Biomedical subjects

Robert T O'Neill

Publications and source records attributed to Robert T O'Neill.

10 recordsLinked to original sources

PubMed2006

A regulatory view on adaptive/flexible clinical trial design.

Recently there is growing interest in use of adaptive or flexible designs for development of pharmaceutical products. Statistical methodology has been greatly advanced in the literature. However, there are still some important issues with the methodology and application. In addition, there are many other challenges with these designs, including efficiency of these designs in the entire development program, trial conduct and logistics, the infrastructure of an adaptive trial, the regulatory evaluation of trial results and trial conduct, etc. Up till now, regulatory experience in these designs is very limited. We share some of the challenges.

Biometry↗

PubMed2005

Multicentre trials: a US regulatory perspective.

Multicentre trials are very common in the field of drug development. In recent years, multicentre trials have taken on a multinational and multiregional aspect. We provide a conceptual framework for the use of multicentre trials in the context of drug development, from the perspective of drug regulation in the United States. In this paper, we review some regulatory history, milestones and standards as they relate to multicentre trials. Special attention is given to the similarities and differences in the approaches to multicentre trials in the following documents; Guideline for the Format and Content of the Clinical and Statistical Sections of New Drug Applications, International Conference on Harmonization, Draft Guideline on Statistical Principles for clinical trials and the Guidance for Industry Providing Clinical Evidence of Effectiveness for Human Drug and Biologic Products. The paper includes a consideration of some of the issues in the analysis of data from multicentre trials.

Drug Approval↗

PubMed2004

Better reporting of harms in randomized trials: an extension of the CONSORT statement.

In response to overwhelming evidence and the consequences of poor-quality reporting of randomized, controlled trials (RCTs), many medical journals and editorial groups have now endorsed the CONSORT (Consolidated Standards of Reporting Trials) statement, a 22-item checklist and flow diagram. Because CONSORT primarily aimed at improving the quality of reporting of efficacy, only 1 checklist item specifically addressed the reporting of safety. Considerable evidence suggests that reporting of harms-related data from RCTs also needs improvement. Members of the CONSORT Group, including journal editors and scientists, met in Montebello, Quebec, Canada, in May 2003 to address this problem. The result is the following document: the standard CONSORT checklist with 10 new recommendations about reporting harms-related issues, accompanying explanation, and examples to highlight specific aspects of proper reporting. We hope that this document, in conjunction with other CONSORT-related materials (http://www.consort-statement.org), will help authors improve their reporting of harms-related data from RCTs. Better reporting will help readers critically appraise and interpret trial results. Journals can support this goal by revising Instructions to Authors so that they refer authors to this document.

Evidence-Based Medicine↗

PubMed2004

Independence of the statistician who analyses unblinded data.

This discussion considers arguments for and against separating responsibility for the unblinded interim analysis of a clinical trial from responsibility for trial management and modifications to the ongoing trial. The degree to which one or different statisticians carry out these responsibilities and thus the degree of statistician independence for the two activities can vary, but a sponsor should recognize that giving a single statistician both responsibilities might limit flexibility in managing the trial, particularly with respect to modifying an ongoing trial.

Clinical Trials Data Monitoring Committees↗

PubMed2003

Sam Greenhouse: his contributions as a consultant to the Food and Drug Administration.

This paper recounts contributions made by Sam Greenhouse to the Food and Drug Administration during his tenure as an advisory committee member and as chair of the committee. The events and topics are taken from available recollections and minutes and selectively describe a range of topic areas and issues about which Sam Greenhouse played a substantial leadership role. Published in 2003 by John Wiley & Sons, Ltd.

Advisory Committees↗

PubMed2002

Regulatory perspectives on data monitoring.

