John Whitehead

Lancaster University, Lancaster, England, United Kingdom

Are you John Whitehead?

Claim your profile

Publications (83)244.37 Total impact

  • Source
    John Whitehead · Piero Olliaro · Trudie Lang · Peter Horby
    [Show abstract] [Hide abstract]
    ABSTRACT: Tragically, the outbreak of Ebola that started in West Africa in 2014 has been far more extensive and damaging than any previous outbreaks. The duration of the outbreak has, for the first time, allowed the clinical evaluation of Ebola treatments. This article discusses the designs used for two such clinical trials which have recruited patients in Liberia and Sierra Leone. General principles are outlined for trial designs intended to be deployed quickly, adapt flexibly and provide results soon enough to influence the course of the current epidemic rather than just providing evidence for use should Ebola break out again. Lessons are drawn for the conduct of clinical research in future outbreaks of infectious diseases, where the sequence of events may or may not be similar to the West African Ebola epidemic.
    Preview · Article · Jan 2016 · Clinical Trials
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: As of September 30, 2015, a total of 1589 laboratory-confirmed cases of infection with the Middle East respiratory syndrome coronavirus (MERS-CoV) have been reported to the World Health Organization (WHO). At present there is no effective specific therapy against MERS-CoV. The use of convalescent plasma (CP) has been suggested as a potential therapy based on existing evidence from other viral infections. We aim to study the feasibility of CP therapy as well as its safety and clinical and laboratory effects in critically ill patients with MERS-CoV infection. We will also examine the pharmacokinetics of the MERS-CoV antibody response and viral load over the course of MERS-CoV infection. This study will inform a future randomized controlled trial that will examine the efficacy of CP therapy for MERS-CoV infection. In the CP collection phase, potential donors will be tested by the enzyme linked immunosorbent assay (ELISA) and the indirect fluorescent antibody (IFA) techniques for the presence of anti-MERS-CoV antibodies. Subjects with anti-MERS-CoV IFA titer of ≥1:160 and no clinical or laboratory evidence of MERS-CoV infection will be screened for eligibility for plasma donation according to standard donation criteria. In the CP therapy phase, 20 consecutive critically ill patients admitted to intensive care unit with laboratory-confirmed MERS-CoV infection will be enrolled and each will receive 2 units of CP. Post enrollment, patients will be followed for clinical and laboratory outcomes that include anti-MERS-CoV antibodies and viral load. This protocol was developed collaboratively by King Abdullah International Medical Research Center (KAIMRC), Gulf Cooperation Council (GCC) Infection Control Center Group and the World Health Organization—International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC-WHO) MERS-CoV Working Group. It was approved in June 2014 by the Ministry of the National Guard Health Affairs Institutional Review Board (IRB). A data safety monitoring board (DSMB) was formulated. The study is registered at http://www.clinicaltrials.gov (NCT02190799).
    Full-text · Article · Dec 2015 · SpringerPlus
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Introduction: Telmisartan, an angiotensin receptor blocker, has beneficial effects on insulin resistance and cardiovascular health in non-HIV populations. This trial will evaluate whether telmisartan can reduce insulin resistance in HIV-positive individuals on combination antiretroviral therapy. Methods and analysis: This is a phase II, multicentre, randomised, open-labelled, dose-ranging trial of telmisartan in 336 HIV-positive individuals over a period of 48 weeks. The trial will use an adaptive design to inform the optimal dose of telmisartan. Patients will be randomised initially 1:1:1:1 to receive one of the three doses of telmisartan (20, 40 and 80 mg) or no intervention (control). An interim analysis will be performed when half of the planned maximum of 336 patients have been followed up for at least 24 weeks. The second stage of the study will depend on the results of interim analysis. The primary outcome measure is a reduction in insulin resistance (as measured by Homeostatic Model Assessment-Insulin Resistance (HOMA-IR)) in telmisartan treated arm(s) after 24 weeks of treatment in comparison with the non-intervention arm. The secondary outcome measures include changes in lipid profile; body fat redistribution (as measured by MRI); plasma and urinary levels of various biomarkers of cardiometabolic and renal health at 12, 24 and 48 weeks. Serious adverse events will be compared between different telmisartan treated dose arm(s) and the control arm. Ethics and dissemination: The study, this protocol and related documents have been approved by the National Research Ethics Service Committee North West-Liverpool Central (Ref: 12/NW/0214). On successful completion, study data will be shared with academic collaborators. The findings from TAILoR will be disseminated through peer-reviewed publications, at scientific conferences, the media and through patient and public involvement. Trial registration numbers: 04196/0024/001-0001; EUDRACT: 2012-000935-18; ISRCTN: 51069819.
    Full-text · Article · Oct 2015 · BMJ Open
  • [Show abstract] [Hide abstract]
    ABSTRACT: One of the main aims of early phase clinical trials is to identify a safe dose with an indication of therapeutic benefit to administer to subjects in further studies. Ideally therefore, dose-limiting events (DLEs) and responses indicative of efficacy should be considered in the dose-escalation procedure. Several methods have been suggested for incorporating both DLEs and efficacy responses in early phase dose-escalation trials. In this paper, we describe and evaluate a Bayesian adaptive approach based on one binary response (occurrence of a DLE) and one continuous response (a measure of potential efficacy) per subject. A logistic regression and a linear log-log relationship are used respectively to model the binary DLEs and the continuous efficacy responses. A gain function concerning both the DLEs and efficacy responses is used to determine the dose to administer to the next cohort of subjects. Stopping rules are proposed to enable efficient decision making. Simulation results shows that our approach performs better than taking account of DLE responses alone. To assess the robustness of the approach, scenarios where the efficacy responses of subjects are generated from an Emax model, but modelled by the linear log-log model are also considered. This evaluation shows that the simpler log-log model leads to robust recommendations even under this model showing that it is a useful approximation to the difficulty in estimating Emax model. Additionally, we find comparable performance to alternative approaches using efficacy and safety for dose-finding. Copyright © 2015 John Wiley & Sons, Ltd.
    No preview · Article · Sep 2015 · Pharmaceutical Statistics
  • Source

