Evidence map›Paper›PMID 37789257›Full record

ArticleBMC medical research methodology2023

Sequential regression and simulation: a method for estimating causal effects from heterogeneous clinical trials without a common control group.

Vivek A Rudrapatna, Vignesh G Ravindranath, Douglas V Arneson, Arman Mosenia, Atul J Butte, Shan Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical research methodology, 2023. 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
1.4field-weighted citation impact, top 17% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  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

6 authors at 2 institutions in 1 country.

Vivek A RudrapatnaDivision of Gastroenterology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA. vivek.rudrapatna@ucsf.edu.
Vignesh G RavindranathBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Douglas V ArnesonBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Arman MoseniaSchool of Medicine, University of California, San Francisco, San Francisco, CA, USA.
Atul J ButteBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Shan WangDepartment of Mathematics and Statistics, University of San Francisco, San Francisco, CA, USA. swang151@usfca.edu.
University of California, San Francisco · USUniversity of San Francisco · US

Funding

Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
TRAINING PROGRAM IN DISGESTIVE DISEASES AND NUTRITIONT32DK007007 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI AVERIL I MA · 1986 to 2026
$6.4M
ADA3: a novel target for HPV16 E6 oncoproteinR01CA094143 · NCI · UNIVERSITY OF NEBRASKA MEDICAL CENTER · PI BAND, VIMLA · 2002 to 2006
$1.7M
Colon Polyp Surveillance with DNA Analysis of StoolR21CA094117 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SCHOENFELD, PHILIP S · 2002 to 2003
$303k
A data science framework for transforming electronic health records into real-world evidenceK99LM014099 · NLM · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI RUDRAPATNA, VIVEK ASHOK · 2023 to 2024
$178k
NCATS NIH HHS UL1 TR001872NCI NIH HHS R01 CA094143NCI NIH HHS R21 CA094117NIDDK NIH HHS T32 DK007007NLM NIH HHS K99 LM014099
6 · The paper itself

Abstract

backgroundThe advent of clinical trial data sharing platforms has created opportunities for making new discoveries and answering important questions using already collected data. However, existing methods for meta-analyzing these data require the presence of shared control groups across studies, significantly limiting the number of questions that can be confidently addressed. We sought to develop a method for meta-analyzing potentially heterogeneous clinical trials even in the absence of a common control group.

methodsThis work was conducted within the context of a broader effort to study comparative efficacy in Crohn's disease. Following a search of clnicaltrials.gov we obtained access to the individual participant data from nine trials of FDA-approved treatments in Crohn's Disease (N = 3392). We developed a method involving sequences of regression and simulation to separately model the placebo- and drug-attributable effects, and to simulate head-to-head trials against an appropriately normalized background. We validated this method by comparing the outcome of a simulated trial comparing the efficacies of adalimumab and ustekinumab against the recently published results of SEAVUE, an actual head-to-head trial of these drugs. This study was pre-registered on PROSPERO (#157,827) prior to the completion of SEAVUE.

resultsUsing our method of sequential regression and simulation, we compared the week eight outcomes of two virtual cohorts subject to the same patient selection criteria as SEAVUE and treated with adalimumab or ustekinumab. Our primary analysis replicated the corresponding published results from SEAVUE (p = 0.9). This finding proved stable under multiple sensitivity analyses.

conclusionsThis new method may help reduce the bias of individual participant data meta-analyses, expand the scope of what can be learned from these already-collected data, and reduce the costs of obtaining high-quality evidence to guide patient care.

Indexed as

Crohn DiseaseUstekinumabAdalimumabClinical Trials as TopicControl GroupsHumansRemission InductionAdalimumabUstekinumabBiostatisticsComparative effectivenessComparative efficacyCrohn’s diseaseEvidence synthesisIndividual participant data meta-analysisRandomized clinical trials

Identifiers

PMID37789257
PMCPMC10546672
OpenAlexW4387310220

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.