Data monitoring is a critical component of the conduct of clinical trials that provide the evidence of efficacy and safety of investigational drugs. These trials may be conducted either by a pharmaceutical sponsor or by the government, especially those large trials that assess the impact of therapies on serious morbidity and/or mortality. While not extensive, I will review a regulatory history of FDA's evolving concerns and positions on data monitoring. I will review the key aspects of data monitoring and interim analysis of clinical trials contained in the recently published International Conference on Harmonization's statistical guidance as well as some other issues being considered for a draft guidance on data monitoring. Finally, some suggestions for improving and enhancing tools and statistical methods for monitoring clinical trials for safety assessment will be offered. This latter area deserves more consideration by statisticians than it has received to date.

Clinical Trials Data Monitoring Committees↗

PubMed2002

Short of complete abstinence: an analysis exploration of multiple drinking episodes in alcoholism treatment trials.

BACKGROUND: In alcoholism treatment clinical trials, conventional analysis of efficacy outcomes often focuses on the time to a first event, where the event may be "any drinking", "safe (or low risk) drinking", "moderate drinking" or "heavy drinking," in addition to multiple outcomes such as frequency of drinking days, percent abstinence days, etc. METHODS: We consider the multivariate failure time analytic methods. In alcoholism treatment trials, the naturalistic course of drinking behavior during treatment intervention often presents with a gradual change in drinking before the emergence of a more stable drinking or abstinence pattern. Thus, for each subject, evaluation of all drinking events, and incorporating the event times over a defined duration, may give a more comprehensive description of his/her drinking pattern. As a consequence, the efficacy of a new treatment for alcoholism may be elevated with greater statistical sensitivity. RESULTS: The utility of the multiple failure time method is demonstrated via a real case study for evaluation of alcoholism treatments. The multiple event time analyses showed that the risk of having "any drinking days" or "heavy drinking days" during the entire duration of the study was significantly lower with experimental treatment than with placebo. Further explorations showed that the treatment effect was primarily observed in the later relapse events and not the first event with respect to relapse to any drinking episodes. Such effect would have missed using the traditional time to first event analysis approach. The observed effect of treatment with respect to relapse to multiple heavy drinking episodes was shown not only in the first event but also in the later events. CONCLUSION: The multiple failure time approach may be applicable when 'drinking failure' is variously defined as a single drink, one at-risk drinking day, one heavy drinking day, or one alcohol-related social, occupational or medical problem. If "a drinking episode" is properly defined and the design gains statistical efficiency, the multiple event analytic strategy should provide improved statistical power to detect treatment effects.

Alcohol Drinking↗

PubMed2002

Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database.

Since 1998, the US Food and Drug Administration (FDA) has been exploring new automated and rapid Bayesian data mining techniques. These techniques have been used to systematically screen the FDA's huge MedWatch database of voluntary reports of adverse drug events for possible events of concern. The data mining method currently being used is the Multi-Item Gamma Poisson Shrinker (MGPS) program that replaced the Gamma Poisson Shrinker (GPS) program we originally used with the legacy database. The MGPS algorithm, the technical aspects of which are summarised in this paper, computes signal scores for pairs, and for higher-order (e.g. triplet, quadruplet) combinations of drugs and events that are significantly more frequent than their pair-wise associations would predict. MGPS generates consistent, redundant, and replicable signals while minimising random patterns. Signals are generated without using external exposure data, adverse event background information, or medical information on adverse drug reactions. The MGPS interface streamlines multiple input-output processes that previously had been manually integrated. The system, however, cannot distinguish between already-known associations and new associations, so the reviewers must filter these events. In addition to detecting possible serious single-drug adverse event problems, MGPS is currently being evaluated to detect possible synergistic interactions between drugs (drug interactions) and adverse events (syndromes), and to detect differences among subgroups defined by gender and by age, such as paediatrics and geriatrics. In the current data, only 3.4% of all 1.2 million drug-event pairs ever reported (with frequencies > or = 1) generate signals [lower 95% confidence interval limit of the adjusted ratios of the observed counts over expected (O/E) counts (denoted EB05) of > or = 2]. The total frequency count that contributed to signals comprised 23% (2.4 million) of the total number, 10.4 million of drug-event pairs reported, greatly facilitating a more focused follow-up and evaluation. The algorithm provides an objective, systematic view of the data alerting reviewers to critically important, new safety signals. The study of signals detected by current methods, signals stored in the Center for Drug Evaluation and Research's Monitoring Adverse Reports Tracking System, and the signals regarding cerivastatin, a cholesterol-lowering drug voluntarily withdrawn from the market in August 2001, exemplify the potential of data mining to improve early signal detection. The operating characteristics of data mining in detecting early safety signals, exemplified by studying a drug recently well characterised by large clinical trials confirms our experience that the signals generated by data mining have high enough specificity to deserve further investigation. The application of these tools may ultimately improve usage recommendations.

