Deesha Patel, Linda Beer, Xin Yuan, Yunfeng Tie, Amy R Baugher, William L Jeffries, Andre Dailey, Kirk D Henny
Explaining racial and ethnic disparities in antiretroviral therapy adherence and viral suppression among U.S. men who have sex with men
AIDS (London, England)
bicycle safety emerged pressing concern within vulnerable transportation community numerous studies conducted identify significant factors contribute severity cyclist injuries yet findings subject uncertainty due unobserved heterogeneity class imbalance research aims address issues developing model examine impact key factors cyclist injury severity accounting data heterogeneity imbalance incorporate unobserved heterogeneity total 3895 bicycle accidents categorized three homogeneous subaccident clusters using latent class cluster analysis lca additionally five oversampling techniques employed mitigate effects data imbalance accident cluster category subsequently bayesian network bn structure learning algorithms utilized construct 32 bn models pairing accident data four accident cluster types sampling optimal bn models accident cluster type provided insights key factors associated cyclist injury severity results indicate key factors influencing serious cyclist injuries vary heterogeneously across different accident clusters female cyclists adverse weather conditions rain snow offpeak periods identified key factors several subclasses accident clusters conversely factors week accident characteristics trafficway season drivers failing yield rightofway distracted cyclists years driving experience found key factors one subcluster accident clusters additionally factors time crash gender cyclist weather conditions exhibit varying levels heterogeneity across different accident clusters cases exhibit opposing effects