Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study.
暂无分享,去创建一个
[1] P. Heagerty. Marginally Specified Logistic‐Normal Models for Longitudinal Binary Data , 1999, Biometrics.
[2] P. Albert,et al. Models for longitudinal data: a generalized estimating equation approach. , 1988, Biometrics.
[3] G. Pap,et al. Asymptotic properties of maximum-likelihood estimators for Heston models based on continuous time observations , 2013, 1310.4783.
[4] Inbal Nahum-Shani,et al. Randomised trials for the Fitbit generation , 2015, Significance.
[5] K. Liang,et al. Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests under Nonstandard Conditions , 1987 .
[6] Xihong Lin,et al. Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models , 2007, Biometrics.
[7] Jee-Seon Kim,et al. Omitted Variables in Multilevel Models , 2006 .
[8] T. Louis,et al. Marginalized Binary Mixed‐Effects Models with Covariate‐Dependent Random Effects and Likelihood Inference , 2004, Biometrics.
[9] Matt P. Wand,et al. Smoothing and mixed models , 2003, Comput. Stat..
[10] S. Vansteelandt. On Confounding, Prediction and Efficiency in the Analysis of Longitudinal and Cross‐sectional Clustered Data , 2007 .
[11] J. Suls,et al. How robust is the association between smoking and depression in adults? A meta-analysis using linear mixed-effects models. , 2014, Addictive behaviors.
[12] Per B. Brockhoff,et al. lmerTest Package: Tests in Linear Mixed Effects Models , 2017 .
[13] P. Heagerty,et al. Longitudinal structural mixed models for the analysis of surgical trials with noncompliance , 2012, Statistics in medicine.
[14] Susan A. Murphy,et al. Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary Outcomes , 2019 .
[15] Ambuj Tewari,et al. Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support , 2017, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.
[16] D. Stram,et al. Variance components testing in the longitudinal mixed effects model. , 1994, Biometrics.
[17] J. Roy,et al. Conditional Inference Methods for Incomplete Poisson Data With Endogenous Time-Varying Covariates , 2006 .
[18] P. Heagerty,et al. Regression analysis of longitudinal binary data with time-dependent environmental covariates: bias and efficiency. , 2005, Biostatistics.
[19] U. Böckenholt,et al. Regressor and random‐effects dependencies in multilevel models , 2004 .
[20] T. Mostafa,et al. Solving endogeneity problems in multilevel estimation: an example using education production functions , 2012 .
[21] K Y Liang,et al. Longitudinal data analysis for discrete and continuous outcomes. , 1986, Biometrics.
[22] M. Pepe,et al. A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data , 1994 .
[23] M. Kenward,et al. The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines , 1999 .
[24] F. E. Satterthwaite. Synthesis of variance , 1941 .
[25] N. Bolger,et al. Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research , 2013 .
[26] J. Robins,et al. Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments , 2001 .
[27] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[28] Charles E. McCulloch,et al. Separating between‐ and within‐cluster covariate effects by using conditional and partitioning methods , 2006 .
[29] G. Gurtner,et al. Statistics in medicine. , 2011, Plastic and reconstructive surgery.
[30] Hairul Azlan Annuar,et al. Foreign investors' interests and corporate tax avoidance: Evidence from an emerging economy , 2015 .
[31] D. Ruppert,et al. Likelihood ratio tests in linear mixed models with one variance component , 2003 .
[32] M. Arellano,et al. Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations , 1991 .
[33] M. Wand,et al. Penalized Splines and Reproducing Kernel Methods , 2006 .
[34] Nicholas J. Seewald,et al. Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps. , 2018, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.
[35] J. Robins,et al. Specifying the correlation structure in inverse-probability- weighting estimation for repeated measures. , 2012, Epidemiology.
[36] L. Dempfle. Comparison of several sire evaluation methods in dairy cattle breeding , 1977, Annales de génétique et de sélection animale.
[37] S. Murphy,et al. Assessing Time-Varying Causal Effect Moderation in Mobile Health , 2016, Journal of the American Statistical Association.
[38] S. Greenland,et al. The Intensity‐Score Approach to Adjusting for Confounding , 2003, Biometrics.
[39] Romain Neugebauer,et al. Causal inference in longitudinal studies with history-restricted marginal structural models. , 2007, Electronic journal of statistics.
