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Incorporating scientific knowledge into phenotype development: Penalized latent class regression
Author(s) -
Leoutsakos JeannieMarie S.,
BandeenRoche Karen,
GarrettMayer Elizabeth,
Zandi Peter P.
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4137
Subject(s) - covariate , latent class model , computer science , latent variable , biostatistics , econometrics , bayesian probability , regression , feature selection , bayes' theorem , machine learning , statistics , data mining , artificial intelligence , mathematics , medicine , nursing , public health
The field of psychiatric genetics is hampered by the lack of a clear taxonomy for disorders. Building on the work of Houseman and colleagues (Feature‐specific penalized latent class analysis for genomic data. Harvard University Biostatistics Working Paper Series, Working Paper 22 , 2005), we describe a penalized latent class regression aimed at allowing additional scientific information to influence the estimation of the measurement model, while retaining the standard assumption of non‐differential measurement. In simulation studies, ridge and LASSO penalty functions improved the precision of estimates and, in some cases of differential measurement, also reduced bias. Class‐specific penalization enhanced separation of latent classes with respect to covariates, but only in scenarios where there was a true separation. Penalization proved to be less computationally intensive than an analogous Bayesian analysis by a factor of 37. This methodology was then applied to data from normal elderly subjects from the Cache County Study on Memory and Aging. Addition of APO‐E genotype and a number of baseline clinical covariates improved the dementia prediction utility of the latent classes; application of class‐specific penalization improved precision while retaining that prediction utility. This methodology may be useful in scenarios with large numbers of collinear covariates or in certain cases where latent class model assumptions are violated. Investigation of novel penalty functions may prove fruitful in further refining psychiatric phenotypes. Copyright © 2010 John Wiley & Sons, Ltd.