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

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.

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Faculty Opinions recommendation of Randomised trial of a parent-mediated intervention for infants at high risk for autism: longitudinal outcomes to age 3 years.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.727495422.793530795 ◽

2017 ◽

Author(s):

Patricia Howlin

Keyword(s):

High Risk ◽

Randomised Trial ◽

Longitudinal Outcomes

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

Discrete Dynamics in Nature and Society ◽

10.1155/2020/8893594 ◽

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.

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A Joint Model for Longitudinal Outcomes with Potential Floor or Ceiling Effects and Survival Times, with Applications to Analysis of Quality of Life Data from a Cancer Clinical Trial

Stat ◽

10.1002/sta4.412 ◽

2021 ◽

Author(s):

Zhanfeng Wang ◽

Honghong Xu ◽

Haijiao Liu ◽

Hui Song ◽

Dongsheng Tu

Keyword(s):

Quality Of Life ◽

Clinical Trial ◽

Joint Model ◽

Cancer Clinical Trial ◽

Survival Times ◽

Longitudinal Outcomes ◽

Life Data ◽

Ceiling Effects

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

Psych ◽

10.3390/psych3020017 ◽

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.

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