ArticlePharmacoEconomics2018
Different Methods for Modelling Severe Hypoglycaemic Events: Implications for Effectiveness, Costs and Health Utilities.
Article in PharmacoEconomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed, 2 citations in OpenAlex.
- Shared parameter model for competing risks and different data summaries in meta-analysis: Implications for common and rare outcomes.Research synthesis methods · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundClinical trials report severe hypoglycaemic events as the number of patients with at least one event out of the total randomised or number of events for a given total exposure. Different network meta-analysis models have been used to analyse these different data types.
objectiveThis aim of this article was to establish the impact of using the different models on effectiveness, costs and health utility estimates.
methodsWe analysed a dataset used in a recent network meta-analysis of severe hypoglycaemic events conducted to inform National Institute for Health and Care Excellence recommendations regarding basal insulin choice for patients with type 1 diabetes mellitus. We fitted a model with a binomial likelihood reporting odds ratios (using a logit link) or hazard ratios (complementary log-log link), a model with a Poisson likelihood reporting hazard ratios and a shared-parameter model combining different types of data. We compared the results in terms of relative effects and resulting cost and disutility estimates.
resultsRelative treatment effects are similar regardless of which model or scale is used. Differences were seen in the probability of having an event on the baseline treatment with the logit model giving a baseline probability of 0.07, the complementary log-log 0.17 and the Poisson 0.29. These translate into differences of up to £110 in the yearly cost of a hypoglycaemic event and 0.004 in disutility.
conclusionWhile choice of network meta-analysis model does not have a meaningful impact on relative effects for this outcome, care should be taken to ensure that the baseline probabilities used in an economic model are accurate to avoid misrepresenting costs and effects.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.