ArticlePsychometrika2012
PREDICTING LATENT CLASS SCORES FOR SUBSEQUENT ANALYSIS.
Article in Psychometrika, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- De-prescribing opioids among Medicaid patients with long-term opioid use.Journal of substance use and addiction treatment · 2025Article
- Identifiability of Latent Class Models with Covariates.Psychometrika · 2022Article
- What's important to you? Socioeconomic inequalities in the perceived importance of health compared to other life domains.BMC public health · 2022Article
- Article
- Escape expectancies and sexualized substance use among gay, bisexual, and other men who have sex with men.AIDS care · 2020Article
- Two-Step Estimation of Models Between Latent Classes and External Variables.Psychometrika · 2018Article
- A Proposed Approach for Joint Modeling of the Longitudinal and Time-To-Event Data in Heterogeneous Populations: An Application to HIV/AIDS's Disease.BioMed research international · 2018Article
- Comparing the Performance of Improved Classify-Analyze Approaches For Distal Outcomes in Latent Profile Analysis.Methodology : European journal of research methods for the behavioral & social sciences · 2016Article
- ADDRESSING CONFOUNDING WHEN ESTIMATING THE EFFECTS OF LATENT CLASSES ON A DISTAL OUTCOME.Health services & outcomes research methodology · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose a new method for predicting class scores that, in contrast to posterior probability-based methods, yields consistent estimators of the parameters in the third step. Additionally, in simulation studies the new methodology exhibited only a minor loss of efficiency. Finally, the new and the posterior probability-based methods are compared in an analysis of mobility/exercise.
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Registered trials
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.