Evidence map›Paper›PMID 25620844›Full record

ArticleJournal of applied econometrics (Chichester, England)

ESTIMATING PERSON-CENTERED TREATMENT (PeT) EFFECTS USING INSTRUMENTAL VARIABLES: AN APPLICATION TO EVALUATING PROSTATE CANCER TREATMENTS.

Anirban Basu

Abstract read
In one paragraph

Article in Journal of applied econometrics (Chichester, England). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
    Review
  7. Article
  8. DAta Linkage to Enhance Cancer Care (DaLECC): Protocol of a Large Australian Data Linkage Study.International journal of environmental research and public health · 2023
    Article
  9. Local Instrumental Variable Methods to Address Confounding and Heterogeneity when Using Electronic Health Records: An Application to Emergency Surgery.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  10. A Machine-Learning Approach for Estimating Subgroup- and Individual-Level Treatment Effects: An Illustration Using the 65 Trial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
    Article
  11. Article
  12. Article
  13. Comparative effectiveness of gastric bypass and sleeve gastrectomy on predicted 10-year risk of cardiovascular disease 5 years after surgery.Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery · 2022
    Article
  14. Article
  15. Observational
  16. Article
  17. Article
  18. Optimal individualized decision rules using instrumental variable methods.Journal of the American Statistical Association · 2021
    Article
  19. Article
  20. 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

1 author.

Anirban BasuDepartments of Health Services and Economics and Pharmaceutical Outcomes Research and Policy Program (PORPP), University of Washington, Seattle, WA, USA ; NBER, Cambridge, MA, USA.

Funding

Advancing Instrumental Variable Methods in Comparative Effectiveness ResearchRC4CA155809 · NCI · UNIVERSITY OF WASHINGTON · PI BASU, ANIRBAN · 2010 to 2010
$1.5M
Instrumental Variable Methods for Censored Cost Data and an Application in ProstaR01CA155329 · NCI · UNIVERSITY OF WASHINGTON · PI BASU, ANIRBAN · 2011 to 2013
$1.3M
NCI NIH HHS N01 PC035136NCI NIH HHS N01 PC035139NCI NIH HHS N02 PC015105NCI NIH HHS R01 CA155329NCI NIH HHS RC4 CA155809
6 · The paper itself

Abstract

This paper builds on the methods of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to estimate person-centered treatment (PeT) effects that are conditioned on the person's observed characteristics and averaged over the potential conditional distribution of unobserved characteristics that lead them to their observed treatment choices. PeT effects are more individualized than conditional treatment effects from a randomized setting with the same observed characteristics. PeT effects can be easily aggregated to construct any of the mean treatment effect parameters and, more importantly, are well suited to comprehend individual-level treatment effect heterogeneity. The paper presents the theory behind PeT effects, and applies it to study the variation in individual-level comparative effects of prostate cancer treatments on overall survival and costs.

Identifiers

PMID25620844
PMCPMC4300990

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.