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

The practice of latent class (LC) modeling using a bias-adjusted three-step approach has become widely popular. However, the current three-step approach has one important drawback–its key assumption of conditional independence between external variables and latent class indicators is often violated in practice, such as when a (nominal) covariate represents subgroups showing measurement non-invariance (MNI) or differential item functioning (DIF). In this article, we demonstrate how the current three-step approach should be modified to account for MNI; that is, covariates causing DIF should be included in the step-one model and the step-three classification error adjustment should differ across the values of the DIF covariates. We also propose a model-building strategy that makes the new methodology practically applicable also when it is unknown which of the external variables cause DIF. The new approach, implemented in the program Latent GOLD, is illustrated using a synthetic and a real data example

Testing for homogenous or heterogenous doers in Longitudinal latent class regression framework

Latent class regression analysis is applied in context of conditional and unconditional analysis. The empirical analysis is conducted in novel way for exploratory and confirmatory perspective utilizing longitudinal British household data of Understanding society. The study aims to explore the profile differences for subjective satisfaction towards work and confirms the absence of differential effects of job-related variables across the explored broad classes of satisfied and non satisfied job doers. For further insights into behaviour of selected classes, conditional models are employed. Step 3 approach is utilized in this regard for investigating the contribution of background variables such as gender, age, occupation and quality of life for shaping their response of being satisfied or non satisfied with their jobs. This study overall tests and confirms the absence of heterogenous triggers for job satisfaction in British society. &nbsp

Row mixture-based clustering with covariates for ordinal responses

Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal data, using finite mixtures to cluster the rows (observations) of the matrix. These models can incorporate the main effects of individual rows and columns, as well as cluster effects, to model the matrix of responses. However, many real-world applications also include available covariates, which provide insights into the main characteristics of the clusters and determine clustering structures based on both the individuals’ similar patterns of responses and the effects of the covariates on the individuals' responses. In our research we have extended the mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization algorithm and assess performance using a simulation study. We also illustrate an application of the models to the well-known arthritis clinical trial data set"This work has been supported by the Ministerio de Ciencia e Innovación (Spain) [PID2019-104830RB-I00/ DOI (AEI): 10.13039/501100011033], and by Grant 2021 SGR 01421 (GRBIO) administrated by the Departament de Recerca i Universitats de la Generalitat de Catalunya (Spain). Daniel Fernández is member of the Centro de Investigación Biomédica en Red de Salud Mental, Instituto de Salud Carlos III (CIBERSAM). Daniel Fernández is a Serra Húnter Fellow"Peer ReviewedPostprint (published version

Estimating the Relative Treatment Effects of Natural Clusters of Adolescent Substance Abuse Treatment Services: Combining Latent Class Analysis and Propensity Score Methods

Objectives: The motivating substantive aim of this dissertation was to identify common clusters of drug treatment services that adolescents receive in practice that are effective in terms of improving substance use outcomes. We first identified clusters of drug treatment services that adolescents in outpatient treatment report receiving, as well as examined factors associated with each class of treatment services (Chapter 2). Our statistical approach for estimating the effect of treatment service classes on outcomes was latent class regression with a distal outcome; we review various statistical methods for implementing latent class regression with a distal outcome in Chapter 3. Addressing potential confounding arising from baseline differences among youth receiving different classes of treatment services was a key concern; Chapter 4 describes emerging methods to address confounding in the context of latent class regression with a distal outcome, highlighting the challenges that arise when the treatment of interest is a latent variable. Methods: Chapters 2 and 4 used data on 5,527 adolescents receiving drug treatment services through treatment providers funded through the Substance Abuse and Mental Health Services Administration’s Center for Substance Abuse Treatment. Latent class analysis was used to identify classes of substance use treatment services reported by youth. A simulation study to compare 5 statistical methods for latent class regression with a distal outcome was performed in Chapter 3. An additional simulation study to compare 3 methods for addressing confounding in this context was performed in Chapter 4; these methods were also applied to our adolescent data. Results: Distinct classes of outpatient treatment services received by adolescents were empirically identified using latent class analysis; youth receiving different classes of treatment services were found to be significantly different on numerous baseline characteristics. Statistical performance varied notably across methods for latent class regression with a distal outcome. Finally, failing to account for potential confounding in this setting can lead to significantly biased estimates of the association between the latent class and the distal outcome; the 1-step method we examined performed particularly well in terms of reducing bias. Conclusions: Emerging methods for modeling the treatment of interest as a latent variable are quite relevant for social and behavior researchers. However, like studies with fully observed variables, care must be taken to address potential confounding; future work should continue to develop methods to address confounding in this context

