CS计算机代考程序代写 Bayesian Biostatistics M235 Final project ideas

Biostatistics M235 Final project ideas
Ideas based on reviewing articles in the literature
 Theory underlying propensity-score matching
Rubin, D.B., Thomas, N. (1992). “Affinely Invariant Matching Methods with Ellipsoidal
Distributions,” Annals of Statistics, 20:1079-1093.
Rubin, D.B., Thomas, N. (1992). “Characterizing the Effect of Matching Using Linear
Propensity Score Methods with Normal Distributions,” Biometrika, 79:797-809.
Rubin, D.B., Thomas, N. (1996). “Matching Using Estimated Propensity Scores: Relating
Theory to Practice,” Biometrics, 52:249-264.
Rubin, D.B., Thomas, N. (2000). “Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates,” Journal of the American Statistical Association, 95:573-585.
 Role of multiple control groups
Rosenbaum, P.R. (1987), “The Role of a Second Control Group in Observational
Studies” (with discussion), Statistical Science, 2: 292-316
Stuart, E.A., Rubin, D.B. (2008), “Matching with Multiple Control Groups with Adjustment for Group Differences,” Journal of Educational and Behavioral Statistics, 33: 279-306.
 Causal inference in dose-response settings
Efron, B., Feldman, D. (1991). “Compliance as an Explanatory Variable in Clinical Trials”
(with discussion), Journal of the American Statistical Association, 86:9-26.
Jin, H., Rubin, D.B. (2008), “Principal Stratification for Causal Inference with Extended Partial Compliance,” Journal of the American Statistical Association, 103: 101- 111.
 Inference for causal effects regarding air pollution regulations
Zigler CM, Dominici F, and Wang Y. Estimating causal effects of air quality regulations Using principal stratification for spatially-correlated multivariate intermediate outcomes. Biostatistics 2012; 13: 289-302.
Kim C, Daniels MJ, Hogan JW, Choirat C, Zigler CM. Bayesian methods for multiple mediators: Relating principal stratification and causal mediation in the analysis of power plant emission controls. Annals of Applied Statistics, 2019; 13: 1927-1956.
1

 Direct and indirect effects
Holland, P.W. (1988). “Causal Inference, Path Analysis, and Recursive Structural Equations
Models,” Sociological Methodology, 18:449-484.
Robins, J.M., Greenland, S. (1992). “Identifiability and Exchangeability for Direct and
Indirect Effects,” Epidemiology, 3:143-155.
Rubin, D.B. (2004). “Direct and Indirect Causal Effects via Potential Outcomes,”
Scandinavian Journal of Statistics, 31: 161-170.
 Missing data and modeling uncertainty in propensity-score analysis
D’Agostino, Jr. RB, Rubin DB. Estimating and using propensity scores with partially missing data. Journal of the American Statistical Association, 2000; 95: 749-759.
Song J, Belin TR, Lee MB, Gao X, Rotheram-Borus MJ. Handling baseline differences and missing items in a longitudinal study of HIV risk among runaway youths. Health Services and Outcomes Research Methodology, 2001; 2:317-329.
Zigler CM, Dominici F. Uncertainty in propensity score estimation: Bayesian methods for variable selection and model-averaged causal effects. Journal of the American Statistical Association, 2014; 109: 95-107.
Wang C, Dominici F, Parmigiani G, Zigler CM. Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models. Biometrics, 2015; 71: 654-665.
 Bayesian inference for causal effects from a potential-outcomes perspective, relaxing the assumption of “no defiers”
Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables (with discussion). Journal of the American Statistical Association, 1996; 91: 444-472.
Imbens GW, Rubin DB. Bayesian inference for causal effects in randomized experiments with noncompliance. The Annals of Statistics, 1997; 25:305-327.
 Missing data in studies with non-compliance
Frangakis C, Rubin DB Addressing complications of intention-to-treat analysis in the combined presence of all-or-none treatment noncompliance and subsequent missing outcomes. Biometrika, 1999; 86:366-379.
Zhou XH, Li SM. ITT analysis of randomized encouragement design studies with missing data. Statistics in Medicine, 2005; 25: 2737-2761.
2

