08.Latent variable modeling05.Latent class modeling - sporedata/researchdesigneR GitHub Wiki

1. Use cases: in which situations should I use this method?

  1. When renaming or recategorizing a disease [1].

2. Input: what kind of data does the method require?

  1. A dataset where one believes that there is an underlying clustering (latent class).
  2. A latent variable categorical.

3. Algorithm: how does the method work?

Model mechanics

Reporting guidelines

Latent class modeling is similar to unsupervised learning but where the probability distribution is provided.

Data science packages

  • BayesLCA: Bayesian Latent Class Analysis [2].
  • hdpGLM - Hierarchical Dirichlet Process Generalized Linear Models, performs a clusterization (latent class) with regression.

Suggested companion methods

Learning materials

  1. Books

    • Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences [3].

  2. Articles

4. Output: how do I interpret this method's results?

Mock conclusions or most frequent format for conclusions reached at the end of a typical analysis.

Tables, plots, and their interpretation

5. SporeData-specific

Templates

Data science functions

References

[1] Doust J, Vandvik PO, Qaseem A, Mustafa RA, Horvath AR, Frances A, Al-Ansary L, Bossuyt P, Ward RL, Kopp I, Gollogly L. Guidance for modifying the definition of diseases: A checklist. JAMA Internal Medicine. 2017 Jul 1;177(7):1020-5.

[2] White A, Murphy TB. BayesLCA: An R Package for Bayesian Latent Class Analysis. Journal of Statistical Software. 2014. 61(13), 1–28.

[3] Collins LM, Lanza ST. Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences. John Wiley & Sons; 2009 Dec 14.

[4] Tegegne TK, Islam SMS, Maddison R. Efects of lifestyle risk behaviour clustering on cardiovascular disease among UK adults: latent class analysis with distal outcomes . Scientific reports, 12(1), 1-8.

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