Bayesian methods for modelling non-random missing data mechanisms in longitudinal studies
暂无分享,去创建一个
[1] Andrew Gelman,et al. Exploratory Data Analysis for Complex Models , 2004 .
[2] David J. Spiegelhalter,et al. Analysis of longitudinal binary data from multiphase sampling , 1998 .
[3] Donald B. Rubin,et al. Nested multiple imputation of NMES via partially incompatible MCMC , 2003 .
[4] Nicky J Welton,et al. Allowing for uncertainty due to missing data in meta‐analysis—Part 2: Hierarchical models , 2008, Statistics in medicine.
[5] Peter Congdon. Bayesian statistical modelling , 2002 .
[6] William J. Browne,et al. Implementation and performance issues in the Bayesian and likelihood fitting of multilevel models , 2000, Comput. Stat..
[7] R. Christensen,et al. A New Perspective on Priors for Generalized Linear Models , 1996 .
[8] Garrett M. Fitzmaurice,et al. Methods for Handling Dropouts in Longitudinal Clinical Trials , 2003 .
[9] J. Kadane,et al. Experiences in elicitation , 1998 .
[10] S. Pocock,et al. Coping with missing data in clinical trials: A model‐based approach applied to asthma trials , 2002, Statistics in medicine.
[11] Joseph B. Kadane,et al. Comparing Harm Done by Mobility and Class Absence: Missing Students and Missing Data , 2003 .
[12] Bradley P. Carlin,et al. Markov Chain Monte Carlo in Practice: A Roundtable Discussion , 1998 .
[13] M. Kenward,et al. A comparison of multiple imputation and doubly robust estimation for analyses with missing data , 2006 .
[14] Geert Molenberghs,et al. Missing Data in Clinical Studies , 2007 .
[15] P. Diggle,et al. Analysis of Longitudinal Data. , 1997 .
[16] D. Rubin,et al. On Jointly Estimating Parameters and Missing Data by Maximizing the Complete-Data Likelihood , 1983 .
[17] Jeremy E. Oakley,et al. Uncertain Judgements: Eliciting Experts' Probabilities , 2006 .
[18] Nathaniel Schenker,et al. Combining information from multiple surveys to enhance estimation of measures of health , 2007, Statistics in medicine.
[19] S. Richardson,et al. Hierarchical related regression for combining aggregate and individual data in studies of socio‐economic disease risk factors , 2007 .
[20] S. Paddock. Bayesian variable selection for longitudinal substance abuse treatment data subject to informative censoring , 2007 .
[21] D J Spiegelhalter,et al. Approximate cross‐validatory predictive checks in disease mapping models , 2003, Statistics in medicine.
[22] M. Kenward,et al. The analysis of longitudinal ordinal data with nonrandom drop-out , 1997 .
[23] Thomas A. Louis,et al. Graphical Elicitation of a Prior Distribution for a Clinical Trial , 1993 .
[24] Susanne Rässler,et al. A Non‐Iterative Bayesian Approach to Statistical Matching , 2003 .
[25] D. Cox,et al. An Analysis of Transformations , 1964 .
[26] Ian Plewis,et al. National Child Development Study and 1970 British Cohort Study Technical Report: Changes in the NCDS and BCS70 Populations and Samples over Time , 2004 .
[27] David J. Lunn,et al. A Bayesian toolkit for genetic association studies , 2006, Genetic epidemiology.
[28] P. Rousseeuw,et al. The Shape of Correlation Matrices , 1994 .
[29] Jon Wakefield,et al. Ecological inference for 2 × 2 tables , 2004 .
[30] Roderick J. A. Little,et al. Statistical Analysis with Missing Data , 1988 .
[31] Daniel F Heitjan,et al. An index of local sensitivity to nonignorable drop‐out in longitudinal modelling , 2005, Statistics in medicine.
[32] M. Kenward,et al. Every missingness not at random model has a missingness at random counterpart with equal fit , 2008 .
[33] Joel B Greenhouse,et al. Using prior distributions to synthesize historical evidence: comments on the Goodman–Sladky case study of IVIg in Guillain–Barré syndrome , 2005, Clinical trials.
[34] Ken P Kleinman,et al. Much Ado About Nothing , 2007, The American statistician.
[35] Nathaniel Schenker,et al. Combining Information From Two Surveys to Estimate County-Level Prevalence Rates of Cancer Risk Factors and Screening , 2007 .
[36] A. Gelfand,et al. Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models , 1999 .
[37] A. Rotnitzky,et al. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis by DANIELS, M. J. and HOGAN, J. W , 2009 .
[38] S Greenland,et al. Basic methods for sensitivity analysis of biases. , 1996, International journal of epidemiology.
[39] T. Raghunathan,et al. Combining exposure information from various sources in an analysis of a case-control study , 1998 .
[40] Roderick J. A. Little,et al. Modeling the Drop-Out Mechanism in Repeated-Measures Studies , 1995 .
