Arterial Occlusive Disease Measurements for 16 Patients with Repeated Leg Data

A historical dataset from a study proposing a Bayesian hierarchical modeling framework for mixed-type outcomes. It contains 256 repeated leg measurements from 16 patients with arterial occlusive diseases. The dataset was created by Endris Assen Ebrahim and last updated on 2026-04-15.

Use Cases

• Comparing computational methods like Metropolis-Hastings, Gibbs sampling, and Hamiltonian Monte Carlo based on the described modeling framework.
• Evaluating model performance using criteria such as WAIC, LOO-IC, and DIC as mentioned in the study.
• Developing hierarchical models for repeated measures of binary and continuous outcomes in medical research.
• Analyzing small-sample datasets with complex dependency structures, as referenced in the description.

Strengths

• Dataset size is precisely 5.5 KB, indicating a compact and focused collection.
• Includes 256 observations from 16 patients, providing a specific scale for analysis.
• Licensed under CC-BY-4.0, allowing for open sharing and adaptation.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The description notes findings may depend on specific priors and may not generalize to all small-sample datasets.

Provenance

Source

figshare, authored by Endris Assen Ebrahim.

Collection Method

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

Freshness

Last updated 2026-04-15 17:50:51; freshness should be verified.