scholarly journals Use of external evidence for design and Bayesian analysis of clinical trials: a qualitative study of trialists’ views

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gemma L. Clayton ◽  
Daisy Elliott ◽  
Julian P. T. Higgins ◽  
Hayley E. Jones
Keyword(s):  
Clinical Trials ◽  
Qualitative Study ◽  
Bayesian Analysis ◽  
Bayesian Methods ◽  
Trial Design ◽  
Evidence Base ◽  
Bayesian Framework ◽  
Current Evidence ◽  
External Evidence ◽  
Existing Data

Abstract Background Evidence from previous studies is often used relatively informally in the design of clinical trials: for example, a systematic review to indicate whether a gap in the current evidence base justifies a new trial. External evidence can be used more formally in both trial design and analysis, by explicitly incorporating a synthesis of it in a Bayesian framework. However, it is unclear how common this is in practice or the extent to which it is considered controversial. In this qualitative study, we explored attitudes towards, and experiences of, trialists in incorporating synthesised external evidence through the Bayesian design or analysis of a trial. Methods Semi-structured interviews were conducted with 16 trialists: 13 statisticians and three clinicians. Participants were recruited across several universities and trials units in the United Kingdom using snowball and purposeful sampling. Data were analysed using thematic analysis and techniques of constant comparison. Results Trialists used existing evidence in many ways in trial design, for example, to justify a gap in the evidence base and inform parameters in sample size calculations. However, no one in our sample reported using such evidence in a Bayesian framework. Participants tended to equate Bayesian analysis with the incorporation of prior information on the intervention effect and were less aware of the potential to incorporate data on other parameters. When introduced to the concepts, many trialists felt they could be making more use of existing data to inform the design and analysis of a trial in particular scenarios. For example, some felt existing data could be used more formally to inform background adverse event rates, rather than relying on clinical opinion as to whether there are potential safety concerns. However, several barriers to implementing these methods in practice were identified, including concerns about the relevance of external data, acceptability of Bayesian methods, lack of confidence in Bayesian methods and software, and practical issues, such as difficulties accessing relevant data. Conclusions Despite trialists recognising that more formal use of external evidence could be advantageous over current approaches in some areas and useful as sensitivity analyses, there are still barriers to such use in practice.

scholarly journals Trial Reporting in Immuno-Oncology (TRIO): An American Society of Clinical Oncology-Society for Immunotherapy of Cancer Statement

2019 ◽  
Vol 37 (1) ◽  
pp. 72-80 ◽  
Author(s):  
Apostolia M. Tsimberidou ◽  
Laura A. Levit ◽  
Richard L. Schilsky ◽  
Steven D. Averbuch ◽  
Daniel Chen ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Care ◽  
Evidence Base ◽  
Rapid Expansion ◽  
Evidence Based ◽  
Medical Oncologists ◽  
Immunotherapy Of Cancer ◽  
American Society ◽  
Existing Data ◽  
Support Evidence

Purpose To develop recommendations for clinical trial reporting that address the unique efficacy, toxicity, and combination and sequencing aspects of immuno-oncology (IO) treatments. Methods ASCO and the Society for Immunotherapy of Cancer (SITC) convened a working group that consisted of practicing medical oncologists, immunologists, clinical researchers, biostatisticians, and representatives from industry and government to develop Trial Reporting in Immuno-Oncology (TRIO) recommendations. These recommendations are based on expert consensus, given that existing data to support evidence-based recommendations are limited. Conclusion The TRIO recommendations are intended to improve the reporting of IO clinical trials and thus provide more complete evidence on the relative benefits and risks of an IO therapeutic approach. Given the rapid expansion of the number of IO clinical trials and ongoing improvements to the evidence base supporting the use of IO treatments in clinical care, these recommendations will likely need regular revision as the IO field develops.

scholarly journals Bayesian clinical trials at The University of Texas MD Anderson Cancer Center: An update

2019 ◽  
Vol 16 (6) ◽  
pp. 645-656 ◽  
Author(s):  
Rebecca S Slack Tidwell ◽  
S Andrew Peng ◽  
Minxing Chen ◽  
Diane D Liu ◽  
Ying Yuan ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Personalized Medicine ◽  
Bayesian Methods ◽  
Trial Design ◽  
Large Scale ◽  
Dose Finding ◽  
Phase Iii ◽  
Ethical Review ◽  
Cancer Center ◽  
Md Anderson