Adverse Drug Reaction Reporting Systems↗

PubMedDate not supplied

Adapting the sample size planning of a phase III trial based on phase II data.

Traditionally, in clinical development plan, phase II trials are relatively small and can be expected to result in a large degree of uncertainty in the estimates based on which Phase III trials are planned. Phase II trials are also to explore appropriate primary efficacy endpoint(s) or patient populations. When the biology of the disease and pathogenesis of disease progression are well understood, the phase II and phase III studies may be performed in the same patient population with the same primary endpoint, e.g. efficacy measured by HbA1c in non-insulin dependent diabetes mellitus trials with treatment duration of at least three months. In the disease areas that molecular pathways are not well established or the clinical outcome endpoint may not be observed in a short-term study, e.g. mortality in cancer or AIDS trials, the treatment effect may be postulated through use of intermediate surrogate endpoint in phase II trials. However, in many cases, we generally explore the appropriate clinical endpoint in the phase II trials. An important question is how much of the effect observed in the surrogate endpoint in the phase II study can be translated into the clinical effect in the phase III trial. Another question is how much of the uncertainty remains in phase III trials. In this work, we study the utility of adaptation by design (not by statistical test) in the sense of adapting the phase II information for planning the phase III trials. That is, we investigate the impact of using various phase II effect size estimates on the sample size planning for phase III trials. In general, if the point estimate of the phase II trial is used for planning, it is advisable to size the phase III trial by choosing a smaller alpha level or a higher power level. The adaptation via using the lower limit of the one standard deviation confidence interval from the phase II trial appears to be a reasonable choice since it balances well between the empirical power of the launched trials and the proportion of trials not launched if a threshold lower than the true effect size of phase III trial can be chosen for determining whether the phase III trial is to be launched.

Clinical Trials, Phase II as Topic↗

PubMedDate not supplied

Methodological issues with adaptation of clinical trial design.

Adaptation of clinical trial design generates many issues that have not been resolved for practical applications, though statistical methodology has advanced greatly. This paper focuses on some methodological issues. In one type of adaptation such as sample size re-estimation, only the postulated value of a parameter for planning the trial size may be altered. In another type, the originally intended hypothesis for testing may be modified using the internal data accumulated at an interim time of the trial, such as changing the primary endpoint and dropping a treatment arm. For sample size re-estimation, we make a contrast between an adaptive test weighting the two-stage test statistics with the statistical information given by the original design and the original sample mean test with a properly corrected critical value. We point out the difficulty in planning a confirmatory trial based on the crude information generated by exploratory trials. In regards to selecting a primary endpoint, we argue that the selection process that allows switching from one endpoint to the other with the internal data of the trial is not very likely to gain a power advantage over the simple process of selecting one from the two endpoints by testing them with an equal split of alpha (Bonferroni adjustment). For dropping a treatment arm, distributing the remaining sample size of the discontinued arm to other treatment arms can substantially improve the statistical power of identifying a superior treatment arm in the design. A common difficult methodological issue is that of how to select an adaptation rule in the trial planning stage. Pre-specification of the adaptation rule is important for the practicality consideration. Changing the originally intended hypothesis for testing with the internal data generates great concerns to clinical trial researchers.

Clinical Trials as Topic↗