Random Effects Models for Longitudinal Data

[1]  C. McCulloch,et al.  Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data , 2002 .

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

[3]  Geert Molenberghs,et al.  A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data , 2008, Comput. Stat. Data Anal..

[4]  Geert Molenberghs,et al.  Simplified hierarchical linear models for the evaluation of surrogate endpoints , 2003 .

[5]  Geert Molenberghs,et al.  Evaluation of Surrogate Endpoints , 2006, Handbook of Statistical Methods for Randomized Controlled Trials.

[6]  Louise Ryan,et al.  Bivariate Latent Variable Models for Clustered Discrete and Continuous Outcomes , 1992 .

[7]  M J Daniels,et al.  Meta-analysis for the evaluation of potential surrogate markers. , 1997, Statistics in medicine.

[8]  A. E. Ferentz Integrating pharmacogenomics into drug development. , 2002, Pharmacogenomics.

[9]  P. Altham,et al.  Two Generalizations of the Binomial Distribution , 1978 .

[10]  Raymond J. Carroll,et al.  Estimation and comparison of changes in the presence of informative right censoring by modeling the censoring process , 1988 .

[11]  Geert Verbeke,et al.  High dimensional multivariate mixed models for binary questionnaire data , 2006 .

[12]  Daniel Commenges,et al.  Bivariate linear mixed models using SAS proc MIXED , 2002, Comput. Methods Programs Biomed..

[13]  R. Elashoff,et al.  Missing Observations in Multivariate Statistics I. Review of the Literature , 1966 .

[14]  L. Lesko,et al.  Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies. , 2001, Annual review of pharmacology and toxicology.

[15]  D. Bates,et al.  Mixed-Effects Models in S and S-PLUS , 2001 .

[16]  Geert Verbeke,et al.  Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles , 2006, Biometrics.

[17]  L. Zhao,et al.  Estimating equations for parameters in means and covariances of multivariate discrete and continuous responses. , 1991, Biometrics.

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

[19]  Yeow Meng Thum,et al.  Hierarchical Linear Models for Multivariate Outcomes , 1997 .

[20]  Nanny Wermuth,et al.  Response models for mixed binary and quantitative variables , 1992 .

[21]  Ning Li,et al.  A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types , 2008, Biometrics.

[22]  G. Molenberghs,et al.  Pseudolikelihood Modeling of Multivariate Outcomes in Developmental Toxicology , 1999 .

[23]  J. Robins,et al.  Analysis of semiparametric regression models for repeated outcomes in the presence of missing data , 1995 .

[24]  David A. Schoenfeld,et al.  A Random-Effects Model for Multiple Characteristics with Possibly Missing Data , 1997 .

[25]  G. Molenberghs,et al.  Surrogate Marker Evaluation from an Information Theory Perspective , 2007, Biometrics.

[26]  R. Prentice Covariate measurement errors and parameter estimation in a failure time regression model , 1982 .

[27]  Barry C. Arnold,et al.  PSEUDOLIKELIHOOD ESTIMATION : SOME EXAMPLES , 2016 .

[28]  G. Molenberghs,et al.  Marginal Modeling of Correlated Ordinal Data Using a Multivariate Plackett Distribution , 1994 .

[29]  R J Carroll,et al.  On meta-analytic assessment of surrogate outcomes. , 2000, Biostatistics.

[30]  James M. Robins,et al.  Semiparametric Regression for Repeated Outcomes With Nonignorable Nonresponse , 1998 .

[31]  F. Hsieh,et al.  Joint modelling of accelerated failure time and longitudinal data , 2005 .

[32]  N. Cox Estimation of the correlation between a continuous and a discrete variable. , 1974, Biometrics.

[33]  D. Hedeker,et al.  A random-effects ordinal regression model for multilevel analysis. , 1994, Biometrics.

[34]  G. Molenberghs,et al.  Marginal modelling of multivariate categorical data. , 1999, Statistics in medicine.

[35]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[36]  Nathaniel Schenker,et al.  Analysis of Censored Survival Data with Intermittently Observed Time-Dependent Binary Covariates , 1998 .

[37]  Geert Molenberghs,et al.  Shared parameter models under random effects misspecification , 2008 .