[40] S. Vansteelandt,et al. Analysis of Longitudinal Studies With Repeated Outcome Measures: Adjusting for Time-Dependent Confounding Using Conventional Methods , 2017, American journal of epidemiology.
[41] Michelle Shardell,et al. Joint mixed-effects models for causal inference with longitudinal data. , 2018, Statistics in medicine.
[42] C. R. Henderson,et al. Best linear unbiased estimation and prediction under a selection model. , 1975, Biometrics.
[43] Ambuj Tewari,et al. Sample size calculations for micro‐randomized trials in mHealth , 2015, Statistics in medicine.
[44] Semiparametric estimation in generalized linear mixed models with auxiliary covariates: A pairwise likelihood approach , 2014, Biometrics.
[45] D. Miglioretti,et al. Marginal modeling of multilevel binary data with time-varying covariates. , 2004, Biostatistics.
[46] B L De Stavola,et al. Methods for dealing with time‐dependent confounding , 2013, Statistics in medicine.
[47] G. Wahba. Spline models for observational data , 1990 .
[48] Takeshi Amemiya,et al. Instrumental-variable estimation of an error-components model , 1986 .
[49] J. Booth,et al. 2. Random-Effects Modeling of Categorical Response Data , 2000 .
[50] M. Berger,et al. Robust designs for linear mixed effects models , 2004 .
[51] Jerry A. Hausman,et al. Panel Data and Unobservable Individual Effects , 1981 .
[52] James M. Robins,et al. Marginal Structural Models versus Structural nested Models as Tools for Causal inference , 2000 .
[53] Tanya P. Garcia,et al. Optimal Estimator for Logistic Model with Distribution‐free Random Intercept , 2016, Scandinavian journal of statistics, theory and applications.
[54] M. Wand,et al. Explicit connections between longitudinal data analysis and kernel machines , 2009 .
[55] Jeffrey M. Wooldridge,et al. Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data , 2003 .
[56] M. Arellano,et al. Another look at the instrumental variable estimation of error-components models , 1995 .
[57] P. Diggle,et al. Analysis of Longitudinal Data , 2003 .
[58] Y. Mundlak. On the Pooling of Time Series and Cross Section Data , 1978 .
[59] Anthony S. Bryk,et al. Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .
[60] Predrag Klasnja,et al. Rapidly Personalizing Mobile Health Treatment Policies with Limited Data , 2020, ArXiv.
[61] Carla Rampichini,et al. The Role of Sample Cluster Means in Multilevel Models , 2011 .
[62] J. Pearl,et al. Causal Inference , 2011, Twenty-one Mental Models That Can Change Policing.
[63] D. Bates,et al. Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.
[64] J M Robins,et al. Correction for non-compliance in equivalence trials. , 1998, Statistics in medicine.
[65] J. Robins. Correcting for non-compliance in randomized trials using structural nested mean models , 1994 .
[66] J. Robins. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect , 1986 .
[67] Scott L. Zeger,et al. Marginalized Multilevel Models and Likelihood Inference , 2000 .
[68] T. Louis,et al. A Note on Marginal Linear Regression with Correlated Response Data , 2000 .
[69] J. Ware,et al. Random-effects models for longitudinal data. , 1982, Biometrics.
[70] D. Lindley,et al. Bayes Estimates for the Linear Model , 1972 .
[71] K Y Liang,et al. An overview of methods for the analysis of longitudinal data. , 1992, Statistics in medicine.
[72] M. Cheung. A model for integrating fixed-, random-, and mixed-effects meta-analyses into structural equation modeling. , 2008, Psychological methods.
[73] Jee-Seon Kim,et al. Multilevel Modeling with Correlated Effects , 2007 .
[74] G. Robinson. That BLUP is a Good Thing: The Estimation of Random Effects , 1991 .
[75] K. Larsen,et al. Interpreting Parameters in the Logistic Regression Model with Random Effects , 2000, Biometrics.
[76] James M. Robins,et al. Causal Inference from Complex Longitudinal Data , 1997 .
[77] Jeffrey M. Wooldridge,et al. Estimating Panel Data Models in the Presence of Endogeneity and Selection , 2010 .
[78] Michael Rosenblum,et al. Marginal Structural Models , 2011 .
[79] S. Vansteelandt,et al. Conditional Generalized Estimating Equations for the Analysis of Clustered and Longitudinal Data , 2008, Biometrics.