Literature DB >> 24274626

Using a shared parameter mixture model to estimate change during treatment when termination is related to recovery speed.

Nisha C Gottfredson1, Daniel J Bauer2, Scott A Baldwin3, John C Okiishi3.   

Abstract

OBJECTIVE: This study demonstrates how to use a shared parameter mixture model (SPMM) in longitudinal psychotherapy studies to accommodate missingness that is due to a correlation between rate of improvement and termination of therapy. Traditional growth models assume that such a relationship does not exist (i.e., assume that data are missing at random) and produce biased results if this assumption is incorrect.
METHOD: We used longitudinal data from 4,676 patients enrolled in a naturalistic study of psychotherapy to compare results from a latent growth model and an SPMM.
RESULTS: In this data set, estimates of the rate of improvement during therapy differed by 6.50%-6.66% across the two models, indicating that participants with steeper trajectories left psychotherapy earliest, thereby potentially biasing inference for the slope in the latent growth model.
CONCLUSION: We conclude that reported estimates of change during therapy may be underestimated in naturalistic studies of therapy in which participants and their therapists determine the end of treatment. Because non-randomly missing data can also occur in randomized controlled trials or in observational studies of development, the utility of the SPMM extends beyond naturalistic psychotherapy data. PsycINFO Database Record (c) 2014 APA, all rights reserved.

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Mesh:

Year:  2013        PMID: 24274626      PMCID: PMC4032810          DOI: 10.1037/a0034831

Source DB:  PubMed          Journal:  J Consult Clin Psychol        ISSN: 0022-006X


  23 in total

1.  A comparison of inclusive and restrictive strategies in modern missing data procedures.

Authors:  L M Collins; J L Schafer; C M Kam
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2.  Distributional assumptions of growth mixture models: implications for overextraction of latent trajectory classes.

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Journal:  Psychol Methods       Date:  2003-09

3.  Latent pattern mixture models for informative intermittent missing data in longitudinal studies.

Authors:  Haiqun Lin; Charles E McCulloch; Robert A Rosenheck
Journal:  Biometrics       Date:  2004-06       Impact factor: 2.571

4.  The role of coding time in estimating and interpreting growth curve models.

Authors:  Jeremy C Biesanz; Natalia Deeb-Sossa; Alison A Papadakis; Kenneth A Bollen; Patrick J Curran
Journal:  Psychol Methods       Date:  2004-03

5.  Modeling longitudinal data with nonignorable dropouts using a latent dropout class model.

Authors:  Jason Roy
Journal:  Biometrics       Date:  2003-12       Impact factor: 2.571

6.  Shared parameter models for the joint analysis of longitudinal data and event times.

Authors:  Edward F Vonesh; Tom Greene; Mark D Schluchter
Journal:  Stat Med       Date:  2006-01-15       Impact factor: 2.373

7.  Longitudinal Analysis when the Experimenter does not Determine when Treatment Ends: What is Dose-Response?

Authors:  Daniel J Feaster; Frederick L Newman; Christopher Rice
Journal:  Clin Psychol Psychother       Date:  2003

8.  Dose-effect relations and responsive regulation of treatment duration: the good enough level.

Authors:  Michael Barkham; Janice Connell; William B Stiles; Jeremy N V Miles; Frank Margison; Chris Evans; John Mellor-Clark
Journal:  J Consult Clin Psychol       Date:  2006-02

9.  A latent-class mixture model for incomplete longitudinal Gaussian data.

Authors:  Caroline Beunckens; Geert Molenberghs; Geert Verbeke; Craig Mallinckrodt
Journal:  Biometrics       Date:  2007-06-30       Impact factor: 2.571

10.  Outcomes management, expected treatment response, and severity-adjusted provider profiling in outpatient psychotherapy.

Authors:  Wolfgang Lutz; Zoran Martinovich; Kenneth I Howard; Scott C Leon
Journal:  J Clin Psychol       Date:  2002-10
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  1 in total

1.  A Note on the Use of Mixture Models for Individual Prediction.

Authors:  Veronica T Cole; Daniel J Bauer
Journal:  Struct Equ Modeling       Date:  2016-05-09       Impact factor: 6.125

  1 in total

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