From 20aeb724e5d935c21fac05610c721dd68245b075 Mon Sep 17 00:00:00 2001 From: theorashid Date: Tue, 12 Dec 2023 12:21:58 +0000 Subject: [PATCH] cancer paper --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 518cfc9..0e9c9cd 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,7 @@ This code is used in: - Rashid, T., Bennett, J.E. et al. (2021). [Life expectancy and risk of death in 6791 communities in England from 2002 to 2019: high-resolution spatiotemporal analysis of civil registration data](https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(21)00205-X/fulltext). _The Lancet Public Health_. - Bennett, J.E., Rashid, T. et al. (2023). [Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data](https://www.thelancet.com/journals/lanepe/article/PIIS2666-7762(22)00276-9/fulltext). _The Lancet Regional Health Europe_. +- Rashid, T., Bennett, J.E. et al. (2023). [Mortality from leading cancers in districts of England from 2002 to 2019: a population-based, spatiotemporal study](https://www.thelancet.com/journals/lanonc/article/PIIS1470-2045(23)00530-2/fulltext). _The Lancet Oncology_. ## Table of models @@ -15,7 +16,7 @@ nested.bug | Rashid 2021 | gamma-Poisson | $α_0 + β_0 t + α_{1s} + β_{1s} t+ BYM.bug | Rashid 2021 | gamma-Poisson | $α_0 + β_0 t + α_{1s} + β_{1s} t+ α_{2a} + β_{2a} t + ξ_{as} + γ_{at} + ν_{st}$ | BYM nested_bb.bug | Bennett 2023 | beta-binomial | $α_0 + β_0 t + α_{1s} + β_{1s} t+ α_{2a} + β_{2a} t + ξ_{as} + γ_{at} + ν_{st}$ | nested nested.py | - | binomial | $α_0 + β_0 t + α_{1s} + β_{1s} t+ α_{2a} + β_{2a} t + ξ_{as} + γ_{at}$ | nested -car.py | - | binomial | $α_0 + β_0 t + α_{1s} + β_{1s} t+ α_{2a} + β_{2a} t + ξ_{as} + γ_{at}$ | ICAR +car.py | Rashid 2023 | binomial | $α_0 + β_0 t + α_{1s} + β_{1s} t+ α_{2a} + β_{2a} t + ξ_{as} + γ_{at}$ | ICAR ### nimble models @@ -40,7 +41,7 @@ For the full explanation of the options available, run Rscript run_model.R --help ``` -### numpyro models (experimental) +### numpyro models By porting the model to [numpyro](https://num.pyro.ai/), I have seen massive speedups, both in terms of run time and effective samples per second. This is thanks to numpyro's jax backend allowing sampling on a GPU, which is beneficial for large models, and using NUTS over nimble's conjugate Gibbs/RWMH samplers.