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
Group-Based Trajectory Modeling in Clinical Research
TL;DR: The challenges associated with the application of both group-based trajectory and growth mixture modeling are discussed, and a set of preliminary guidelines for applied researchers to follow when reporting model results are proposed.
Journal ArticleDOI
The Prevention and Treatment of Missing Data in Clinical Trials
Roderick J. A. Little,Ralph B. D'Agostino,Michael L. Cohen,Kay Dickersin,Scott S. Emerson,John T. Farrar,Constantine Frangakis,Joseph W. Hogan,Geert Molenberghs,Susan A. Murphy,James D. Neaton,Andrea Rotnitzky,Daniel O. Scharfstein,Weichung Joe Shih,Jay P. Siegel,Hal S. Stern +15 more
TL;DR: Methods for preventing missing data and, failing that, dealing with data that are missing in clinical trials are reviewed.
Journal ArticleDOI
Causal inference using potential outcomes: Design, modeling, decisions
TL;DR: The assignment mechanism as discussed by the authors is a probabilistic model for the treatment each unit receives as a function of covariates and potential outcomes, and it is defined as comparisons of potential outcomes under different treatments on a common set of units.
Journal ArticleDOI
Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective
Joseph L Schafer,Maren K. Olsen +1 more
TL;DR: The key ideas of multiple imputation are reviewed, the software programs currently available are discussed, and their use on data from the Adolescent Alcohol Prevention Trial is demonstrated.
Journal ArticleDOI
Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
TL;DR: In this paper, a general-purpose, multiple imputation model for missing data is proposed, which is considerably faster and easier to use than the leading method recommended in the statistics literature.
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.