Incorporating covariate information in the covariance structure of misaligned spatial data
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DOI: 10.1002/env.2623
Abstract: Incorporating covariates in the second‐ structure of spatial processes is an effective way of building flexible nonstationary covariance models. Fitting these covariances requires covariates to already exist at locations where there is response data. However,… read more here.
Keywords: covariance structure; response data; structure; covariate ... See more keywords
Understanding the effects of conditional dependence in research studies involving imperfect diagnostic tests.
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DOI: 10.1002/sim.7148
Abstract: When two imperfect diagnostic tests are carried out on the same subject, their results may be correlated even after conditioning on the true disease status. While past work has focused on the consequences of ignoring… read more here.
Keywords: dependence; diagnostic tests; imperfect diagnostic; conditional dependence ... See more keywords
A covariate-specific time-dependent receiver operating characteristic curve for correlated survival data.
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DOI: 10.1002/sim.8550
Abstract: Several studies for the clinical validity of circulating tumor cells (CTCs) in metastatic breast cancer were conducted showing that it is a prognostic biomarker of overall survival. In this work, we consider an individual patient… read more here.
Keywords: time dependent; specific time; curve; covariate ... See more keywords
Nonparametric regression with right‐censored covariate via conditional density function
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DOI: 10.1002/sim.9343
Abstract: Censoring often occurs in data collection. This article, considers nonparametric regression when the covariate is censored under general settings. In contrast to censoring in the response variable in survival analysis, regression with censored covariates is… read more here.
Keywords: right censored; function; regression; nonparametric regression ... See more keywords
Bayesian analysis for partly linear Cox model with measurement error and time‐varying covariate effect
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DOI: 10.1002/sim.9531
Abstract: The Cox proportional hazards model is commonly used to estimate the association between time‐to‐event and covariates. Under the proportional hazards assumption, covariate effects are assumed to be constant in the follow‐up period of study. When… read more here.
Keywords: time; effect; measurement error; error ... See more keywords
Calculating the power to examine treatment‐covariate interactions when planning an individual participant data meta‐analysis of randomized trials with a binary outcome
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DOI: 10.1002/sim.9538
Abstract: Before embarking on an individual participant data meta‐analysis (IPDMA) project, researchers and funders need assurance it is worth their time and cost. This should include consideration of how many studies are promising their IPD and,… read more here.
Keywords: participant data; individual participant; power; data meta ... See more keywords
A multiple imputation‐based sensitivity analysis approach for regression analysis with a missing not at random covariate
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DOI: 10.1002/sim.9723
Abstract: Missing covariate problems are common in biomedical and electrical medical record data studies while evaluating the relationship between a biomarker and certain clinical outcome, when biomarker data are not collected for all subjects. However, missingness… read more here.
Keywords: sensitivity; sensitivity analysis; analysis; analysis approach ... See more keywords
The mean residual life model for the right-censored data in the presence of covariate measurement errors.
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DOI: 10.1002/sim.9736
Abstract: In this article, we consider the mean residual life regression model in the presence of covariate measurement errors. In the whole cohort, the surrogate variable of the error-prone covariate is available for each subject, while… read more here.
Keywords: covariate; mean residual; measurement errors; cohort ... See more keywords
The quandary of covarying: A brief review and empirical examination of covariate use in structural neuroimaging studies on psychological variables
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DOI: 10.1016/j.neuroimage.2019.116225
Abstract: Although covarying for potential confounds or nuisance variables is common in psychological research, relatively little is known about how the inclusion of covariates may influence the relations between psychological variables and indices of brain structure.… read more here.
Keywords: quandary covarying; structural neuroimaging; covariate sets; covarying brief ... See more keywords
DeepNull models non-linear covariate effects to improve phenotypic prediction and association power
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DOI: 10.1038/s41467-021-27930-0
Abstract: Genome-wide association studies (GWASs) examine the association between genotype and phenotype while adjusting for a set of covariates. Although the covariates may have non-linear or interactive effects, due to the challenge of specifying the model,… read more here.
Keywords: deepnull; non linear; covariate; power ... See more keywords
Controlling Measurement Heterogeneity in Longitudinal Data Using Covariate Residualized Indicators
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DOI: 10.1080/00273171.2017.1404895
Abstract: Implicit in the modeling of a construct’s latent trajectory is the assumption that strong factorial invariance (i.e., invariant item intercepts and factor loadings) holds across repeated measurements. Yet, many researchers who examine latent means do… read more here.
Keywords: longitudinal data; measurement heterogeneity; invariance; covariate residualized ... See more keywords