A Fast EM Algorithm for Fitting Joint Models of a Binary Response and Multiple Longitudinal Covariates Subject to Detection Limits.

Paul W Bernhardt, Daowen Zhang, Huixia Judy Wang

Abstract read

In one paragraph

Article in Computational statistics & data analysis, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Mediation analysis in a case-control study when the mediator is a censored variable.Statistics in medicine · 2019
Article
Estimation of indirect effect when the mediator is a censored variable.Statistical methods in medical research · 2018
Article

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Paul W BernhardtDepartment of Mathematics and Statistics, Villanova University, Villanova, PA, USA.

Daowen ZhangDepartment of Statistics, North Carolina State University, Raleigh, NC, USA.

Huixia Judy WangDepartment of Statistics, George Washington University, Washington, DC, USA.

Funding

Statistical Methods for AIDS Clinical TrialsR37AI031789 · NIAID · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI TSIATIS, ANASTASIOS A · 2003 to 2012

$3.2M

Flexible Statistical Methods for Biomedical DataR01CA085848 · NCI · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI DAVIDIAN, MARIE · 2000 to 2015

$3.1M

NCI NIH HHS R01 CA085848NIAID NIH HHS R37 AI031789

6 · The paper itself

Abstract

Joint modeling techniques have become a popular strategy for studying the association between a response and one or more longitudinal covariates. Motivated by the GenIMS study, where it is of interest to model the event of survival using censored longitudinal biomarkers, a joint model is proposed for describing the relationship between a binary outcome and multiple longitudinal covariates subject to detection limits. A fast, approximate EM algorithm is developed that reduces the dimension of integration in the E-step of the algorithm to one, regardless of the number of random effects in the joint model. Numerical studies demonstrate that the proposed approximate EM algorithm leads to satisfactory parameter and variance estimates in situations with and without censoring on the longitudinal covariates. The approximate EM algorithm is applied to analyze the GenIMS data set.

Indexed as

Detection limitEM algorithmJoint modelLogistic regressionMultiple longitudinal covariatesNormal approximation

Identifiers

PMID25598564

DOI10.1016/j.csda.2014.11.011

PMCPMC4295570

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.