Non-linear Association Structures

How it works

Benefits

Preparation

Highlighted claims

• The method represents the association function as f(nu)=g(nu)nu with f(0)=0. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
• The hierarchy includes a standard linear level, a quadratic level, and a spline-deviation level. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
• The Level 3 model uses knots and an orthogonalized second-order random walk to model smooth non-linear departures. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
• The hierarchy can shrink toward simpler association structures when the data do not justify non-linearity. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
• The marker must be centered at a meaningful reference point because zero defines the reference risk. — Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA