Doubly robust estimation and causal inference in longitudinal studies with dropout and truncation by death

Biostatistics

Volumn 16, Issue 1, 2015, Pages 155-168

  Shardell, Michelle ^a   Hicks, Gregory E ^b   Ferrucci, Luigi ^c  

^a UNIVERSITY OF MARYLAND SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF DELAWARE   (United States)

^c NATIONAL INSTITUTE ON AGING   (United States)

Author keywords

Causal inference; Longitudinal data analysis; Missing data; Observational studies

Indexed keywords

HUMAN; MORTALITY; OUTCOME ASSESSMENT; PATIENT DROPOUT; PROCEDURES; STATISTICAL ANALYSIS; STATISTICAL BIAS;

BIAS (EPIDEMIOLOGY); DATA INTERPRETATION, STATISTICAL; HUMANS; MORTALITY; OUTCOME ASSESSMENT (HEALTH CARE); PATIENT DROPOUTS;

EID: 84922540923     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxu032     Document Type: Article

References (46)

1. BANG, H. AND ROBINS, J. M. (2005).Doubly robust estimation inmissing data and causal inference models. Biometrics 61, 962-972.
2. BRYAN, J.,YU, Z. AND VAN DER LAAN, M. J. (2004). Analysis of longitudinal marginal structural models. Biostatistics 5, 361-380.
3. EGLESTON, B. L., SCHARFSTEIN, D. O., FREEMAN, E. E. AND WEST, S. K. (2007). Causal inference for non-mortality outcomes in the presence of death. Biostatistics 8, 526-545.
4. FERRUCCI, L., BANDINELLI, S., BENVENUTI, E., Di IORIO, A., MACCHI, C., HARRIS, T. B. AND GURALNIK, J. M. (2000). Subsystems contributing to the decline in ability to walk: bridging the gap between epidemiology and geriatric practice in the InCHIANTI study. Journal of the American Geriatrics Society 48, 1618-1625.
5. FRANGAKIS, C. E. AND RUBIN, D. B. (2002). Principal stratification in causal inference. Biometrics 58, 21-29.
6. GLYMOUR, M. M., MUJAHID, M., Wu, Q., WHITE, K. AND TCHETGEN TCHETGEN E. J. (2010). Neighborhood disadvantage and self-assessed health, disability, and depressive symptoms: Longitudinal results from the health and retirement study. Annals of Epidemiology 20, 856-861.
7. HAYDEN, D., PAULER, D. K. AND SCHOENFELD, D. (2005). An estimator for treatment comparisons among survivors in randomized trials. Biometrics 61, 305-310.
8. HERN ÁN, M. A. (2005). Invited commentary: hypothetical interventions to define causal effects-afterthought or prerequisite? American Journal of Epidemiology 162, 618-620
9. HERN ÁN, M. A., HERN ÁNDEZ-DIAZ, E. F. AND ROBINS, J. M. (2004). A structural approach to selection bias. Epidemiology 15, 615-625
10. HERN ÁN, M. A. AND VANDERWEELE, T. J. (2011). Compound treatments and transportability of causal inference. Epidemiology 22, 368-377.
11. HIV-CAUSAL COLLABORATION (2012). Impact of antiretroviral therapy on tuberculosis incidence among HIVpositive patients in high-income countries. Clinical Infectious Diseases 54, 1364-1372.
12. HOLICK, M. F., BINKLEY, N. C., BISCHOFF-FERRARI, H. A., GORDON, C. M., HANLEY, D. A., HEANEY, R. P.,MURAD, M. H. AND WEAVER, C. M. (2011). Evaluation, treatment, and prevention of vitamin D deficiency: an Endocrine Society clinical practice guideline. Journal of Clinical Endocrinology and Metabolism 96, 1911-1930.
13. HOLICK, M. F., BINKLEY, N. C., BISCHOFF-FERRARI, H. A., GORDON, C. M., HANLEY, D. A., HEANEY, R. P.,MURAD, M. H. AND WEAVER, C. M. (2012). Guidelines for preventing and treating vitamin D deficiency and insufficiency revisited. Journal of Clinical Endocrinology and Metabolism 97, 1153-1158.
14. Institute of medicine(2011) Dietary Reference Intakes for Calcium and Vitamin D. Washington DC: The National Academies Press.
15. JEMIAI, Y., ROTNITZKY, A., SHEPHERD, B. E. AND GILBERT, P. B. (2007). Semiparametric estimation of treatment effects given baseline covariates on an outcome measured after a post-randomization event. Journal of the Royal Statistical Society: Series B 69, 879-901.
16. JOFFE, M. (2011). Principal stratification and attribution prohibition: good ideas taken too far. The International Journal of Biostatistics 7, article 35.
17. KURLAND, B. F. AND HEAGERTY, P. J. (2005). Directly parameterized regression conditioning on being alive: analysis of longitudinal data truncated by deaths. Biostatistics 6, 241-258.
18. LEE, L., DANIELS, M. J., AND SARGENT, D. J. (2010). Causal effects of treatment for informative missing data due to progression/death. Journal of the American Statistical Association 105, 912-929.
19. LIANG, K. Y. AND ZEGER, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13-22.
20. PEARL, J. (2009). Causality: Models, Reasoning, and Inference, 2nd edition. New York: Cambridge.
21. PEARL, J. (2011). Principal stratification-a goal or a tool? The International Journal of Biostatistics 7, article 20.
