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

Correlated component regression: A prediction/classification methodology for possibly many features

Abstract A new ensemble dimension reduction regression technique, called Correlated Component Regression (CCR), is proposed that predicts the dependent variable based on K correlated components. For K = 1, CCR is equivalent to the corresponding Naïve Bayes solution, and for K = P, CCR is equivalent to traditional regression with P predictors. An optional step-down variable selection procedure provides a sparse solution, with each component defined as a linear combination of only P* < P predictors. For high-dimensional data, simulation results suggest that good prediction is generally attainable for K = 3 or 4 regardless of the number of predictors, and estimation is fast. When predictors include one or more suppressor variables, common with gene expression data, simulations based on linear regression, logistic regression and discriminant analysis suggest that CCR predicts outside the sample better than comparable approaches based on stepwise regression, penalized regression and/or PLS regression. A major reason for the improvement is that the CCR/step-down algorithm is much better than other sparse techniques in capturing important suppressor variables among the final predictors

Transcriptome Profiling of Whole Blood Cells Identifies PLEK2 and C1QB in Human Melanoma

Developing analytical methodologies to identify biomarkers in easily accessible body fluids is highly valuable for the early diagnosis and management of cancer patients. Peripheral whole blood is a "nucleic acid-rich" and "inflammatory cell-rich" information reservoir and represents systemic processes altered by the presence of cancer cells.We conducted transcriptome profiling of whole blood cells from melanoma patients. To overcome challenges associated with blood-based transcriptome analysis, we used a PAXgene™ tube and NuGEN Ovation™ globin reduction system. The combined use of these systems in microarray resulted in the identification of 78 unique genes differentially expressed in the blood of melanoma patients. Of these, 68 genes were further analyzed by quantitative reverse transcriptase PCR using blood samples from 45 newly diagnosed melanoma patients (stage I to IV) and 50 healthy control individuals. Thirty-nine genes were verified to be differentially expressed in blood samples from melanoma patients. A stepwise logit analysis selected eighteen 2-gene signatures that distinguish melanoma from healthy controls. Of these, a 2-gene signature consisting of PLEK2 and C1QB led to the best result that correctly classified 93.3% melanoma patients and 90% healthy controls. Both genes were upregulated in blood samples of melanoma patients from all stages. Further analysis using blood fractionation showed that CD45(-) and CD45(+) populations were responsible for the altered expression levels of PLEK2 and C1QB, respectively.The current study provides the first analysis of whole blood-based transcriptome biomarkers for malignant melanoma. The expression of PLEK2, the strongest gene to classify melanoma patients, in CD45(-) subsets illustrates the importance of analyzing whole blood cells for biomarker studies. The study suggests that transcriptome profiling of blood cells could be used for both early detection of melanoma and monitoring of patients for residual disease

Statistical Innovations Inc.

Latent class (LC) modeling was initially introduced by Lazarsfeld and Henry (1968) as a way of formulating latent attitudinal variables from dichotomous survey items. In contrast to factor analysis, which posits continuous latent variables, LC models assume that the latent variable is categorical, and areas of application are more wide-ranging. Th

Hierarchical mixture models for nested data structures

Abstract. A hierarchical extension of the finite mixture model is presented that can be used for the analysis of nested data structures. The model permits a simultaneous model-based clustering of lower- and higher-level units. Lower-level observations within higher-level units are assumed to be mutually independent given cluster membership of the higher-level units. The proposed model can be seen as a finite mixture model in which the prior class membership probabilities are assumed to be random, which makes it very similar to the grade-of-membership (GoM) model. The new model is illustrated with an example from organizational psychology.