[41] Leon Feinstein,et al. Inequality in the Early Cognitive Development of British Children in the 1970 Cohort , 2003 .
[42] J. Robins,et al. Analysis of semi-parametric regression models with non-ignorable non-response. , 1997, Statistics in medicine.
[43] A. Troxel,et al. AN INDEX OF LOCAL SENSITIVITY TO NONIGNORABILITY , 2004 .
[44] F. Peracchi,et al. Survey response and survey characteristics: microlevel evidence from the European Community Household Panel , 2005 .
[45] Geert Molenberghs,et al. Selection models and pattern‐mixture models to analyse longitudinal quality of life data subject to drop‐out , 2002, Statistics in medicine.
[46] Geert Molenberghs,et al. Monotone missing data and pattern‐mixture models , 1998 .
[47] Peter Green,et al. Highly Structured Stochastic Systems , 2003 .
[48] Joseph L Schafer,et al. Analysis of Incomplete Multivariate Data , 1997 .
[49] James R Carpenter,et al. Sensitivity analysis after multiple imputation under missing at random: a weighting approach , 2007, Statistical methods in medical research.
[50] N M Laird,et al. Maximum likelihood analysis of generalized linear models with missing covariates , 1999, Statistical methods in medical research.
[51] F. Vella. Estimating Models with Sample Selection Bias: A Survey , 1998 .
[52] Ian R White,et al. Eliciting and using expert opinions about influence of patient characteristics on treatment effects: a Bayesian analysis of the CHARM trials , 2005, Statistics in medicine.
[53] J. Schafer. Multiple imputation: a primer , 1999, Statistical methods in medical research.
[54] Harvey Goldstein,et al. Multilevel models with multivariate mixed response types , 2009 .
[55] Michael G Kenward,et al. Multiple imputation: current perspectives , 2007, Statistical methods in medical research.
[56] Joseph G Ibrahim,et al. Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates , 2005, Biometrics.
[57] Peter J. Diggle,et al. Analysis of longitudinal data with drop‐out: objectives, assumptions and a proposal , 2007 .
[58] Ian Plewis,et al. The Contribution of Residential Mobility to Sample Loss in a Birth Cohort Study: Evidence from the First Two Waves of the UK Millennium Cohort Study , 2008 .
[59] G Molenberghs,et al. Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach , 2001, Biometrics.
[60] N. Longford,et al. Analysis of a long‐term study of neurotic disorder, with insights into the process of non‐response , 2006 .
[61] Gareth O. Roberts,et al. Convergence assessment techniques for Markov chain Monte Carlo , 1998, Stat. Comput..
[62] Andrew Gelman,et al. R2WinBUGS: A Package for Running WinBUGS from R , 2005 .
[63] D. Rubin. Multiple Imputation After 18+ Years , 1996 .
[64] H. Boshuizen,et al. Multiple imputation of missing blood pressure covariates in survival analysis. , 1999, Statistics in medicine.
[65] S. Lipsitz,et al. Missing-Data Methods for Generalized Linear Models , 2005 .
[66] Mario Peruggia,et al. On the variability of case-deletion importance sampling weights in the Bayesian linear model , 1997 .
[67] S. Chib,et al. Bayesian analysis of binary and polychotomous response data , 1993 .
[68] G Molenberghs,et al. Parametric models for incomplete continuous and categorical longitudinal data , 1999, Statistical methods in medical research.
[69] A. Gelman. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) , 2004 .
[70] H. Goldstein,et al. Patterns of Attainment. , 1978 .
[71] D. Collett. Modelling Binary Data , 1991 .
[72] Matthew Hotopf,et al. Using number of failed contact attempts to adjust for non‐ignorable non‐response , 2006 .
[73] M. Kenward,et al. Informative dropout in longitudinal data analysis (with discussion) , 1994 .
[74] D. S. Bunch,et al. Estimability in the Multinomial Probit Model , 1989 .
[75] I. Plewis,et al. Modelling non‐response in the National Child Development Study , 2006 .
[76] Peter E. Rossi,et al. An exact likelihood analysis of the multinomial probit model , 1994 .
[77] Andrew Gelman,et al. Diagnostics for multivariate imputations , 2007 .
[78] Jun S. Liu,et al. Metropolized independent sampling with comparisons to rejection sampling and importance sampling , 1996, Stat. Comput..
[79] Gabriele B. Durrant,et al. Using data augmentation to correct for non‐ignorable non‐response when surrogate data are available: an application to the distribution of hourly pay , 2006 .
[80] Xiao-Li Meng,et al. Applications of multiple imputation in medical studies: from AIDS to NHANES , 1999, Statistical methods in medical research.
[81] P. Allison. Multiple Imputation for Missing Data , 2000 .
[82] Sylvia Richardson,et al. Using Bayesian graphical models to model biases in observational studies and to combine multiple sources of data: application to low birth weight and water disinfection by‐products , 2009 .
