A Bayesian Bernoulli-Exponential joint model for binary longitudinal outcomes and informative time with applications to bladder cancer recurrence data
Abstract Background A variety of methods exist for the analysis of longitudinal data, many of which are characterized with the assumption of fixed visit time points for study individuals. This, however is not always a tenable assumption. Phenomenon that alter subject visit patterns such as adverse e...
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| Format: | Article |
| Language: | English |
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BMC
2024-03-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-024-02160-2 |