Practitioner's Guide to Latent Class Analysis: Methodological Considerations and Common Pitfalls

被引：535

作者：

Sinha, Pratik ^{[1
,2
]}

Calfee, Carolyn S. ^{[1
,2
]}

Delucchi, Kevin L. ^{[3
]}

机构：

[1] Univ Calif San Francisco, Dept Med, Div Pulm Crit Care Allergy & Sleep Med, San Francisco, CA 94143 USA

[2] Univ Calif San Francisco, San Francisco, CA 94143 USA

[3] Univ Calif San Francisco, Dept Psychiat, San Francisco, CA USA

来源：

CRITICAL CARE MEDICINE | 2021年 / 49卷 / 01期

关键词：

clustering algorithms; data science; heterogeneity; latent class analysis; phenotypes; MODEL SELECTION; LUNG-FUNCTION; MONTE-CARLO; SUBPHENOTYPES; NUMBER; PERFORMANCE; VALIDATION; PHENOTYPES; COMPONENTS; ARDS;

D O I：

10.1097/CCM.0000000000004710

中图分类号：

R4 [临床医学];

学科分类号：

1002 ; 100602 ;

摘要：

Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and statistical inference. There has been a recent upsurge in the application of latent class analysis in the fields of critical care, respiratory medicine, and beyond. In this review, we present a brief overview of the principles behind latent class analysis. Furthermore, in a stepwise manner, we outline the key processes necessary to perform latent class analysis including some of the challenges and pitfalls faced at each of these steps. The review provides a one-stop shop for investigators seeking to apply latent class analysis to their data.

引用

页码：E63 / E79

页数：17

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