taubman S, Robins J, Mittleman M, Hernan M. Alternative approaches to estimating the effects of hypothetical interventions. Proceedings of the 2008 Joint Statistical Meeting, Denver, Colorado, 2008.