Arterial Occlusive Disease Measurements for 16 Patients, 256 Observations

A historical dataset of repeated leg measurements from 16 patients, resulting in 256 total observations, used to model arterial occlusive diseases. The data was analyzed in a study by Endris Assen Ebrahim, comparing Bayesian MCMC methods, with results last updated in April 2026. The dataset is provided in an XLS format under a CC-BY-4.0 license.

Use Cases

• Comparing MCMC algorithm performance (Metropolis-Hastings, Gibbs, HMC) based on the methodological study described.
• Evaluating model selection criteria (WAIC, LOO-IC, DIC) for hierarchical models based on the described analysis framework.
• Benchmarking predictive accuracy of random effects models for repeated measures data based on the study's focus.

Strengths

• Dataset size is precisely defined as 5.5 KB.
• Study design includes 16 patients with 256 repeated observations.
• Model performance was evaluated using multiple established information criteria (WAIC, LOO-IC, DIC, BIC-ICOMP).

Limitations

• Row count and column definitions are unknown, limiting suitability assessment.
• The dataset is described as a small-sample, historical dataset, which may limit generalizability.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Endris Assen Ebrahim via figshare

Collection Method

Likely collected as part of a medical research study on arterial occlusive diseases.

Freshness

Last updated 2026-04-15 17:50:52; freshness should be verified.