2022
Missing Data
Tong G, Li F, Allen A. Missing Data. 2022, 1681-1701. DOI: 10.1007/978-3-319-52636-2_117.Peer-Reviewed Original ResearchLikelihood-based analysisMissingness modelMissingness processData mechanismAverage treatment effectStatistical methodsComplete case analysisConsistent estimatesRobust approachInverse probability weightingBiased estimatesMissingnessOutcome distributionModeling approachProbability weightingData processSensitivity analysisOutcome modelModelEstimatesBrief discussionPractical considerationsInferenceApproachImputation
2019
Missing Data
Tong G, Li F, Allen A. Missing Data. 2019, 1-21. DOI: 10.1007/978-3-319-52677-5_117-1.Peer-Reviewed Original ResearchLikelihood-based analysisMissingness modelMissingness processData mechanismAverage treatment effectStatistical methodsComplete case analysisConsistent estimatesRobust approachInverse probability weightingBiased estimatesMissingnessOutcome distributionModeling approachProbability weightingData processSensitivity analysisOutcome modelModelEstimatesBrief discussionPractical considerationsInferenceApproachImputation
2018
Missing data treatments matter: an analysis of multiple imputation for anterior cervical discectomy and fusion procedures
Ondeck NT, Fu MC, Skrip LA, McLynn RP, Cui JJ, Basques BA, Albert TJ, Grauer JN. Missing data treatments matter: an analysis of multiple imputation for anterior cervical discectomy and fusion procedures. The Spine Journal 2018, 18: 2009-2017. PMID: 29649614, DOI: 10.1016/j.spinee.2018.04.001.Peer-Reviewed Original ResearchConceptsSevere adverse eventsNational Surgical Quality Improvement ProgramAnterior cervical discectomyAdverse eventsPreoperative anemiaPreoperative hypoalbuminemiaPreoperative albuminCervical discectomyHospital readmissionAdverse outcomesComplete case analysisSurgical Quality Improvement ProgramAdverse outcome variablesOne-level ACDFPreoperative laboratory valuesBody mass indexFusion proceduresMultiple imputationLogistic regression analysisQuality Improvement ProgramPreoperative hematocritPostoperative outcomesRetrospective reviewMass indexLaboratory values
2017
Treatments of Missing Values in Large National Data Affect Conclusions: The Impact of Multiple Imputation on Arthroplasty Research
Ondeck NT, Fu MC, Skrip LA, McLynn RP, Su EP, Grauer JN. Treatments of Missing Values in Large National Data Affect Conclusions: The Impact of Multiple Imputation on Arthroplasty Research. The Journal Of Arthroplasty 2017, 33: 661-667. PMID: 29153865, DOI: 10.1016/j.arth.2017.10.034.Peer-Reviewed Original ResearchConceptsUnicompartmental knee arthroplastyPreoperative albuminAdverse outcomesKnee arthroplastyNational Surgical Quality Improvement ProgramComplete case analysisHematocrit valuesSurgical Quality Improvement ProgramDemographics of patientsPreoperative laboratory valuesMultiple imputationQuality Improvement ProgramArthroplasty researchSelection biasHealthy patientsLaboratory valuesPotential selection biasPatientsJoint surgeonsComplete dataArthroplastyOnly caseNational datasetOutcomesConclusion
2011
The use of complete-case and multiple imputation-based analyses in molecular epidemiology studies that assess interaction effects
Desai M, Esserman DA, Gammon MD, Terry MB. The use of complete-case and multiple imputation-based analyses in molecular epidemiology studies that assess interaction effects. Epidemiologic Perspectives & Innovations 2011, 8: 1-17. PMID: 21978450, PMCID: PMC3217865, DOI: 10.1186/1742-5573-8-5.Peer-Reviewed Original Research
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