22. RAJAN, K. B. AND LEURGANS, S. E. (2010). Joint modeling of missing data due to non-participation and death in longitudinal aging studies. Statistics in Medicine 29, 2260-2268.
23. ROBINS, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods-application to control of the healthy worker survivor effect. Mathematical Modelling, 7, 1393-1512.
24. ROBINS, J. M. (1998).Marginal structural models. In 1997 Proceedings of the American Statistical Association Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association, pp. 1-10.
25. ROBINS, J. M. (1999). Marginal structural models versus structural nested models as tools for causal inference. In Halloran, M. E. and Berry, D (editors). Statistical Models in Epidemiology, the Environment, and Clinical Trials. New York: Springer, pp. 95-133.
26. ROBINS, J. M. (2000). Robust estimation in sequentially ignorable missing data and causal inference. In 1999 Proceedings of the American Statistical Association Section on Bayesian Statistical Science. Alexandria, VA: American Statistical Association, pp. 6-10.
27. ROBINS, J. M. AND GREENLAND, S. (2000). Comment on "Causal inference without counterfactuals." Journal of the American Statistical Association 95, 431-435.
28. ROBINS, J. M., HERN ÁN, M. A. AND BRUMBACK, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology 11, 550-560.
29. ROBINS, J. M. AND ROTNITZKY, A. (1992). Recovery of information and adjustment for dependent censoring using surrogate markers. In Jewell, N., Dietz, K. and Farewell, V. (editors). AIDS Epidemiology-Methodological Issues Boston: Birkhüser, pp. 297-331.
30. ROSEN, C. J. and others (2012). IOM committee members respond to Endocrine Society vitamin D guideline. Journal of Clinical Endocrinology and Metabolism 97, 1146-1152.
31. ROTNITZKY, A., LEI, Q., SUED, M. AND ROBINS, J. M. (2012). Improved double-robust estimation in missing data and causal inference models. Biometrika 99, 439-456.
32. RUBIN, D. B. (1976). Inference and missing data. Biometrika 63, 581-592.
33. RUBIN, D. B. (2006). Causal inference through potential outcomes and principal stratification: application to studies with "censoring" due to death. Statistical Science 21, 299-309.
34. SHARDELL, M., D'ADAMO, C., ALLEY, D. E.,MILLER, R. R., HICKS, G. E.,MILANESCHI, Y., SEMBA, R. D., CHERUBINI, A.,BANDINELLI, S. AND FERRUCCI, L. (2012). Serum 25-hydroxyvitamin D, transitions between frailty states, and mortality in older adults: the Invecchiare in Chianti Study. Journal of the American Geriatrics Society 60, 256-264.
35. SHARDELL, M., HICKS, G. E., MILLER, R. R., AND MAGAZINER, J. (2010). Semiparametric regression models for repeated measures of mortal cohorts with non-monotone missing outcomes and time-dependent covariates. Statistics in Medicine 29, 2282-2296.
36. SHARDELL, M. AND MILLER, R. R. (2008). Weighted estimating equations for longitudinal studies with death and non-monotone missing time-dependent covariates and outcomes. Statistics in Medicine 27, 1008-1025.
37. SHEPHERD, B. E., GILBERT, P. B., JEMIAI, Y. AND ROTNITZKY, A. (2006). Sensitivity analyses comparing outcomes only existing in a subset selected post-randomization, conditional on covariates, with application to HIV vaccine trials. Biometrics 62, 332-342.
38. TAN, Z. (2010). Bounded, efficient and doubly robust estimation with inverse weighting. Biometrika 97, 661-682.
39. TCHETGEN TCHETGEN, E. J. (2013). Identification and estimation of survivor average causal effects. Harvard University Biostatistics Working Paper Series, working paper 167.
40. TCHETGEN TCHETGEN, E. J., GLYMOUR, M. M., SHPITSER, I. AND WEUVE J. (2012a). To weight or not to weight? On the relation between inverse-probability weighting and principal stratification for truncation by death. Epidemiology 23, 132-137.
41. TCHETGEN TCHETGEN, E. J.,GLYMOUR, M. M.,WEUVE J. AND ROBINS J. (2012b). Specifying the correlation structure in inverse-probability-weighting estimation for repeated measures. Epidemiology 23, 644-646.
42. TSIATIS, A. A. (2006). Semiparametric Theory and Missing Data. New York: Springer.
43. TSIATIS, A. A., DAVIDIAN, M. AND CAO, W. (2011). Improved doubly robust estimation when data are monotonely coarsened, with application to longitudinal studies with dropout. Biometrics 67, 536-545.
44. VAN DER LAAN, M. J (2010). Targeted maximum likelihood based causal inference: part I. International Journal of Biostatistics 6, article 2.
45. VAN DER LAAN, M. J., HAIGHT, T. J. AND TAGER, I. B. (2005). Response to "Hypothetical interventions to define causal effects." American Journal of Epidemiology 162, 621-622.
46. VAN DER LAAN, M. J. AND ROBINS, J. (2003). Unified Methods for Censored Longitudinal Data and Causality. New York: Springer.