Comparison of covariate selection methods with correlated covariates: prior information versus data information, or a mixture of both?

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MARC

Chasseloup, EstelleUppsala universitet,Institutionen för farmaceutisk biovetenskap,Uppsala Univ, Fac Pharm, Dept Pharmaceut Biosci, Room BMC B3 4 Biomed Ctr BMC,Husargatan 3,Box 591, S-75124 Uppsala, Sweden. (author)

Comparison of covariate selection methods with correlated covariates : prior information versus data information, or a mixture of both?

Article/chapterEnglish2020

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2020-07-13
SPRINGER/PLENUM PUBLISHERS,2020
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LIBRIS-ID:oai:DiVA.org:uu-439275
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-439275URI
https://doi.org/10.1007/s10928-020-09700-5DOI

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Language:English
Summary in:English

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

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The inclusion of covariates in population models during drug development is a key step to understanding drug variability and support dosage regimen proposal, but high correlation among covariates often complicates the identification of the true covariate. We compared three covariate selection methods balancing data information and prior knowledge: (1) full fixed effect modelling (FFEM), with covariate selection prior to data analysis, (2) simplified stepwise covariate modelling (sSCM), data driven selection only, and (3) Prior-Adjusted Covariate Selection (PACS) mixing both. PACS penalizes the a priori less likely covariate model by adding to its objective function value (OFV) a prior probability-derived constant: 2(*) ln(Pr(X)/(1 - Pr(X))), Pr(X) being the probability of the more likely covariate. Simulations were performed to compare their external performance (average OFV in a validation dataset of 10,000 subjects) in selecting the true covariate between two highly correlated covariates: 0.5, 0.7, or 0.9, after a training step on datasets of 12, 25 or 100 subjects (increasing power). With low power data no method was superior, except FFEM when associated with highly correlated covariates (r = 0.9), sSCM and PACS suffering both from selection bias. For high power data, PACS and sSCM performed similarly, both superior to FFEM. PACS is an alternative for covariate selection considering both the expected power to identify an anticipated covariate relation and the probability of prior information being correct. A proposed strategy is to use FFEM whenever the expected power to distinguish between contending models is < 80%, PACS when > 80% but < 100%, and SCM when the expected power is 100%.

Subject headings and genre

MEDICIN OCH HÄLSOVETENSKAP Medicinska och farmaceutiska grundvetenskaper Farmakologi och toxikologi hsv//swe
MEDICAL AND HEALTH SCIENCES Basic Medicine Pharmacology and Toxicology hsv//eng
Covariates
Prior
Correlation
Stepwise covariate modelling
Full fixed effects modelling
Prior-adjusted covariate selection

Added entries (persons, corporate bodies, meetings, titles ...)

Yngman, GunnarUppsala universitet,Institutionen för farmaceutisk biovetenskap,Uppsala Univ, Fac Pharm, Dept Pharmaceut Biosci, Room BMC B3 4 Biomed Ctr BMC,Husargatan 3,Box 591, S-75124 Uppsala, Sweden.(Swepub:uu)gunyn322 (author)
Karlsson, MatsUppsala universitet,Institutionen för farmaceutisk biovetenskap,Institutionen för farmaci(Swepub:uu)matskarl (author)
Uppsala universitetInstitutionen för farmaceutisk biovetenskap (creator_code:org_t)

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In:Journal of Pharmacokinetics and Pharmacodynamics: SPRINGER/PLENUM PUBLISHERS47:5, s. 485-4921567-567X1573-8744

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