Article in Communications in statistics: theory and methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing 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.
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
8 authors.
Kevin P JoseyDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA.
Brandy M RinghamLifecourse Epidemiology of Adiposity and Disease (LEAD) Center, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA.ORCID 0000-0001-5677-373X
Anna E BarónDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA.
Margaret SchenkmanPhysical Therapy Program, School of Medicine, University of Colorado Denver, Denver, Colorado, USA.
Katherine A SauderDepartment of Pediatrics, School of Medicine, University of Colorado Denver, Denver, Colorado, USA.
Keith E MullerDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Dana DabeleaDepartment of Epidemiology and the Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado Denver, Denver, Colorado, USA.
Deborah H GlueckDepartment of Pediatrics, School of Medicine, University of Colorado Denver, Denver, Colorado, USA.ORCID 0000-0003-0047-8939
Funding
PILOT STUDY--SUBSTRATE METABOLISM IN EXTREMELY LOW BIRTH WEIGHT INFANTSP30DK048520 · NIDDK · UNIVERSITY OF COLORADO DENVER · PI Wendy M Kohrt · 1995 to 2026
$32.6M
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
Exercise, Physical Function, and Parkinson's DiseaseR01HD043770 · NICHD · UNIVERSITY OF COLORADO DENVER · PI SCHENKMAN, MARGARET L · 2003 to 2010
$1.7M
A Master Course on Power for Multilevel and Longitudinal Health Behavior StudiesR25GM111901 · NIGMS · UNIVERSITY OF FLORIDA · PI GLUECK, DEBORAH HELEN, MULLER, KEITH E · 2014 to 2018
$1.0M
Prevalence of FMRI Expansions in Movement DisordersK23NS052487 · NINDS · UNIVERSITY OF COLORADO DENVER · PI HALL, DEBORAH A · 2006 to 2010
$881k
Colorado Clinical and Translational Science Institute (TL1)TL1RR025778 · NCRR · UNIVERSITY OF COLORADO DENVER · PI SOKOL, RONALD J. · 2008 to 2011
$730k
Power and Sample Size for Multilevel and Longitudinal Designs in Health ResearchG13LM011879 · NLM · UNIVERSITY OF FLORIDA · PI GLUECK, DEBORAH HELEN, MULLER, KEITH E · 2014 to 2016
When designing repeated measures studies, both the amount and the pattern of missing outcome data can affect power. The chance that an observation is missing may vary across measurements, and missingness may be correlated across measurements. For example, in a physiotherapy study of patients with Parkinson's disease, increasing intermittent dropout over time yielded missing measurements of physical function. In this example, we assume data are missing completely at random, since the chance that a data point was missing appears to be unrelated to either outcomes or covariates. For data missing completely at random, we propose noncentral
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.
Power for balanced linear mixed models with complex missing data processes. · full record | OpenQuestion