[38]  Robin Henderson,et al.  Diagnostics for joint longitudinal and dropout time modeling. , 2003, Biometrics.

[39]  Paul J. Catalano,et al.  Bivariate Dose-Response Modeling and Risk Estimation in Developmental Toxicology , 1999 .

[40]  Yan Wang,et al.  Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome , 2001 .

[41]  Nan M. Laird,et al.  Regression Models for a Bivariate Discrete and Continuous Outcome with Clustering , 1995 .

[42]  Nanny Wermuth,et al.  A note on the quadratic exponential binary distribution , 1994 .

[43]  G. Molenberghs,et al.  Criteria for the validation of surrogate endpoints in randomized experiments. , 1998, Biometrics.

[44]  Geert Molenberghs,et al.  The validation of surrogate end points by using data from randomized clinical trials: a case‐study in advanced colorectal cancer , 2004 .

[45]  Ross L. Prentice,et al.  Likelihood inference in a correlated probit regression model , 1984 .

[46]  Geert Verbeke,et al.  The Practical Use of Different Strategies to Handle Dropout in Longitudinal Studies , 2001 .

[47]  Geert Molenberghs,et al.  Pseudo-likelihood inference for clustered binary data , 1997 .

[48]  Thomas R. Fleming,et al.  Surrogate Endpoints in Clinical Trials , 1996 .

[49]  James M. Robins,et al.  Unified Methods for Censored Longitudinal Data and Causality , 2003 .

[50]  Joseph G Ibrahim,et al.  Joint Models for Multivariate Longitudinal and Multivariate Survival Data , 2006, Biometrics.

[51]  F. Hsieh,et al.  Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited , 2006, Biometrics.

[52]  S. Zeger,et al.  Multivariate Regression Analyses for Categorical Data , 1992 .

[53]  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.

[54]  F. Oort,et al.  Three-mode models for multivariate longitudinal data. , 2001, The British journal of mathematical and statistical psychology.

[55]  Geert Molenberghs,et al.  Validation of Surrogate Endpoints in Multiple Randomized Clinical Trials with Discrete Outcomes , 2002 .

[56]  Geert Molenberghs,et al.  Multiple‐Imputation‐Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival Outcomes , 2010, Biometrics.

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

[58]  B. Leroux,et al.  Efficiency of regression estimates for clustered data. , 1996, Biometrics.

[59]  Marie Davidian,et al.  A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time‐to‐Event Data , 2002, Biometrics.

[60]  G Molenberghs,et al.  Evaluation of surrogate endpoints in randomized experiments with mixed discrete and continuous outcomes , 2001, Statistics in medicine.

[61]  Rizopoulos Dimitris,et al.  Joint Modeling of Longitudinal and Time-to-Event Data , 2014 .

[62]  J. Ibrahim,et al.  A Flexible B‐Spline Model for Multiple Longitudinal Biomarkers and Survival , 2005, Biometrics.

[63]  Robert F. Tate,et al.  Correlation Between a Discrete and a Continuous Variable. Point-Biserial Correlation , 1954 .

[64]  D. Clayton A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence , 1978 .

[65]  J Stare,et al.  Explained variation in survival analysis. , 1996, Statistics in medicine.

[66]  Xihong Lin,et al.  Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes , 2000, Biometrics.

[67]  D. Rubin,et al.  Handling “Don't Know” Survey Responses: The Case of the Slovenian Plebiscite , 1995 .

[68]  Robert F. Tate,et al.  THE THEORY OF CORRELATION BETWEEN TWO CONTINUOUS VARIABLES WHEN ONE IS DICHOTOMIZED , 1955 .

[69]  Eric R. Ziegel,et al.  Multivariate Statistical Modelling Based on Generalized Linear Models , 2002, Technometrics.

[70]  Donald Hedeker,et al.  A Mixed‐Effects Regression Model for Longitudinal Multivariate Ordinal Data , 2006, Biometrics.

[71]  M M Regan,et al.  Likelihood Models for Clustered Binary and Continuous Out comes: Application to Developmental Toxicology , 1999, Biometrics.

[72]  R. Little,et al.  Maximum likelihood estimation for mixed continuous and categorical data with missing values , 1985 .

