Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches.

Abstract:

:Simple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits to simulated data, where parameters (and future behaviour) are known, to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).

journal_name

Stat Methods Med Res

authors

Li M,Dushoff J,Bolker BM

doi

10.1177/0962280217747054

subject

Has Abstract

pub_date

2018-07-01 00:00:00

pages

1956-1967

issue

7

eissn

0962-2802

issn

1477-0334

journal_volume

27

pub_type

杂志文章
  • Inferences about a linear combination of proportions.

    abstract::Statistical methods for carrying out asymptotic inferences (tests or confidence intervals) relative to one or two independent binomial proportions are very frequent. However, inferences about a linear combination of K independent proportions L = Σβ(i)p(i) (in which the first two are special cases) have had very little...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280209347953

    authors: Martín Andrés A,Alvarez Hernández M,Herranz Tejedor I

    更新日期:2011-08-01 00:00:00

  • On the power of the Cochran-Armitage test for trend in the presence of misclassification.

    abstract::The Cochran-Armitage (CA) test is commonly used in both epidemiology and genetics to test for linear trend in two-way tables with a binary outcome. There has been increasing interest in the power and size of the test and in determination of sample size, especially when there is potential misclassification in the 'expo...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280211406424

    authors: Buonaccorsi JP,Laake P,Veierød MB

    更新日期:2014-06-01 00:00:00

  • Interval estimation of a population mean using existing knowledge or data on effect sizes.

    abstract::Bayes or empirical Bayes methods to improve inferential accuracy for a population mean has been widely adopted in medical research. As the joint prior distribution of both the mean and variance parameters can be difficult to specify or estimate, most of these methods have relied on certain level of simplifications of ...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280218773537

    authors: Shen C

    更新日期:2019-06-01 00:00:00

  • Sample size calculation for treatment effects in randomized trials with fixed cluster sizes and heterogeneous intraclass correlations and variances.

    abstract::When comparing two different kinds of group therapy or two individual treatments where patients within each arm are nested within care providers, clustering of observations may occur in both arms. The arms may differ in terms of (a) the intraclass correlation, (b) the outcome variance, (c) the cluster size, and (d) th...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280214563100

    authors: Candel MJ,van Breukelen GJ

    更新日期:2015-10-01 00:00:00

  • Multiplicity adjustments in trials with two correlated comparisons of interest.

    abstract::Clinical trials investigating the efficacy of two or more doses of an experimental treatment compared to a single reference arm are not uncommon. In such situations, if each dose is compared to the reference arm using an un-adjusted significance level, consideration of the Type I familywise error is likely to be requi...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280210378943

    authors: Fernandes N,Stone A

    更新日期:2011-12-01 00:00:00

  • Sample size for binary logistic prediction models: Beyond events per variable criteria.

    abstract::Binary logistic regression is one of the most frequently applied statistical approaches for developing clinical prediction models. Developers of such models often rely on an Events Per Variable criterion (EPV), notably EPV ≥10, to determine the minimal sample size required and the maximum number of candidate predictor...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280218784726

    authors: van Smeden M,Moons KG,de Groot JA,Collins GS,Altman DG,Eijkemans MJ,Reitsma JB

    更新日期:2019-08-01 00:00:00

  • Semi-supervised identification of cancer subgroups using survival outcomes and overlapping grouping information.

    abstract::Identification of cancer patient subgroups using high throughput genomic data is of critical importance to clinicians and scientists because it can offer opportunities for more personalized treatment and overlapping treatments of cancers. In spite of tremendous efforts, this problem still remains challenging because o...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217752980

    authors: Wei W,Sun Z,da Silveira WA,Yu Z,Lawson A,Hardiman G,Kelemen LE,Chung D

    更新日期:2019-07-01 00:00:00

  • Statistical methods in genetic research on smoking.

    abstract::A growing body of evidence suggests that genetic factors have an important influence on the onset and course of smoking. Here we review some of the statistical methods that have been used to test for genetic influences on smoking behaviour, with a particular focus on studies of large national twin samples. We show how...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章,评审

    doi:10.1177/096228029800700205

    authors: Heath AC,Madden PA,Martin NG

    更新日期:1998-06-01 00:00:00

  • Forensic inference from genetic markers.

    abstract::This review provides an overview of forensic inference from genetic markers. Because the judge and jurors are charged with decision-making, the forensic expert's job is to provide a useful summary of the evidence to the court. Hence, this review focuses on the likelihood ratio as a means of summarizing the genetic dat...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章,评审

    doi:10.1177/096228029300200304

    authors: Devlin B

    更新日期:1993-01-01 00:00:00

  • Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator.