    Full-text · Conference Paper · May 2015
  • Source

    Full-text · Conference Paper · May 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Experimental treatments for Ebola virus disease (EVD) might reduce EVD mortality. There is uncertainty about the ability of different clinical trial designs to identify effective treatments, and about the feasibility of implementing individually randomised controlled trials during an Ebola epidemic. A treatment evaluation programme for use in EVD was devised using a multi-stage approach (MSA) with two or three stages, including both non-randomised and randomised elements. The probabilities of rightly or wrongly recommending the experimental treatment, the required sample size, and the consequences for epidemic outcomes over 100 d under two epidemic scenarios were compared for the MSA, a sequential randomised controlled trial (SRCT) with up to 20 interim analyses, and, as a reference case, a conventional randomised controlled trial (RCT) without interim analyses. Assuming 50% 14-d survival in the population treated with the current standard of supportive care, all designs had similar probabilities of identifying effective treatments correctly, while the MSA was less likely to recommend treatments that were ineffective. The MSA led to a smaller number of cases receiving ineffective treatments and faster roll-out of highly effective treatments. For less effective treatments, the MSA had a high probability of including an RCT component, leading to a somewhat longer time to roll-out or rejection. Assuming 100 new EVD cases per day, the MSA led to between 6% and 15% greater reductions in epidemic mortality over the first 100 d for highly effective treatments compared to the SRCT. Both the MSA and SRCT led to substantially fewer deaths than a conventional RCT if the tested interventions were either highly effective or harmful. In the proposed MSA, the major threat to the validity of the results of the non-randomised components is that referral patterns, standard of care, or the virus itself may change during the study period in ways that affect mortality. Adverse events are also harder to quantify without a concurrent control group. The MSA discards ineffective treatments quickly, while reliably providing evidence concerning effective treatments. The MSA is appropriate for the clinical evaluation of EVD treatments.
    Full-text · Article · Apr 2015 · PLoS Medicine
  • Source

