Evidence map›Paper›PMID 30686847›Full record

ArticleRevista Colombiana de estadistica

Using an Anchor to Improve Linear Predictions with Application to Predicting Disease Progression.

Alex Karanevich, Jianghua He, Byron J Gajewski

Abstract read
In one paragraph

Article in Revista Colombiana de estadistica. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Alex KaranevichDepartment of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160.
Jianghua HeDepartment of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160.
Byron J GajewskiDepartment of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160.

Funding

Heartland Institute for Clinical and Translational ResearchUL1TR000001 · NCATS · UNIVERSITY OF KANSAS MEDICAL CENTER · PI BAROHN, RICHARD J. · 2012 to 2015
$13.0M
HEARTLAND INSTITUTE FOR CLINICAL AND TRANSLATIONAL RESEARCHUL1RR033179 · NCRR · UNIVERSITY OF KANSAS MEDICAL CENTER · PI BAROHN, RICHARD J. · 2011 to 2011
$3.2M
NCATS NIH HHS UL1 TR000001NCRR NIH HHS UL1 RR033179
6 · The paper itself

Abstract

Linear models are some of the most straightforward and commonly used modelling approaches. Consider modelling approximately monotonic response data arising from a time-related process. If one has knowledge as to when the process began or ended, then one may be able to leverage additional assumed data to reduce prediction error. This assumed data, referred to as the "anchor," is treated as an additional data-point generated at either the beginning or end of the process. The response value of the anchor is equal to an intelligently selected value of the response (such as the upper bound, lower bound, or 99

Indexed as

amyotrophic lateral sclerosisanchorbiased regressionLinear modelsordinary least squares

Identifiers

PMID30686847
PMCPMC6345390

What OpenQuestion holds

Textmetadata
LicenceTDM
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.