Statistical Methodologies for Dealing with Incomplete Longitudinal Outcomes Due to Dropout Missing at Random

2017 ◽  
pp. 179-209
Author(s):  
A. Satty ◽  
H. Mwambi ◽  
G. Molenberghs
Keyword(s):  
Missing At Random ◽  
Longitudinal Outcomes

scholarly journals Estimating Inverse-probability Weights for Longitudinal Data with Dropout or Truncation: The Xtrccipw Command

2017 ◽  
Vol 17 (2) ◽  
pp. 253-278 ◽  
Author(s):  
Eric J. Daza ◽  
Michael G. Hudgens ◽  
Amy H. Herring
Keyword(s):  
Longitudinal Study ◽  
Longitudinal Data ◽  
Simulation Study ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Missing At Random ◽  
Drop Out ◽  
Longitudinal Outcomes ◽  
Inverse Probability ◽  
Inverse Probability Weights

Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study.

scholarly journals Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hanji He ◽  
Guangming Deng
Keyword(s):  
Empirical Likelihood ◽  
Missing At Random ◽  
Confidence Regions ◽  
Likelihood Inference ◽  
High Dimensional ◽  
Finite Sample ◽  
The Mean ◽  
Data Missing ◽  
Consistency And Asymptotic Normality ◽  
The Impact

We extend the mean empirical likelihood inference for response mean with data missing at random. The empirical likelihood ratio confidence regions are poor when the response is missing at random, especially when the covariate is high-dimensional and the sample size is small. Hence, we develop three bias-corrected mean empirical likelihood approaches to obtain efficient inference for response mean. As to three bias-corrected estimating equations, we get a new set by producing a pairwise-mean dataset. The method can increase the size of the sample for estimation and reduce the impact of the dimensional curse. Consistency and asymptotic normality of the maximum mean empirical likelihood estimators are established. The finite sample performance of the proposed estimators is presented through simulation, and an application to the Boston Housing dataset is shown.

scholarly journals Evaluating the Observed Log-Likelihood Function in Two-Level Structural Equation Modeling with Missing Data: From Formulas to R Code

Psych ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 197-232
Author(s):  
Yves Rosseel
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Likelihood Function ◽  
Likelihood Estimation ◽  
Missing At Random ◽  
Equation Modeling ◽  
Computationally Efficient ◽  
Log Likelihood ◽  
Efficient Expression ◽  
Special Case

This paper discusses maximum likelihood estimation for two-level structural equation models when data are missing at random at both levels. Building on existing literature, a computationally efficient expression is derived to evaluate the observed log-likelihood. Unlike previous work, the expression is valid for the special case where the model implied variance–covariance matrix at the between level is singular. Next, the log-likelihood function is translated to R code. A sequence of R scripts is presented, starting from a naive implementation and ending at the final implementation as found in the lavaan package. Along the way, various computational tips and tricks are given.

scholarly journals Joint Longitudinal Models for Dealing With Missing at Random Data in Trial-Based Economic Evaluations

2021 ◽  
Author(s):  
Andrea Gabrio ◽  
Rachael Hunter ◽  
Alexina J. Mason ◽  
Gianluca Baio
Keyword(s):  
Missing At Random ◽  
Economic Evaluations ◽  
Longitudinal Models ◽  
Random Data

The longitudinal outcomes of applying non-selective beta-blockers in portal hypertension: real-world multicenter study

2021 ◽  
Author(s):  
Seong Hee Kang ◽  
Minjong Lee ◽  
Moon Young Kim ◽  
Jun Hyeok Lee ◽  
Baek Gyu Jun ◽  
...  
Keyword(s):  
Portal Hypertension ◽  
Multicenter Study ◽  
Real World ◽  
Beta Blockers ◽  
Longitudinal Outcomes

Longitudinal outcomes of body mass index in overweight and obese children with chronic kidney disease

2021 ◽  
Author(s):  
Nancy M. Rodig ◽  
Jennifer Roem ◽  
Michael F. Schneider ◽  
Patricia W. Seo-Mayer ◽  
Kimberly J. Reidy ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Body Mass ◽  
Obese Children ◽  
Longitudinal Outcomes

scholarly journals West Nile virus neuroinvasive disease: neurological manifestations and prospective longitudinal outcomes

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
John Hart ◽  
◽  
Gail Tillman ◽  
Michael A Kraut ◽  
Hsueh-Sheng Chiang ◽  
...  
Keyword(s):  
West Nile Virus ◽  
Neurological Manifestations ◽  
West Nile ◽  
Longitudinal Outcomes ◽  
Neuroinvasive Disease ◽  
Prospective Longitudinal
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