    Full-text · Article · Oct 2014 · The Lancet
  • John Whitehead
    [Show abstract] [Hide abstract]
    ABSTRACT: This work is motivated by trials in rapidly lethal cancers or cancers for which measuring shrinkage of tumours is infeasible. In either case, traditional phase II designs focussing on tumour response are unsuitable. Usually, tumour response is considered as a substitute for the more relevant but longer-term endpoint of death. In rapidly lethal cancers such as pancreatic cancer, there is no need to use a surrogate, as the definitive endpoint is (sadly) available so soon. In uveal cancer, there is no counterpart to tumour response, and so, mortality is the only realistic response available. Cytostatic cancer treatments do not seek to kill tumours, but to mitigate their effects. Trials of such therapy might also be based on survival times to death or progression, rather than on tumour shrinkage.Phase II oncology trials are often conducted with all study patients receiving the experimental therapy, and this approach is considered here. Simple extensions of one-stage and two-stage designs based on binary responses are presented. Outcomes based on survival past a small number of landmark times are considered: here, the case of three such times is explored in examples. This approach allows exact calculations to be made for both design and analysis purposes. Simulations presented here show that calculations based on normal approximations can lead to loss of power when sample sizes are small. Two-stage versions of the procedure are also suggested. Copyright © 2014 John Wiley & Sons, Ltd.
    No preview · Article · Sep 2014 · Statistics in Medicine
  • Source
    John Whitehead · Faye Cleary · Amanda Turner
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, a Bayesian approach is developed for simultaneously comparing multiple experimental treatments with a common control treatment in an exploratory clinical trial. The sample size is set to ensure that, at the end of the study, there will be at least one treatment for which the investigators have a strong belief that it is better than control, or else they have a strong belief that none of the experimental treatments are substantially better than control. This criterion bears a direct relationship with conventional frequentist power requirements, while allowing prior opinion to feature in the analysis with a consequent reduction in sample size. If it is concluded that at least one of the experimental treatments shows promise, then it is envisaged that one or more of these promising treatments will be developed further in a definitive phase III trial. The approach is developed in the context of normally distributed responses sharing a common standard deviation regardless of treatment. To begin with, the standard deviation will be assumed known when the sample size is calculated. The final analysis will not rely upon this assumption, although the intended properties of the design may not be achieved if the anticipated standard deviation turns out to be inappropriate. Methods that formally allow for uncertainty about the standard deviation, expressed in the form of a Bayesian prior, are then explored. Illustrations of the sample sizes computed from the new method are presented, and comparisons are made with frequentist methods devised for the same situation. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
    Preview · Article · Aug 2014 · Statistics in Medicine
  • Source
    Amanda Turner · John Whitehead
    [Show abstract] [Hide abstract]
    ABSTRACT: Gaussian comparison inequalities provide a way of bounding probabilities relating to multivariate Gaussian random vectors in terms of probabilities of random variables with simpler correlation structures. In this paper, we establish the partial stochastic dominance result that the cumulative distribution function of the maximum of a multivariate normal random vector, with positive intraclass correlation coefficient, intersects the cumulative distribution function of a standard normal random variable at most once. This result can be applied to the Bayesian design of a clinical trial in which several experimental treatments are compared to a single control.
    Preview · Article · Jul 2014 · Statistics [?] Probability Letters
  • Source

