Evidence map›Paper›PMID 32981946›Full record

ArticleEconometrica : journal of the Econometric Society2020

UNDERSTANDING DOCTOR DECISION MAKING: THE CASE OF DEPRESSION TREATMENT.

Janet M Currie, W Bentley Macleod

Abstract read
In one paragraph

Article in Econometrica : journal of the Econometric Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025
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  4. Doing More with Less: Predicting Primary Care Provider Effectiveness.The review of economics and statistics · 2025
    Article
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  10. Treatment of mental illness in American adolescents varies widely within and across areas.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  11. UNDERSTANDING DOCTOR DECISION MAKING: THE CASE OF DEPRESSION TREATMENT.Econometrica : journal of the Econometric Society · 2020
    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

2 authors.

Janet M CurrieWoodrow Wilson School, Princeton University and NBER.
W Bentley MacleodDepartment of Economics, Columbia University and NBER.

Funding

Scientific CoreP30AG024928 · NIA · PRINCETON UNIVERSITY · PI CURRIE, JANET M. · 2004 to 2018
$5.2M
NIA NIH HHS P30 AG024928
6 · The paper itself

Abstract

Treatment for depression is complex, requiring decisions that may involve trade-offs between exploiting treatments with the highest expected value and experimenting with treatments with higher possible payoffs. Using patient claims data, we show that among skilled doctors, using a broader portfolio of drugs predicts better patient outcomes, except in cases where doctors' decisions violate loose professional guidelines. We introduce a behavioral model of decision making guided by our empirical observations. The model's novel feature is that the trade-off between exploitation and experimentation depends on the doctor's diagnostic skill. The model predicts that higher diagnostic skill leads to greater diversity in drug choice and better matching of drugs to patients even among doctors with the same initial beliefs regarding drug effectiveness. Consistent with the finding that guideline violations predict poorer patient outcomes, simulations of the model suggest that increasing the number of possible drug choices can lower performance.

Indexed as

bounded rationalityDecision theoryexpert decision makinghuman capitallearning

Identifiers

PMID32981946
PMCPMC7514077

What OpenQuestion holds

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LicenceTDM
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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.