Evidence map›Paper›PMID 38883969›Full record

ArticleInternational journal of statistics in medical research2023

High-Dimensional Fixed Effects Profiling Models and Applications in End-Stage Kidney Disease Patients: Current State and Future Directions.

Danh V Nguyen, Qi Qian, Amy S You, Esra Kurum, Connie M Rhee, Damla Senturk

Abstract read
In one paragraph

Article in International journal of statistics in medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Danh V NguyenDepartment of Medicine, University of California Irvine, Orange, CA 92868, USA.
Qi QianDepartment of Biostatistics, University of California, Los Angeles, CA 90095, USA.
Amy S YouDepartment of Medicine, University of California Irvine, Orange, CA 92868, USA.
Esra KurumDepartment of Statistics, University of California, Riverside, CA 92521, USA.
Connie M RheeDepartment of Medicine, University of California, Los Angeles, CA 90095, USA.
Damla SenturkDepartment of Biostatistics, University of California, Los Angeles, CA 90095, USA.

Funding

Multilevel Time-Dynamic Modeling of Hospitalization and Survival in Patients on DialysisR01DK092232 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI KURUM, ESRA, NGUYEN, DANH V · 2011 to 2025
$4.9M
NIDDK NIH HHS R01 DK092232
6 · The paper itself

Abstract

Profiling analysis aims to evaluate health care providers, including hospitals, nursing homes, or dialysis facilities among others with respect to a patient outcome, such as 30-day unplanned hospital readmission or mortality. Fixed effects (FE) profiling models have been developed over the last decade, motivated by the overall need to (a) improve accurate identification or "flagging" of under-performing providers, (b) relax assumptions inherent in random effects (RE) profiling models, and (c) take into consideration the unique disease characteristics and care/treatment processes of end-stage kidney disease (ESKD) patients on dialysis. In this paper, we review the current state of FE methodologies and their rationale in the ESKD population and illustrate applications in four key areas: profiling dialysis facilities for (1) patient hospitalizations over time (longitudinally) using standardized dynamic readmission ratio (SDRR), (2) identification of dialysis facility characteristics (e.g., staffing level) that contribute to hospital readmission, and (3) adverse recurrent events using standardized event ratio (SER). Also, we examine the operating characteristics with a focus on FE profiling models. Throughout these areas of applications to the ESKD population, we identify challenges for future research in both methodology and clinical studies.

Indexed as

Dialysis facility staffingend-stage kidney diseasefixed effectsgeneralized linear mixed modelhigh-dimensional parametersmultilevel varying coefficient modelPoisson regressionpropensity scorerandom effectsUnited States Renal Data System

Identifiers

PMID38883969
PMCPMC11178325

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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.