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2.
Trials ; 19(1): 499, 2018 Sep 17.
Article in English | MEDLINE | ID: mdl-30223881

ABSTRACT

BACKGROUND: The classification of phase 3 trials as superiority or non-inferiority has become routine, and it is widely accepted that there are important differences between the two types of trial in their design, analysis and interpretation. MAIN TEXT: There is a clear rationale for the superiority/non-inferiority framework in the context of regulatory trials. The focus of our article is non-regulatory trials with a public health objective. First, using two examples from infectious disease research, we show that the classification of superiority or non-inferiority trials is not always straightforward. Second, we show that several arguments for different approaches to the design, analysis and interpretation of superiority and non-inferiority trials are unconvincing when examined in detail. We consider, in particular, the calculation of sample size (and the choice of delta or the non-inferiority margin), intention-to-treat versus per-protocol analyses, and one-sided versus two-sided confidence intervals. We argue that the superiority/non-inferiority framework is not just unnecessary but can have a detrimental effect, being a barrier to clear scientific thought and communication. In particular, it places undue emphasis on tests for significance or non-inferiority at the expense of estimation. We emphasise that these concerns apply to phase 3 non-regulatory trials in general, not just to those where the classification of the trial as superiority or non-inferiority is ambiguous. CONCLUSIONS: Guidelines and statistical practice should abandon the sharp division between superiority and non-inferiority phase 3 non-regulatory trials and be more closely aligned to the clinical and public health questions that motivate the trial.


Subject(s)
Clinical Trials, Phase III as Topic/classification , Equivalence Trials as Topic , Research Design , Clinical Trials, Phase III as Topic/statistics & numerical data , Data Interpretation, Statistical , Humans , Intention to Treat Analysis , Research Design/statistics & numerical data , Sample Size , Terminology as Topic
3.
Trials ; 19(1): 134, 2018 Feb 21.
Article in English | MEDLINE | ID: mdl-29467027

ABSTRACT

BACKGROUND: Despite more than 60 years of clinical trials, tuberculosis (TB) still causes a high global burden of mortality and morbidity. Treatment currently requires multiple drugs in combination, taken over a prolonged period. New drugs are needed to shorten treatment duration, prevent resistance and reduce adverse events. However, to improve on current methodology in drug development, a more complete understanding of the existing clinical evidence base is required. METHODS: A systematic review was undertaken to summarise outcomes reported in phase III trials of patients with newly diagnosed pulmonary TB. A systematic search of databases (PubMed, MEDLINE, EMBASE, CENTRAL and LILACs) was conducted on 30 November 2017 to retrieve relevant peer-reviewed articles. Reference lists of included studies were also searched. This systematic review considered all reported outcomes. RESULTS: Of 248 included studies, 229 considered "on-treatment" outcomes whilst 148 reported "off-treatment" outcomes. There was wide variation and ambiguity in the definition of reported outcomes, including their relationship to treatment and in the time points evaluated. Additional challenges were observed regarding the analysis approach taken (per protocol versus intention to treat) and the varying durations of "intensive" and "continuation" phases of treatment. Bacteriological outcomes were most frequently reported but radiological and clinical data were often included as an implicit or explicit component of the overall definition of outcome. CONCLUSIONS: Terminology used to define long-term outcomes in phase III trials is inconsistent, reflecting evolving differences in protocols and practices. For successful future cumulative meta-analysis, the findings of this review suggest that greater availability of individual patient data and the development of a core outcome set would be desirable. In the meantime, we propose a simple and logical approach which should facilitate combination of key evidence and inform improvements in the methodology of TB drug development and clinical trials.


Subject(s)
Antitubercular Agents/therapeutic use , Clinical Trials, Phase III as Topic/standards , Data Accuracy , Endpoint Determination/standards , Outcome Assessment, Health Care/standards , Research Design/standards , Tuberculosis, Pulmonary/drug therapy , Antitubercular Agents/adverse effects , Clinical Trials, Phase III as Topic/classification , Drug Therapy, Combination , Endpoint Determination/classification , Evidence-Based Medicine/standards , Humans , Outcome Assessment, Health Care/classification , Terminology as Topic , Time Factors , Treatment Outcome , Tuberculosis, Pulmonary/diagnosis , Tuberculosis, Pulmonary/microbiology , Tuberculosis, Pulmonary/mortality
4.
Trials ; 16: 430, 2015 Sep 28.
Article in English | MEDLINE | ID: mdl-26416387

