Evidence map›Paper›PMID 32808656›Full record

Trial reportBiostatistics (Oxford, England)2022

A sparse additive model for treatment effect-modifier selection.

Hyung Park, Eva Petkova, Thaddeus Tarpey, R Todd Ogden

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Hyung ParkDivision of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA.
Eva PetkovaDivision of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA.
Thaddeus TarpeyDivision of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA.
R Todd OgdenDivision of Biostatistics, Department of Population Health, New York University, New York, NY, USA and Department of Biostatistics, Columbia University, New York, NY, USA.

Funding

Characterizing Placebo ResponseR01MH099003 · NIMH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI PETKOVA, EVA, TARPEY, THADDEUS · 2013 to 2021
$4.5M
NIMH NIH HHS R01 MH099003
6 · The paper itself

Abstract

Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.

Indexed as

Research DesignComputer SimulationHumansBiomarkersIndividualized treatment rulesSparse additive modelsTreatment effect-modifiers

Identifiers

PMID32808656
PMCPMC9308457

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.