4.4 Article

Three-Step Latent Class Analysis with Inverse Propensity Weighting in the Presence of Differential Item Functioning

Related references

Note: Only part of the references are listed.
Article Statistics & Probability

A new three-step method for using inverse propensity weighting with latent class analysis

F. J. Clouth et al.

Summary: The bias-adjusted three-step LCA is extended to incorporate IPW, separating the estimation of the measurement model from the treatment effect estimation. This new approach solves conceptual issues and makes it easier for model selection and the use of multiple imputation. The implementation of this method in Latent GOLD is evaluated in a simulation study and illustrated with data of prostate cancer patients.

ADVANCES IN DATA ANALYSIS AND CLASSIFICATION (2022)

Article Mathematics, Interdisciplinary Applications

A Guide to Detecting and Modeling Local Dependence in Latent Class Analysis Models

Marieke Visser et al.

Summary: Latent class analysis (LCA) assigns individuals to exclusive classes based on response patterns to a set of indicators. The assumption of local independence is crucial, and failure to model correlated indicators can lead to biased parameter estimates. This comprehensive resource provides tools for researchers to detect and model violations of local independence, improving the accuracy of their analyses.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2022)

Article Mathematics, Interdisciplinary Applications

Stepwise Latent Class Analysis in the Presence of Missing Values on the Class Indicators

Oe Emre C. Alagoez et al.

Summary: This study investigates how missing values affect latent class modeling using bias-adjusted stepwise approaches and proposes a new analysis method. Using synthetic data sets, the results show that unbiased parameter estimates can be obtained in stepwise LC analysis when the MAR assumption holds in the first step model. However, bias is introduced when this assumption does not hold, especially with modal class assignments. The amount of bias is influenced by the deviation from MAR, the proportion of missing values, and the separation between classes.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2022)

Article Statistics & Probability

CAUSAL INFERENCE FOR TIME-VARYING TREATMENTS IN LATENT MARKOV MODELS: AN APPLICATION TO THE EFFECTS OF REMITTANCES ON POVERTY DYNAMICS

Federico Tullio et al.

Summary: This study assesses the effectiveness of remittances on the poverty level of recipient households using a causal inference approach. The study utilizes longitudinal data and time-varying treatments and combines propensity score techniques with a latent Markov framework. The findings indicate that remittances decrease the probability of falling into poverty, but have no impact on the probability of moving out of poverty.

ANNALS OF APPLIED STATISTICS (2022)

Article Mathematics, Interdisciplinary Applications

How to perform three-step latent class analysis in the presence of measurement non-invariance or differential item functioning

Jeroen K. Vermunt et al.

Summary: This article highlights a drawback in the current three-step approach used in latent class modeling, which is the assumption of conditional independence is often violated. The study proposes a modification to account for measurement non-invariance (MNI) and differential item functioning (DIF), as well as a new model-building strategy to address these issues practically. The new approach is implemented in the Latent GOLD program and demonstrated using synthetic and real data examples.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2021)

Article Oncology

Heterogeneity in Quality of Life of Long-Term Colon Cancer Survivors: A Latent Class Analysis of the Population-Based PROFILES Registry

Felix J. Clouth et al.

Summary: This study identified five distinct clusters of long-term colon cancer survivors with similar patterns of health-related quality of life using latent class analysis (LCA) and latent transition analysis (LTA). The clusters were stable over time and influenced by factors like time since diagnosis, age, and gender.

ONCOLOGIST (2021)

Article Mathematics, Interdisciplinary Applications

The Detection and Modeling of Direct Effects in Latent Class Analysis

Jeroen H. M. Janssen et al.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2019)

Article Mathematics, Interdisciplinary Applications

Mostly Harmless Direct Effects: A Comparison of Different Latent Markov Modeling Approaches

Roberto Di Mari et al.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2018)

Article Medicine, Research & Experimental

Different ways to estimate treatment effects in randomised controlled trials

J. Twisk et al.

CONTEMPORARY CLINICAL TRIALS COMMUNICATIONS (2018)

Article Mathematics, Interdisciplinary Applications

Measurement Invariance and Differential Item Functioning in Latent Class Analysis With Stepwise Multiple Indicator Multiple Cause Modeling

Katherine E. Masyn

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2017)

Article Mathematics, Interdisciplinary Applications

Robustness of Stepwise Latent Class Modeling With Continuous Distal Outcomes

Zsuzsa Bakk et al.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2016)

Article Mathematics, Interdisciplinary Applications

Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration

Karen Nylund-Gibson et al.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2016)

Article Education & Educational Research

Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies

Francesco Bartolucci et al.

JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS (2016)

Article Mathematics, Interdisciplinary Applications

Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus

Tihomir Asparouhov et al.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2014)

Article Mathematics, Interdisciplinary Applications

A Latent Transition Mixture Model Using the Three-Step Specification

Karen Nylund-Gibson et al.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2014)

Article Health Care Sciences & Services

Addressing confounding when estimating the effects of latent classes on a distal outcome

Megan S. Schuler et al.

HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY (2014)

Article Mathematics, Interdisciplinary Applications

Causal Inference in Latent Class Analysis

Stephanie T. Lanza et al.

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL (2013)

Article Statistics & Probability

A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models

Daniel L. Oberski et al.

ADVANCES IN DATA ANALYSIS AND CLASSIFICATION (2013)

Article Mathematics, Interdisciplinary Applications

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

Peter C. Austin

MULTIVARIATE BEHAVIORAL RESEARCH (2011)

Article Political Science

Latent Class Modeling with Covariates: Two Improved Three-Step Approaches

Jeroen K. Vermunt

POLITICAL ANALYSIS (2010)

Article Public, Environmental & Occupational Health

Estimating causal effects from epidemiological data

MA Hernán et al.

JOURNAL OF EPIDEMIOLOGY AND COMMUNITY HEALTH (2006)

Article Public, Environmental & Occupational Health

Marginal structural models and causal inference in epidemiology

JM Robins et al.

EPIDEMIOLOGY (2000)