A simulation study comparing different statistical approaches for the identification of predictive biomarkers
type: Item
instance of: scholarly article
title: A simulation study comparing different statistical approaches for the identification of predictive biomarkers
zbMATH Open document ID: 1423.92086
DOI: 10.1155/2019/7037230
author: Bernhard Haller
author: K. Ulm
author: A. Hapfelmeier
published in: Computational \& Mathematical Methods in Medicine
publication date: 2019-07-09T00:00:00Z
review text: Summary: Identification of relevant biomarkers that are associated with a treatment effect is one requirement for adequate treatment stratification and consequently to improve health care by administering the best available treatment to an individual patient. Various statistical approaches were proposed that allow assessing the interaction between a continuous covariate and treatment. Nevertheless, categorization of a continuous covariate, e.g., by splitting the data at the observed median value, appears to be very prevalent in practice. In this article, we present a simulation study considering data as observed in a randomized clinical trial with a time-to-event outcome performed to compare properties of such approaches, namely, Cox regression with linear interaction, multivariable fractional polynomials for interaction (MFPI), local partial-likelihood bootstrap (LPLB), and the subpopulation treatment effect pattern plot (STEPP) method, and of strategies based on categorization of continuous covariates (splitting the covariate at the median, splitting at quartiles, and using an ``optimal'' split by maximizing a corresponding test statistic). In different scenarios with no interactions, linear interactions or nonlinear interactions, type I error probability and the power for detection of a true covariate-treatment interaction were estimated. The Cox regression approach was more efficient than the other methods for scenarios with monotonous interactions, especially when the number of observed events was small to moderate. When patterns of the biomarker-treatment interaction effect were more complex, MFPI and LPLB performed well compared to the other approaches. Categorization of data generally led to a loss of power, but for very complex patterns, splitting the data into multiple categories might help to explore the nature of the interaction effect. Consequently, we recommend application of statistical methods developed for assessment of interactions between continuo…
Mathematics Subject Classification ID: 92C50
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62H05
Mathematics Subject Classification ID: 62F40
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 7077768
zbMATH Keywords: biomarkers identification
zbMATH Keywords: randomized clinical trial
zbMATH Keywords: comparison of statistical approaches
describes a project that uses: car
describes a project that uses: Stata
describes a project that uses: carData
describes a project that uses: R
describes a project that uses: survival
describes a project that uses: stepp
describes a project that uses: lplb
describes a project that uses: GitHub
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2019/7037230
OpenAlex ID: W2949292400
Wikidata QID: Q91939435
cites work: Multivariable Model‐Building
cites work: A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
cites work: Local partial-likelihood estimation for lifetime data
cites work: Modeling survival data: extending the Cox model
cites work: Investigation of age–treatment interaction in the SPACE trial using different statistical approaches
description: scientific article; zbMATH DE number 7077768