Alan Girling University of Birmingham, UK

Slides:

Advertisements

Similar presentations

Econometric analysis informing policies UNICEF workshop, 13 May 2008 Christian Stoff Statistics Division, UNESCAP,

Advertisements

Mustafa Cayci INFS 795 An Evaluation on Feature Selection for Text Clustering.

A systematic review of interventions for children with cerebral palsy: state of the evidence Rohini R Rattihalli

An Introduction to Sparse Coding, Sparse Sensing, and Optimization Speaker: Wei-Lun Chao Date: Nov. 23, 2011 DISP Lab, Graduate Institute of Communication.

Chapter 5 Introduction to Factorial Designs

Sandrine Dudoit1 Microarray Experimental Design and Analysis Sandrine Dudoit jointly with Yee Hwa Yang Division of Biostatistics, UC Berkeley

1 Chapter 5 Introduction to Factorial Designs Basic Definitions and Principles Study the effects of two or more factors. Factorial designs Crossed:

Stopping Trials for Futility RSS/NIHR HTA/MRC 1 day workshop 11 Nov 2008.

Tirgul 7. Find an efficient implementation of a dynamic collection of elements with unique keys Supported Operations: Insert, Search and Delete. The keys.

Using ranking and DCE data to value health states on the QALY scale using conventional and Bayesian methods Theresa Cain.

1Carl-Fredrik Burman, 11 Nov 2008 RSS / MRC / NIHR HTA Futility Meeting Futility stopping Carl-Fredrik Burman, PhD Statistical Science Director AstraZeneca.

Chapter 6: Transform and Conquer Genetic Algorithms The Design and Analysis of Algorithms.

Design Optimization School of Engineering University of Bradford 1 Formulation of a multi-objective problem Pareto optimum set consists of the designs.

Clinical Trials 2015 Practical Session 1. Q1: List three parameters (quantities) necessary for the determination of sample size (n) for a Phase III clinical.

Design of Engineering Experiments Part 4 – Introduction to Factorials

The return of the 5 year plan Mathematical programming for allocation of health care resources David Epstein, Karl Claxton, Mark Sculpher (CHE) Zaid Chalabi.

CIKM’09 Date:2010/8/24 Advisor: Dr. Koh, Jia-Ling Speaker: Lin, Yi-Jhen 1.

HSRP 734: Advanced Statistical Methods June 19, 2008.

QMB 4701 MANAGERIAL OPERATIONS ANALYSIS

LOGO A Convolution Accelerator for OR1200 Dawei Fan.

Optimization of personalized therapies for anticancer treatment Alexei Vazquez The Cancer Institute of New Jersey.

Duraid Y. Mohammed Philip J. Duncan Francis F. Li. School of Computing Science and Engineering, University of Salford UK Audio Content Analysis in The.

Genetic Algorithms What is a GA Terms and definitions Basic algorithm.

EBayesMet - on the main activities and results regarding to e-learning Kraków, Mateusz Nikodem, CASPolska Association.

Funded through the ESRC’s Researcher Development Initiative Department of Education, University of Oxford Session 2.1 – Revision of Day 1.

1 ALLOCATION. 2 DETERMINING SAMPLE SIZE Problem: Want to estimate. How choose n to obtain a margin of error not larger than e? Solution: Solve the inequality.

Engineering Statistics Design of Engineering Experiments.

High Speed Heteroskedasticity Review. 2 Review: Heteroskedasticity Heteroskedasticity leads to two problems: –OLS computes standard errors on slopes incorrectly.

Diabetes Research Network Professor Azeem Majeed Imperial College, London.

CHAPTER 14: THE NUTS AND BOLTS OF OTHER SPECIALIZED DESIGNS.

Dense-Region Based Compact Data Cube

Module 9: Choosing the Sampling Strategy

What size of trial do I need?

You've got mail: Using to recruit a representative cohort for a healthy lifestyles research study Kayla Confer, BS1, Jessica Garber, MPH1, Jody.

