Evidence map›Paper›PMID 35371519›Full record

ReviewNeuro-oncology practice2022

Understanding the effect of categorization of a continuous predictor with application to neuro-oncology.

Ruchi Gupta, Courtney N Day, Wlliam O Tobin, Cynthia S Crowson

Abstract readReview
In one paragraph

Review in Neuro-oncology practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

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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

4 authors.

Ruchi GuptaDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
Courtney N DayDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
Wlliam O TobinDepartment of Neurology, Mayo Clinic, Rochester, Minnesota, USA.
Cynthia S CrowsonDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0001-5847-7475

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many neuro-oncology studies commonly assess the association between a prognostic factor (predictor) and disease or outcome, such as the association between age and glioma. Predictors can be continuous (eg, age) or categorical (eg, race/ethnicity). Effects of categorical predictors are frequently easier to visualize and interpret than effects of continuous variables. This makes it an attractive, and seemingly justifiable, option to subdivide the continuous predictors into categories (eg, age <50 years vs age ≥50 years). However, this approach results in loss of information (and power) compared to the continuous version. This review outlines the use cases for continuous and categorized predictors and provides tips and pitfalls for interpretation of these approaches.

Indexed as

analysiscategoricalcategorizecontinuousstatistics

Identifiers

PMID35371519
PMCPMC8965047

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.