Advancing Research Methodology and Educational Policy: An Application of Mixture Modeling Using School Climate

School climate is a well-studied issue in educational research. However, surveys of school climate tend to be analyzed using item-centered as opposed to person-centered methods. The current study evaluated the 2018 South Carolina School Climate Survey using advanced applications of mixture modeling in an attempt to identify latent profiles at the student and school levels. The relatively new manual BCH 3-Step approach was applied given its usefulness in analyzing multilevel data with covariates and distal outcomes. However, its application to multilevel mixture models leaves room for advancement and prompted the adoption of an alternative analysis plan that included separate analyses for students and schools. A latent profile analysis was conducted at the student level and resulted in the identification of six student profiles. At the school level, the manual BCH 3-Step process was applied, allowing for the incorporation of a covariate for school poverty level and distal outcomes related to academic achievement. Two profiles were identified at the school level, but because schools were also assigned to \u27known classes\u27 based on type (elementary, middle, high), a total of six profiles were created and analyzed in relation to the covariate and distal outcomes. A discussion of the results and methodological challenges associated with this study follows alongside considerations about how school climate can and should be analyzed, interpreted, and applied from both methodological and policy perspectives

Prognostic factors for epilepsy

Introduction and Aims: Epilepsy is a neurological disorder and is a heterogeneous condition both in terms of cause and prognosis. Prognostic factors identify patients at varying degrees of risk for specific outcomes which facilitates treatment choice and aids patient counselling. Few prognostic models based on prospective cohorts or randomised controlled trial data have been published in epilepsy. Patients with epilepsy can be loosely categorised as having had a first seizure, being newly diagnosed with epilepsy, having established epilepsy or frequent unremitting seizures despite optimum treatment. This thesis concerns modelling prognostic factors for these patient groups, for outcomes including seizure recurrence, seizure remission and treatment failure. Methods: Methods for modelling prognostic factors are discussed and applied to several examples including eligibility to drive following a first seizure and following withdrawal of treatment after a period of remission from seizures. Internal and external model validation techniques are reviewed. The latter is investigated further in a simulation study, the results of which are demonstrated in a motivating example. Mixture modelling is introduced and assessed to better predict whether a patient would achieve remission from seizures immediately, at a later time point, or whether they may never achieve remission. Results: Multivariable models identified a number of significant factors. Future risk of a seizure was therefore obtained for various patient subgroups. The models identified that the chance of a second seizure was below the risk threshold for driving, set by the DVLA, after six months, and the risk of a seizure following treatment withdrawal after a period of remission from seizures was below the risk threshold after three months. Selected models were found to be internally valid and the simulation study indicated that concordance and a variety of imputation methods for handling covariates missing from the validation dataset were useful approaches for external validation of prognostic models. Assessing these methods for a selected model indicated that the model was valid in independent datasets. Mixture modelling techniques begin to show an improved prognostic model for the frequently reported outcome time to 12-month remission. Conclusions: The models described within this thesis can be used to predict outcome for patients with first seizures or epilepsy aiding individual patient risk stratification and the design and analysis of future epilepsy trials. Prognostic models are not commonly externally validated. A method of external validation in the presence of a missing covariate has been proposed and may facilitate validation of prognostic models making the evidence base more transparent and reliable and instil confidence in any significant findings