RETRACTED: Part II: A step-by-step guide to latent class analysis (vol 148, pg 170, 2022)

被引：16

作者：

Aflaki, Kayvan ^{[1
]}

Vigod, Simone ^{[2
]}

Ray, Joel G. ^{[3
,4
,5
]}

机构：

[1] Univ Toronto, Inst Med Sci, Toronto, ON, Canada

[2] Womens Coll Hosp, Dept Psychiat, Toronto, ON, Canada

[3] St Michaels Hosp, Dept Med, Toronto, ON, Canada

[4] St Michaels Hosp, Dept Obstet & Gynaecol, Toronto, ON, Canada

[5] St Michaels Hosp, 30 Bond St, Toronto, ON M5B 18W, Canada

来源：

JOURNAL OF CLINICAL EPIDEMIOLOGY | 2023年 / 159卷

关键词：

Latent class analysis; Model-based clustering; Subgroup analysis; Unsupervised methods; Mixture modeling; Bayesian information criterion; Aikake information criterion; SAMPLE-SIZE;

D O I：

10.1016/j.jclinepi.2023.05.025

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Latent class analysis (LCA) is an analytical approach for the identification of more homogeneous subgroups within an otherwise dissimilar patient population. In the current paper, Part II, we present a practical step-by-step guide for LCA of clinical data, including when LCA might be applied, selecting indicator variables, and choosing a final class solution. We also identify common pitfalls of LCA, and related solutions. (C) 2023 Published by Elsevier Inc.

引用

页码：348 / 351

页数：4

共 22 条

[1] Part I: A friendly introduction to latent class analysis [J].