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Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. We consider…

Methodology · Statistics 2020-06-24 Susanna Makela , Yajuan Si , Andrew Gelman

This paper develops Bayesian sample size formulae for experiments comparing two groups. We assume the experimental data will be analysed in the Bayesian framework, where pre-experimental information from multiple sources can be represented…

Methodology · Statistics 2022-03-09 Haiyan Zheng , Thomas Jaki , James M. S. Wason

Prior probabilities of clinical hypotheses are not systematically used for clinical trial design yet, due to a concern that poor priors may lead to poor decisions. To address this concern, a conservative approach to Bayesian trial design is…

Methodology · Statistics 2023-09-21 Fabio Rigat

Bayesian experimental design (BED) is a framework that uses statistical models and decision making under uncertainty to optimise the cost and performance of a scientific experiment. Sequential BED, as opposed to static BED, considers the…

Machine Learning · Statistics 2020-03-23 Steven Kleinegesse , Christopher Drovandi , Michael U. Gutmann

We propose a new procedure named PASOA, for Bayesian experimental design, that performs sequential design optimization by simultaneously providing accurate estimates of successive posterior distributions for parameter inference. The…

Machine Learning · Statistics 2024-10-16 Jacopo Iollo , Christophe Heinkelé , Pierre Alliez , Florence Forbes

A current challenge for many Bayesian analyses is determining when to terminate high-dimensional Markov chain Monte Carlo simulations. To this end, we propose using an automated sequential stopping procedure that terminates the simulation…

Computation · Statistics 2014-03-24 Lei Gong , James M. Flegal

The design of an experiment can be always be considered at least implicitly Bayesian, with prior knowledge used informally to aid decisions such as the variables to be studied and the choice of a plausible relationship between the…

Methodology · Statistics 2017-01-03 David C. Woods , Antony M. Overstall , Maria Adamou , Timothy W. Waite

Crossover designs are an extremely useful tool to investigators, whilst group sequential methods have proven highly proficient at improving the efficiency of parallel group trials. Yet, group sequential methods and crossover designs have…

Methodology · Statistics 2017-10-11 Michael Grayling , James Wason , Adrian Mander

We describe group sequential tests which efficiently incorporate information from multiple endpoints allowing for early stopping at pre-planned interim analyses. We formulate a testing procedure where several outcomes are examined, and…

Methodology · Statistics 2024-05-09 Abigail J. Burdon , Thomas Jaki

Due to the high cost and high failure rate of Phase III trials, seamless Phase II/III designs are more and more popular to trial efficiency. A potential attraction of Phase II/III design is to allow a randomized proof-of-concept stage prior…

Applications · Statistics 2022-06-28 Guanhong Miao , Jason J. Z. Liao , Jing Yang , Keaven Anderson

This paper deals with the problem of estimating the volume of the excursion set of a function $f:\mathbb{R}^d \to \mathbb{R}$ above a given threshold, under a probability measure on $\mathbb{R}^d$ that is assumed to be known. In the…

Computation · Statistics 2012-04-26 Julien Bect , David Ginsbourger , Ling Li , Victor Picheny , Emmanuel Vazquez

When prospectively developing a new clinical prediction model (CPM), fixed sample size calculations are typically conducted before data collection based on sensible assumptions. But if the assumptions are inaccurate the actual sample size…

Optimal design is a critical yet challenging task within many applications. This challenge arises from the need for extensive trial and error, often done through simulations or running field experiments. Fortunately, sequential optimal…

Machine Learning · Computer Science 2025-03-25 Xubo Yue , Raed Al Kontar , Albert S. Berahas , Yang Liu , Blake N. Johnson

Sequential design is a highly active field of research in active learning which provides a general framework for designing computer experiments with limited computational budgets. It aims to create efficient surrogate models to replace…

Methodology · Statistics 2025-01-03 Paul Lartaud , Philippe Humbert , Josselin Garnier

Inferring the causal structure of a system typically requires interventional data, rather than just observational data. Since interventional experiments can be costly, it is preferable to select interventions that yield the maximum amount…

Methodology · Statistics 2021-03-30 Michele Zemplenyi , Jeffrey W. Miller

Sample size determination is crucial in experimental design, especially in traffic and transport research. Frequentist statistics require a fixed sample size determined by power analysis, which cannot be adjusted once the experiment starts.…

Methodology · Statistics 2025-03-04 Xiaomi Yang , Carol Flannagan , Jonas Bärgman

In a group sequential clinical trial, accumulated data are analysed at numerous time-points in order to allow early decisions about a hypothesis of interest. These designs have historically been recommended for their ethical, administrative…

Computation · Statistics 2017-11-29 Michael Grayling , James Wason , Adrian Mander

A group sequential clinical trial design can be an attractive option when planning a pivotal trial as this approach has the ability to stop the trial early for success, whilst also being well accepted from a regulatory review perspective.…

Applications · Statistics 2023-03-03 Fraser I Lewis

Traditional accelerated life test plans are typically based on optimizing the C-optimality for minimizing the variance of an interested quantile of the lifetime distribution. The traditional methods rely on some specified planning values…

Applications · Statistics 2018-12-04 Lu Lu , I-Chen Lee , Yili Hong

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at…

Methodology · Statistics 2023-08-24 Özge Sürer , Matthew Plumlee , Stefan M. Wild