stgenreg: A Stata Package for General Parametric Survival Analysis

In this paper we present the Stata package stgenreg for the parametric analysis of survival data. Any user-defined hazard function can be specified, with the model estimated using maximum likelihood utilising numerical quadrature. Models that can be fitted range from the Weibull proportional hazards model to the generalized gamma model, mixture models, cure rate models, accelerated failure time models and relative survival models. We illustrate the features of stgenreg through application to a cohort of women diagnosed with breast cancer with outcome all-cause death.

[1]  D. Cox.  Regression Models and Life-Tables , 1972 .

[2]  T. Mok,et al.  Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. , 2009, The New England journal of medicine.

[3]  William Gould,et al.  Maximum likelihood estimation with stata , 1999 .

[4]  P C Lambert,et al.  Estimating the crude probability of death due to cancer and other causes using relative survival models , 2010, Statistics in medicine.

[5]  Niels Keiding,et al.  Statistical Models Based on Counting Processes , 1993 .

[6]  P. Royston,et al.  Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. , 1994 .

[7]  Milton C Weinstein,et al.  Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices--Modeling Studies. , 2003, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[8]  R. Simon,et al.  Flexible regression models with cubic splines. , 1989, Statistics in medicine.

[9]  Begnaud Francis Hildebrand,et al.  Introduction to numerical analysis: 2nd edition , 1987 .

[10]  Jong-Hyeon Jeong,et al.  Breast cancer adjuvant therapy: time to consider its time-dependent effects. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  Henrik Møller,et al.  Quantifying differences in breast cancer survival between England and Norway. , 2011, Cancer epidemiology.

[12]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[13]  Paul C Lambert,et al.  Flexible parametric models for relative survival, with application in coronary heart disease , 2007, Statistics in medicine.

[14]  Arnold Neumaier,et al.  Introduction to Numerical Analysis , 2001 .

[15]  Haitao Chu,et al.  Parametric survival analysis and taxonomy of hazard functions for the generalized gamma distribution , 2007, Statistics in medicine.

[16]  Paul C. Lambert,et al.  Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model , 2011 .

[17]  K. Abrams,et al.  Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study , 2011, BMC medical research methodology.

[18]  P. Royston,et al.  Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects , 2002, Statistics in medicine.