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
The peaceful mind program: a pilot test of a cognitive-behavioral therapy-based intervention for anxious patients with dementia.
Melinda A. Stanley,Jessica Calleo,Amber L. Bush,Nancy Wilson,A. Lynn Snow,A. Lynn Snow,Cynthia Kraus-Schuman,Amber L. Paukert,Nancy J. Petersen,Nancy J. Petersen,Gretchen A. Brenes,Paul E. Schulz,Susan P. Williams,Mark E. Kunik +13 more
TL;DR: Feasibility was demonstrated with regard to recruitment, attrition, and treatment characteristics, and results support that Peaceful Mind is ready for future comparative clinical trials.
Journal ArticleDOI
Dose-effect relationship in routine outpatient psychotherapy: does treatment duration matter?
TL;DR: The dose-effect model, which posits diminishing effects of additional sessions in later treatment phases, is compared against a model that assumes a linear and steady treatment progress through termination, and the rate of change was found to be inversely related to the length of treatment.
Journal ArticleDOI
Modeling nonignorable missing data with item response theory (irt)
TL;DR: This paper analyzed the effects of treating omitted responses either as missing or as wrong, as is done in the majority of international studies, and compared these data-treatment solutions to model-based approaches to treat omitted responses.
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Effectiveness of Valid Mean Substitution in Treating Missing Data in Attitude Assessment
TL;DR: The results indicated that both procedures are similarly effective in estimating the means and standard deviations of the scales, however, VMS is superior in estimating parameters.
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Clustered encouragement designs with individual noncompliance: bayesian inference with randomization, and application to advance directive forms.
TL;DR: Bayesian methodology for causal inference for the effect of the new target treatment versus the control target treatment in the randomized CED with all-or-none compliance at the unit level is proposed, which generalizes the approach of Hirano et al. (2000) in important and surprisingly subtle ways.
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.