Over the last 20 years, comprehensive strategies for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters have emerged. Focusing on both established and novel approaches, "Measurement Error: Models, Methods, and Applications" provides an overview of the main techniques and illustrates their application in various models. It describes the impacts of measurement errors on naive analyses that ignore them and presents ways to correct for them across a variety of statistical models, from simple one-sample problems to regression models to more complex mixed and time series models. The book covers correction methods based on known measurement error parameters, replication, internal or external validation data, and, for some models, instrumental variables. It emphasizes the use of several relatively simple methods, moment corrections, regression calibration, simulation extrapolation (SIMEX), modified estimating equation methods, and likelihood techniques. The author uses SAS-IML and Stata to implement many of the techniques in the examples. Accessible to a broad audience, this book explains how to model measurement error, the effects of ignoring it, and how to correct for it. More applied than most books on measurement error, it describes basic models and methods, their uses in a range of application areas, and the associated terminology.
1 Introduction 1
2 Misclassification in estimating a proportion 11
3 Misclassification in two-way tables 33
4 Simple linear regression 73
5 Multiple linear regression 105
6 Measurement error in regression : a general overview 143
7 Binary regression 223
8 Linear models with nonadditive error 259
9 Nonlinear regression 319
10 Error in the response 325
11 Mixed/longitudinal models 361
12 Time series 385
13 Background material 409
References 413
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