Journal ArticleDOI
Inference and missing data
TLDR
In this article, it was shown that ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are missing at random and the observed data are observed at random, and then such inferences are generally conditional on the observed pattern of missing data.Abstract:
Two results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are “missing at random” and the observed data are “observed at random,” and then such inferences are generally conditional on the observed pattern of missing data. Second, ignoring the process that causes missing data when making Bayesian inferences about θ is generally appropriate if and only if the missing data are missing at random and the parameter of the missing data is “independent” of θ. Examples and discussion indicating the implications of these results are included.read more
Citations
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Journal ArticleDOI
Traditional Risk factors and subclinical disease measures as predictors of first myocardial infarction in older adults: The Cardiovascular Health study
Bruce M. Psaty,Curt D. Furberg,Lewis H. Kuller,Diane E. Bild,Pentti M. Rautaharju,Joseph F. Polak,Edwin G. Bovill,John S. Gottdiener +7 more
TL;DR: In this article, the authors assess the individual and joint contributions made by both traditional risk factors and measures of subclinical disease for myocardial infarction (MI) in older adults.
Journal ArticleDOI
Draw the Line/Respect the Line: A Randomized Trial of a Middle School Intervention to Reduce Sexual Risk Behaviors
TL;DR: The Draw the Line/Respect the Line curriculum was effective for boys, but not for girls and Psychosocial effects for girls were limited.
Journal ArticleDOI
Intention-to-treat approach to data from randomized controlled trials: a sensitivity analysis.
C.C. Wright,Jenny Sim +1 more
TL;DR: The analysis shows that biased estimates of effect may occur when deviation is nonrandom, when a large percentage of participants switch treatments or are lost to follow-up, and when the method of estimating missing values accounts inadequately for the process causing loss to following-up.
Journal ArticleDOI
The differential effects of parental divorce and marital conflict on young adult romantic relationships
Ming Cui,Frank D. Fincham +1 more
TL;DR: In this paper, structural equation modeling supported the hypothesis that parental divorce and marital conflict were independently associated with young adult children's romantic relationships through different mechanisms: Parental divorce was associated with low level of relationship quality through a negative attitude toward marriage (positive attitude toward divorce) and lack of commitment to their own current relationships.
Journal ArticleDOI
Worst-Rank Score Analysis with Informatively Missing Observations in Clinical Trials
TL;DR: Under a specific model that the imputation of a worst-rank score for informatively missing observations provides an unbiased test against a restricted alternative, generalizations that employ the actual times of the informative event are described.
References
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Book
Bayesian inference in statistical analysis
George E. P. Box,George C. Tiao +1 more
TL;DR: In this article, the effect of non-normality on inference about a population mean with generalizations was investigated. But the authors focused on the effect on the mean with information from more than one source.
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Bayesian Inference in Statistical Analysis
Journal ArticleDOI
Maximum Likelihood Estimates for a Multivariate Normal Distribution when Some Observations are Missing
TL;DR: In this paper, the authors give an approach to derive maximum likelihood estimates of parameters of multivariate normal distributions in cases where some observations are missing (Edgett [2] and Lord [3], [4]).
Journal ArticleDOI
Missing Observations in Multivariate Statistics I. Review of the Literature
TL;DR: In this paper, a review of the literature on the problem of handling multivariate data with observations missing on some or all of the variables under study is presented, where the authors examine the ways that statisticians have devised to estimate means, variances, correlations and linear regression functions.