arXiv subjects

Matthew Sperrin

Publications and source records attributed to Matthew Sperrin.

23 records · Page 2Linked to original sources

arXiv2017

Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models

Objectives: Clinical prediction models (CPMs) can inform decision-making concerning treatment initiation. Here, one requires predicted risks assuming that no treatment is given. This is challenging since CPMs are often derived in datasets where patients receive treatment; moreover, treatment can commence post-baseline - treatment drop-ins. This study presents a novel approach of using marginal structural models (MSMs) to adjust for treatment drop-in. Study Design and Setting: We illustrate the use of MSMs in the CPM framework through simulation studies, representing randomised controlled trials and observational data. The simulations include a binary treatment and a covariate, each recorded at two timepoints and having a prognostic effect on a binary outcome. The bias in predicted risk was examined in a model ignoring treatment, a model fitted on treatment naïve patients (at baseline), a model including baseline treatment, and the MSM. Results: In all simulation scenarios, all models except the MSM under-estimated the risk of outcome given absence of treatment. Consequently, CPMs that do not acknowledge treatment drop-in can lead to under-allocation of treatment. Conclusion: When developing CPMs to predict treatment-naïve risk, authors should consider using MSMs to adjust for treatment drop-in. MSMs also allow estimation of individual treatment effects.

stat.ME↗

arXiv2011

Proximity penalty priors for Bayesian mixture models

When using mixture models it may be the case that the modeller has a-priori beliefs or desires about what the components of the mixture should represent. For example, if a mixture of normal densities is to be fitted to some data, it may be desirable for components to focus on capturing differences in location rather than scale. We introduce a framework called proximity penalty priors (PPPs) that allows this preference to be made explicit in the prior information. The approach is scale-free and imposes minimal restrictions on the posterior; in particular no arbitrary thresholds need to be set. We show the theoretical validity of the approach, and demonstrate the effects of using PPPs on posterior distributions with simulated and real data.

stat.ME↗

arXiv2011

Recovering Direct Effects in Genetics: A Comparison

In genetics it is often of interest to discover single nucleotide polymorphisms (SNPs) that are directly related to a disease, rather than just being associated with it. Few methods exist, however, addressing this so-called `true sparsity recovery' issue. In a thorough simulation study, we show that for moderate or low correlation between predictors, lasso-based methods perform well at true sparsity recovery, despite not being specifically designed for this purpose. For large correlations, however, more specialised methods are needed. Stability selection and direct effect testing perform well in all situations, including when the correlation is large.

stat.AP↗

arXiv2011

Modelling time to event with observations made at arbitrary times

We introduce new methods of analysing time to event data via extended versions of the proportional hazards and accelerated failure time (AFT) models. In many time to event studies, the time of first observation is arbitrary, in the sense that no risk modifying event occurs. This is particularly common in epidemiological studies. We show formally that, in these situations, it is not sensible to take the first observation as the time origin, either in AFT or proportional hazards type models. Instead, we advocate using age of the subject as the time scale. We account for the fact that baseline observations may be made at different ages in different patients via a two stage procedure. First, we marginally regress any potentially age-varying covariates against age, retaining the residuals. These residuals are then used as covariates in the fitting of either an AFT model or a proportional hazards model. We call the procedures residual accelerated failure time (RAFT) regression and residual proportional hazards (RPH) regression respectively. We compare standard AFT with RAFT, and demonstrate superior predictive ability of RAFT in real examples. In epidemiology, this has real implications in terms of risk communication to both patients and policy makers.

stat.ME↗

arXiv2010

A Novel Chronic Disease Policy Model

We develop a simulation tool to support policy-decisions about healthcare for chronic diseases in defined populations. Incident disease-cases are generated in-silico from an age-sex characterised general population using standard epidemiological approaches. A novel disease-treatment model then simulates continuous life courses for each patient using discrete event simulation. Ideally, the discrete event simulation model would be inferred from complete longitudinal healthcare data via a likelihood or Bayesian approach. Such data is seldom available for relevant populations, therefore an innovative approach to evidence synthesis is required. We propose a novel entropy-based approach to fit survival densities. This method provides a fully flexible way to incorporate the available information, which can be derived from arbitrary sources. Discrete event simulation then takes place on the fitted model using a competing hazards framework. The output is then used to help evaluate the potential impacts of policy options for a given population.

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