[73]  R. Potthoff,et al.  A generalized multivariate analysis of variance model useful especially for growth curve problems , 1964 .

[74]  L. Ryan,et al.  Latent Variable Models for Mixed Discrete and Continuous Outcomes , 1997 .

[75]  G. Molenberghs,et al.  Models for Discrete Longitudinal Data , 2005 .

[76]  Anastasios A. Tsiatis,et al.  A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error , 2001 .

[77]  B. Graubard,et al.  Statistical validation of intermediate endpoints for chronic diseases. , 1992, Statistics in medicine.

[78]  Geert Molenberghs,et al.  Prentice's Approach and the Meta‐Analytic Paradigm: A Reflection on the Role of Statistics in the Evaluation of Surrogate Endpoints , 2004, Biometrics.

[79]  G. Molenberghs,et al.  A unified framework for the evaluation of surrogate endpoints in mental-health clinical trials , 2010, Statistical methods in medical research.

[80]  Jane-Ling Wang,et al.  Modeling Longitudinal Data with Nonparametric Multiplicative Random Effects Jointly with Survival Data , 2008, Biometrics.

[81]  R Henderson,et al.  Joint modelling of longitudinal measurements and event time data. , 2000, Biostatistics.

[82]  R. Gueorguieva,et al.  A multivariate generalized linear mixed model for joint modelling of clustered outcomes in the exponential family , 2001 .

[83]  J. Fozard,et al.  Age changes in pure-tone hearing thresholds in a longitudinal study of normal human aging. , 1990, The Journal of the Acoustical Society of America.

[84]  N. Breslow,et al.  Approximate inference in generalized linear mixed models , 1993 .

[85]  M. Gail,et al.  The promise and peril of surrogate end points in cancer research , 2002, Nature Reviews Cancer.

[86]  J. Kalbfleisch,et al.  A Comparison of Cluster-Specific and Population-Averaged Approaches for Analyzing Correlated Binary Data , 1991 .

[87]  Brian Everitt,et al.  Principles of Multivariate Analysis , 2001 .

[88]  Andrzej T. Galecki,et al.  General class of covariance structures for two or more repeated factors in longitudinal data analysis , 1994 .

[89]  I. Rossman,et al.  Normal Human Aging: The Baltimore Longitudinal Study of Aging , 1986 .

[90]  Jonathan J. Forster,et al.  Model‐based inference for categorical survey data subject to non‐ignorable non‐response , 1998 .

[91]  J. Dale Global cross-ratio models for bivariate, discrete, ordered responses. , 1986, Biometrics.

[92]  E. Lesaffre,et al.  A 12–week treatment for dermatophyte toe onychomycosis terbinafine 250mg/day vs. itraconazole 200mg/day—a double‐blind comparative trial , 1996, The British journal of dermatology.

[93]  R. Wolfinger,et al.  Generalized linear mixed models a pseudo-likelihood approach , 1993 .

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

[95]  Geert Molenberghs,et al.  A unifying approach for surrogate marker validation based on Prentice's criteria , 2006, Statistics in medicine.

[96]  J. Ibrahim,et al.  A Bayesian semiparametric joint hierarchical model for longitudinal and survival data. , 2003, Biometrics.

[97]  Joseph L Schafer,et al.  Analysis of Incomplete Multivariate Data , 1997 .

[98]  C. Morrell,et al.  Modelling hearing thresholds in the elderly. , 1991, Statistics in medicine.

[99]  G. Molenberghs,et al.  Linear Mixed Models for Longitudinal Data , 2001 .

[100]  E. Metter,et al.  Gender differences in a longitudinal study of age-associated hearing loss. , 1995, The Journal of the Acoustical Society of America.

[101]  Geert Verbeke,et al.  Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data , 2009 .

[102]  G Molenberghs,et al.  Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach , 2001, Biometrics.

[103]  Lue Ping Zhao,et al.  Multivariate Mean Parameter Estimation by Using a Partly Exponential Model , 1992 .

[104]  Roger A. Sugden,et al.  Multiple Imputation for Nonresponse in Surveys , 1988 .

[105]  M. Wulfsohn,et al.  Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS , 1995 .

