Analysis of longitudinal censored semicontinuous data with application to the study of executive dysfunction: The Towers Task

Spencer Lourens; Ying Zhang; Jeffrey D Long; Jane S Paulsen

doi:10.1177/0962280214560187

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Analysis of longitudinal censored semicontinuous data with application to the study of executive dysfunction: The Towers Task

Journal article

Peer reviewed

Analysis of longitudinal censored semicontinuous data with application to the study of executive dysfunction: The Towers Task

Spencer Lourens, Ying Zhang, Jeffrey D Long and Jane S Paulsen

Statistical methods in medical research, Vol.26(2), pp.865-879

04/2017

DOI: 10.1177/0962280214560187

PMID: 25431462

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https://pmc.ncbi.nlm.nih.gov/articles/PMC10481431/pdf/nihms-1904320.pdfView

Open Access

Abstract

Executive dysfunction is a deficiency in skills of planning and problem solving that characterizes many neuropsychiatric disorders. The Towers Task is a commonly used measure of planning and problem solving for assessing executive function. Towers Task data are usually zero-inflated and right-censored, and ignoring these features can result in biased inference for the disease characterization of executive dysfunction. In this manuscript, a mixed-effects model for longitudinal censored semicontinuous data is developed for analyzing longitudinal Towers Task data from the PREDICT-HD study. The model is contrasted with current practice, and implications for general use are discussed.

Data Interpretation, Statistical

Humans

Huntington Disease - psychology

Problem Solving

Male

Models, Statistical

Disease Progression

Likelihood Functions

Biostatistics - methods

Computer Simulation

Adult

Female

Nonlinear Dynamics

Executive Function

Longitudinal Studies

Task Performance and Analysis

Details

Title: Subtitle: Analysis of longitudinal censored semicontinuous data with application to the study of executive dysfunction: The Towers Task
Creators: Spencer Lourens - 1 Department of Biostatistics, University of Iowa, Iowa City, USA
Ying Zhang - 3 Department of Mathematics, Shanghai Jiao Tong University, Shanghai, China
Jeffrey D Long - 4 Departments of Psychiatry and Biostatistics, University of Iowa, Iowa City, USA
Jane S Paulsen - 5 Departments of Psychiatry, Neurology and Psychology, Carver College of Medicine, University of Iowa, Iowa City, USA
Resource Type: Journal article
Publication Details: Statistical methods in medical research, Vol.26(2), pp.865-879
DOI: 10.1177/0962280214560187
PMID: 25431462
NLM abbreviation: Stat Methods Med Res
ISSN: 0962-2802
eISSN: 1477-0334
Publisher: England
Grant note: R01 NS040068 / NINDS NIH HHS U01 NS082085 / NINDS NIH HHS UL1 TR000442 / NCATS NIH HHS U01 NS082086 / NINDS NIH HHS U01 NS082083 / NINDS NIH HHS R01 NS054893 / NINDS NIH HHS
Language: English
Date published: 04/2017
Academic Unit: Psychiatry; Psychological and Brain Sciences; Biostatistics
Record Identifier: 9984003963602771

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