Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models.
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Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola PatientsLongitudinal associations of sleep curtailment with metabolic risk in mid-childhood.Maternal lipid change in relation to length of gestation: a prospective cohort study with preconception enrollment of womenParental urinary biomarkers of preconception exposure to bisphenol A and phthalates in relation to birth outcomesBirth outcomes and background exposures to select elements, the Longitudinal Investigation of Fertility and the Environment (LIFE)Perfluoroalkyl Substances, Sex Hormones, and Insulin-like Growth Factor-1 at 6-9 Years of Age: A Cross-Sectional Analysis within the C8 Health ProjectImproving survey methods in sero-epidemiological studies of injecting drug users: a case example of two cross sectional surveys in Serbia and Montenegro.Missing data analysis using multiple imputation: getting to the heart of the matter.Multiple Imputation to Deal with Missing Clinical Data in Rheumatologic Surveys: an Application in the WHO-ILAR COPCORD Study in Iran.Principled missing data methods for researchers.The impact of missing data on clinical trials: a re-analysis of a placebo controlled trial of Hypericum perforatum (St Johns wort) and sertraline in major depressive disorder.Integration of high-volume molecular and imaging data for composite biomarker discovery in the study of melanoma.Genetic diversity analysis of highly incomplete SNP genotype data with imputations: an empirical assessmentML versus MI for Missing Data with Violation of Distribution ConditionsFrailty index of deficit accumulation and falls: data from the Global Longitudinal Study of Osteoporosis in Women (GLOW) Hamilton cohortIdentifying reprioritization response shift in a stroke caregiver population: a comparison of missing data methods.Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index?Comparison between frailty index of deficit accumulation and phenotypic model to predict risk of falls: data from the global longitudinal study of osteoporosis in women (GLOW) Hamilton cohort.Simultaneous Treatment of Missing Data and Measurement Error in HIV Research Using Multiple Overimputation.Censored Data Analysis Reveals Effects of Age and Hepatitis C Infection on C-Reactive Protein Levels in Healthy Adult Chimpanzees (Pan troglodytes).Frailty Change and Major Osteoporotic Fracture in the Elderly: Data from the Global Longitudinal Study of Osteoporosis in Women 3-Year Hamilton Cohort.Multiple imputation and analysis for high-dimensional incomplete proteomics dataDoes Age Matter? Association Between Usual Source of Care and Hypertension Control in the US Population: Data From NHANES 2007-2012Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study.Filling gaps in notification data: a model-based approach applied to travel related campylobacteriosis cases in New Zealand.Information for decision making from imperfect national data: tracking major changes in health care use in Kenya using geostatisticsWhat impact do assumptions about missing data have on conclusions? A practical sensitivity analysis for a cancer survival registry.[Multiple imputations for missing data: a simulation with epidemiological data].Multiple imputation with large data sets: a case study of the Children's Mental Health Initiative.Multiple imputation for missing data in epidemiological and clinical research: potential and pitfallsMissing Data in Longitudinal Trials - Part B, Analytic Issues.Techniques for handling missing data in secondary analyses of large surveys.Milk, Fruit and Vegetable, and Total Antioxidant Intakes in Relation to Mortality Rates: Cohort Studies in Women and MenPerioperative adjuvant corticosteroids for post-operative analgesia in elective knee surgery - A systematic review.Myocardial infarction and stroke associated with diuretic based two drug antihypertensive regimens: population based case-control study.Child abuse as a predictor of gendered sexual orientation disparities in body mass index trajectories among U.S. youth from the Growing Up Today Study.Chronic sleep curtailment and adiposity.The advantage of imputation of missing income data to evaluate the association between income and self-reported health status (SRH) in a Mexican American cohort studyUndiagnosed diabetes in kidney transplant candidates: a case-finding strategyReview of inverse probability weighting for dealing with missing data.
P2860
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P2860
Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models.
description
2007 nî lūn-bûn
@nan
2007 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Much ado about nothing: A comp ...... mplete data regression models.
@ast
Much ado about nothing: A comp ...... mplete data regression models.
@en
type
label
Much ado about nothing: A comp ...... mplete data regression models.
@ast
Much ado about nothing: A comp ...... mplete data regression models.
@en
prefLabel
Much ado about nothing: A comp ...... mplete data regression models.
@ast
Much ado about nothing: A comp ...... mplete data regression models.
@en
P2860
P356
P1476
Much ado about nothing: A comp ...... mplete data regression models.
@en
P2093
Ken P Kleinman
P2860
P356
10.1198/000313007X172556
P407
P577
2007-02-01T00:00:00Z