Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis

An application of Measurement error evaluation using latent class analysis

‎Latent class analysis (LCA) is a method of evaluating non sampling errors‎, ‎especially measurement error in categorical data‎. ‎Biemer (2011) introduced four latent class modeling approaches‎: ‎probability model parameterization‎, ‎log linear model‎, ‎modified path model‎, ‎and graphical model using path diagrams‎. ‎These models are interchangeable‎. ‎Latent class probability models express l...

Eliminating bias/living with bias?

The scientific method and elements of bias are fundamentally at odds with one another. While the prior seeks to identify truth through objective analysis, the latter wishes to prove one's preconceptions through artificially created or filtered observations. Both the scientific method and bias are purpose-driven notions, both serve an intrinsic value. Arguably the more general and truly-lasting ...

Latent Class Analysis to Classify Patients with Transthyretin Amyloidosis by Signs and Symptoms

INTRODUCTION The aim of this study was to develop an empirical approach to classifying patients with transthyretin amyloidosis (ATTR) based on clinical signs and symptoms. METHODS Data from 971 symptomatic subjects enrolled in the Transthyretin Amyloidosis Outcomes Survey were analyzed using a latent class analysis approach. Differences in health status measures for the latent classes were ex...

Optimization in latent class analysis

In latent class analysis (LCA) one seeks a clustering of categorical data, such as patterns of symptoms of a patient, in terms of locally independent stochastic models. This leads to practical definitions of criteria, e.g., whether to include patients in further diagnostic examinations. The clustering is often determined by parameters that are estimated by the maximum likelihood method. The lik...

Latent Class Cluster Analysis