Evidence map›Paper›PMID 31360594›Full record

ArticleJournal of biometrics & biostatistics2018

Simple Power and Sample Size Estimation for Non-Randomized Longitudinal Difference in Differences Studies.

Yirui Hu, D R Hoover

Abstract read
In one paragraph

Article in Journal of biometrics & biostatistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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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

2 authors.

Yirui HuBiomedical and Translational Informatics, Geisinger, Danville, 17821, USA.
D R HooverDepartment of Statistics and Biostatistics and the Institute for Health, Health Care Policy and Aging Research, Rutgers University, Piscataway, 08854, USA.

Funding

Women's Interagency HIV Study (WIHS-V)U01AI035004 · NIAID · MONTEFIORE MEDICAL CENTER (BRONX, NY) · PI ANASTOS, KATHRYN M., SHARMA, ANJALI · 1993 to 2018
$65.7M
"Enhancing Cancer Research in Central Africa IeDEA"U01AI096299 · NIAID · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI Denis Nash, Marcel Yotebieng · 2011 to 2026
$35.8M
Reducing Urban Women's HIV Risk: Soap Opera Videos on Video Capable Cell PhonesR01NR010860 · NINR · RUTGERS THE STATE UNIV OF NJ NEWARK · PI JONES, RACHEL · 2009 to 2012
$2.0M
Reducing urban women's HIV risk: Soap opera videos streamed to mobile devicesR01NR014632 · NINR · NORTHEASTERN UNIVERSITY · PI JONES, RACHEL · 2015 to 2018
$2.0M
Acute Effects of Cannabis on Cognition and Mobility in Older HIV-Infected and Uninfected Women"R21AG059505 · NIA · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI SHARMA, ANJALI · 2018 to 2019
$318k
NIAID NIH HHS U01 AI035004NIAID NIH HHS U01 AI096299NIA NIH HHS R21 AG059505NINR NIH HHS R01 NR010860NINR NIH HHS R01 NR014632
6 · The paper itself

Abstract

Intervention effects on continuous longitudinal normal outcomes are often estimated in two-arm pre-post interventional studies with b≥1 pre- and k≥1 post-intervention measures using "Difference-in-Differences" (DD) analysis. Although randomization is preferred, non-randomized designs are often necessary due to practical constraints. Power/sample size estimation methods for non-randomized DD designs that incorporate the correlation structure of repeated measures are needed. We derive Generalized Least Squares (GLS) variance estimate of the intervention effect. For the commonly assumed compound symmetry (CS) correlation structure (where the correlation between all repeated measures is a constant

Indexed as

Compound symmetry covariance matrixNon-randomized longitudinal studyOptimal pre-post intervention allocationRepeated measure

Identifiers

PMID31360594
PMCPMC6663085

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.