Analysis with Mismeasured Responses

In many settings, precise measurements of variables are difficult or expensive to obtain. Both response and covariate variables are equally likely to be mismeasured. Measurement error in covariates has received extensive research interest. A large body of analysis methods, as discussed in the aforementioned chapters, has been developed in the literature. Issues on mismeasured responses, on the other hand, have been relatively less explored.

[1]  T. Louis Finding the Observed Information Matrix When Using the EM Algorithm , 1982 .

[2]  Christopher R. Bollinger,et al.  Estimation With Response Error and Nonresponse , 2001 .

[3]  Man Yu Wong,et al.  Likelihood estimation of a simple linear regression model when both variables have error , 1989 .

[4]  Wei Pan,et al.  Does it always help to adjust for misclassification of a binary outcome in logistic regression? , 2005, Statistics in medicine.

[5]  Raymond J. Carroll,et al.  A Semiparametric Correction for Attenuation , 1994 .

[6]  Richard F. Gunst,et al.  Stochastic Regression with Errors in Both Variables , 1986 .

[7]  Grace Y. Yi,et al.  Marginal methods for correlated binary data with misclassified responses , 2011 .

[8]  J. Neuhaus,et al.  Binomial Regression with Misclassification , 2003, Biometrics.

[9]  Jungsywan H. Sepanski On a repeated-measurement model with errors in dependent variable , 2001 .

[10]  Grace Y Yi,et al.  Marginal analysis of longitudinal ordinal data with misclassification in both response and covariates , 2014, Biometrical journal. Biometrische Zeitschrift.

[11]  Raymond J. Carroll,et al.  Semiparametric quasilikelihood and variance function estimation in measurement error models , 1993 .

[12]  J. Neuhaus Bias and efficiency loss due to misclassified responses in binary regression , 1999 .

[13]  Raymond J. Carroll,et al.  Semiparametric Estimation in Logistic Measurement Error Models , 1989 .

[14]  N. Breslow,et al.  Statistical methods in cancer research. Vol. 1. The analysis of case-control studies. , 1981 .

[15]  M. Green,et al.  Use of predictive value to adjust relative risk estimates biased by misclassification of outcome status. , 1983, American journal of epidemiology.

[16]  P S Albert,et al.  Latent Model for Correlated Binary Data with Diagnostic Error , 1999, Biometrics.

[17]  Richard F. Gunst,et al.  Structural model estimation with correlated measurement errors , 1985 .

[18]  D L Preston,et al.  The effect of diagnostic misclassification on non-cancer and cancer mortality dose response in A-bomb survivors. , 1992, Biometrics.

[19]  Huey-Miin Hsueh,et al.  ESTIMATION OF A LOGISTIC REGRESSION MODEL WITH MISMEASURED OBSERVATIONS , 2003 .

[20]  S. Lipsitz,et al.  Generalized estimating equations for correlated binary data: Using the odds ratio as a measure of association , 1991 .

[21]  A. Wald The Fitting of Straight Lines if Both Variables are Subject to Error , 1940 .

[22]  Zhijian Chen,et al.  Analysis of Correlated Data with Measurement Error in Responses or Covariates , 2010 .

[23]  M. Pepe,et al.  A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data , 1994 .

[24]  Richard F. Gunst,et al.  Estimation of parameters in linear structural relationships: Sensitivity to the choice of the ratio of error variances , 1984 .

[25]  J. Neuhaus,et al.  Analysis of Clustered and Longitudinal Binary Data Subject to Response Misclassification , 2002, Biometrics.

[26]  John P. Buonaccorsi,et al.  Measurement error in the response in the general linear model , 1996 .

[27]  C. Bollinger,et al.  Modeling Discrete Choice with Response Error: Food Stamp Participation , 1997 .

[28]  P. McCullagh,et al.  Generalized Linear Models , 1984 .

[29]  A. Tsiatis Semiparametric Theory and Missing Data , 2006 .

[30]  Surupa Roy,et al.  Analysis of misclassified correlated binary data using a multivariate probit model when covariates are subject to measurement error. , 2009, Biometrical journal. Biometrische Zeitschrift.

[31]  J. Hausman,et al.  Misclassification of the dependent variable in a discrete-response setting , 1998 .

[32]  Tapabrata Maiti,et al.  Measurement error model for misclassified binary responses , 2005, Statistics in medicine.

[33]  Grace Y. Yi,et al.  A NOTE ON MIS-SPECIFIED ESTIMATING FUNCTIONS , 2010 .

[34]  A. Madansky The fitting of straight lines when both variables are subject to error , 1959 .

[35]  Lung-fei Lee,et al.  Estimation of Linear and Nonlinear Errors-in-Variables Models Using Validation Data , 1995 .

[36]  O. Reiersøl Identifiability of a Linear Relation between Variables Which Are Subject to Error , 1950 .

[37]  M Palta,et al.  Latent variables, measurement error and methods for analysing longitudinal binary and ordinal data. , 1999, Statistics in medicine.

[38]  Margaret S. Pepe,et al.  Inference using surrogate outcome data and a validation sample , 1992 .

[39]  R. Prentice,et al.  Correlated binary regression with covariates specific to each binary observation. , 1988, Biometrics.

[40]  R. Prentice Surrogate endpoints in clinical trials: definition and operational criteria. , 1989, Statistics in medicine.

[41]  Wayne A. Fuller,et al.  A Model for Multinomial Response Error Applied to Labor Flows , 1987 .

[42]  Yasuo Amemiya,et al.  Prediction When Both Variables are Subject to Error, with Application to Earthquake Magnitudes , 1983 .

[43]  Clifford H. Spiegelman,et al.  Two Pitfalls of Using Standard Regression Diagnostics When Both X and Y Have Measurement Error , 1986 .

[44]  S. Hui,et al.  Evaluation of diagnostic tests without gold standards , 1998, Statistical methods in medical research.

[45]  Thomas R. Fleming,et al.  Auxiliary outcome data and the mean score method , 1994 .

[46]  Richard J. Cook,et al.  Marginal Methods for Incomplete Longitudinal Data Arising in Clusters , 2002 .

[47]  John W. Van Ness,et al.  On Estimating Linear Relationships When Both Variables are Subject to Errors , 1994 .

[48]  H. Fujisawa,et al.  Inference about Misclassification Probabilities from Repeated Binary Responses , 2000, Biometrics.

[49]  J. Seaman,et al.  Binary Regression with Misclassified Response and Covariate Subject to Measurement Error: a Bayesian Approach , 2008, Biometrical journal. Biometrische Zeitschrift.

[50]  R A Kronmal,et al.  The effects of measurement error in response variables and tests of association of explanatory variables in change models. , 1998, Statistics in medicine.