The DELTA2 Study: Summary of Methodology and Results

Challenges of statistical analysis in surgical trials

Chapter 5 Introduction to Factorial Designs

Brennan Kahan NIHR Doctoral Research Fellow

Re-randomising patients within clinical trials

Precision Farming Profitability

Location Recommendation — for Out-of-Town Users in Location-Based Social Network Yina Meng.

Association between risk-of-bias assessments and results of randomized trials in Cochrane reviews: the ROBES study Jelena Savović1, Becky Turner2, David.

S1316 analysis details Garnet Anderson Katie Arnold

Chair and Discussant: Karla Hemming University of Birmingham

Methods of Economic Investigation Lecture 12

Chair and Discussant: Karla Hemming, PhD University of Birmingham

Working Group Land Cover and Land Use Statistics

Yield Optimization: Divide and Conquer Method

The European Statistical Training Programme (ESTP)

Ch10 Analysis of Variance.

Evaluation of nurse training to deliver an integrated care review for patients with inflammatory rheumatological conditions in primary care: a mixed methods.

Sampling and Power Slides by Jishnu Das.

Impact Evaluation Designs for Male Circumcision

Slide presentation title to go here

Ensemble learning Reminder - Bagging of Trees Random Forest

Exact Test Fisher’s Statistics

Experimental Design All experiments consist of two basic structures:

CHAPTER 10 Comparing Two Populations or Groups

Slide presentation title to go here

Slide presentation title to go here

Non-Experimental designs

Slide presentation title to go here

Reinforcement Learning (2)

Cryptography Lecture 15.

Adaptive mixed-mode design WP1

Alan Girling University of Birmingham, UK

Cluster Crossovers with Multiple periods

Rita Faria, MSc Centre for Health Economics University of York, UK

Reinforcement Learning (2)

Design Issues Lecture Topic 6.

Presentation transcript:

Alan Girling University of Birmingham, UK A.J.Girling@bham.ac.uk Optimality in stepped wedge cluster trials: When and Where to take measurements Introduction Alan Girling University of Birmingham, UK A.J.Girling@bham.ac.uk Funding support (AG) from the NIHR through: The NIHR Collaborations for Leadership in Applied Health Research and Care for West Midlands (CLAHRC WM). The HiSLAC study (NIHR ref 12/128/07) SCT/ICTMC Liverpool, May 2017

Standard Stepped Wedge Layout Clusters  Time  1 0: Control (untreated) Cell 1: Treated Cell Aim: maximise the precision of the treatment effect estimate by changing the design in some way

Essential Constraints Allow for Time Effects Otherwise get a “Before & After” Design Treatment implementation cannot be reversed If it can a Cross-Over Design is best

1. Optimising the layout in a ‘complete’ trial Assume uniform sampling over time in each cluster Choose when to implement the treatment in each cluster. For example, Is better than ? And what is the best layout overall? Lawrie J, Carlin JB, Forbes AB. (2015) Optimal stepped wedge designs. Statistics & Probability Letters , 99: 210-214. Girling, A. J., and Hemming, K. (2016) Statistical efficiency and optimal design for stepped cluster studies under linear mixed effects models. Statist. Med., 35: 2149–2166.

For ‘large studies’ the best design looks like: Parallel Clusters 100R% Stepped-Wedge Clusters A ‘Hybrid’ combination of Parallel and Stepped Wedge clusters

2. Optimising the sampling rate in each cluster over time In a standard SW design, some of the cells contribute almost nothing to the treatment effect estimate Better estimates can be got by re-allocating observations from less to more influential cells Optimal allocation depends on constraints E.g. Fixed total number of observations; Fixed number in each cluster…. Solution can be a sparse design

E.g. A Sparse Design with 6 groups of clusters 5 8 9 This is Optimal for fixed total number of observations unless this number, or the ICC, is ‘too large’. (unpubl)

The Way Ahead Exact Results Algorithmic Approaches Useful designs (Optimal, or near-Optimal)

Speakers Jennifer Thompson (LSHTM) Jessica Kasza (Monash): The optimal design of the stepped-wedge trial and a comparison to other trial designs Jessica Kasza (Monash): Information content of cluster-periods in stepped-wedge trials Richard Hooper (Queen Mary) Trimming the fat from stepped wedge trials: an algorithmic search for the trial design requiring the fewest participants