Pre-Posterior Distributions in Drug Development and Their Properties.

The topic of this article is pre-posterior distributions of success or failure. These distributions, determined before a study is run and based on all our assumptions, are what we should believe about the treatment effect if we are told only that the study has been successful, or unsuccessful. I show how the pre-posterior distributions of success and failure can be used during the planning phase of a study to investigate whether the study is able to discriminate between effective and ineffective treatments.

Probability of success and group sequential designs.

In this article, I extend the use of probability of success calculations, previously developed for fixed sample size studies to group sequential designs (GSDs) both for studies planned to be analyzed by standard frequentist techniques or Bayesian approaches. The structure of GSDs lends itself to sequential learning which in turn allows us to consider how knowledge about the result of an interim analysis can influence our assessment of the study's probability of success. In this article, I build on work by Temple and Robertson who introduced the idea of conditional probability of success, an idea which I also treated in a recent monograph.

On implementing Jeffreys' substitution likelihood for Bayesian inference concerning the medians of unknown distributions.

When statisticians are uncertain as to which parametric statistical model to use to analyse experimental data, they will often resort to a non-parametric approach. The purpose of this paper is to provide insight into a simple approach to take when it is unclear as to the appropriate parametric model and plan to conduct a Bayesian analysis. I introduce an approximate, or substitution likelihood, first proposed by Harold Jeffreys in 1939 and show how to implement the approach combined with both a non-informative and an informative prior to provide a random sample from the posterior distribution of the median of the unknown distribution.

Optimising the trade-off between type I and II error rates in the Bayesian context.

For any decision-making study, there are two sorts of errors that can be made, declaring a positive result when the truth is negative, and declaring a negative result when the truth is positive. Traditionally, the primary analysis of a study is a two-sided hypothesis test, the type I error rate will be set to 5% and the study is designed to give suitably low type II error - typically 10 or 20% - to detect a given effect size. These values are standard, arbitrary and, other than the choice between 10 and 20%, do not reflect the context of the study, such as the relative costs of making type I and II errors and the prior belief the drug will be placebo-like.

How to design a dose-finding study using the continual reassessment method.

Introduction: The continual reassessment method (CRM) is a model-based design for phase I trials, which aims to find the maximum tolerated dose (MTD) of a new therapy. The CRM has been shown to be more accurate in targeting the MTD than traditional rule-based approaches such as the 3 + 3 design, which is used in most phase I trials. Furthermore, the CRM has been shown to assign more trial participants at or close to the MTD than the 3 + 3 design.

Statistical issues in first-in-human studies on BIA 10-2474: Neglected comparison of protocol against practice.

By setting the regulatory-approved protocol for a suite of first-in-human studies on BIA 10-2474 against the subsequent French investigations, we highlight 6 key design and statistical issues, which reinforce recommendations by a Royal Statistical Society Working Party, which were made in the aftermath of cytokine release storm in 6 healthy volunteers in the United Kingdom in 2006. The 6 issues are dose determination, availability of pharmacokinetic results, dosing interval, stopping rules, appraisal by safety committee, and clear algorithm required if combining approvals for single and multiple ascending dose studies.

Response-adaptive clinical trials: case studies in the medical literature.

The past 15 years has seen many pharmaceutical sponsors consider and implement adaptive designs (AD) across all phases of drug development. Given their arrival at the turn of the millennium, we might think that they are a recent invention. That is not the case.

Simulation-based sample-sizing and power calculations in logistic regression with partial prior information.

There have been many approximations developed for sample sizing of a logistic regression model with a single normally-distributed stimulus. Despite this, it has been recognised that there is no consensus as to the best method. In pharmaceutical drug development, simulation provides a powerful tool to characterise the operating characteristics of complex adaptive designs and is an ideal method for determining the sample size for such a problem.

The predictive distribution of the residual variability in the linear-fixed effects model for clinical cross-over trials.

In the linear model for cross-over trials, with fixed subject effects and normal i.i.d.

Idle thoughts of a 'well-calibrated' Bayesian in clinical drug development.

The use of Bayesian approaches in the regulated world of pharmaceutical drug development has not been without its difficulties or its critics. The recent Food and Drug Administration regulatory guidance on the use of Bayesian approaches in device submissions has mandated an investigation into the operating characteristics of Bayesian approaches and has suggested how to make adjustments in order that the proposed approaches are in a sense calibrated. In this paper, I present examples of frequentist calibration of Bayesian procedures and argue that we need not necessarily aim for perfect calibration but should be allowed to use procedures, which are well-calibrated, a position supported by the guidance.

