2015
An analysis of moderators in the COMBINE study: Identifying subgroups of patients who benefit from acamprosate
Gueorguieva R, Wu R, Tsai WM, O’Connor P, Fucito L, Zhang H, O’Malley S. An analysis of moderators in the COMBINE study: Identifying subgroups of patients who benefit from acamprosate. European Neuropsychopharmacology 2015, 25: 1586-1599. PMID: 26141511, PMCID: PMC4600651, DOI: 10.1016/j.euroneuro.2015.06.006.Peer-Reviewed Original ResearchConceptsAcamprosate effectHeavy drinkingShort abstinenceEnhanced treatment responseMonths of treatmentSubgroup of patientsBody mass indexDrug plasma levelsIdentification of subgroupsBetter prognosisLower BMIMass indexPlasma levelsGlutamatergic hyperactivityTreatment responseAcamprosateCOMBINE StudyPrior treatmentLarger studyConsecutive daysAbstinencePretreatment abstinenceTreatment effectsCognitive inefficiencySubgroups
2010
Decision trees for identifying predictors of treatment effectiveness in clinical trials and its application to ovulation in a study of women with polycystic ovary syndrome
Zhang H, Legro RS, Zhang J, Zhang L, Chen X, Huang H, Casson PR, Schlaff WD, Diamond MP, Krawetz SA, Coutifaris C, Brzyski RG, Christman GM, Santoro N, Eisenberg E. Decision trees for identifying predictors of treatment effectiveness in clinical trials and its application to ovulation in a study of women with polycystic ovary syndrome. Human Reproduction 2010, 25: 2612-2621. PMID: 20716558, PMCID: PMC2939757, DOI: 10.1093/humrep/deq210.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAndrogensAnovulationBody Mass IndexClomipheneDecision TreesDrug Therapy, CombinationFemaleFertility Agents, FemaleHumansInfertility, FemaleMetforminOrgan SizeOvulation InductionPolycystic Ovary SyndromePregnancyProinsulinRandomized Controlled Trials as TopicSex Hormone-Binding GlobulinTreatment OutcomeWaist-Hip RatioConceptsPolycystic ovary syndromeCombination of CCClomiphene citrateClinical trialsOvarian volumeOvary syndromeOvulation inductionHigher sex hormone-binding globulin levelsSex hormone-binding globulin levelsHormone-binding globulin levelsTreatment effectivenessHigher baseline insulinHip circumference ratioIncidence of ovulationBaseline physical characteristicsBaseline laboratoryOvulatory rateClinical parametersHigher waistBaseline variablesPredictive biomarkersGlobulin levelsCircumference ratioOvulatory responseTreatment outcomes
2006
Recursive partitioning–based preoperative risk stratification for atrial fibrillation after coronary artery bypass surgery
Sedrakyan A, Zhang H, Treasure T, Krumholz HM. Recursive partitioning–based preoperative risk stratification for atrial fibrillation after coronary artery bypass surgery. American Heart Journal 2006, 151: 720-724. PMID: 16504639, DOI: 10.1016/j.ahj.2005.05.010.Peer-Reviewed Original ResearchConceptsCoronary artery bypass graft surgeryAtrial fibrillationLow-risk groupAggressive prophylaxisRelative riskPredictors of AFArtery bypass graft surgeryCoronary artery bypass surgeryRisk of AFYale-New Haven HospitalOnly ejection fractionPreoperative atrial fibrillationBypass graft surgeryArtery bypass surgeryCoronary artery diseasePreoperative risk stratificationHigh-risk groupHeart disease severityPossible adverse eventsOlder age subgroupsArrhythmia prophylaxisGraft surgeryProphylactic therapyAdverse eventsBypass surgery
2005
A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking
Ye Y, Zhong X, Zhang H. A genome-wide tree- and forest-based association analysis of comorbidity of alcoholism and smoking. BMC Genomic Data 2005, 6: s135. PMID: 16451594, PMCID: PMC1866801, DOI: 10.1186/1471-2156-6-s1-s135.Peer-Reviewed Original ResearchConceptsAssociation analysisGenetic Analysis Workshop 14Single nucleotide polymorphism dataJoint association analysisNew genesSingle nucleotide polymorphismsGenetic mechanismsPolymorphism dataAssociation studiesDeterministic forestsGenetics of AlcoholismGenesTreesUseful candidateGeneticsForestPolymorphismFuture studiesStudy of alcoholismData mining
Cupples LA, Bailey J, Cartier KC, Falk CT, Liu K, Ye Y, Yu R, Zhang H, Zhao H. Data mining. Genetic Epidemiology 2005, 29: s103-s109. PMID: 16342179, DOI: 10.1002/gepi.20117.Peer-Reviewed Original Research
2003
Cell and tumor classification using gene expression data: Construction of forests
Zhang H, Yu CY, Singer B. Cell and tumor classification using gene expression data: Construction of forests. Proceedings Of The National Academy Of Sciences Of The United States Of America 2003, 100: 4168-4172. PMID: 12642676, PMCID: PMC153066, DOI: 10.1073/pnas.0230559100.Peer-Reviewed Original Research
1996
Tree-based, Two-stage Risk Factor Analysis for Spontaneous Abortion
Zhang H, Bracken M. Tree-based, Two-stage Risk Factor Analysis for Spontaneous Abortion. American Journal Of Epidemiology 1996, 144: 989-996. PMID: 8916510, DOI: 10.1093/oxfordjournals.aje.a008869.Peer-Reviewed Original ResearchConceptsSpontaneous abortionPutative risk factorsRisk factorsPotential confoundersFirst monthYears of smokingNegative pregnancy outcomesNumber of pregnanciesRisk factor analysisNew risk factorsPotential confounding factorsPregnancy outcomesGynecologic problemsMaternal ageMore cupsMother's heightPregnancyConfounding factorsPassive exposureCocaine useLogistic regressionMantel-HaenszelAbortionConfoundersBirth controlA TREE‐BASED METHOD OF ANALYSIS FOR PROSPECTIVE STUDIES
ZHANG H, HOLFORD T, BRACKEN M. A TREE‐BASED METHOD OF ANALYSIS FOR PROSPECTIVE STUDIES. Statistics In Medicine 1996, 15: 37-49. PMID: 8614744, DOI: 10.1002/(sici)1097-0258(19960115)15:1<37::aid-sim144>3.0.co;2-0.Peer-Reviewed Original Research