Covariate selection is an activity routinely performed during pharmacometric analysis. Many are familiar with the stepwise procedures, but perhaps not as many are familiar with some of the issues associated with such methods. Recently, attention has focused on selection procedures that do not suffer from these issues and maintain good predictive properties. In this review, we endeavour to put the main variable selection procedures into a framework that facilitates comparison. We highlight some issues that are unique to pharmacometric analyses and provide some thoughts and strategies for pharmacometricians to consider when planning future analyses.
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http://dx.doi.org/10.1111/bcp.12451 | DOI Listing |
Mol Genet Metab Rep
March 2025
Department of Pediatrics, University of Iowa, Iowa City, IA, USA.
Background: Immediately after birth, adaptation to the extrauterine environment includes an upregulation of fatty acid catabolism. Cystic fibrosis and untreated hypothyroidism exert a life-long impact on fatty acid metabolism, but their influence during this transitional period is unknown. Children and adults with cystic fibrosis exhibit unbalanced fatty acid composition, most prominently a relative deficit of linoleic acid.
View Article and Find Full Text PDFHeliyon
July 2024
Department of Breast Surgery, Institute of Breast Disease, Second Hospital of Dalian Medical University, Zhongshan Road, Dalian, 116023, Liaoning, China.
Identifying driver genes in cancer is a difficult task because of the heterogeneity of cancer as well as the complex interactions among genes. As sequencing data become more readily available, there is a growing need for detecting cancer driver genes based on statistical and mathematical modeling methods. Currently, plenty of driver gene identification algorithms have been published, but they fail to achieve consistent results.
View Article and Find Full Text PDFSci Rep
January 2025
Division of Public Health Science, Department of Health Sciences, Mid Sweden University, Sundsvall, Sweden.
Intimate Partner Violence (IPV) during pregnancy poses a serious threat to maternal health, particularly in low- and lower-middle-income countries (LMICs). Despite these known risks, the role of spousal educational differences in IPV during pregnancy remains poorly understood. This study aimed to examine this influence, analyzing data from multiple countries across five continents.
View Article and Find Full Text PDFBiometrics
January 2025
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1G1, Canada.
Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling these effect differences is important for etiological goals and for purposes of optimizing treatment. Structural nested mean models (SNMMs) are useful causal models for estimating the potentially heterogeneous effect of a time-varying exposure on the mean of an outcome in the presence of time-varying confounding.
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