Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.
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The rise of multiple imputation: a review of the reporting and implementation of the method in medical researchIs Anti-Müllerian Hormone Associated With Fecundability? Findings From the EAGeR TrialUrinary paracetamol and time-to-pregnancyPreterm, low-birth-weight deliveries, and farmwork among Latinas in CaliforniaImmune activation, CD4+ T cell counts, and viremia exhibit oscillatory patterns over time in patients with highly resistant HIV infectionPrevalence and Risk Factors of Hookworm-Related Cutaneous Larva Migrans (HrCLM) in a Resource-Poor Community in Manaus, BrazilAdherence to guidelines for creatinine and potassium monitoring and discontinuation following renin-angiotensin system blockade: a UK general practice-based cohort studyDeterminants of aortic stiffness: 16-year follow-up of the Whitehall II studyEarly ART Results in Greater Immune Reconstitution Benefits in HIV-Infected Infants: Working with Data Missingness in a Longitudinal DatasetTackling missing radiographic progression data: multiple imputation technique compared with inverse probability weights and complete case analysis.Doubly robust estimators of causal exposure effects with missing data in the outcome, exposure or a confounder.Using full-cohort data in nested case-control and case-cohort studies by multiple imputation.Combining multiple imputation and meta-analysis with individual participant data.Nonlinear multiple imputation for continuous covariate within semiparametric Cox model: application to HIV data in Senegal.The impact of missing data on analyses of a time-dependent exposure in a longitudinal cohort: a simulation study.Introduction to multiple imputation for dealing with missing data.Commentary: Berkson's fallacy and missing data.Transparent reporting of missing outcome data in clinical trials: applying the general principles of CONSORT 2010.Are missing data adequately handled in cluster randomised trials? A systematic review and guidelines.Handling incomplete smoking history data in survival analysis.Effectiveness of biologic DMARDs in monotherapy versus in combination with synthetic DMARDs in rheumatoid arthritis: data from the Swiss Clinical Quality Management Registry.Statistical analysis and handling of missing data in cluster randomised trials: protocol for a systematic review.Using decision trees to understand structure in missing data.What are the appropriate methods for analyzing patient-reported outcomes in randomized trials when data are missing?Quantification of the smoking-associated cancer risk with rate advancement periods: meta-analysis of individual participant data from cohorts of the CHANCES consortiumBayesian analysis of censored response data in family-based genetic association studies.Statin use and the risk of herpes zoster: a nested case-control study using primary care data from the U.K. Clinical Research Practice DatalinkMultiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study.Breast Cancer and Modifiable Lifestyle Factors in Argentinean Women: Addressing Missing Data in a Case-Control StudyExploratory Factor Analysis With Small Samples and Missing Data.What impact do assumptions about missing data have on conclusions? A practical sensitivity analysis for a cancer survival registry.Herpes zoster risk after 21 specific cancers: population-based case-control study.Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5·24 million UK adultsAdverse outcome analyses of observational data: assessing cardiovascular risk in HIV diseaseMissing covariate data in clinical research: when and when not to use the missing-indicator method for analysisOn shrinkage and model extrapolation in the evaluation of clinical center performanceImproving upon the efficiency of complete case analysis when covariates are MNAR.Practical considerations for sensitivity analysis after multiple imputation applied to epidemiological studies with incomplete data.A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures.Iron status predicts malaria risk in Malawian preschool children
P2860
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P2860
Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年学术文章
@wuu
2010年学术文章
@zh
2010年学术文章
@zh-cn
2010年学术文章
@zh-hans
2010年学术文章
@zh-my
2010年学术文章
@zh-sg
2010年學術文章
@yue
2010年學術文章
@zh-hant
name
Bias and efficiency of multipl ...... for missing covariate values.
@en
Bias and efficiency of multipl ...... for missing covariate values.
@nl
type
label
Bias and efficiency of multipl ...... for missing covariate values.
@en
Bias and efficiency of multipl ...... for missing covariate values.
@nl
prefLabel
Bias and efficiency of multipl ...... for missing covariate values.
@en
Bias and efficiency of multipl ...... for missing covariate values.
@nl
P356
P1476
Bias and efficiency of multipl ...... for missing covariate values.
@en
P2093
Ian R White
John B Carlin
P304
P356
10.1002/SIM.3944
P407
P577
2010-12-01T00:00:00Z