Inverse probability weighting with missing predictors of treatment assignment or missingness

Communications in Statistics - Theory and Methods

Volumn 43, Issue 16, 2014, Pages 3499-3515

  Seaman, Shaun ^a   White, Ian ^a  

^a MRC BIOSTATISTICS UNIT   (United Kingdom)

Author keywords

Causal inference; Confounding; Horwitz Thompson estimator; Missing at random; Missing Not at random

Indexed keywords

STATISTICS;

CAUSAL INFERENCES; CONFOUNDING; HORWITZ-THOMPSON ESTIMATOR; MISSING AT RANDOMS; MISSING NOT AT RANDOM;

MATHEMATICAL TECHNIQUES;

EID: 84905483303     PISSN: 03610926     EISSN: 1532415X     Source Type: Journal    
DOI: 10.1080/03610926.2012.700371     Document Type: Article

References (42)

1. Atherton, K., Fuller, E., Shepherd, P., Strachan, D.P., Power, C. (2008). Loss and representativeness in a biomedical survey at age 45 years: 1958 British birth cohort. J. Epidemiol. Comm. Health 62: 216-223.
2. Cepeda, M.S., Boston, R., Farrar, J.T., Strom, B.L. (2003). Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am. J. Epidemiol. 158: 280-287.
3. DAgostino, R.B., Rubin, D.B. (2000). Estimating and using propensity scores with partially missing data. J. Am. Stat. Assoc. 95: 749-759.
4. Hayes, J.R., Groner, J.I. (2008). Using multiple imputation and propensity scores to test the effect of car seats and seat belt usage on injury severity from trauma registry data. J. Pediatr. Surg. 43: 924-927.
5. Horwitz, D.G., Thompson, D.J. (1985). A generalisation of sampling without replacement from a finite universe. J. Am. Stat. Assoc. 47: 663-685.
6. Lunceford, J.K., Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat. Med. 23: 2937-2960.
7. Mattei, A. (2009). Estimating and using propensity scores in presence of missing background data: an application to assess the impact of childbearing on wellbeing. Stat. Methods Appl. 18: 257-273.
8. Mitra, R., Reiter, J.P. (2011). Estimating propensity scores with missing covariate data using general location mixture models. Stat. Med. 30: 627-641.
9. Nielsen, S.F. (2003). Proper and improper multiple imputation. Int. Stat. Rev. 71: 593-627.
10. Qu, Y., Lipkovich, I. (2009). Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach. Stat. Med. 28: 1402-1414.
11. Robins, J.M., Wang, N. (2000). Inference for imputation estimators. Biometrika 87: 113-124.
12. Rosenbaum, P.R., Rubin, R.J.A. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70: 41-55.
13. Rotnitzky, A., Wypij, D. (1994). A note on the bias of estimators with missing data. Biometrics 44: 1163-1170.
14. Royston, J.P. (2005). Multiple imputation of missing values: Update of ice. Stat. J. 5: 527-536.
15. Schafer, J.L. (2003). Multiple imputation in multivariate problems when the imputation and analysis models differ. Stat. Neerlandica 57: 19-35.
16. Seaman, S.R., White, I.R. (2013). Review of inverse probability weighting for dealing with missing data. Stat. Methods Med. Res. 22: 278-295.
17. Seaman, S.R., White, I.R., Copas, A.J., Li, L. (2012). Combining multiple imputation and inverse-probability weighting. Biometrics 68: 129-137.
18. Song, J., Belin, T.R., Lee, M.B., Gao, X., Rotheram-Borus, M.J. (2001). Handling baseline differences and missing items in a longitudinal study of HIV risk among runaway youths. Health Serv. Outcomes Res. Method. 2: 317-329.
19. Stürmer, T., Schneeweiss, S., Brookhart, M.A., Rothman, K.J., Avorn, J., Glynn, R.J. (2005). Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: Nonsteroidal antiinflammatory drugs and short-term mortality in the elderly. Am. J. Epidemiol. 161: 891-898.
20. Thomas, C., Hypponen, E., Power, C. (2007). Prenatal exposures and glucose metabolism in adulthood. Diabetes Care 30: 918-924.
21. Tsiatis, A.A. (2006). Semiparametric Theory and Missing Data. (pp. 30, 206), New York: Springer.
22. Atherton, K., Fuller, E., Shepherd, P., Strachan, D. P., Power, C. (2008). Loss and representativeness in a biomedical survey at age 45 years: 1958 British birth cohort. J. Epidemiol. Comm. Health 62:216-223.
23. Cepeda, M. S., Boston, R., Farrar, J. T., Strom, B. L. (2003). Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am. J. Epidemiol. 158:280-287.
24. D'Agostino, R. B., Rubin, D. B. (2000). Estimating and using propensity scores with partially missing data. J. Am. Stat. Assoc. 95:749-759.
25. Hayes, J. R., Groner, J. I. (2008). Using multiple imputation and propensity scores to test the effect of car seats and seat belt usage on injury severity from trauma registry data. J. Pediatr. Surg. 43:924-927.
26. Horwitz, D. G., Thompson, D. J. (1985). A generalisation of sampling without replacement from a finite universe. J. Am. Stat. Assoc. 47:663-685.
27. Lunceford, J. K., Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study. Stat. Med. 23:2937-2960.
28. Mattei, A. (2009). Estimating and using propensity scores in presence of missing background data: an application to assess the impact of childbearing on wellbeing. Stat. Methods Appl. 18:257-273.
29. Mitra, R., Reiter, J. P. (2011). Estimating propensity scores with missing covariate data using general location mixture models. Stat. Med. 30:627-641.
30. Nielsen, S. F. (2003). Proper and improper multiple imputation. Int. Stat. Rev. 71:593-627.
31. Qu, Y., Lipkovich, I. (2009). Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach. Stat. Med. 28:1402-1414.
32. Robins, J. M., Wang, N. (2000). Inference for imputation estimators. Biometrika 87:113-124.
33. Rosenbaum, P. R., Rubin, R. J. A. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70:41-55.
34. Rotnitzky, A., Wypij, D. (1994). A note on the bias of estimators with missing data. Biometrics 44:1163-1170.
35. Royston, J. P. (2005). Multiple imputation of missing values: Update of ice. Stat. J. 5:527-536.
36. Schafer, J. L. (2003). Multiple imputation in multivariate problems when the imputation and analysis models differ. Stat. Neerlandica 57:19-35.
37. Seaman, S. R., White, I. R. (2013). Review of inverse probability weighting for dealing with missing data. Stat. Methods Med. Res. 22:278-295.
38. Seaman, S. R., White, I. R., Copas, A. J., Li, L. (2012). Combining multiple imputation and inverseprobability weighting. Biometrics 68:129-137.
39. Song, J., Belin, T. R., Lee, M. B., Gao, X., Rotheram-Borus, M. J. (2001). Handling baseline differences and missing items in a longitudinal study of HIV risk among runaway youths. Health Serv. Outcomes Res. Method. 2:317-329.
40. Stürmer, T., Schneeweiss, S., Brookhart, M. A., Rothman, K. J., Avorn, J., Glynn, R. J. (2005). Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: Nonsteroidal antiinflammatory drugs and short-term mortality in the elderly. Am. J. Epidemiol. 161:891-898.
41. Thomas, C., Hypponen, E., Power, C. (2007). Prenatal exposures and glucose metabolism in adulthood. Diabetes Care 30:918-924.
42. Tsiatis, A. A. (2006). Semiparametric Theory and Missing Data. (pp. 30, 206), New York: Springer.