ABSTRACT

BACKGROUND: Despite the promising benefits of adaptive designs (ADs), their routine use, especially in confirmatory trials, is lagging behind the prominence given to them in the statistical literature. Much of the previous research to understand barriers and potential facilitators to the use of ADs has been driven from a pharmaceutical drug development perspective, with little focus on trials in the public sector. In this paper, we explore key stakeholders' experiences, perceptions and views on barriers and facilitators to the use of ADs in publicly funded confirmatory trials. METHODS: Semi-structured, in-depth interviews of key stakeholders in clinical trials research (CTU directors, funding board and panel members, statisticians, regulators, chief investigators, data monitoring committee members and health economists) were conducted through telephone or face-to-face sessions, predominantly in the UK. We purposively selected participants sequentially to optimise maximum variation in views and experiences. We employed the framework approach to analyse the qualitative data. RESULTS: We interviewed 27 participants. We found some of the perceived barriers to be: lack of knowledge and experience coupled with paucity of case studies, lack of applied training, degree of reluctance to use ADs, lack of bridge funding and time to support design work, lack of statistical expertise, some anxiety about the impact of early trial stopping on researchers' employment contracts, lack of understanding of acceptable scope of ADs and when ADs are appropriate, and statistical and practical complexities. Reluctance to use ADs seemed to be influenced by: therapeutic area, unfamiliarity, concerns about their robustness in decision-making and acceptability of findings to change practice, perceived complexities and proposed type of AD, among others. CONCLUSIONS: There are still considerable multifaceted, individual and organisational obstacles to be addressed to improve uptake, and successful implementation of ADs when appropriate. Nevertheless, inferred positive change in attitudes and receptiveness towards the appropriate use of ADs by public funders are supportive and are a stepping stone for the future utilisation of ADs by researchers.


Subject(s)
Attitude of Health Personnel , Clinical Trials, Phase III as Topic/methods , Perception , Research Design , Research Personnel/psychology , Adult , Clinical Trials, Phase III as Topic/classification , Clinical Trials, Phase III as Topic/economics , Comprehension , Female , Health Knowledge, Attitudes, Practice , Humans , Interviews as Topic , Male , Middle Aged , Public Sector/economics , Qualitative Research , Research Support as Topic , Terminology as Topic
5.
Control Clin Trials ; 20(2): 172-86, 1999 Apr.
Article in English | MEDLINE | ID: mdl-10227416

ABSTRACT

For decades, biostatisticians have developed and refined the methodology for clinical trials with the intent of giving trial participants a better representation than traditional, equal-allocation, fixed sample-size designs. Despite these methodologic advances and ethical advantages, alternative or data-dependent designs for phase III clinical trials, including sequential designs, Bayesian methods, and adaptive designs, have not been widely adopted in practice. We attempt to characterize situations under which these designs are feasible and desirable from ethical and logistical standpoints. In particular, we describe the role of individual and collective ethics in designing clinical trials and argue that greater attention should be paid to the former. We give examples of those alternative designs that have been used in practice, including discussion of their strengths and shortcomings. We conclude that alternative designs are applicable in limited classes of trials and that investigators should consider them more often when planning clinical trials.


Subject(s)
Clinical Trials, Phase III as Topic/methods , Ethics, Medical , Randomized Controlled Trials as Topic/methods , Research Design , Bayes Theorem , Biometry , Clinical Trials, Phase III as Topic/classification , Cross-Over Studies , Feasibility Studies , Humans , Linear Models , Medical Informatics , Practice Guidelines as Topic , Random Allocation , Randomized Controlled Trials as Topic/classification , Sample Size , Selection Bias
6.
Int J Radiat Oncol Biol Phys ; 39(4): 859-61, 1997 Nov 01.
Article in English | MEDLINE | ID: mdl-9369135

ABSTRACT

Results of Phase III randomized clinical trials can be categorized into three groups: positive, null, and negative. The jargon used in discussing results of comparative studies requires clarification because misclassification can result in incorrect interpretation. A positive result indicates that the experimental therapy(ies) is(are) superior to standard therapy. A null result indicates that no statistically significant difference between therapies was found; hence, standard therapy should not be replaced. A negative result indicates that the experimental therapy had a deleterious effect compared to standard therapy. This article presents a discussion of these categories and examples of each.


Subject(s)
Clinical Trials, Phase III as Topic/classification , Data Interpretation, Statistical , Randomized Controlled Trials as Topic/classification , Treatment Outcome , Humans
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