Evidence map›Paper›PMID 26463684›Full record

ArticleTrials2015

An internal pilot design for prospective cancer screening trials with unknown disease prevalence.

John T Brinton, Brandy M Ringham, Deborah H Glueck

Open access · goldAbstract read
In one paragraph

Article in Trials, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 95% of its field
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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Article
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

3 authors at 2 institutions in 1 country.

John T BrintonDenver Health Medical Center, 777 Bannock St., MC 6551, Denver, Colorado, 80204, USA. john.brinton@dhha.org.
Brandy M RinghamDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, Aurora, Colorado, 80045, USA. brandy.ringham@ucdenver.edu.
Deborah H GlueckDepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 E. 17th Place, Aurora, Colorado, 80045, USA. deborah.glueck@ucdenver.edu.
Colorado School of Public Health · USDenver Health Medical Center · US

Funding

Multilevel and Longitudinal Study Sample Size Tools for Behavioral ScientistsR01DE020832 · NIDCR · UNIVERSITY OF FLORIDA · PI MULLER, KEITH E · 2011 to 2014
$2.3M
Efficacy of Oral Cancer Screening Adjunctive TechniquesRC2DE020779 · NIDCR · UNIVERSITY OF CHICAGO · PI LINGEN, MARK W. · 2009 to 2009
$770k
NIDCR NIH HHS NIDCR 1 R01 DE020832-01A1NIDCR NIH HHS NIDCR RC2DE020779NIDCR NIH HHS R01 DE020832NIDCR NIH HHS RC2 DE020779
6 · The paper itself

Abstract

backgroundFor studies that compare the diagnostic accuracy of two screening tests, the sample size depends on the prevalence of disease in the study population, and on the variance of the outcome. Both parameters may be unknown during the design stage, which makes finding an accurate sample size difficult.

methodsTo solve this problem, we propose adapting an internal pilot design. In this adapted design, researchers will accrue some percentage of the planned sample size, then estimate both the disease prevalence and the variances of the screening tests. The updated estimates of the disease prevalence and variance are used to conduct a more accurate power and sample size calculation.

resultsWe demonstrate that in large samples, the adapted internal pilot design produces no Type I inflation. For small samples (N less than 50), we introduce a novel adjustment of the critical value to control the Type I error rate. We apply the method to two proposed prospective cancer screening studies: 1) a small oral cancer screening study in individuals with Fanconi anemia and 2) a large oral cancer screening trial.

conclusionConducting an internal pilot study without adjusting the critical value can cause Type I error rate inflation in small samples, but not in large samples. An internal pilot approach usually achieves goal power and, for most studies with sample size greater than 50, requires no Type I error correction. Further, we have provided a flexible and accurate approach to bound Type I error below a goal level for studies with small sample size.

Indexed as

Research DesignClinical Trials as TopicData Interpretation, StatisticalEarly Detection of CancerHumansMouth NeoplasmsPilot ProjectsPredictive Value of TestsPrevalenceProspective StudiesReproducibility of ResultsSample Size

Identifiers

PMID26463684
PMCPMC4604650
OpenAlexW1899438797

What OpenQuestion holds

Textmetadata
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.