Evidence map›Paper›PMID 29258428›Full record

ArticleBMC medical research methodology2017

Tweedie distributions for fitting semicontinuous health care utilization cost data.

Christoph F Kurz

Abstract read
In one paragraph

Article in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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.

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3 · Its place in the literature

Who cites it

43 citing papers in PubMed.

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  16. Examining Demand and Substitutability of Usual Brand Little Cigars/Cigarillos and Cigarettes in Young Adults Who Dual Use.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024
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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

1 author.

Christoph F KurzInstitute of Health Economics and Health Care Management, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany. christoph.kurz@helmholtz-muenchen.de.ORCID http://orcid.org/0000-0001-9498-8002

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe statistical analysis of health care cost data is often problematic because these data are usually non-negative, right-skewed and have excess zeros for non-users. This prevents the use of linear models based on the Gaussian or Gamma distribution. A common way to counter this is the use of Two-part or Tobit models, which makes interpretation of the results more difficult. In this study, I explore a statistical distribution from the Tweedie family of distributions that can simultaneously model the probability of zero outcome, i.e. of being a non-user of health care utilization and continuous costs for users.

methodsI assess the usefulness of the Tweedie model in a Monte Carlo simulation study that addresses two common situations of low and high correlation of the users and the non-users of health care utilization. Furthermore, I compare the Tweedie model with several other models using a real data set from the RAND health insurance experiment.

resultsI show that the Tweedie distribution fits cost data very well and provides better fit, especially when the number of non-users is low and the correlation between users and non-users is high.

conclusionThe Tweedie distribution provides an interesting solution to many statistical problems in health economic analyses.

Indexed as

AlgorithmsModels, EconomicStatistical DistributionsComputer SimulationHealth Care CostsHealth Services ResearchHumansMonte Carlo MethodPatient Acceptance of Health CareCost dataHealth care utilizationHealth economicsTweedie distribution

Identifiers

PMID29258428
PMCPMC5735804

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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.