An imputation approach for fitting two-part mixed effects models for longitudinal semi-continuous data.

Statistical Methods in Medical Research
Hyoyoung Choo-WosobaPaul S Albert

Abstract

Two-part mixed effects models are often used for analyzing longitudinal data with many zeros. Typically, these models are formulated with binary and continuous components separately with random effects that are correlated between the two components. Researchers have developed maximum-likelihood and Bayesian approaches for fitting these models that often require using particular software packages or very specialized software. We propose an imputation approach that will allow practitioners to separately use standard linear and generalized linear mixed models to estimate the fixed effects for two-part mixed effects models with complex random effects structures. An approximation to the conditional distribution of positive measurements given an individual's pattern of non-zero measurements is proposed that can be easily estimated and then imputed from. We show that for a wide range of parameter values, the imputation approach results in nearly unbiased estimation and can be implemented with standard software. We illustrate the proposed imputation approach for the analysis of longitudinal clinical trial data with many zeros.

References

Aug 29, 2002·Statistical Methods in Medical Research·Janet A ToozeRichard H Jones
Jan 26, 2008·Statistics in Medicine·Lei LiuBankole A Johnson
Jan 13, 2009·Biostatistics·Li SuVernon T Farewell
Sep 23, 2011·The Annals of Applied Statistics·Paul S Albert, Joanna H Shih
Apr 14, 2015·Journal of the Royal Statistical Society. Series B, Statistical Methodology·Jessica BarrettDavid Taylor-Robinson
May 9, 2015·The International Journal of Behavioral Nutrition and Physical Activity·Tonja R NanselAiyi Liu
Sep 12, 2017·Annual Review of Statistics and Its Application·V T FarewellL Su

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