A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data
نویسندگان
چکیده
A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data A. F. Donneau, M. Mauer, G. Molenberghs & A. Albert a Medical Informatics and Biostatistics, University of Liège, Liège, Belgium b EORTC Headquarters, Departments of Statistics and Quality of Life, Brussels, Belgium c I-BioStat, University of Hasselt, Diepenbeek, Belgium I-BioStat, Katholieke University of Leuven, Leuven, Belgium Accepted author version posted online: 07 Apr 2014.Published online: 23 Oct 2014.
منابع مشابه
چند رویکرد برخورد با مقادیر گمشده متغیرهای کمی و بررسی اثر آنها بر نتایج حاصل از یک کارآزمایی بالینی
Background and Objectives: A major challenge that affects the longitudinal studies is the problem of missing data. Missing in the data may result in the loss of part of the information which reduces the accuracy of the estimator and obtain the results will be biased and inaccurate. Therefore, it is necessary to evaluate the missing data mechanism from a longitudinal research and to consider thi...
متن کاملEstimating the effect of multiple imputation on incomplete longitudinal data with application to a randomized clinical study.
For analyzing incomplete longitudinal data, there has been recent interest in comparing estimates with and without the use of multiple imputation along with mixed effects model and generalized estimating equations. Empirically, the additional use of multiple imputation generally led to overestimated variances and may yield more heavily biased estimates than the use of last observation carried f...
متن کاملDoubly Robust Imputation of Incomplete Binary Longitudinal Data
Estimation in binary longitudinal data by using generalized estimating equation (GEE) becomes complicated in the presence of missing data because standard GEEs are only valid under the restrictive missing completely at random assumption. Weighted GEE has therefore been proposed to allow the validity of GEE's under the weaker missing at random assumption. Multiple imputation offers an attractive...
متن کاملDual imputation model for incomplete longitudinal data.
Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation (MI) is a well-known likelihood-based method that has optimal properties in terms of efficiency and consistency if the imputation model is correctly specified. Doubly robust (DR) weighing-based methods protect against misspecification bias if one of the models, but not necessarily both, for the data o...
متن کاملBeta - Binomial and Ordinal Joint Model with Random Effects for Analyzing Mixed Longitudinal Responses
The analysis of discrete mixed responses is an important statistical issue in various sciences. Ordinal and overdispersed binomial variables are discrete. Overdispersed binomial data are a sum of correlated Bernoulli experiments with equal success probabilities. In this paper, a joint model with random effects is proposed for analyzing mixed overdispersed binomial and ordinal longitudinal respo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Communications in Statistics - Simulation and Computation
دوره 44 شماره
صفحات -
تاریخ انتشار 2015