    Preview · Article · Nov 2013 · Trials
  • Thomas Jaki · Valérie André · Ting-Li Su · John Whitehead
    [Show abstract] [Hide abstract]
    ABSTRACT: In phase III cancer clinical trials, overall survival is commonly used as the definitive endpoint. In phase II clinical trials, however, more immediate endpoints such as incidence of complete or partial response within 1 or 2 months or progression-free survival (PFS) are generally used. Because of the limited ability to detect change in overall survival with response, the inherent variability of PFS and the long wait for progression to be observed, more informative and immediate alternatives to overall survival are desirable in exploratory phase II trials. In this paper, we show how comparative trials can be designed and analysed using change in tumour size as the primary endpoint. The test developed is based on the framework of score statistics and will formally incorporate the information of whether patients survive until the time at which change in tumour size is assessed. Using an example in non-small cell lung cancer, we show that the sample size requirements for a trial based on change in tumour size are favourable compared with alternative randomized trials and demonstrate that these conclusions are robust to our assumptions. Copyright © 2012 John Wiley & Sons, Ltd.
    No preview · Article · Jul 2013 · Statistics in Medicine
  • [Show abstract] [Hide abstract]
    ABSTRACT: The Cancer Research UK study CR0720-11 is a trial to determine the tolerability and effect on survival of using two agents in combination in patients with advanced pancreatic cancer. In particular, the trial is designed first to identify the most suitable combination of doses of the two agents in terms of the incidence of dose-limiting toxicities. Then, the survival of all patients who have received that dose combination in the study so far, together with additional patients assigned to that dose combination to ensure that the total number is sufficient, will be analysed. If the survival outcomes show promise, then a definitive randomised study of that dose combination will be recommended. The first two patients in the trial will be treated with the lowest doses of each agent in combination. An adaptive Bayesian procedure based only on monotonicity constraints concerning the risks of toxicity at different dose levels will then be used to suggest dose combinations for subsequent patients. The survival analysis will concern only patients who received the chosen dose combination, and will compare observed mortality with that expected from an exponential model based on the known survival rates associated with current treatment. In this paper, the Bayesian dose-finding procedure is described and illustrated, and its properties are evaluated through simulation. Computation of the appropriate sample size for the survival investigation is also discussed.
    No preview · Article · Aug 2012 · Statistics in Medicine
  • [Show abstract] [Hide abstract]
    ABSTRACT: Almost all uveal melanomas showing chromosome 3 loss (i.e., monosomy 3) are fatal. Randomized clinical trials are therefore needed to evaluate various systemic adjuvant therapies. Conventional trial designs require large numbers of patients, which are difficult to achieve in a rare disease. The aim of this study was to use existing data to estimate how sample size and study duration could be reduced by selecting high-risk patients and adopting multistage trial designs. We identified 217 patients with a monosomy 3 melanoma exceeding 15 mm in basal diameter; these patients had a median survival of 3.27 years. Several trial designs comparing overall survival were explored for such a population. A power of 0.90 to detect a hazard ratio of 0.737 was set, and recruitment of 16 patients per month was assumed. A suitable single-stage study would require 960 patients and a duration of 76 months. A two-stage design with an interim analysis based on 852 patients after 53.3 months would have a 50% probability of stopping because no statistically significant treatment effect is seen. Encouraging but inconclusive results would require a further 108 patients and prolongation of the study to 77.2 months. A multistage design would have a 43% probability of stopping before 47 months having recruited 759 patients. Prospects for clinical studies of systemic adjuvant therapy for uveal melanoma are enhanced by multistage trial designs enrolling only high-risk patients.
    No preview · Article · Jun 2012 · Investigative ophthalmology & visual science
  • Source
    D. Magirr · T. Jaki · J. Whitehead
    [Show abstract] [Hide abstract]
    ABSTRACT: We generalize the Dunnett test to derive efficacy and futility boundaries for a flexible multi-arm multi-stage clinical trial for a normally distributed endpoint with known variance. We show that the boundaries control the familywise error rate in the strong sense. The method is applicable for any number of treatment arms, number of stages and number of patients per treatment per stage. It can be used for a wide variety of boundary types or rules derived from α-spending functions. Additionally, we show how sample size can be computed under a least favourable configuration power requirement and derive formulae for expected sample sizes.
    Preview · Article · May 2012 · Biometrika
  • Susan Todd · M. Fazil Baksh · John Whitehead
    [Show abstract] [Hide abstract]
