Marginal models for the analysis of longitudinal measurements with nonignorable non-monotone missing data

SUMMARY We propose methods for the analysis of continuous responses subject to nonignorable non-monotone missing data. We form a pseudolikelihood by naively assuming independence over time and using a product of marginal likelihoods at each time point, and we obtain consistent and asymptotically normal estimators of the mean and missingness parameters. Our primary interest is in estimating the parameters of the marginal model at each time point, and we make no assumption about the correlation structure. An approximate variance is found for the parameter estimates using a sandwich estimate. We provide an example, and present a simulation study that assesses the performance of the model.

[1]  C H Brown,et al.  Protecting against nonrandomly missing data in longitudinal studies. , 1990, Biometrics.

[2]  Daniel O. Stram,et al.  Analysis of Repeated Ordered Categorical Outcomes with Possibly Missing Observations and Time-Dependent Covariates , 1988 .

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

[4]  Marie Davidian,et al.  The Nonlinear Mixed Effects Model with a Smooth Random Effects Density , 1993 .

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

[6]  J. W. Akitt.  Function Minimisation Using the Nelder and Mead Simplex Method with Limited Arithmetic Precision: The Self Regenerative Simplex , 1977, Comput. J..

[7]  M D Schluchter,et al.  Methods for the analysis of informatively censored longitudinal data. , 1992, Statistics in medicine.

[8]  R. Gelber,et al.  Quality of life measures for patients receiving adjuvant therapy for breast cancer: an international trial. The International Breast Cancer Study Group. , 1992, European journal of cancer.

[9]  J. Robins,et al.  Semiparametric Efficiency in Multivariate Regression Models with Missing Data , 1995 .

[10]  Roderick J. A. Little,et al.  Modeling the Drop-Out Mechanism in Repeated-Measures Studies , 1995 .

[11]  J. Robins,et al.  Analysis of semi-parametric regression models with non-ignorable non-response. , 1997, Statistics in medicine.

[12]  N M Laird,et al.  Missing data in longitudinal studies. , 1988, Statistics in medicine.

[13]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[14]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[15]  M. Kenward,et al.  The analysis of longitudinal ordinal data with nonrandom drop-out , 1997 .

[16]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[17]  Gail Gong,et al.  Pseudo Maximum Likelihood Estimation: Theory and Applications , 1981 .

[18]  Peter E. Rossi,et al.  Nonlinear dynamic structures , 1993 .

[19]  S G Baker,et al.  Marginal regression for repeated binary data with outcome subject to non-ignorable non-response. , 1995, Biometrics.

[20]  M. Kenward,et al.  Informative Drop‐Out in Longitudinal Data Analysis , 1994 .