A New u-Statistic with Superior Design Sensitivity in Matched Observational Studies

Biometrics

Volumn 67, Issue 3, 2011, Pages 1017-1027

  Rosenbaum, Paul R ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Design sensitivity; Observational study; Sensitivity analysis; U statistic

Indexed keywords

MEDICINE; SAMPLING; STATISTICS;

COVARIATES; DESIGN SENSITIVITY; HIGH POWER; OBSERVATIONAL STUDY; POWER; RANDOM ASSIGNMENT; RANDOMIZATION TESTS; RANDOMIZED EXPERIMENTS; TREATMENT EFFECTS; U-STATISTICS;

SENSITIVITY ANALYSIS;

BIOMETRY; CLINE; DESIGN; EPIDEMIOLOGY; EXPERIMENTAL STUDY; GENOTOXICITY; HYPOTHESIS TESTING; MEDICINE; OBSERVATIONAL METHOD; PROBABILITY; SENSITIVITY ANALYSIS; VARIANCE ANALYSIS;

EID: 80052784619     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2010.01535.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. Copas, J. and Eguchi, S. (2001). Local sensitivity approximations for selectivity bias. Journal of the Royal Statistical Society, Series B 63, 871-896.
3. 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.
4. Deng, H., Zhang, M., He, J., Wu, W., Jin, L., Zheng, W., Lou, J., and Wang, B. (2005). Investigating genetic damage in workers occupationally exposed to methotrexate using three genetic end-points. Mutagenesis 20, 351-357.
5. Diprete, T. A. and Gangl, M. (2004). Assessing bias in the estimation of causal effects. Sociological Methodology 34, 271-310.
6. Feller, W. (1968). Introduction to Probability Theory, I. New York Wiley.
7. Fisher, R. A. (1935). The Design of Experiments. Edinburgh Oliver & Boyd.
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. Glynn, R. J., Schneeweiss, S., Wang, P. S., Levin, R., and Avorn, J. (2006). Selective prescribing led to overestimation of the benefits of lipid-lowering drugs. Journal of Clinical Epidemiology 59, 819-828.
10. Greenhouse, S. (1982). Cornfield's contributions to epidemiology. Biometrics 38S, 33-46.
11. Hajek, J. and Sidak, Z. (1967). Theory of Rank Tests. New York Academic Press.
12. Heller, R., Rosenbaum, P. R., and Small, D. (2009). Split samples and design sensitivity in observational studies. Journal of the American Statistical Association 104, 1090-1101.
13. Hettmansperger, T. P. (1984). Statistical Inference Based on Rank. New York: John Wiley.
14. Hoeffding, W. (1948). A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics 19, 293-325.
15. Imbens, G. W. (2003). Sensitivity to exogeneity assumptions in program evaluation. American Economic Review 93, 126-132.
16. Lehmann, E. L. (1975). Nonparametrics. San Francisco Holden Day.
17. Lin, D. Y., Psaty, B. M., and Kronmal, R. A. (1998). Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics 54, 948-963.
18. Marcus, S. (1997). Using omitted variable bias to assess uncertainty in the estimation of an AIDS education treatment effect. Journal of Educational Statistics 22, 193-201.
19. Morton, D., Saah, A., Silberg, S., Owens, W., Roberts, M., and Saah, M. (1982). Lead absorption in children of employees in a lead related industry. American Journal of Epidemiology 115, 549-555.
20. Neyman, J. (1923). On the application of probability theory to agricultural experiments: Essay on principles, Section 9. Reprinted in Statistical Science 5, 463-480.
21. Pratt, J. W. and Gibbons, J. D. (1981). Concepts of Nonparametric Theory. New York Springer.
22. Robins, J. M., Rotnitzky, A., and Scharfstein, D. (1999). Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference. In Statistical Models in Epidemiology. E. Halloran and D. Berry (eds), 1-94. New York Springer.
23. Rosenbaum, P. R. (2002a). Observational Studies, 2nd edition. New York Springer.
24. Rosenbaum, P. R. (2002b). Covariance adjustment in randomized experiments and observational studies (with discussion). Statistical Science 17, 286-327.
25. Rosenbaum, P. R. (2004). Design sensitivity in observational studies. Biometrika 91, 153-164.
26. Rosenbaum, P. R. (2007). Confidence intervals for uncommon but dramatic responses to treatment. Biometrics 63, 1164-1171.
27. Rosenbaum, P. R. (2010a). Design of Observational Studies. New York Springer.
28. Rosenbaum, P. R. (2010b). Design sensitivity and efficiency in observational studies. Journal of the American Statistical Association 105, 692-702.
29. Rosenbaum, P. R. and Silber, J. H. (2009). Amplification of sensitivity analysis in observational studies. Journal of the American Statistical Association 102, 75-83.
30. Rosenbaum, P. R., Ross R. N., and Silber, J. H. (2007). Minimum distance matched sampling with fine balance in an observational study of treatment for ovarian cancer. Journal of the American Statistical Association 102, 75-83.
31. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 688-701.
32. Silber, J. H., Rosenbaum, P. R., Polsky, D., Ross, R., Even-Shoshan, O., Schwartz, S., Armstrong, K., and Randall, T. (2007). Does ovarian cancer treatment and survival differ by the specialty providing chemotherapy Journal of Clinical Oncology 25, 1169-1175.
33. Slade, E. P., Stuart, E. A., Alkever, D. S. S., Karakus, M., Green, K. M., and Ialongo, N. (2008). Impacts of age of onset of substance use disorders on risk of adult incarceration among disadvantaged urban youth. Drug and Alcohol Dependence 95, 1-13.
34. Small, D. (2007). Sensitivity analysis for instrumental variables regression with overidentifying restrictions. Journal of the American Statistical Association 102, 1049-1058.
35. Stephenson, W. R. (1981). A general class of one-sample nonparametric test statistics based on subsamples. Journal of the American Statistical Association 76, 960-966.
36. Yanagawa, T. (1984). Case-control studies: Assessing the effect of a confounding factor. Biometrika 71, 191-194.
37. Yu, B. B., Gastwirth, J. L. (2005). Sensitivity analysis for trend tests: Application to the risk of radiation exposure. Biostatistics 6, 201-209.