    ABSTRACT: A study or experiment can be described as sequential if its design includes one or more interim analyses at which it is possible to stop the study, having reached a definitive conclusion concerning the primary question of interest. The potential of the sequential study to terminate earlier than the equivalent fixed sample size study means that, typically, there are ethical and economic advantages to be gained from using a sequential design. These advantages have secured a place for the methodology in the conduct of many clinical trials of novel therapies. Recently, there has been increasing interest in pharmacogenetics: the study of how DNA variation in the human genome affects the safety and efficacy of drugs. The potential for using sequential methodology in pharmacogenetic studies is considered and the conduct of candidate gene association studies, family-based designs and genome-wide association studies within the sequential setting is explored. The objective is to provide a unified framework for the conduct of these types of studies as sequential designs and hence allow experimenters to consider using sequential methodology in their future pharmacogenetic studies.
    No preview · Article · May 2012 · Computational Statistics & Data Analysis
  • Ting-Li Su · Ekkehard Glimm · John Whitehead · Mike Branson
    [Show abstract] [Hide abstract]
    ABSTRACT: The issues and dangers involved in testing multiple hypotheses are well recognised within the pharmaceutical industry. In reporting clinical trials, strenuous efforts are taken to avoid the inflation of type I error, with procedures such as the Bonferroni adjustment and its many elaborations and refinements being widely employed. Typically, such methods are conservative. They tend to be accurate if the multiple test statistics involved are mutually independent and achieve less than the type I error rate specified if these statistics are positively correlated. An alternative approach is to estimate the correlations between the test statistics and to perform a test that is conditional on those estimates being the true correlations. In this paper, we begin by assuming that test statistics are normally distributed and that their correlations are known. Under these circumstances, we explore several approaches to multiple testing, adapt them so that type I error is preserved exactly and then compare their powers over a range of true parameter values. For simplicity, the explorations are confined to the bivariate case. Having described the relative strengths and weaknesses of the approaches under study, we use simulation to assess the accuracy of the approximate theory developed when the correlations are estimated from the study data rather than being known in advance and when data are binary so that test statistics are only approximately normally distributed.
    No preview · Article · Mar 2012 · Pharmaceutical Statistics
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We describe a dose escalation procedure for a combined phase I/II clinical trial. The procedure is based on a Bayesian model for the joint distribution of the occurrence of a dose limiting event and of some indicator of efficacy (both considered binary variables), making no assumptions other than monotonicity. Thus, the chances of each outcome are assumed to be non-decreasing in dose level. We applied the procedure to the design of a placebo-controlled, sequential trial in rheumatoid arthritis, in each stage of which patients were randomized between placebo and all dose levels that currently appeared safe and non-futile. On the basis of data from a pilot study, we constructed five different scenarios for the dose-response relationships under which we simulated the trial and assessed the performance of the procedure. The new design appears to have satisfactory operating characteristics and can be adapted to the requirements of a range of trial situations. Copyright © 2012 John Wiley & Sons, Ltd.
    Full-text · Article · Dec 2011 · Pharmaceutical Statistics
  • John Whitehead · Helene Thygesen · Anne Whitehead
    [Show abstract] [Hide abstract]
    ABSTRACT: Many formal statistical procedures for phase I dose-finding studies have been proposed. Most concern a single novel agent available at a number of doses and administered to subjects participating in a single treatment period and returning a single binary indicator of toxicity. Such a structure is common when evaluating cytotoxic drugs for cancer. This paper concerns studies of combinations of two agents, both available at several doses. Subjects participate in one treatment period and provide two binary responses: one an indicator of benefit and the other of harm. The word 'benefit' is used loosely here: the response might be an early indicator of physiological change which, if induced in patients, is of potential therapeutic value. The context need not be oncology, but might be any study intended to meet both the phase I aim of establishing which doses are safe and the phase II goal of exploring potential therapeutic activity. A Bayesian approach is used based on an assumption of monotonicity in the relationship between the strength of the dose-combination and the distribution of the bivariate outcome. Special cases are described, and the procedure is evaluated using simulation. The parameters that define the model have immediate and simple interpretation. Graphical representations of the posterior opinions about model parameters are shown, and these can be used to inform the discussions of the trial safety committee.
    No preview · Article · Jul 2011 · Statistics in Medicine

Publication Stats

886 Citations
244.37 Total Impact Points

Institutions

  • 2007-2015
    • Lancaster University
      • Department of Mathematics and Statistics
      Lancaster, England, United Kingdom
  • 1995-2008
    • University of Reading
      • Department of Mathematics and Statistics
      Reading, England, United Kingdom