[106]  G. Molenberghs,et al.  Topics in Modelling of Clustered Data , 2002 .

[107]  R. MacCallum,et al.  Studying Multivariate Change Using Multilevel Models and Latent Curve Models. , 1997, Multivariate behavioral research.

[108]  Menggang Yu,et al.  JOINT LONGITUDINAL-SURVIVAL-CURE MODELS AND THEIR APPLICATION TO PROSTATE CANCER , 2004 .

[109]  Geert Molenberghs,et al.  Strategies to fit pattern-mixture models. , 2002, Biostatistics.

[110]  B. Efron Double Exponential Families and Their Use in Generalized Linear Regression , 1986 .

[111]  J. Ware,et al.  Applied Longitudinal Analysis , 2004 .

[112]  Nanny Wermuth,et al.  Multivariate Dependencies: Models, Analysis and Interpretation , 1996 .

[113]  Stephen A. Sivo,et al.  Multiple Indicator Stationary Time Series Models , 2001 .

[114]  D. Rubin,et al.  Statistical Analysis with Missing Data , 1988 .

[115]  D. Hedeker,et al.  MIXOR: a computer program for mixed-effects ordinal regression analysis. , 1996, Computer methods and programs in biomedicine.

[116]  Geert Molenberghs,et al.  Missing Data in Clinical Studies , 2007 .

[117]  P. Diggle,et al.  Analysis of Longitudinal Data. , 1997 .

[118]  A. Agresti,et al.  Simultaneously Modeling Joint and Marginal Distributions of Multivariate Categorical Responses , 1994 .

[119]  Bert F. Green,et al.  Adaptive Estimation When the Unidimensionality Assumption of IRT is Violated , 1989 .

[120]  D. Follmann,et al.  An approximate generalized linear model with random effects for informative missing data. , 1995, Biometrics.

[121]  J. Klein,et al.  Statistical Models Based On Counting Process , 1994 .

[122]  S. Zeger,et al.  Joint analysis of longitudinal data comprising repeated measures and times to events , 2001 .

[123]  M. Wulfsohn,et al.  A joint model for survival and longitudinal data measured with error. , 1997, Biometrics.

[124]  Pranab K Sen,et al.  Estimating correlation by using a general linear mixed model: evaluation of the relationship between the concentration of HIV‐1 RNA in blood and semen , 2003, Statistics in medicine.

[125]  A. Dreher Modeling Survival Data Extending The Cox Model , 2016 .

[126]  Yudi Pawitan,et al.  Modeling a Marker of Disease Progression and Onset of Disease , 1992 .

[127]  Geert Molenberghs,et al.  Validation of surrogate markers in multiple randomized clinical trials with repeated measurements: canonical correlation approach. , 2004 .

[128]  G. Molenberghs,et al.  The validation of surrogate endpoints in meta-analyses of randomized experiments. , 2000, Biostatistics.

[129]  J. Schafer Multiple Imputation in Multivariate Problems When the Imputation and Analysis Models Differ , 2003 .

[130]  T. Louis,et al.  Inferences on the association parameter in copula models for bivariate survival data. , 1995, Biometrics.

[131]  Geert Verbeke,et al.  Joint modelling of multivariate longitudinal profiles: pitfalls of the random‐effects approach , 2004, Statistics in medicine.

[132]  C. Genest,et al.  The Joy of Copulas: Bivariate Distributions with Uniform Marginals , 1986 .

[133]  P J Catalano,et al.  Bivariate modelling of clustered continuous and ordered categorical outcomes. , 1997, Statistics in medicine.

[134]  G. Molenberghs,et al.  The meta-analytic framework for the evaluation of surrogate endpoints in clinical trials , 2008 .

[135]  Ingram Olkin,et al.  Multivariate Correlation Models with Mixed Discrete and Continuous Variables , 1961 .

[136]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[137]  Christl A. Donnelly,et al.  Information on sexual behaviour when some data are missing , 1999 .

[138]  G. Molenberghs,et al.  Validation of surrogate end points in multiple randomized clinical trials with failure time end points , 2001 .

[139]  Cécile Proust-Lima,et al.  Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach , 2009, Comput. Stat. Data Anal..