Calibrating sensitivity analyses to observed covariates in observational studies

Biometrics

Volumn 69, Issue 4, 2013, Pages 803-811

  Hsu, Jesse Y ^a   Small, Dylan S ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b CHILDREN S HOSPITAL OF PHILADELPHIA   (United States)

Author keywords

Causal inference; Hidden bias; Simultaneous sensitivity analysis

Indexed keywords

CAUSAL INFERENCES; COVARIATES; HIDDEN BIAS; MEDICAL SCIENCE; OBSERVATIONAL STUDY; PRE-TREATMENTS; SENSITIVITY ANALYZES; SENSITIVITY PARAMETERS; SIMULTANEOUS SENSITIVITY ANALYSE; SUBJECT MATTER EXPERTS;

SENSITIVITY ANALYSIS;

CALIBRATION; DISEASE TREATMENT; HEALTH RISK; OBSERVATIONAL METHOD; PARAMETERIZATION; SENSITIVITY ANALYSIS; SMOKING;

UNITED STATES;

LEAD;

ARTICLE; BIOMETRY; BLOOD; CALIBRATION; CAUSAL INFERENCE; EPIDEMIOLOGY; FEMALE; HIDDEN BIAS; HUMAN; MALE; METHODOLOGY; MIDDLE AGED; OBSERVATIONAL STUDY; RISK FACTOR; SIMULTANEOUS SENSITIVITY ANALYSIS; SMOKING; STANDARD; STATISTICAL ANALYSIS; STATISTICS; UNITED STATES;

CAUSAL INFERENCE; HIDDEN BIAS; SIMULTANEOUS SENSITIVITY ANALYSIS;

BIOMETRY; CALIBRATION; DATA INTERPRETATION, STATISTICAL; EPIDEMIOLOGIC METHODS; FEMALE; HUMANS; LEAD; MALE; MATCHED-PAIR ANALYSIS; MIDDLE AGED; OBSERVATIONAL STUDY AS TOPIC; RISK FACTORS; SMOKING; UNITED STATES;

EID: 84890310015     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12101     Document Type: Article

References (28)

1. Bingenheimer, J. B., Brennan, R. T., and Earls, F. J. (2005). Firearm violence exposure and serious violent behavior. Science 308, 1323-1326.
2. Copas, J. B. and Li, H. G. (1997). Inference for non-random samples. Journal of the Royal Statistical Society. Series B (Methodological) 59, 55-95.
3. Cornfield, J., Haenszel, W., Hammond, E. C., Lilienfeld, A. M., Shimkin, M. B., and Wynder, E. L. (1959). Smoking and lung cancer. Journal of the National Cancer Institute 22, 173-203.
4. Dawid, A. P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society. Series B (Methodological) 41, 1-31.
5. Fisher, R. A. (1935). The Design of Experiments. Edinburgh, Scotland: Oliver and Boyd.
6. Gail, M. H., Wieand, S., and Piantadosi, S. (1984). Biased estimates of treatment effect in randomized experiments with nonlinear regressions and omitted covariates. Biometrika 71, 431-444.
7. Gastwirth, J. L. (1992). Methods for assessing the sensitivity of statistical comparisons used in title VII cases to omitted variables. Jurimetrics 33, 19-34.
8. Gastwirth, J. L., Krieger, A. M., and Rosenbaum, P. R. (1998). Dual and simultaneous sensitivity analysis for matched pairs. Biometrika 85, 907-920.
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 (Statistical Methodology) 62, 545-555.
10. Gelman, A. (2008). Scaling regression inputs by dividing by two standard deviations. Statistics in Medicine 27, 2865-2873.
11. Geronimus, A. T. and Bound, J. (1998). Use of census-based aggregate variables to proxy for socioeconomic group: Evidence from national samples. American Journal of Epidemiology 148, 475-486.
12. Guo, J. and Geng, Z. (1995). Collasibility of logistic regression coefficients. Journal of the Royal Statistical Society. Series B (Methodological) 57, 263-267.
13. Hansen, B. B. (2007). Optmatch: Flexible, optimal matching for observational studies. R News 7, 18-24.
14. Holden, C. (2005). Controversial study suggests seeing gun violence promotes it. Science 308, 1239-1240.
15. Hosman, C. A., Hansen, B. B., and Holland, P. W. (2010). The sensitivity of linear regression coefficients' confidence limits to the omission of a confounder. The Annals of Applied Statistics 4, 849-870.
16. Hsu, J. Y., Lorch, S. A., and Small, D. S. (2012). Perils and prospects of using aggregate area level socioeconomic information as a proxy for individual level socioeconomic confounders in instrumental variables regression. Health Services and Outcomes Research Methodology 12, 119-140.
17. Imbens, G. W. (2003). Sensitivity to exogeneity assumptions in program evaluation. The American Economic Review 93, 126-132.
18. Marcus, S. M. (1997). Using omitted variable bias to assess uncertainty in the estimation of an AIDS education treatment effect. Journal of Educational and Behavioral Statistics 22, 193-201.
19. Neyman, J. (1923). On the application of probability theory to agricultural experiments. Reprint in Statistical Science( 5:465-480, 1923).
20. Pan, W. and Frank, K. A. (2003). A probability index of the robustness of a causal inference. Journal of Educational and Behavioral Statistics 28, 315-337.
21. Rosenbaum, P. R. (1986). Dropping out of high school in the united states: An observational study. Journal of Educational Statistics 11, 207-224.
22. Rosenbaum, P. R. (2002). Observational Studies, 2nd edition. New York, NY, USA: Springer.
23. Rosenbaum, P. R. and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55.
24. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 688-701.
25. Shepherd, B. E., Gilbert, P. B., and Mehrotra, D. V. (2007). Eliciting a counterfactual sensitivity parameter. The American Statistician 61, 56-63.
26. Small, D. S. (2007). Sensitivity analysis for instrumental variables regression with overidentifying restructions. Journal of the American Statistical Association 102, 1049-1058.
27. Small, D. S., Gastwirth, J. L., Krieger, A. M., and Rosenbaum, P. R. (2009). Simultaneous sensitivity analysis for observational studies using full matching or matching with multiple controls. Statistics and Its Interface 2, 203-211.
28. Wang, L. and Krieger, A. M. (2006). Causal conclusions are most sensitive to unobserved binary covariates. Statistics in Medicine 25, 2257-2271.