 Studies of quality of life where death is a possible outcome
Frangakis C, Rubin DB. Principal stratification in causal inference. Biometrics, 2002; 58:
21-29.
Zhang JL, Rubin DB. Estimation of causal effects via principal stratification when some outcomes are truncated by “death”. Journal of Educational and Behavioral Statistics, 2003; 28: 353-368
 Inverse-probability-weighting strategies and double-robustness in causal inference Bang H, Robins JM. Doubly robust estimation in missing data and causal inference
models. Biometrics, 2005; 61: 962-973.
Yu, Z., and van der Laan, M. J. Double robust estimation in longitudinal marginal structural models. Journal of Statistical Planning and Inference, 2006; 136: 1061–1089.
Kang JDY, Schafer, JL. Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data (with discussion). Statistical Science, 2007; 22: 523–539.
Zubizarreta JR. Stable weights that balance covariates for estimation with incomplete outcome data. Journal of the American Statistical Association, 2015; 110: 910– 922.
 Penalized-spline-of-propensity treatment comparison for causal inference
Zhou T, Elliott MR, Little RJA. Penalized spline of propensity methods for treatment
comparison. Journal of the American Statistical Association, 2019; 114: 1-38.
 Causal inference with interfering units
Rosenbaum PR. Interference between units in randomized experiments. Journal of the American Statistical Association, 2007; 102: 191-200.
Hudgens MG, Halloran ME. Toward causal inference with interference. Journal of the American Statistical Association, 2008; 103: 832-842.
Papadogeorgou G, Mealli F, Zigler CM. Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 2019; 75: 778-787.
Zigler CM, Papadogeorgou G. Bipartite causal inference with interference. Statistical Science, 2021; 36: 109-123.
3

 Multiple imputation of missing potential outcomes
Gutman R, Rubin DB. Estimation of causal effects of binary treatments in unconfounded
studies. Statistics in Medicine, 2015; 34: 3381-3398.
Gutman R, Rubin DB. Estimation of causal effects of binary treatments in unconfounded studies with one continuous covariate. Statistical Methods in Medical Research, 2017; 26: 1199-1215.
 Evaluating and achieving fine balance in observational studies
Rosenbaum PR, Ross RN, Silber JH. Minimum-distance matched sampling with fine balance in an observational study of treatment for ovarian cancer. Journal of the American Statistical Association, 2007;102:75-83.
Silber, JH, Rosenbaum, PR, Polsky, D, Ross,RN, Even-Shoshan, O, Schwartz, S, Armstrong,KA, Randall, TC. Does ovarian cancer treatment and survival differ by the specialty providing chemotherapy? Journal of Clinical Oncology (JCO), 2007; 25: 1169-1175. Editorial: Cannistra, SA. Gynecologic oncology or medical oncology: What’s in a name? JCO 2007; 25: 1157-1159. 5 letters and 2 rejoinders from S Blank, J Curtin, ABerchuck, M Hoffman, U Iqbal, M Markham, W McGuire, JH Silber, PR Rosenbaum, S Cannistra. JCO 2007; 25: 1151-1158
See also 2019 Fisher Lecture by Paul Rosenbaum, beginning in the 32nd minute of
https://ww2.amstat.org/meetings/jsm/2019/webcasts/index.cfm
 Extensions of ignorability in principal causal effect estimation
Harel O, Schafer JL. Partial and latent ignorability in missing-data problems.
Biometrika, 2009; 96:37-50.
Jo B, Stuart EA. On the use of propensity scores in principal causal effect estimation.
Statistics in Medicine, 2009; 28: 2857-2875.
Zigler CM, Belin TR. A Bayesian approach to improved estimation of causal effect
predictiveness for a principal surrogate endpoint. Biometrics 2012; 68: 922-932.
4

