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
Multiple Imputation With Large Data Sets: A Case Study of the Children's Mental Health Initiative
TL;DR: The method of multiple imputation by chained equations, which iterates through the data, imputing one variable at a time conditional on the others, was used to ensure that data analysis samples reflect the full population of youth participating in this program.
Journal ArticleDOI
How Important are High Response Rates for College Surveys
TL;DR: In a recent study, this paper found that survey researchers across a number of social science disciplines in America and abroad have witnessed a gradual decrease in survey participation over time (Brick & Wil-2017).
Book
Missing data in randomised controlled trials: a practical guide
Carpenter,Michael G. Kenward +1 more
TL;DR: A principled approach to handling missing data in clinical trials is proposed, and how this principled approach can be practically, and directly, applied to the majority of trials with longitudinal follow-up is outlined.
Journal ArticleDOI
Increased Risk of Fragility Fractures among HIV Infected Compared to Uninfected Male Veterans
Julie A. Womack,Joseph L. Goulet,Cynthia Gibert,Cynthia Brandt,Chung Chou Chang,Barbara Gulanski,Liana Fraenkel,Kristin M. Mattocks,David Rimland,Maria C. Rodriguez-Barradas,Janet P. Tate,Michael T. Yin,Amy C. Justice +12 more
TL;DR: HIV infection is associated with fragility fracture risk, and this risk is attenuated by BMI, which reduces the risk of incident hip, vertebral, or upper arm fracture.
Book
Mixed Effects Models for Complex Data
TL;DR: This chapter discusses Mixed Effects Models with Missing Covariates, Joint Modeling for Longitudinal Data and Survival Data, and Bayesian Joint Models of Longitudinal and Survival data.
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.
Journal ArticleDOI
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.