In genetic studies, the transmission/disequilibrium test (TDT) using case-parent triads has gained popularity attributable to its robustness to population admixture. Several extensions have been proposed to accommodate incomplete triads. Some strategies assume that parental genotypes are missing completely at random (MCAR) to insure an unbiased conclusion and some methods allow parental genotypes to be missing informatively, resulting in reduced power when the missing data pattern is indeed MCAR. However, these tests assumed that offspring genotypes were MCAR. Recently, Guo indicated that when offspring genotypes were missing informatively, an occurrence that can be considered as ascertainment bias, inflated type-I error and/or reduced power may occur using the TDT when incomplete triads are excluded. In an effort to avoid an erroneous conclusion, we propose a strategy called testing informative missingness (TIM) that compares conditional distributions of parental genotypes among complete triads and incomplete data with only one parent to examine the missing data pattern. Through computer simulations, TIM has decent power to detect informative missingness and is robust to population admixture. In addition, we illustrate TIM with an application to the Framingham Heart Study.
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http://dx.doi.org/10.1038/ejhg.2008.38 | DOI Listing |
PLoS One
January 2025
Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada.
There is a growing need to document sociodemographic factors in electronic medical records to produce representative cohorts for medical research and to perform focused research for potentially vulnerable populations. The objective of this work was to assess the content of family physicians' electronic medical records and characterize the quality of the documentation of sociodemographic characteristics. Descriptive statistics were reported for each sociodemographic characteristic.
View Article and Find Full Text PDFBiometrics
January 2025
MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, United Kingdom.
Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice, estimating an optimal DTR using observational data from electronic medical record (EMR) databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates.
View Article and Find Full Text PDFHealthc Technol Lett
December 2024
Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems Ulster University, Magee campus Derry∼Londonderry Northern Ireland UK.
Missing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder-based imputation of missing key features of heterogeneous data that comprised tau-PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history.
View Article and Find Full Text PDFOpen Heart
December 2024
Cedars-Sinai Medical Center, Los Angeles, California, USA
Background: Cardiac amyloidosis (CA) is an underdiagnosed, progressive and lethal disease. Machine learning applied to common measurements derived from routine echocardiogram studies can inform suspicion of CA.
Objectives: Our objectives were to test a random forest (RF) model in detecting CA.
Biometrics
October 2024
Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, United States.
Analyses of cluster randomized trials (CRTs) can be complicated by informative missing outcome data. Methods such as inverse probability weighted generalized estimating equations have been proposed to account for informative missingness by weighing the observed individual outcome data in each cluster. These existing methods have focused on settings where missingness occurs at the individual level and each cluster has partially or fully observed individual outcomes.
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