[83] Sylvia Richardson,et al. Improving ecological inference using individual‐level data , 2006, Statistics in medicine.
[84] C. Robert,et al. Deviance information criteria for missing data models , 2006 .
[85] G. King,et al. Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation , 2001, American Political Science Review.
[86] Jun S. Liu,et al. Sequential Imputations and Bayesian Missing Data Problems , 1994 .
[87] David B. Dunson,et al. Bayesian Data Analysis , 2010 .
[88] Andrew M. Jones,et al. Health‐related non‐response in the British Household Panel Survey and European Community Household Panel: using inverse‐probability‐weighted estimators in non‐linear models , 2006 .
[89] J. Schafer. Multiple Imputation in Multivariate Problems When the Imputation and Analysis Models Differ , 2003 .
[90] N M Laird,et al. Model-based approaches to analysing incomplete longitudinal and failure time data. , 1997, Statistics in medicine.
[91] Anthony N. Pettitt,et al. A Bayesian hierarchical model for categorical longitudinal data from a social survey of immigrants , 2006 .
[92] Joseph B. Kadane,et al. Subjective Bayesian analysis for surveys with missing data , 1993 .
[93] Daniel O Scharfstein,et al. Incorporating prior beliefs about selection bias into the analysis of randomized trials with missing outcomes. , 2003, Biostatistics.
[94] S. van Buuren,et al. Flexibele multiple imputation by chained equations of the AVO-95 Survey , 1999 .
[95] Bradley P. Carlin,et al. Bayesian measures of model complexity and fit , 2002 .
[96] D. Dunson,et al. Bayesian latent variable models for clustered mixed outcomes , 2000 .
[97] Ian R White,et al. Allowing for uncertainty due to missing data in meta‐analysis—Part 1: Two‐stage methods , 2008, Statistics in medicine.
[98] Geert Verbeke,et al. Multiple Imputation for Model Checking: Completed‐Data Plots with Missing and Latent Data , 2005, Biometrics.
[99] William A Ghali,et al. Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses. , 2002, Journal of clinical epidemiology.
[100] J. Schafer,et al. Missing data: our view of the state of the art. , 2002, Psychological methods.
[101] Geert Molenberghs,et al. Sensitivity Analysis of Continuous Incomplete Longitudinal Outcomes , 2003 .
[102] Michael G. Kenward,et al. Using Hierarchical Likelihood for Missing Data Problems , 2007 .
[103] A. Brix. Bayesian Data Analysis, 2nd edn , 2005 .
[104] E. Boyko,et al. The millennium Cohort Study: a 21-year prospective cohort study of 140,000 military personnel. , 2002, Military medicine.
[105] Nicholas J. Horton,et al. Multiple Imputation in Practice , 2001 .
[106] J. Schafer,et al. A comparison of inclusive and restrictive strategies in modern missing data procedures. , 2001, Psychological methods.
[107] Andrew Gelman,et al. General methods for monitoring convergence of iterative simulations , 1998 .
[108] D. Rubin. INFERENCE AND MISSING DATA , 1975 .
[109] Ulrich Rendtel,et al. Extent and Determinants of Panel Attrition in the European Community Household Panel , 2005 .
[110] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[111] I. White,et al. Eliciting and using expert opinions about dropout bias in randomized controlled trials , 2007, Clinical trials.
[112] D. Rubin,et al. Fully conditional specification in multivariate imputation , 2006 .
[113] Sander Greenland,et al. Multiple‐bias modelling for analysis of observational data , 2005 .
[114] M. Kenward. Selection models for repeated measurements with non-random dropout: an illustration of sensitivity. , 1998, Statistics in medicine.
[115] David J. Lunn,et al. Generic reversible jump MCMC using graphical models , 2009, Stat. Comput..
[116] Peter E. Rossi,et al. A Bayesian analysis of the multinomial probit model with fully identified parameters , 2000 .
[117] Bradley P. Carlin,et al. Markov Chain Monte Carlo conver-gence diagnostics: a comparative review , 1996 .
[118] Jonathan J. Forster,et al. Model‐based inference for categorical survey data subject to non‐ignorable non‐response , 1998 .
[119] Robert E. Kass,et al. A default conjugate prior for variance components in generalized linear mixed models (comment on article by Browne and Draper) , 2006 .
[120] R. Little. Pattern-Mixture Models for Multivariate Incomplete Data , 1993 .
[121] S. Chib,et al. Analysis of multivariate probit models , 1998 .
[122] I. Plewis. Non‐Response in a Birth Cohort Study: The Case of the Millennium Cohort Study , 2007 .
[123] B. R. Dansie. PARAMETER ESTIMABILITY IN THE MULTINOMIAL PROBIT MODEL , 1985 .
[124] D. Spiegelhalter,et al. Bayesian Analysis of Realistically Complex Models , 1996 .
[125] H. Goldstein,et al. Social Factors associated with changes in Educational Attainment between 7 and 11 Tears of Age , 1976 .