Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects

By Patrick Royston, Mahesh K B Parmar. Statistics in Medicine, 2002

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Beyond Logit and Probit: Cox Duration Models of Single, Repeating, and Competing Events for State Policy Adoption (2005, methodology): “Among these are flexible parametric models (Royston and Parmar 2001, 2002), dependent-risks models (Gordon 2002), and frailty or random-coefficients models (Box-Steffensmeier and Jones 2004).”
flexsurv: A Platform for Parametric Survival Modeling in R. (2016, methodology): “number of knots (Royston and Parmar 2002) and 3–4 parameter generalized gamma and F distribution families.”
Most Likely Transformations (2015, methodology): “Mallick and Walker (2003); Chang et al. (2005) and McLain and Ghosh (2013) used Bernstein polynomials for hY , and Royston and Parmar (2002) proposed a maximum likelihood approach using cubic splines for modelling hY and also time-varying effects.”