Evidence map›Paper›PMID 39281709›Full record

ArticleThe annals of applied statistics2024

COMPOSITE SCORES FOR TRANSPLANT CENTER EVALUATION: A NEW INDIVIDUALIZED EMPIRICAL NULL METHOD.

Nicholas Hartman, Joseph M Messana, Jian Kang, Abhijit S Naik, Tempie H Shearon, Kevin He

Abstract read
In one paragraph

Article in The annals of applied statistics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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

  1. Pooled it
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  4. 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.

Nicholas HartmanDepartment of Biostatistics, University of Michigan, Ann Arbor.
Joseph M MessanaDivision of Nephrology, University of Michigan, Ann Arbor.
Jian KangDepartment of Biostatistics, University of Michigan, Ann Arbor.
Abhijit S NaikDivision of Nephrology, University of Michigan, Ann Arbor.
Tempie H ShearonDepartment of Biostatistics, University of Michigan, Ann Arbor.
Kevin HeDepartment of Biostatistics, University of Michigan, Ann Arbor.

Funding

Improve Statistical Methods for Profiling of Healthcare ProvidersR01DK129539 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Zhi Kevin He · 2022 to 2026
$1.6M
NIDDK NIH HHS R01 DK129539
6 · The paper itself

Abstract

Risk-adjusted quality measures are used to evaluate healthcare providers with respect to national norms while controlling for factors beyond their control. Existing healthcare provider profiling approaches typically assume that the between-provider variation in these measures is entirely due to meaningful differences in quality of care. However, in practice, much of the between-provider variation will be due to trivial fluctuations in healthcare quality, or unobservable confounding risk factors. If these additional sources of variation are not accounted for, conventional methods will disproportionately identify larger providers as outliers, even though their departures from the national norms may not be "extreme" or clinically meaningful. Motivated by efforts to evaluate the quality of care provided by transplant centers, we develop a composite evaluation score based on a novel individualized empirical null method, which robustly accounts for overdispersion due to unobserved risk factors, models the marginal variance of standardized scores as a function of the effective sample size, and only requires the use of publicly-available center-level statistics. The evaluations of United States kidney transplant centers based on the proposed composite score are substantially different from those based on conventional methods. Simulations show that the proposed empirical null approach more accurately classifies centers in terms of quality of care, compared to existing methods.

Indexed as

Empirical nullEnd-stage renal diseaseProvider profilingUnmeasured confounders

Identifiers

PMID39281709
PMCPMC11395314

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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.