Contents
Preface xiii
I Casual inference and observational studies 1
1 An overview of methods for causal inference from observational
studies, by Sander Greenland 3
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Approachesbasedoncausalmodels................. 3
1.3 Canonical inference . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4 Methodologic modeling . . . . . . . . . . . . . . . . . . . . . . . 10
1.5 Conclusion .............................. 13
2 Matching in observational studies, by Paul R. Rosenbaum 15
2.1 The role of matching in observational studies . . . . . . . . . . . . 15
2.2 Whymatch?.............................. 16
2.3 Twokeyissues:balanceandstructure................ 17
2.4 Additionalissues ........................... 21
3 Estimating causal effects in nonexperimental studies,
by Rajeev Dehejia 25
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2 Identifying and estimating the average treatment effect . . . . . . . 27
3.3 TheNSWdata ............................ 29
3.4 Propensity score estimates . . . . . . . . . . . . . . . . . . . . . . 31
3.5 Conclusions.............................. 35

4 Medication cost sharing and drug spending in Medicare,
by Alyce S. Adams 37
4.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2 Results................................. 40
4.3 Study limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.4 Conclusionsandpolicyimplications................. 46
5 A comparison of experimental and observational data analyses,
by Jennifer L. Hill, Jerome P. Reiter, and Elaine L. Zanutto 49
5.1 Experimentalsample ......................... 50
5.2 Constructed observational study . . . . . . . . . . . . . . . . . . . 51
5.3 Concludingremarks ......................... 60
6 Fixing broken experiments using the propensity score,
by Bruce Sacerdote 61
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6.2 Thelotterydata............................ 62
6.3 Estimating the propensity scores . . . . . . . . . . . . . . . . . . . 63
6.4 Results................................. 65
6.5 Concludingremarks ......................... 71

7 The propensity score with continuous treatments,
by Keisuke Hirano and Guido W. Imbens 73
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
7.2 Thebasicframework......................... 74
7.3 BiasremovalusingtheGPS..................... 76
7.4 Estimation and inference . . . . . . . . . . . . . . . . . . . . . . . 78
7.5 Application: the Imbens–Rubin–Sacerdote lottery sample . . . . . 79
7.6 Conclusion .............................. 83
8 Causal inference with instrumental variables, by Junni L. Zhang 85
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
8.2 Key assumptions for the LATE interpretation of the IV estimand . 87
8.3 Estimating causal effects with IV . . . . . . . . . . . . . . . . . . 90
8.4 Some recent applications . . . . . . . . . . . . . . . . . . . . . . . 95
8.5 Discussion............................... 95
9 Principal stratification, by Constantine E. Frangakis 97
9.1 Introduction: partially controlled studies . . . . . . . . . . . . . . . 97
9.2 Examples of partially controlled studies . . . . . . . . . . . . . . . 97
9.3 Principalstratification......................... 101
9.4 Estimands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
9.5 Assumptions ............................. 104
9.6 Designs and polydesigns . . . . . . . . . . . . . . . . . . . . . . . 107
II Missing data modeling 109
10 Nonresponse adjustment in government statistical agencies:
constraints, inferential goals, and robustness issues,
by John L. Eltinge 111
10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in
government statistical agencies . . . . . . . . . . . . . . . . . . . . 111
10.2Constraints .............................. 112
10.3 Complex estimand structures, inferential goals, and utility functions 112
10.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
10.5Closingremarks............................ 113
11 Bridging across changes in classification systems, by Nathaniel
Schenker 117
11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
11.2 Multiple imputation to achieve comparability of industry and occu-
pationcodes.............................. 118
11.3 Bridging the transition from single-race reporting to multiple-race
reporting................................ 123
11.4Conclusion .............................. 128
12 Representing the Census undercount by multiple imputation