    abstract::Censored data make survival analysis more complicated because exact event times are not observed. Statistical methodology developed to account for censored observations assumes that patients' withdrawal from a study is independent of the event of interest. However, in practice, some covariates might be associated to b...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280216628900

    authors: Willems S,Schat A,van Noorden MS,Fiocco M

    更新日期:2018-02-01 00:00:00

  • Practical issues arising in an exploratory analysis evaluating progression-free survival as a surrogate endpoint for overall survival in advanced colorectal cancer.

    abstract::This paper is based on a conference presentation in which several authors presented results from analyses of the same dataset concerning the evaluation of progression-free survival (PFS) as a surrogate endpoint for overall survival in advanced colorectal cancer clinical trials. In evaluating a potential surrogate endp...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280207081860

    authors: Hughes MD

    更新日期:2008-10-01 00:00:00

  • Bayesian modeling and prediction of accrual in multi-regional clinical trials.

    abstract::In multi-regional trials, the underlying overall and region-specific accrual rates often do not hold constant over time and different regions could have different start-up times, which combined with initial jump in accrual within each region often leads to a discontinuous overall accrual rate, and these issues associa...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280214557581

    authors: Deng Y,Zhang X,Long Q

    更新日期:2017-04-01 00:00:00

  • Functional data analysis in longitudinal settings using smoothing splines.

    abstract::Data in many experiments arise as curves and therefore it is natural to use a curve as a basic unit in the analysis, which is termed functional data analysis (FDA). In longitudinal studies, recent developments in FDA have extended classical linear models and linear mixed effects models to functional linear models (als...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章,评审

    doi:10.1191/0962280204sm352ra

    authors: Guo W

    更新日期:2004-02-01 00:00:00

  • Probability intervals of toxicity and efficacy design for dose-finding clinical trials in oncology.

    abstract::Immunotherapy, gene therapy or adoptive cell therapies, such as the chimeric antigen receptor+ T-cell therapies, have demonstrated promising therapeutic effects in oncology patients. We consider statistical designs for dose-finding adoptive cell therapy trials, in which the monotonic dose-response relationship assumed...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280220977009

    authors: Lin X,Ji Y

    更新日期:2020-12-16 00:00:00

  • Semiparametric integrative interaction analysis for non-small-cell lung cancer.

    abstract::In genomic analysis, it is significant though challenging to identify markers associated with cancer outcomes or phenotypes. Based on the biological mechanisms of cancers and the characteristics of datasets, we propose a novel integrative interaction approach under a semiparametric model, in which genetic and environm...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280220909969

    authors: Li Y,Wang F,Li R,Sun Y

    更新日期:2020-10-01 00:00:00

  • The application of methods to quantify attributable risk in medical practice.

    abstract::Several epidemiological parameters have been introduced for quantifying the population impact of a certain exposure on morbidity on a population level, termed 'attributable risk' (AR). Of these definitions, the AR as suggested by Levin in 1953 or some algebraic transformations of it are most commonly used. A structure...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/096228020101000305

    authors: Uter W,Pfahlberg A

    更新日期:2001-06-01 00:00:00

  • Estimation of half-life periods in nonlinear data with fractional polynomials.

    abstract::Regression models are frequently used to model the functional relationship between an interesting outcome parameter and one or more potentially relevant explanatory variables. Objectives can be to set up as a prognostic model, for example, or an estimation model for a certain parameter of interest. Determining half-li...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280213502403

    authors: Mayer B,Keller F,Syrovets T,Wittau M

    更新日期:2016-10-01 00:00:00

  • Advanced colorectal neoplasia risk stratification by penalized logistic regression.

    abstract::Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratif...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280213497432

    authors: Lin Y,Yu M,Wang S,Chappell R,Imperiale TF

    更新日期:2016-08-01 00:00:00

  • Statistical challenges in assessing potential efficacy of complex interventions in pilot or feasibility studies.

    abstract::Early phase trials of complex interventions currently focus on assessing the feasibility of a large randomised control trial and on conducting pilot work. Assessing the efficacy of the proposed intervention is generally discouraged, due to concerns of underpowered hypothesis testing. In contrast, early assessment of e...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280215589507

    authors: Wilson DT,Walwyn RE,Brown J,Farrin AJ,Brown SR

    更新日期:2016-06-01 00:00:00

  • Bayesian spatially dependent variable selection for small area health modeling.

    abstract::Statistical methods for spatial health data to identify the significant covariates associated with the health outcomes are of critical importance. Most studies have developed variable selection approaches in which the covariates included appear within the spatial domain and their effects are fixed across space. Howeve...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280215627184

    authors: Choi J,Lawson AB

    更新日期:2018-01-01 00:00:00

  • Study design for epidemiologic studies with measurement error.

