Evidence map›Paper›PMID 17610445›Full record

ArticleHealth services research2007

Overestimating outcome rates: statistical estimation when reliability is suboptimal.

Rodney A Hayward, Michele Heisler, John Adams, R Adams Dudley, Timothy P Hofer

Abstract read
In one paragraph

Article in Health services research, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

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  18. The HANDDS program: a systematic approach for addressing disparities in the provision of bystander cardiopulmonary resuscitation.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2014
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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

5 authors.

Rodney A HaywardDepartment of Veterans Affairs, VA Center for Practice Management & Outcomes Research, VA Ann Arbor Healthcare System, Ann Arbor, MI 48113-0170, USA.
Michele Heisler
John Adams
R Adams Dudley
Timothy P Hofer

Funding

PUBLIC HEALTH DEMONSTRATIONP60DK020572 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MYERS, MARTIN G · 1985 to 2012
$26.5M
NIDDK NIH HHS P60 DK020572NIDDK NIH HHS P60 DK20572PHS HHS P20 H511540-01
6 · The paper itself

Abstract

objectiveTo demonstrate how failure to account for measurement error in an outcome (dependent) variable can lead to significant estimation errors and to illustrate ways to recognize and avoid these errors. DATA SOURCES: Medical literature and simulation models. STUDY DESIGN/DATA COLLECTION: Systematic review of the published and unpublished epidemiological literature on the rate of preventable hospital deaths and statistical simulation of potential estimation errors based on data from these studies. PRINCIPAL

findingsMost estimates of the rate of preventable deaths in U.S. hospitals rely upon classifying cases using one to three physician reviewers (implicit review). Because this method has low to moderate reliability, estimates based on statistical methods that do not account for error in the measurement of a "preventable death" can result in significant overestimation. For example, relying on a majority rule rating with three reviewers per case (reliability approximately 0.45 for the average of three reviewers) can result in a 50-100 percent overestimation compared with an estimate based upon a reliably measured outcome (e.g., by using 50 reviewers per case). However, there are statistical methods that account for measurement error that can produce much more accurate estimates of outcome rates without requiring a large number of measurements per case.

conclusionThe statistical principles discussed in this case study are critically important whenever one seeks to estimate the proportion of cases belonging to specific categories (such as estimating how many patients have inadequate blood pressure control or identifying high-cost or low-quality physicians). When the true outcome rate is low (<20 percent), using an outcome measure that has low-to-moderate reliability will generally result in substantially overestimating the proportion of the population having the outcome unless statistical methods that adjust for measurement error are used.

Indexed as

Hospital MortalityBiasHumansMedical ErrorsOutcome Assessment, Health CareReproducibility of Results

Identifiers

PMID17610445
PMCPMC1955272

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Registered trials

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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.