5 papers
Bayesian joint modeling of longitudinal patient-reported outcomes and survival: an application to chronic obstructive pulmonary disease
Cristina Galán-Arcicollar, Danilo Alvares, Josu Najera-Zuloaga +1
Questionnaire-based patient-reported outcomes (PROs) are discrete, bounded and overdispersed, yet joint models relating them to survival may ignore these features or estimate both…
The C-index illusion: discrimination without calibration in published survival models
Rafael da Silva, Danilo Alvares
Recent work has argued normatively, on synthetic data, that evaluating survival models by discrimination alone (concordance index) yields systematically misleading model comparison…
Bayesian shared parameter joint models for heterogeneous populations
Sida Chen, Danilo Alvares, Marco Palma +1
Joint models (JMs) for longitudinal and time-to-event data are an important class of biostatistical models in health and medical research. When the study population consists of het…
A Bayesian joint model of multiple longitudinal and categorical outcomes with application to multiple myeloma using permutation-based variable importance
Danilo Alvares, Jessica K. Barrett, François Mercier +5
Joint models have proven to be an effective approach for uncovering potentially hidden connections between various types of outcomes, mainly continuous, time-to-event, and binary.…
A Bayesian joint model of multiple nonlinear longitudinal and competing risks outcomes for dynamic prediction in multiple myeloma: joint estimation and corrected two-stage approaches
Danilo Alvares, Jessica K. Barrett, François Mercier +5
Predicting cancer-associated clinical events is challenging in oncology. In Multiple Myeloma (MM), a cancer of plasma cells, disease progression is determined by changes in biomark…