Evidence map›Paper›PMID 38619866›Full record

ArticleJMIR medical education2024

Rolling the DICE (Design, Interpret, Compute, Estimate): Interactive Learning of Biostatistics With Simulations.

Robert Thiesmeier, Nicola Orsini

Registry-linked trialAbstract read
In one paragraph

Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07016282 (Simulated and Synthetic Health Data), which is not on this 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.

NCT07016282 active not recruitingnot on this mapstarted 2025, after this paper: background citation

Simulated and Synthetic Health Data: Improving Clinical Research on Rare Diseases. A Real-World Data Simulation of Autosomal Dominant Polycystic Kidney Disease (ADPKD) Trials. A Retrospective, Observational Study

TypeobservationalSponsorMario Negri Institute for Pharmacological ResearchRan2025 to 2027Enrolled100ConditionsAutosomal Dominant Polycystic Kidney Disease (ADPKD)
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

2 authors.

Robert Thiesmeier *Department of Global Public Health, Karolinska Institutet, Solna, Sweden.ORCID 0000-0002-4590-5538
Nicola Orsini *Department of Global Public Health, Karolinska Institutet, Solna, Sweden.ORCID 0000-0002-2210-5634

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the increasing relevance of statistics in health sciences, teaching styles in higher education are remarkably similar across disciplines: lectures covering the theory and methods, followed by application and computer exercises in given data sets. This often leads to challenges for students in comprehending fundamental statistical concepts essential for medical research. To address these challenges, we propose an engaging learning approach-DICE (design, interpret, compute, estimate)-aimed at enhancing the learning experience of statistics in public health and epidemiology. In introducing DICE, we guide readers through a practical example. Students will work in small groups to plan, generate, analyze, interpret, and communicate their own scientific investigation with simulations. With a focus on fundamental statistical concepts such as sampling variability, error probabilities, and the construction of statistical models, DICE offers a promising approach to learning how to combine substantive medical knowledge and statistical concepts. The materials in this paper, including the computer code, can be readily used as a hands-on tool for both teachers and students.

Indexed as

BiostatisticsSimulation TrainingBiometryHumansPublic HealthStudentslearning statisticsMonte Carlo simulationsimulation-based learningsurvival analysisWeibull

Identifiers

PMID38619866
PMCPMC11058551

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.