+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on LinkedInFollow on LinkedIn

+ Translate

Bayesian variable selection for longitudinal substance abuse treatment data subject to informative censoring

Bayesian variable selection for longitudinal substance abuse treatment data subject to informative censoring

Journal of the Royal Statistical Society Series C Applied statistics 56(3): 293-311

Measuring the process of care in substance abuse treatment requires analysing repeated client assessments at critical time points during treatment tenure. Assessments are frequently censored because of early departure from treatment. Most analyses accounting for informative censoring define the censoring time to be that of the last observed assessment. However, if missing assessments for those who remain in treatment are attributable to logistical reasons rather than to the underlying treatment process being measured, then the length of stay in treatment might better characterize censoring than would time of measurement. Bayesian variable selection is incorporated in the conditional linear model to assess whether time of measurement or length of stay better characterizes informative censoring. Marginal posterior distributions of the trajectory of treatment process scores are obtained that incorporate model uncertainty. The methodology is motivated by data from an on-going study of the quality of care in in-patient substance abuse treatment.

(PDF emailed within 0-6 h: $19.90)

Accession: 015120617

Download citation: RISBibTeXText

DOI: 10.1111/j.1467-9876.2007.00578.x

Related references

Bayesian Methods for Categorical Data Under Informative General Censoring. Biometrika 82(2): 439-446, 1995

Bayesian methods for categorical data under informative general censoring. Biometrika 82(2): 439-446, 1995

A Bayesian model for time-to-event data with informative censoring. Biostatistics 13(2): 341-354, 2012

Bayesian models for multivariate current status data with informative censoring. Biometrics 58(1): 88, 2002

Analysis of colorectal adenoma recurrence data subject to informative censoring. Cancer Epidemiology, Biomarkers & Prevention 18(3): 712-717, 2009

Joint analysis of longitudinal data with informative right censoring. Biometrics 63(2): 363-371, 2007

Joint modelling of bivariate longitudinal data with informative dropout and left-censoring, with application to the evolution of CD4+ cell count and HIV RNA viral load in response to treatment of HIV infection. Statistics in Medicine 24(1): 65-82, 2004

Discussion: subject selection, recruitment, and retention in longitudinal studies involving perinatal substance abuse and human immunodeficiency virus infection. Nida Research Monograph 117: 183-193, 1992

Random Regression Models for Human Immunodeficiency Virus Ribonucleic Acid Data Subject to Left Censoring and Informative Drop-Outs. Journal of the Royal Statistical Society: Series C 49(4): 485-497, 2000

Random regression models for human immunodeficiency virus ribonucleic acid data subject to left censoring and informative drop-outs. Journal of the Royal Statistical Society: Series C 49(4): 485-497, 2000

Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data. Biometrical Journal. Biometrische Zeitschrift (): -, 2016

Regression analysis of longitudinal data with time-dependent covariates in the presence of informative observation and censoring times. Journal of Statistical Planning and Inference 141(8): 2902-2919, 2011

Bayesian semiparametric modeling for HIV longitudinal data with censoring and skewness. Statistical Methods in Medical Research: 962280218760360-962280218760360, 2018

New variable selection methods for zero-inflated count data with applications to the substance abuse field. Statistics in Medicine 30(18): 2326-2340, 2011

A bayesian two-part latent class model for longitudinal medical expenditure data: assessing the impact of mental health and substance abuse parity. Biometrics 67(1): 280-289, 2011