High-throughput evaluation of T7 promoter variants using biased randomization and DNA barcoding. [PDF]
PLoS ONE, 2018 Cis-regulatory elements (CREs) are one of the important factors in controlling gene expression and elucidation of their roles has been attracting great interest.
Ryo Komura +4 more
doaj +3 more sources
Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies. [PDF]
J Comput Graph Stat, 2023 One central goal of design of observational studies is to embed nonexperimental data into an approximate randomized controlled trial using statistical matching.
Chen K, Heng S, Long Q, Zhang B.
europepmc +3 more sources
Applied Sciences, 2021 In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their ...
Javier Panadero +5 more
doaj +2 more sources
Cluster minimal sufficient balance (CMSB): an efficient covariate balancing randomization method for cluster randomized trials [PDF]
BMC Medical Research MethodologyBackground Cluster randomized trials (CRTs) require balanced baseline covariates to yield unbiased estimates of treatment effects. Existing approaches such as constrained randomization can improve balance but may compromise allocation randomness.
Jiaxin Cai +9 more
doaj +2 more sources
Exact properties of Efron's biased coin randomization procedure [PDF]
, 2010 Efron [Biometrika 58 (1971) 403--417] developed a restricted randomization procedure to promote balance between two treatment groups in a sequential clinical trial. He called this the biased coin design. He also introduced the concept of accidental bias,
Markaryan, Tigran +1 more
core +3 more sources
How to manage missing covariates in randomized controlled trials: a comparison of strategies [PDF]
BMC Medical Research MethodologyBackground When analyzing randomized controlled trials (RCTs) data, covariate adjustment is often employed to increase the precision of estimated treatment effects. Missing data in covariates, if not handled properly, can result in biased and inefficient
Shiyu Zhang, Yajuan Si, John J. Dziak
doaj +2 more sources
On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems [PDF]
Algorithms, 2019 Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory ...
Angel Alejandro Juan +4 more
doaj +5 more sources
Assessing allocation bias in stratified clinical trials with multi-component endpoints evaluated using the stratified Wei-Lachin test. [PDF]
PLoS ONEBackgroundA common issue in rare disease stratified clinical trials with multi-component endpoints is allocation bias, as they frequently lack blinding.
Stefanie Schoenen +2 more
doaj +2 more sources
TAPB: an interventional debiasing framework for alleviating target prior bias in drug-target interaction prediction [PDF]
Nature CommunicationsDrug Target Interaction (DTI) prediction is vital for drug repurposing. Previous DTI studies on BioSNAP and BindingDB datasets often attribute biased predictions to “drug bias,” while our work reveals “target prior bias” as the predominant issue.
Gaoming Lin +6 more
doaj +2 more sources
Common Methods for Performing Mendelian Randomization
Frontiers in Cardiovascular Medicine, 2018 Mendelian randomization (MR) is a framework for assessing causal inference using cross-sectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses
Alexander Teumer, Alexander Teumer
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