Evidence map›Paper›PMID 25452689›Full record

ArticleCancer informatics2014

Type I error control for tree classification.

Sin-Ho Jung, Yong Chen, Hongshik Ahn

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer informatics, 2014. 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
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.

  1. K-Sample comparisons using propensity analysis.Biometrical journal. Biometrische Zeitschrift · 2019
    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.

Sin-Ho JungDepartment of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.
Yong ChenDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA.
Hongshik AhnDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-3600, USA.

Funding

Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVIDIAN, MARIE, KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
NCI NIH HHS P01 CA142538
6 · The paper itself

Abstract

Binary tree classification has been useful for classifying the whole population based on the levels of outcome variable that is associated with chosen predictors. Often we start a classification with a large number of candidate predictors, and each predictor takes a number of different cutoff values. Because of these types of multiplicity, binary tree classification method is subject to severe type I error probability. Nonetheless, there have not been many publications to address this issue. In this paper, we propose a binary tree classification method to control the probability to accept a predictor below certain level, say 5%.

Indexed as

binary treeclassificationpermutationsingle-step procedurestep-down proceduretype I error

Identifiers

PMID25452689
PMCPMC4237155

What OpenQuestion holds

Textmetadata
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.