Evidence map›Paper›PMID 22552989›Full record

ArticlePharmacoepidemiology and drug safety2012

Role of disease risk scores in comparative effectiveness research with emerging therapies.

Robert J Glynn, Joshua J Gagne, Sebastian Schneeweiss

Abstract readComparative Study
In one paragraph

Article in Pharmacoepidemiology and drug safety, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed, 1 pooled it
–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

55 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Characterizing Treatment Effect Heterogeneity Using Real-World Data.Clinical pharmacology and therapeutics · 2025
    Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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.

Robert J GlynnDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Boston, MA 02120, USA. rglynn@rics.bwh.harvard.edu
Joshua J Gagne
Sebastian Schneeweiss

Funding

Propensity Scores and Preventive Drug Use in the ElderlyR01AG023178 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI STURMER, TIL · 2005 to 2014
$2.7M
Medication Use, Cormobidity and Outcomes in Aging PopulationsR01AG018833 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI GLYNN, ROBERT J · 2002 to 2010
$2.0M
Propensity scores and preventive drug use in the elderlyR56AG023178 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI STURMER, TIL · 2015 to 2015
$412k
Medication Use, Comorbidity and Outcomes in Aging populationsR56AG018833 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI GLYNN, ROBERT J · 2007 to 2007
$394k
NIA NIH HHS AG018833NIA NIH HHS AG023178NIA NIH HHS R01 AG018833NIA NIH HHS R01 AG023178NIA NIH HHS R56 AG018833NIA NIH HHS R56 AG023178
6 · The paper itself

Abstract

backgroundUsefulness of propensity scores and regression models to balance potential confounders at treatment initiation may be limited for newly introduced therapies with evolving use patterns.

objectivesTo consider settings in which the disease risk score has theoretical advantages as a balancing score in comparative effectiveness research because of stability of disease risk and the availability of ample historical data on outcomes in people treated before introduction of the new therapy.

methodsWe review the indications for and balancing properties of disease risk scores in the setting of evolving therapies and discuss alternative approaches for estimation. We illustrate development of a disease risk score in the context of the introduction of atorvastatin and the use of high-dose statin therapy beginning in 1997, based on data from 5668 older survivors of myocardial infarction who filled a statin prescription within 30 days after discharge from 1995 until 2004. Theoretical considerations suggested development of a disease risk score among nonusers of atorvastatin and high-dose statins during the period 1995-1997.

resultsObserved risk of events increased from 11% to 35% across quintiles of the disease risk score, which had a C-statistic of 0.71. The score allowed control of many potential confounders even during early follow-up with few study endpoints.

conclusionsBalancing on a disease risk score offers an attractive alternative to a propensity score in some settings such as newly marketed drugs and provides an important axis for evaluation of potential effect modification. Joint consideration of propensity and disease risk scores may be valuable.

Indexed as

Comparative Effectiveness ResearchConfounding Factors, EpidemiologicHydroxymethylglutaryl-CoA Reductase InhibitorsModels, StatisticalPropensity ScoreRandomized Controlled Trials as TopicDose-Response Relationship, DrugEpidemiologic Research DesignHumansMyocardial InfarctionOutcome and Process Assessment, Health CareRisk FactorsHydroxymethylglutaryl-CoA Reductase Inhibitors

Identifiers

PMID22552989
PMCPMC3454457

What Socratic 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 Socratic graph.