How to test hypotheses if you must.

Drug development is not the only industrial-scientific enterprise subject to government regulations. In some fields of ecology and environmental sciences, the application of statistical methods is also regulated by ordinance. Over the past 20years, ecologists and environmental scientists have argued against an unthinking application of null hypothesis significance tests.

Within-person study designs had lower precision and greater susceptibility to bias because of trends in exposure than cohort and nested case-control designs.

Objective: To compare precision and apparent bias between cohort, nested case-control, self-controlled case series, case-crossover, and case-time-control study designs.

Study Design And Setting: Study designs were implemented to evaluate the association between thiazolidinediones (TZDs) and heart failure, TZDs and fracture, and liver enzyme-inducing anticonvulsants and fracture.

Results: Effect estimates were similar for the cohort and case-control study; for the association between TZDs and fracture in women, the hazard ratio was 1.

Cluster randomised trial in the General Practice Research Database: 1. Electronic decision support to reduce antibiotic prescribing in primary care (eCRT study).

Background: The purpose of this research is to develop and evaluate methods for conducting cluster randomised trials in a primary care database that contains electronic patient records for large numbers of family practices. Cluster randomised trials are trials in which the units allocated represent groups of individuals, in this case family practices and their registered patients. Cluster randomised trials often suffer from the limitation that they include too few clusters, leading to problems of insufficient power and only imprecise estimation of the intraclass correlation coefficient, a key design parameter.

Risk and cumulative risk of stroke recurrence: a systematic review and meta-analysis.

Background And Purpose: Estimates of risk of stroke recurrence are widely variable and focused on the short- term. A systematic review and meta-analysis was conducted to estimate the pooled cumulative risk of stroke recurrence.

Methods: Studies reporting cumulative risk of recurrence after first-ever stroke were identified using electronic databases and by manually searching relevant journals and conference abstracts.

Cluster randomized controlled trial of a patient and general practitioner intervention to improve the management of multiple risk factors after stroke: stop stroke.

Background And Purpose: Stroke is a major public health concern worldwide and survivors remain at high risk of recurrence. Secondary prevention requires management of multiple risk factors but current management is suboptimal. Evidence of the effectiveness of interventions to improve poststroke risk factor management from well-designed trials is limited.

Flexible design and efficient implementation of adaptive dose-finding studies.

A dose-finding study with an adaptive design generates three computational problems: fitting the dose-response curve given the current data, identifying the dose to be given to the next patient that is optimal for learning about the dose-response curve, and pretrial simulation in order to establish operating characteristics of alternative designs. Identifying the 'optimal' dose is the rate-limiting step since conventional methods, estimating the full posterior predictive distribution of some utility function under each of the possible doses, are very slow. We explore a simpler strategy based on importance sampling, whereby the posterior mean of the utility at each candidate dose is estimated by taking its average across an empirical distribution for the model parameters from the current Markov chain Monte Carlo (MCMC) run, weighted according to the likelihood of one or more predicted observations.

Adaptive designs in clinical drug development: opportunities, challenges, and scope reflections following PhRMA's November 2006 workshop.

This paper provides reflections on the opportunities, scope and challenges of adaptive design as discussed at PhRMA's workshop held in November 2006. We also provide a status report of workstreams within PhRMA's working group on adaptive designs, which were triggered by the November workshop. Rather than providing a comprehensive review of the presentations given, we limit ourselves to a selection of key statements.

25 years of Bayesian methods in the pharmaceutical industry: a personal, statistical bummel.

Twenty-five years ago the use of Bayesian methods in Pharmaceutical R&D was non-existent. Today that is no longer true. In this paper I describe my own personal journey along the road of discovery of Bayesian methods to routine use in the pharmaceutical industry.

ASTIN: a Bayesian adaptive dose-response trial in acute stroke.

Understanding the dose-response is critical for successful drug development. We describe an adaptive design to efficiently learn about the dose-response and the ED95. A dynamic termination rule allows for early discontinuation either for efficacy or futility.

Acute Stroke Therapy by Inhibition of Neutrophils (ASTIN): an adaptive dose-response study of UK-279,276 in acute ischemic stroke.

Background And Purpose: UK-279,276 (neutrophil inhibitory factor) reduced infarct volume in a rat middle cerebral artery occlusion reperfusion model. ASTIN (Acute Stroke Therapy by Inhibition of Neutrophils) was an adaptive phase 2 dose-response-finding, proof-of-concept study to establish whether UK-279,276 improves recovery in acute ischemic stroke. The prime objective was to determine the dose that gave a clinically relevant effect in patients.