Background/aims In our 2009 article, we showed that Bayesian methods had established a foothold in developing therapies in our institutional oncology trials. In this article, we will document what has happened since that time. In addition, we will describe barriers to implementing Bayesian clinical trials, as well as our experience overcoming them. Methods We reviewed MD Anderson Cancer Center clinical trials submitted to the institutional protocol office for scientific and ethical review between January 2009 and December 2013, the same length time period as the previous article. We tabulated Bayesian methods implemented for design or analyses for each trial and then compared these to our previous findings. Results Overall, we identified 1020 trials and found that 283 (28%) had Bayesian components so we designated them as Bayesian trials. Among MD Anderson–only and multicenter trials, 56% and 14%, respectively, were Bayesian, higher rates than our previous study. Bayesian trials were more common in phase I/II trials (34%) than in phase III/IV (6%) trials. Among Bayesian trials, the most commonly used features were for toxicity monitoring (65%), efficacy monitoring (36%), and dose finding (22%). The majority (86%) of Bayesian trials used non-informative priors. A total of 75 (27%) trials applied Bayesian methods for trial design and primary endpoint analysis. Among this latter group, the most commonly used methods were the Bayesian logistic regression model (N = 22), the continual reassessment method (N = 20), and adaptive randomization (N = 16). Median institutional review board approval time from protocol submission was the same 1.4 months for Bayesian and non-Bayesian trials. Since the previous publication, the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial was the first large-scale decision trial combining multiple treatments in a single trial. Since then, two regimens in breast cancer therapy have been identified and published from the cooperative Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis (I-SPY 2), enhancing cooperation among investigators and drug developers across the nation, as well as advancing information needed for personalized medicine. Many software programs and Shiny applications for Bayesian trial design and calculations are available from our website which has had more than 21,000 downloads worldwide since 2004. Conclusion Bayesian trials have the increased flexibility in trial design needed for personalized medicine, resulting in more cooperation among researchers working to fight against cancer. Some disadvantages of Bayesian trials remain, but new methods and software are available to improve their function and incorporation into cancer clinical research.

scholarly journals Development of Outcome Measures for Large-vessel Vasculitis for Use in Clinical Trials: Opportunities, Challenges, and Research Agenda

2011 ◽  
Vol 38 (7) ◽  
pp. 1471-1479 ◽  
Author(s):  
HANER DIRESKENELI ◽  
SIBEL Z. AYDIN ◽  
TANAZ A. KERMANI ◽  
ERIC L. MATTESON ◽  
MAARTEN BOERS ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Outcome Measures ◽  
Research Agenda ◽  
Trial Design ◽  
Large Vessel Vasculitis ◽  
The Other ◽  
Large Vessel ◽  
Therapeutic Trials ◽  
Core Set ◽  
Existing Data

Giant cell (GCA) and Takayasu’s arteritis (TAK) are 2 forms of large-vessel vasculitis (LVV) that involve the aorta and its major branches. GCA has a predilection for the cranial branches, while TAK tends to affect the extracranial branches. Both disorders may also cause nonspecific constitutional symptoms. Although some clinical features are more common in one or the other disorder and the ages of initial presentation differ substantially, there is enough clinical and histopathologic overlap between these disorders that some investigators suggest GCA and TAK may be 2 processes within the spectrum of a single disease. There have been few randomized therapeutic trials completed in GCA, and none in TAK. The lack of therapeutic trials in LVV is only partially explained by the rarity of these diseases. It is likely that the lack of well validated outcome measures for LVV and uncertainties regarding trial design contribute to the paucity of trials for these diseases. An initiative to develop a core set of outcome measures for use in clinical trials of LVV was launched by the international OMERACT Vasculitis Working Group in 2009 and subsequently endorsed by the OMERACT community at the OMERACT 10 meeting. Aims of this initiative include: (1) to review the literature and existing data related to outcome assessments in LVV; (2) to obtain the opinion of experts and patients on disease content; and (3) to formulate a research agenda to facilitate a more data-based approach to outcomes development.

scholarly journals Improving pragmatic clinical trial design using real-world data

2019 ◽  
Vol 16 (3) ◽  
pp. 273-282 ◽  
Author(s):  
Susan M Shortreed ◽  
Carolyn M Rutter ◽  
Andrea J Cook ◽  
Gregory E Simon
Keyword(s):  
Clinical Trials ◽  
Trial Design ◽  
Suicide Attempts ◽  
Real World Data ◽  
Electronic Health Record Data ◽  
Pragmatic Clinical Trial ◽  
Pragmatic Clinical Trials ◽  
Sample Size Calculations ◽  
Record Data ◽  
Electronic Health

Background Pragmatic clinical trials often use automated data sources such as electronic health records, claims, or registries to identify eligible individuals and collect outcome information. A specific advantage that this automated data collection often yields is having data on potential participants when design decisions are being made. We outline how this data can be used to inform trial design. Methods Our work is motivated by a pragmatic clinical trial evaluating the impact of suicide-prevention outreach interventions on fatal and non-fatal suicide attempts in the 18 months after randomization. We illustrate our recommended approaches for designing pragmatic clinical trials using historical data from the health systems participating in this study. Specifically, we illustrate how electronic health record data can be used to inform the selection of trial eligibility requirements, to estimate the distribution of participant characteristics over the course of the trial, and to conduct power and sample size calculations. Results Data from 122,873 people with patient health questionnaire (PHQ) responses, recorded in their electronic health records between 1 July 2010 and 31 March 2012, were used to show that the suicide attempt rate in the 18 months following completion of the questionnaire varies by response to item nine of the PHQ. We estimated that the proportion of individuals with a prior recorded elevated PHQ (i.e. history of suicidal ideation) would decrease from approximately 50% at the beginning of a trial to about 5%, 50 weeks later. Using electronic health record data, we conducted simulations to estimate the power to detect a 25% reduction in suicide attempts. Simulation-based power calculations estimated that randomizing 8000 participants per randomization arm would allow 90% power to detect a 25% reduction in the suicide attempt rate in the intervention arm compared to usual care at an alpha rate of 0.05. Conclusions Historical data can be used to inform the design of pragmatic clinical trials, a strength of trials that use automated data collection for randomizing participants and assessing outcomes. In particular, realistic sample size calculations can be conducted using real-world data from the health systems in which the trial will be conducted. Data-informed trial design should yield more realistic estimates of statistical power and maximize efficiency of trial recruitment.

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