Evidence map›Paper›PMID 31439981›Full record

ArticleCommunications in statistics: theory and methods2019

On the Distribution of Summary Statistics for Missing Data.

B M Ringham, S M Kreidler, K E Muller, D H Glueck

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Communications in statistics: theory and methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

B M RinghamDepartment of Biostatistics and Informatics, University of Colorado Denver.
S M KreidlerNeptune and Company.
K E MullerDepartment of Health Outcomes and Policy, University of Florida, Gainesville.
D H GlueckDepartment of Biostatistics and Informatics, University of Colorado Denver.

Funding

The Early Life Exposome and Childhood Health - The Colorado Healthy Start 3 Cohort StudyUG3OD023248 · OD · UNIVERSITY OF COLORADO DENVER · PI BEKELMAN, TRACI ALLISON, DABELEA, DANA · 2016 to 2024
$5.9M
Methods and Software for Lifecourse Epidemiology Data and Sample Size AnalysisR01GM121081 · NIGMS · UNIVERSITY OF COLORADO DENVER · PI DABELEA, DANA, GLUECK, DEBORAH HELEN · 2016 to 2019
$2.6M
Multilevel and Longitudinal Study Sample Size Tools for Behavioral ScientistsR01DE020832 · NIDCR · UNIVERSITY OF FLORIDA · PI MULLER, KEITH E · 2011 to 2014
$2.3M
Bias correction optimizes the choice of screening test for early cancer detectionR03CA136048 · NCI · UNIVERSITY OF COLORADO DENVER/HSC DENVER · PI ALONZO, TODD A, GLUECK, DEBORAH HELEN · 2009 to 2010
$171k
NCI NIH HHS R03 CA136048NIDCR NIH HHS R01 DE020832NIGMS NIH HHS R01 GM121081NIH HHS UG3 OD023248
6 · The paper itself

Abstract

Under an assumption that missing values occur randomly in a matrix, formulae are developed for the expected value and variance of six statistics that summarize the number and location of the missing values. For a seventh statistic, a regression model based on simulated data yields an estimate of the expected value. The results can be used in the development of methods to control the Type I error and approximate power and sample size for multilevel and longitudinal studies with missing data.

Indexed as

longitudinalmissing datamultilevelmultinomialpower

Identifiers

PMID31439981
PMCPMC6706086

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.