of households, by Alan M. Zaslavsky 129
12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
12.2Models ................................ 131
12.3Inference ............................... 134
12.4Simulationevaluations ........................ 138
12.5Conclusion .............................. 140
13 Statistical disclosure techniques based on multiple imputation,
by Roderick J. A. Little, Fang Liu, and Trivellore E. Raghunathan 141
13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
13.2Fullsynthesis............................. 143
13.3SMIKeandMIKe........................... 144
13.4Analysisofsyntheticsamples .................... 147
13.5Anapplication ............................ 149
13.6Conclusions.............................. 152
14 Designs producing balanced missing data: examples from the National
Assessment of Educational Progress, by Neal Thomas 153
14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
14.2 Statistical methods in NAEP . . . . . . . . . . . . . . . . . . . . . 155
14.3 Split and balanced designs for estimating population parameters . 157
14.4 Maximum likelihood estimation . . . . . . . . . . . . . . . . . . . 159
14.5 The role of secondary covariates . . . . . . . . . . . . . . . . . . . 160
14.6Conclusions.............................. 162
15 Propensity score estimation with missing data,
by Ralph B. D’Agostino Jr. 163
15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
15.2Notation................................ 165
15.3Appliedexample:MarchofDimesdata............... 168
15.4 Conclusion and future directions . . . . . . . . . . . . . . . . . . . 174

16 Sensitivity to nonignorability in frequentist inference,
by Guoguang Ma and Daniel F. Heitjan 175
16.1 Missing data in clinical trials . . . . . . . . . . . . . . . . . . . . . 175
16.2 Ignorability and bias . . . . . . . . . . . . . . . . . . . . . . . . . 175
16.3 A nonignorable selection model . . . . . . . . . . . . . . . . . . . 176
16.4 Sensitivity of the mean and variance . . . . . . . . . . . . . . . . 177
16.5 Sensitivity of the power . . . . . . . . . . . . . . . . . . . . . . . 178
16.6 Sensitivity of the coverage probability . . . . . . . . . . . . . . . . 180
16.7Anexample.............................. 184
16.8Discussion............................... 185
III Statistical modeling and computation 187
17 Statistical modeling and computation, by D. Michael Titterington 189
17.1Regressionmodels .......................... 190
17.2Latent-variableproblems....................... 191
17.3 Computation: non-Bayesian . . . . . . . . . . . . . . . . . . . . . 191
17.4Computation:Bayesian........................ 192
17.5Prospectsforthefuture........................ 193
18 Treatment effects in before-after data, by Andrew Gelman 195
18.1 Default statistical models of treatment effects . . . . . . . . . . . . 195
18.2 Before-after correlation is typically larger for controls than for
treatedunits.............................. 196
18.3 A class of models for varying treatment effects . . . . . . . . . . . 200
18.4Discussion............................... 201
19 Multimodality in mixture models and factor models, by Eric Loken 203
19.1 Multimodality in mixture models . . . . . . . . . . . . . . . . . . 204
19.2 Multimodal posterior distributions in continuous latent variable
models................................. 209
19.3Summary ............................... 212
20 Modeling the covariance and correlation matrix of repeated measures,
by W. John Boscardin and Xiao Zhang 215
20.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
20.2Modelingthecovariancematrix ................... 216
20.3Modelingthecorrelationmatrix ................... 218
20.4 Modeling a mixed covariance-correlation matrix . . . . . . . . . . 220
20.5 Nonzero means and unbalanced data . . . . . . . . . . . . . . . . 220
20.6 Multivariate probit model . . . . . . . . . . . . . . . . . . . . . . 221
20.7Example:covariancemodeling.................... 222
20.8Example:mixeddata......................... 225
21 Robit regression: a simple robust alternative to logistic and probit
regression, by Chuanhai Liu 227
21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
21.2Therobitmodel ........................... 228
21.3 Robustness of likelihood-based inference using logistic, probit, and
robitregressionmodels ....................... 230
21.4 Complete data for simple maximum likelihood estimation . . . . 231
21.5 Maximum likelihood estimation using EM-type algorithms . . . . 233
