JMbayes2: Joint Models for Longitudinal and Survival Data

Dimitris Rizopoulos developed this software for fitting joint models under a Bayesian framework. It accommodates multiple longitudinal outcomes of mixed type and multiple event times, including competing risks and multi-state processes. The methodology is based on the author's 2012 book (ISBN:9781439872864).

Use Cases

• Modeling disease progression and survival based on repeated biomarker measurements.
• Analyzing competing risks in clinical trials using multiple longitudinal outcomes.
• Estimating joint model parameters for multi-state processes via Bayesian inference.

Strengths

• Supports multiple longitudinal outcomes of mixed type (continuous/categorical).
• Accommodates multiple event times, including competing risks and multi-state processes.
• Implements a Bayesian approach for joint modeling as described in Rizopoulos (2012).

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file formats are unknown, which may limit suitability assessment.

Provenance

Source

Dimitris Rizopoulos

Collection Method

Software implementation for statistical modeling.