 Interpretation of findings from the Women’s Health Initiative
Prentice RL, Langer R, Stefanick M, Howard B, Pettinger M, Anderson G, Barad D,
Curb JD, Kotchen J, Kuller L, Limacher M, Wactawski-Wende J, et al.
Combined postmenopausal hormone therapy and cardiovascular disease: toward
resolving the discrepancy between Women’s Health Initiative Clinical Trial and
Observational Study Results. American Journal of Epidemiology, 2005; 162:
404–414.
Prentice RL, Pettinger M, Anderson GL. Statistical issues arising in the Women’s Health
Initiative (with discussion) Biometrics, 2005; 61: 899–941.
Hernan MA, Robins JM, Garcia Rodriguez LA. Discussion on “Statistical issues arising in the Women’s Health Initiative”, Biometrics, 2005; 61: 922-930.
Hernán MA, Alonso A, Logan R, Grodstein F, Michels KB, Willett WC, Manson JE,
Robins JM. Observational studies analyzed like randomized experiments: an
application to postmenopausal hormone therapy and coronary heart disease.
Epidemiology, 2008; 19:766-779.
Hoover RN. The sound and the fury: Was it all worth it? Epidemiology, 2008; 19: 780-
782.
Stampfer MJ. ITT for observational data: Worst of both worlds? Epidemiology, 2008;
19: 783-784.
Prentice RL. Data analysis methods and the reliability of analytic epidemiologic research. Epidemiology, 2008; 19: 785-793.
Prentice RL, Anderson GL. The Women’s Health Initiative: Lessons learned. Annual Review of Public Health, 2008; 29: 131-150.
Hernán MA, Robins JM. Authors’ response, Part 1: Observational studies analyzed like
randomized experiments: The best of both worlds. Epidemiology, 2008; 19: 789-
792.
Willett WC, Manson JE, Grodstein F. Author’s response, Part II. Epidemiology, 2008;
19: 793.
5

Randomized encouragement designs and doubly randomized preference designs
Hirano, K., Imbens, G.W., Rubin, D.B., Zhou, X.H. (2000). “Assessing the Effect of an Influenza Vaccine in an Encouragement Design,” Biostatistics, 1, 69-88.
http://biostatistics.oxfordjournals.org/content/1/1/69.full.pdf+html
Frangakis, C., Rubin, D.B., Zhou, X. (2002). “Clustered Encouragement Designs with Individual Noncompliance: Bayesian Inference with Randomization, and Application to Advance Directive Forms,” Biostatistics, 3:147-164.
http://biostatistics.oxfordjournals.org/content/3/2/147.full.pdf+html
Ten Have, T.R., Elliott, M.R., Joffe, M., Zanutto, E., Datto, C. (2004). “Causal Models for Randomized Physician Encouragement Trials in Treating Primary Care Depression,” Journal of the American Statistical Association, 99, 16-25.
http://www.jstor.org/stable/27590349?seq=1#page_scan_tab_contents
https://www.jstor.org/stable/27640072
Marcus, S.M., Stuart, E.A., Wang, P., Shadish, W.R., Steiner, P.M. “Estimating the Causal Effect of Randomization versus Treatment Preference in a Doubly-Randomized Preference Trial,” Psychological Methods, 17: 244-254.
Confounding and the ecological paradox
Greenland, S., Robins, J.M. (1986). “Identifiability, Exchangeability, and Confounding,” International Journal of Epidemiology, 15:413-419.
Greenland, S., Morgenstern, H. (1989). “Ecological Bias, Confounding, and Effect Modification,” International Journal of Epidemiology, 18: 269-274.
Freedman D A, Klein S P, Sacks J, Smyth C A, Everett C G (1991). “Ecological regression and voting rights” (with discussion). Evaluation Review 15: 659–817.
Klein, S.A., Freedman, D.A. (1993). “Ecological Regression in Voting Rights Cases,” Chance, 6, No. 3, 38-43.
King, G. (1997) A Solution to the Ecological Inference Problem, Princeton, NJ: Princeton University Press.
King, G., Rosen, O., Tanner, M. (1999). “Binomial-Beta Hierarchical Models for Ecological Inference,” Sociological Methods and Research, 28: 61-90.
Freedman D A, Klein S P, Ostland M, Roberts M R 1998 Review of A Solution to the Ecological Inference Problem. Journal of the American Statistical Association 93: 1518–22; with discussion, vol. 94 (1999) pp. 352–57.
Long, Q., Little, R.J.A., Lin, X. (2004) “Causal Inference in Hybrid Intervention Trials Involving Treatment Choice,” Journal of the American Statistical Association, 103:474- 484.
6

Ideas based on analyzing an observational data set
Using concepts developed in the course to analyze an observational data set can serve as a final project. For example, a project might use the “MatchIt” package for propensity-score matching (https://cran.r-project.org/web/packages/MatchIt/MatchIt.pdf) or sensitivity-analysis software (e.g., http://www-stat.wharton.upenn.edu/~rosenbap/packpaper.pdf) developed in work by Paul Rosenbaum. Ideas should be sketched in a (one-page or less) final-project proposal by March 3.
7