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Sökning: L773:1097 0258 OR L773:0277 6715 > (2020-2024) > Properties of the f...

Properties of the full random-effect modeling approach with missing covariate data

Nyberg, Joakim (författare)
Pharmetheus AB, Uppsala, Sweden.
Jonsson, E. Niclas (författare)
Pharmetheus AB, Uppsala, Sweden.
Karlsson, Mats O. (författare)
Uppsala universitet,Institutionen för farmaci,Pharmetheus AB, Uppsala, Sweden.
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Häggström, Jonas (författare)
Cytel Inc, Wilmington, DE USA.
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Pharmetheus AB, Uppsala, Sweden Institutionen för farmaci (creator_code:org_t)
John Wiley & Sons, 2024
2024
Engelska.
Ingår i: Statistics in Medicine. - : John Wiley & Sons. - 0277-6715 .- 1097-0258. ; 43:5, s. 935-952
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • During drug development, a key step is the identification of relevant covariates predicting between-subject variations in drug response. The full random effects model (FREM) is one of the full-covariate approaches used to identify relevant covariates in nonlinear mixed effects models. Here we explore the ability of FREM to handle missing (both missing completely at random (MCAR) and missing at random (MAR)) covariate data and compare it to the full fixed-effects model (FFEM) approach, applied either with complete case analysis or mean imputation. A global health dataset (20 421 children) was used to develop a FREM describing the changes of height for age Z-score (HAZ) over time. Simulated datasets (n = 1000) were generated with variable rates of missing (MCAR) covariate data (0%-90%) and different proportions of missing (MAR) data condition on either observed covariates or predicted HAZ. The three methods were used to re-estimate model and compared in terms of bias and precision which showed that FREM had only minor increases in bias and minor loss of precision at increasing percentages of missing (MCAR) covariate data and performed similarly in the MAR scenarios. Conversely, the FFEM approaches either collapsed at ≥70% of missing (MCAR) covariate data (FFEM complete case analysis) or had large bias increases and loss of precision (FFEM with mean imputation). Our results suggest that FREM is an appropriate approach to covariate modeling for datasets with missing (MCAR and MAR) covariate data, such as in global health studies.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

covariate model
FREM
full modeling approach
global health
height-for-age Z-score

Publikations- och innehållstyp

ref (ämneskategori)
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