    abstract::Exposure measurement error in epidemiological studies is recognized as a feature that must be considered because of the potential bias that can result in estimates of the exposure-disease association. Most of the work to date has focused on methods of analysis that adjust for the resultant bias, but the implications o...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章,评审

    doi:10.1177/096228029500400405

    authors: Holford TR,Stack C

    更新日期:1995-12-01 00:00:00

  • An ad hoc method for dual adjusting for measurement errors and nonresponse bias for estimating prevalence in survey data: Application to Iranian mental health survey on any illicit drug use.

    abstract::Purpose The prevalence estimates of binary variables in sample surveys are often subject to two systematic errors: measurement error and nonresponse bias. A multiple-bias analysis is essential to adjust for both biases. Methods In this paper, we linked the latent class log-linear and proxy pattern-mixture models to ad...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217690939

    authors: Khalagi K,Mansournia MA,Motevalian SA,Nourijelyani K,Rahimi-Movaghar A,Bakhtiyari M

    更新日期:2018-10-01 00:00:00

  • A comparison of power analysis methods for evaluating effects of a predictor on slopes in longitudinal designs with missing data.

    abstract::In many longitudinal studies, evaluating the effect of a binary or continuous predictor variable on the rate of change of the outcome, i.e. slope, is often of primary interest. Sample size determination of these studies, however, is complicated by the expectation that missing data will occur due to missed visits, earl...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280212437452

    authors: Wang C,Hall CB,Kim M

    更新日期:2015-12-01 00:00:00

  • Mixture modelling for cluster analysis.

    abstract::Cluster analysis via a finite mixture model approach is considered. With this approach to clustering, the data can be partitioned into a specified number of clusters g by first fitting a mixture model with g components. An outright clustering of the data is then obtained by assigning an observation to the component to...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1191/0962280204sm372ra

    authors: McLachlan GJ,Chang SU

    更新日期:2004-10-01 00:00:00

  • A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure.

    abstract::We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of health continues to grow. Likewise, community randomized trials are applied to learn about real-world implementation, sustainability, and popula...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280218774936

    authors: Balzer LB,Zheng W,van der Laan MJ,Petersen ML

    更新日期:2019-06-01 00:00:00

  • Applications of temporal kernel canonical correlation analysis in adherence studies.

    abstract::Adherence to medication is often measured as a continuous outcome but analyzed as a dichotomous outcome due to lack of appropriate tools. In this paper, we illustrate the use of the temporal kernel canonical correlation analysis (tkCCA) as a method to analyze adherence measurements and symptom levels on a continuous s...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280215598805

    authors: John M,Lencz T,Ferbinteanu J,Gallego JA,Robinson DG

    更新日期:2017-10-01 00:00:00

  • Interpretation of mixed models and marginal models with cohort attrition due to death and drop-out.

    abstract::Mixed models estimated by maximum likelihood and marginal models estimated by generalized estimating equations are the standard methods for the analysis of longitudinal data. However, their use is highly debated when attrition may be due to death. While some authors consider that mixed model estimates are interpretabl...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217723675

    authors: Rouanet A,Helmer C,Dartigues JF,Jacqmin-Gadda H

    更新日期:2019-02-01 00:00:00

  • A stochastic estimation procedure for intermittently-observed semi-Markov multistate models with back transitions.

    abstract::Multistate models provide an important method for analyzing a wide range of life history processes including disease progression and patient recovery following medical intervention. Panel data consisting of the states occupied by an individual at a series of discrete time points are often used to estimate transition i...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280217736342

    authors: Aralis H,Brookmeyer R

    更新日期:2019-03-01 00:00:00

  • Modeling fecundity in the presence of a sterile fraction using a semi-parametric transformation model for grouped survival data.

    abstract::The analysis of fecundity data is challenging and requires consideration of both highly timed and interrelated biologic processes in the context of essential behaviors such as sexual intercourse during the fertile window. Understanding human fecundity is further complicated by presence of a sterile population, i.e. co...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/0962280212438646

    authors: McLain AC,Sundaram R,Buck Louis GM

    更新日期:2016-02-01 00:00:00

  • Multilevel models for censored and latent responses.

    abstract::Multilevel models were originally developed to allow linear regression or ANOVA models to be applied to observations that are not mutually independent. This lack of independence commonly arises due to clustering of the units of observations into 'higher level units' such as patients in hospitals. In linear mixed model...

    journal_title:Statistical methods in medical research

    pub_type: 杂志文章

    doi:10.1177/096228020101000604

    authors: Rabe-Hesketh S,Yang S,Pickles A

    更新日期:2001-12-01 00:00:00