Markov transition models for binary repeated measures with ignorable and nonignorable missing values

Xiaowei Yang, Steven Shoptaw, Kun Nie, Juanmei Liu, Thomas R. Belin

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Motivated by problems encountered in studying treatments for drug dependence, where repeated binary outcomes arise from monitoring biomarkers for recent drug use, this article discusses a statistical strategy using Markov transition model for analyzing incomplete binary longitudinal data. When the mechanism giving rise to missing data can be assumed to be 'ignorable', standard Markov transition models can be applied to observed data to draw likelihood-based inference on transition probabilities between outcome events. Illustration of this approach is provided using binary results from urine drug screening in a clinical trial of baclofen for cocaine dependence. When longitudinal data have 'nonignorable' missingness mechanisms, random-effects Markov transition models can be used to model the joint distribution of the binary data matrix and the matrix of missingness indicators. Categorizing missingness patterns into those for occasional or 'intermittent' missingness and those for monotonic missingness or 'missingness due to dropout', the random-effects Markov transition model was applied to a data set containing repeated breath samples analyzed for expired carbon monoxide levels among opioid-dependent, methadone-maintained cigarette smokers in a smoking cessation trial. Markov transition models provide a novel reconceptualization of treatment outcomes, offering both intuitive statistical values and relevant clinical insights.

Original languageEnglish (US)
Pages (from-to)347-364
Number of pages18
JournalStatistical Methods in Medical Research
Volume16
Issue number4
DOIs
StatePublished - Aug 2007

Fingerprint

Cocaine-Related Disorders
Preclinical Drug Evaluations
Baclofen
Transition Model
Repeated Measures
Missing Values
Methadone
Smoking Cessation
Carbon Monoxide
Tobacco Products
Opioid Analgesics
Markov Model
Substance-Related Disorders
Biomarkers
Clinical Trials
Urine
Binary
Drugs
Pharmaceutical Preparations
Binary Data

ASJC Scopus subject areas

  • Epidemiology
  • Health Information Management
  • Nursing(all)

Cite this

Markov transition models for binary repeated measures with ignorable and nonignorable missing values. / Yang, Xiaowei; Shoptaw, Steven; Nie, Kun; Liu, Juanmei; Belin, Thomas R.

In: Statistical Methods in Medical Research, Vol. 16, No. 4, 08.2007, p. 347-364.

Research output: Contribution to journalArticle

Yang, Xiaowei ; Shoptaw, Steven ; Nie, Kun ; Liu, Juanmei ; Belin, Thomas R. / Markov transition models for binary repeated measures with ignorable and nonignorable missing values. In: Statistical Methods in Medical Research. 2007 ; Vol. 16, No. 4. pp. 347-364.
@article{c13b5091ccba487bad299961592b4787,
title = "Markov transition models for binary repeated measures with ignorable and nonignorable missing values",
abstract = "Motivated by problems encountered in studying treatments for drug dependence, where repeated binary outcomes arise from monitoring biomarkers for recent drug use, this article discusses a statistical strategy using Markov transition model for analyzing incomplete binary longitudinal data. When the mechanism giving rise to missing data can be assumed to be 'ignorable', standard Markov transition models can be applied to observed data to draw likelihood-based inference on transition probabilities between outcome events. Illustration of this approach is provided using binary results from urine drug screening in a clinical trial of baclofen for cocaine dependence. When longitudinal data have 'nonignorable' missingness mechanisms, random-effects Markov transition models can be used to model the joint distribution of the binary data matrix and the matrix of missingness indicators. Categorizing missingness patterns into those for occasional or 'intermittent' missingness and those for monotonic missingness or 'missingness due to dropout', the random-effects Markov transition model was applied to a data set containing repeated breath samples analyzed for expired carbon monoxide levels among opioid-dependent, methadone-maintained cigarette smokers in a smoking cessation trial. Markov transition models provide a novel reconceptualization of treatment outcomes, offering both intuitive statistical values and relevant clinical insights.",
author = "Xiaowei Yang and Steven Shoptaw and Kun Nie and Juanmei Liu and Belin, {Thomas R.}",
year = "2007",
month = "8",
doi = "10.1177/0962280206071843",
language = "English (US)",
volume = "16",
pages = "347--364",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications Ltd",
number = "4",

}

TY - JOUR

T1 - Markov transition models for binary repeated measures with ignorable and nonignorable missing values

AU - Yang, Xiaowei

AU - Shoptaw, Steven

AU - Nie, Kun

AU - Liu, Juanmei

AU - Belin, Thomas R.

PY - 2007/8

Y1 - 2007/8

N2 - Motivated by problems encountered in studying treatments for drug dependence, where repeated binary outcomes arise from monitoring biomarkers for recent drug use, this article discusses a statistical strategy using Markov transition model for analyzing incomplete binary longitudinal data. When the mechanism giving rise to missing data can be assumed to be 'ignorable', standard Markov transition models can be applied to observed data to draw likelihood-based inference on transition probabilities between outcome events. Illustration of this approach is provided using binary results from urine drug screening in a clinical trial of baclofen for cocaine dependence. When longitudinal data have 'nonignorable' missingness mechanisms, random-effects Markov transition models can be used to model the joint distribution of the binary data matrix and the matrix of missingness indicators. Categorizing missingness patterns into those for occasional or 'intermittent' missingness and those for monotonic missingness or 'missingness due to dropout', the random-effects Markov transition model was applied to a data set containing repeated breath samples analyzed for expired carbon monoxide levels among opioid-dependent, methadone-maintained cigarette smokers in a smoking cessation trial. Markov transition models provide a novel reconceptualization of treatment outcomes, offering both intuitive statistical values and relevant clinical insights.

AB - Motivated by problems encountered in studying treatments for drug dependence, where repeated binary outcomes arise from monitoring biomarkers for recent drug use, this article discusses a statistical strategy using Markov transition model for analyzing incomplete binary longitudinal data. When the mechanism giving rise to missing data can be assumed to be 'ignorable', standard Markov transition models can be applied to observed data to draw likelihood-based inference on transition probabilities between outcome events. Illustration of this approach is provided using binary results from urine drug screening in a clinical trial of baclofen for cocaine dependence. When longitudinal data have 'nonignorable' missingness mechanisms, random-effects Markov transition models can be used to model the joint distribution of the binary data matrix and the matrix of missingness indicators. Categorizing missingness patterns into those for occasional or 'intermittent' missingness and those for monotonic missingness or 'missingness due to dropout', the random-effects Markov transition model was applied to a data set containing repeated breath samples analyzed for expired carbon monoxide levels among opioid-dependent, methadone-maintained cigarette smokers in a smoking cessation trial. Markov transition models provide a novel reconceptualization of treatment outcomes, offering both intuitive statistical values and relevant clinical insights.

UR - http://www.scopus.com/inward/record.url?scp=34547678497&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34547678497&partnerID=8YFLogxK

U2 - 10.1177/0962280206071843

DO - 10.1177/0962280206071843

M3 - Article

VL - 16

SP - 347

EP - 364

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 4

ER -