Estimating multilevel regional variation in excess mortality of cancer patients using integrated nested Laplace approximation

Seppä, Karri; Rue, Håvard; Hakulinen, Timo; Läärä, Esa; Sillanpää, Mikko J.; Pitkäniemi, Janne

doi:10.1002/sim.8010

Cited by 13 publications

(9 citation statements)

References 51 publications

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“…In this section, we will fit the models using the R-INLA package (Rue et al, 2009(Rue et al, , 2017Seppä et al, 2019), a fast approximate Bayesian inference package catered towards spatial modelling.…”

Section: Undirected Structured Priors Example: Spatial Mrpmentioning

confidence: 99%

Improving Multilevel Regression and Poststratification with Structured Priors

et al. 2021

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A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population. Multilevel regression and poststratification (MRP), a model-based approach, is gaining traction against the traditional weighted approach for survey estimates. MRP estimates are susceptible to bias if there is an underlying structure that the methodology does not capture. This work aims to provide a new framework for specifying structured prior distributions that lead to bias reduction in MRP estimates. We use simulation studies to explore the benefit of these prior distributions and demonstrate their efficacy on non-representative US survey data. We show that structured prior distributions offer absolute bias reduction and variance reduction for posterior MRP estimates in a large variety of data regimes.

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Section: Undirected Structured Priors Example: Spatial Mrpmentioning

confidence: 99%

Improving Multilevel Regression and Poststratification with Structured Priors

et al. 2021

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“…Wakefield, Simpson and Godwin (2016) suggested fitting this model using the R-INLA package to speed up the computation. As the quantity of interest is a non-linear transformation of a number of parameters, we need to use the R-INLA's approximate posterior sampler, which is a relatively recent feature (Seppä et al, 2017).…”

Section: Advi Can Fail For Simple Modelsmentioning

confidence: 99%

Validating Bayesian Inference Algorithms with Simulation-Based Calibration

Talts,

Betancourt,

Simpson

et al. 2018

Preprint

123

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Verifying the correctness of Bayesian computation is challenging. This is especially true for complex models that are common in practice, as these require sophisticated model implementations and algorithms. In this paper we introduce simulation-based calibration (SBC), a general procedure for validating inferences from Bayesian algorithms capable of generating posterior samples. This procedure not only identifies inaccurate computation and inconsistencies in model implementations but also provides graphical summaries that can indicate the nature of the problems that arise. We argue that SBC is a critical part of a robust Bayesian workflow, as well as being a useful tool for those developing computational algorithms and statistical software.Conditioning this joint distribution on an observation, ỹ, yields a posterior distribution, π(θ | ỹ) ∝ π(ỹ, θ),

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“…These are fairly large population coverage areas (ranging from ~ 2.5million to ~ 9 million individuals in 2019). For smaller geographical areas, there will be much greater variation and a modelling approach that smooths through the survival estimates, and also borrows strength across regions, would offer a better analysis strategy [35,36].…”

Section: Discussionmentioning

confidence: 99%

Non-parametric estimation of reference adjusted, standardised probabilities of all-cause death and death due to cancer for population group comparisons

Rutherford

Andersson

Myklebust

et al. 2022

BMC Med Res Methodol

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Background Ensuring fair comparisons of cancer survival statistics across population groups requires careful consideration of differential competing mortality due to other causes, and adjusting for imbalances over groups in other prognostic covariates (e.g. age). This has typically been achieved using comparisons of age-standardised net survival, with age standardisation addressing covariate imbalance, and the net estimates removing differences in competing mortality from other causes. However, these estimates lack ease of interpretability. In this paper, we motivate an alternative non-parametric approach that uses a common rate of other cause mortality across groups to give reference-adjusted estimates of the all-cause and cause-specific crude probability of death in contrast to solely reporting net survival estimates. Methods We develop the methodology for a non-parametric equivalent of standardised and reference adjusted crude probabilities of death, building on the estimation of non-parametric crude probabilities of death. We illustrate the approach using regional comparisons of survival following a diagnosis of rectal cancer for men in England. We standardise to the covariate distribution and other cause mortality of England as a whole to offer comparability, but with close approximation to the observed all-cause region-specific mortality. Results The approach gives comparable estimates to observed crude probabilities of death, but allows direct comparison across population groups with different covariate profiles and competing mortality patterns. In our illustrative example, we show that regional variations in survival following a diagnosis of rectal cancer persist even after accounting for the variation in deprivation, age at diagnosis and other cause mortality. Conclusions The methodological approach of using standardised and reference adjusted metrics offers an appealing approach for future cancer survival comparison studies and routinely published cancer statistics. Our non-parametric estimation approach through the use of weighting offers the ability to estimate comparable survival estimates without the need for statistical modelling.

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Estimating multilevel regional variation in excess mortality of cancer patients using integrated nested Laplace approximation

Cited by 13 publications

References 51 publications

Improving Multilevel Regression and Poststratification with Structured Priors

Improving Multilevel Regression and Poststratification with Structured Priors

Validating Bayesian Inference Algorithms with Simulation-Based Calibration

Non-parametric estimation of reference adjusted, standardised probabilities of all-cause death and death due to cancer for population group comparisons

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