21.6Anumericalexample ........................ 235
21.7Conclusion .............................. 238
22 Using EM and data augmentation for the competing risks model,
by Radu V. Craiu and Thierry Duchesne 239
22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
22.2Themodel............................... 240
22.3EM-basedanalysis .......................... 243
22.4Bayesiananalysis........................... 244
22.5Example................................ 248
22.6Discussionandfurtherwork..................... 250
23 Mixed effects models and the EM algorithm,
by Florin Vaida, Xiao-Li Meng, and Ronghui Xu 253
23.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
23.2 Binary regression with random effects . . . . . . . . . . . . . . . . 254
23.3 Proportional hazards mixed-effects models . . . . . . . . . . . . . 259
24 The sampling/importance resampling algorithm, by Kim-Hung Li 265
24.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
24.2SIRalgorithm............................. 266
24.3 Selection of the pool size . . . . . . . . . . . . . . . . . . . . . . . 267
24.4 Selection criterion of the importance sampling distribution . . . . . 271
24.5Theresamplingalgorithms...................... 272
24.6Discussion............................... 276
IV Applied Bayesian inference 277
25 Whither applied Bayesian inference?, by Bradley P. Carlin 279
25.1Wherewe’vebeen .......................... 279
25.2Whereweare............................. 281
25.3Wherewe’regoing.......................... 282
26 Efficient EM-type algorithms for fitting spectral lines in high-energy
astrophysics, by David A. van Dyk and Taeyoung Park 285
26.1 Application-specific statistical methods . . . . . . . . . . . . . . . 285
26.2 The Chandra X-ray observatory . . . . . . . . . . . . . . . . . . . 287
26.3 Fitting narrow emission lines . . . . . . . . . . . . . . . . . . . . . 289
26.4Modelcheckingandmodelselection ................ 294
27 Improved predictions of lynx trappings using a biological model,
by Cavan Reilly and Angelique Zeringue 297
27.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
27.2Thecurrentbestmodel........................ 298
27.3 Biological models for predator prey systems . . . . . . . . . . . . 299
27.4 Some statistical models based on the Lotka-Volterra system . . . . 300
27.5 Computational aspects of posterior inference . . . . . . . . . . . . 302
27.6 Posterior predictive checks and model expansion . . . . . . . . . . 304
27.7 Prediction with the posterior mode . . . . . . . . . . . . . . . . . 307
27.8Discussion............................... 308
28 Record linkage using finite mixture models, by Michael D. Larsen 309
28.1 Introduction to record linkage . . . . . . . . . . . . . . . . . . . . 309
28.2 Record linkage . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
28.3Mixturemodels ........................... 311
28.4Application .............................. 314
28.5Analysisoflinkedfiles ....................... 316
28.6Bayesianhierarchicalrecordlinkage ................ 317
28.7Summary ............................... 318
29 Identifying likely duplicates by record linkage in a survey
of prostitutes, by Thomas R. Belin, Hemant Ishwaran, Naihua Duan,
Sandra H. Berry, and David E. Kanouse 319
29.1 Concern about duplicates in an anonymous survey . . . . . . . . . 319
29.2Generalframeworksforrecordlinkage ............... 321
29.3 Estimating probabilities of duplication in the Los Angeles Women’s
HealthRiskStudy .......................... 322
29.4Discussion............................... 328
30 Applying structural equation models with incomplete data,
by Hal S. Stern and Yoonsook Jeon 331
30.1Structuralequationmodels...................... 332
30.2 Bayesian inference for structural equation models . . . . . . . . . 334
30.3 Iowa Youth and Families Project example . . . . . . . . . . . . . . 339
30.4Summaryanddiscussion....................... 342
31 Perceptual scaling, by Ying Nian Wu, Cheng-En Guo,
and Song Chun Zhu 343
31.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343
31.2Sparsityandminimaxentropy.................... 347
31.3Complexityscalinglaw........................ 353
31.4 Perceptibility scaling law . . . . . . . . . . . . . . . . . . . . . . . 356
31.5 Texture = imperceptiblestructures.................. 358
31.6 Perceptibility and sparsity . . . . . . . . . . . . . . . . . . . . . . 359
References 361
Index 401

thanks a lot!!!!!!!!!