Impact of Multiple Matched Controls on Design Sensitivity in Observational Studies

Biometrics

Volumn 69, Issue 1, 2013, Pages 118-127

  Rosenbaum, Paul R ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Design sensitivity; M Estimate; Observational study; Sensitivity analysis

Indexed keywords

PRODUCT DESIGN;

% REDUCTIONS; COVARIATES; DESIGN SENSITIVITY; M-ESTIMATES; MATCHED CONTROLS; MATCHINGS; OBSERVATIONAL STUDY; POWER; SAMPLE SIZES; UNTREATED CONTROL;

SENSITIVITY ANALYSIS;

BLOOD; CADMIUM; COVARIANCE ANALYSIS; HEALTH SURVEY; HETEROGENEITY; LEAD; SAMPLING; SENSITIVITY ANALYSIS; SMOKING;

CADMIUM; LEAD;

ARTICLE; BIOMETRY; BLOOD; CASE CONTROL STUDY; COMPUTER SIMULATION; FEMALE; HUMAN; LONGITUDINAL STUDY; MALE; METHODOLOGY; SMOKING; STANDARD; STATISTICAL MODEL;

BIOMETRY; CADMIUM; CASE-CONTROL STUDIES; COMPUTER SIMULATION; FEMALE; HUMANS; LEAD; LONGITUDINAL STUDIES; MALE; MODELS, STATISTICAL; SMOKING;

EID: 84875930150     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2012.01821.x     Document Type: Article

References (37)

1. Conover, W. J. and Salsburg, D. S. (1988). Locally most powerful tests for detecting treatment effects when only a subset of patients can be expected to respond to treatment. Biometrics 44, 189-196.
2. Cornfield, J., Haenszel, W., Hammond, E., Lilienfeld, A., Shimkin, M., and Wynder, E. (1959). Smoking and lung cancer. Journal of the National Cancer Institute 22, 173-203.
3. Diprete, T. A. and Gangl, M. (2004). Assessing bias in the estimation of causal effects. Sociological Methodology 34, 271-310.
4. Doksum, K. (1974). Empirical probability plots and statistical inference for nonlinear models in the two-sample case. Annals of Statistics 2, 267-277.
5. Egleston, B. L., Scharfstein, D. O., and MacKenzie, E. (2009). On estimation of the survivor average causal effect in observational studies when important confounders are missing due to death. Biometrics 65, 497-504.
6. Fisher, R. A. (1935). Design of Experiments. Edinburgh: Oliver & Boyd.
7. Gadbury, G. L. (2001). Randomization inference and the bias of standard errors. American Statistician 55, 310-313.
8. Gastwirth, J. L. (1992). Methods for assessing the sensitivity of statistical comparisons used in Title VII cases to omitted variables. Jurimetrics 33, 19-34.
9. Gastwirth, J. L., Krieger, A. M., and Rosenbaum, P. R. (2000). Asymptotic separability in sensitivity analysis. Journal of the Royal Statistical Society, Series B 62, 545-555.
10. Gilbert, P. C., Bosch, R. J., and Hudgens, M. G. (2003). Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIV vaccine trials. Biometrics 59, 531-541.
11. Hansen, B. B. (2007). Optmatch. R News 7, 18-24.
12. Hansen, B. B. and Klopfer, S. O. (2006). Optimal full matching and related designs via network flows. Journal of Computational and Graphical Statistics 15, 609-627.
13. Heller, R., Rosenbaum, P. R., and Small, D. S. (2010). Using the cross-match test to appraise covariate balance in matched pairs. American Statistician 64, 299-309.
14. Hosman, C. A., Hansen, B. B., and Holland, P. W. H. (2010). The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder. The Annals of Applied Statistics 4, 849-870.
15. Huber, P. J. (1981). Robust Statistics. New York: Wiley.
16. Lu, B., Greevy, R., Xu, X., and Beck, C. (2011). Optimal nonbipartite matching and its statistical applications. American Statistician 65, 21-30.
17. Marcus, S. M. (1997). Using omitted variable bias to assess uncertainty in the estimation of an AIDS education treatment effect. Journal of Educational Statistics 22, 193-201.
18. McCandless, L. C., Gustafson, P., and Levy, A. (2007). Bayesian sensitivity analysis for unmeasured confounding in observational studies. Statistics in Medicine 26, 2331-2347.
19. Maritz, J. (1979). Exact robust confidence intervals for location. Biometrika 66, 163-166.
20. Ming, K. and Rosenbaum, P. R. (2000). Substantial gains in bias reduction from matching with a variable number of controls. Biometrics 56, 118-124.
21. Neyman, J. (1923). Essay on principles. Statistical Science 5, 463-480.
22. Reiter, J. (2000). Using statistics to determine causal relationships. American Mathematical Monthly 107, 24-32.
23. Rosenbaum, P. R. (1991). A characterization of optimal designs for observational studies. Journal of the Royal Statistical Society, Series B 53, 597-610.
24. Rosenbaum, P. R. (2002). Observational Studies. New York: Springer.
25. Rosenbaum, P. R. (2004). Design sensitivity in observational studies. Biometrika 91, 153-164.
26. Rosenbaum, P. R. (2005). Heterogeneity and causality. American Statistician 59, 147-152.
27. Rosenbaum, P. R. (2007). Sensitivity analysis for m-estimates, tests, and confidence intervals in matched observational studies. Biometrics 63, 456-464.
28. Rosenbaum, P. R. (2010a). Design of Observational Studies. New York: Springer.
29. Rosenbaum, P. R. (2010b). Design sensitivity and efficiency in observational studies. Journal of the American Statistical Association 105, 692-702.
30. Rosenbaum, P. R. (2011). Some approximate evidence factors in observational studies. Journal of the American Statistical Association 106, 285-295.
31. Rosenbaum, P. R. and Silber, J. H. (2009). Amplification of sensitivity analysis in observational studies. Journal of the American Statistical Association 104, 1398-1405.
32. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 688-701.
33. Stuart, E. A. (2010). Matching methods for causal inference. Statistical Science 25, 1-21.
34. Wang, L. and Krieger, A. M. (2006). Causal conclusions are most sensitive to unobserved binary covariates. Statistics in Medicine 25, 2257-2271.
35. Welch, B. L. (1937). On the z-test in randomized blocks. Biometrika 29, 21-52.
36. Yanagawa, T. (1984). Case-control studies: Assessing the effect of a confounding factor. Biometrika 71, 191-194.
37. Yu, B. B. and Gastwirth, J. L. (2005). Sensitivity analysis for trend tests: Application to the risk of radiation exposure. Biostatistics 6, 201-209.