Evidence map›Paper›PMID 24168424›Full record

ArticleBMC medical research methodology2013

Direct risk standardisation: a new method for comparing casemix adjusted event rates using complex models.

Jon Nicholl, Richard M Jacques, Michael J Campbell

Abstract read
In one paragraph

Article in BMC medical research methodology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

9 citing papers in PubMed.

  1. Observational
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  3. Observational
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  9. Integrative Protein-Based Prognostic Model for Early-Stage Endometrioid Endometrial Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2016
    Article
4 · The record

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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

3 authors.

Jon NichollSchool of Health and Related Research (ScHARR), University of Sheffield, 30 Regent Street, Sheffield, S1 4DA, UK. j.nicholl@shef.ac.uk.
Richard M Jacques
Michael J Campbell

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundComparison of outcomes between populations or centres may be confounded by any casemix differences and standardisation is carried out to avoid this. However, when the casemix adjustment models are large and complex, direct standardisation has been described as "practically impossible", and indirect standardisation may lead to unfair comparisons. We propose a new method of directly standardising for risk rather than standardising for casemix which overcomes these problems.

methodsUsing a casemix model which is the same model as would be used in indirect standardisation, the risk in individuals is estimated. Risk categories are defined, and event rates in each category for each centre to be compared are calculated. A weighted sum of the risk category specific event rates is then calculated. We have illustrated this method using data on 6 million admissions to 146 hospitals in England in 2007/8 and an existing model with over 5000 casemix combinations, and a second dataset of 18,668 adult emergency admissions to 9 centres in the UK and overseas and a published model with over 20,000 casemix combinations and a continuous covariate.

resultsSubstantial differences between conventional directly casemix standardised rates and rates from direct risk standardisation (DRS) were found. Results based on DRS were very similar to Standardised Mortality Ratios (SMRs) obtained from indirect standardisation, with similar standard errors.

conclusionsDirect risk standardisation using our proposed method is as straightforward as using conventional direct or indirect standardisation, always enables fair comparisons of performance to be made, can use continuous casemix covariates, and was found in our examples to have similar standard errors to the SMR. It should be preferred when there is a risk that conventional direct or indirect standardisation will lead to unfair comparisons.

Indexed as

HospitalizationHumansMulticenter Studies as TopicReference StandardsRisk AssessmentStatistics, NonparametricUnited Kingdom

Identifiers

PMID24168424
PMCPMC3870993

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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.