scholarly journals Bayesian methods in clinical trials: a Bayesian analysis of ECOG trials E1684 and E1690

2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Joseph G Ibrahim ◽  
Ming-Hui Chen ◽  
Haitao Chu
Keyword(s):  
Clinical Trials ◽  
Bayesian Analysis ◽  
Bayesian Methods

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 Is Bayesian Analysis Ready for Use in Phase III Randomized Clinical Trials?

Stroke ◽  
2005 ◽  
Vol 36 (7) ◽  
pp. 1622-1623 ◽  
Author(s):  
George Howard ◽  
Christopher S. Coffey ◽  
Gary R. Cutter
Keyword(s):  
Clinical Trials ◽  
Bayesian Analysis ◽  
Randomized Clinical Trials ◽  
Phase Iii

INFERENCE OF BIOMEDICAL DATA SETS USING BAYESIAN MACHINE LEARNING

2019 ◽  
Vol 31 (04) ◽  
pp. 1950030
Author(s):  
Ayesha Sohail
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Methods ◽  
Relevant Information ◽  
Data Sets ◽  
Hybrid Technique ◽  
Biomedical Data ◽  
Bayesian Techniques ◽  
Clinical Databases ◽  
The Individual ◽  
Available Information

Due to the advancement in data collection and maintenance strategies, the current clinical databases around the globe are rich in a sense that these contain detailed information not only about the individual’s medical conditions, but also about the environmental features, associated with the individual. Classification within this data could provide new medical insights. Data mining technology has become an attraction for researchers due to its affectivity and efficacy in the field of biomedicine research. Due to the diverse structure of such data sets, only few successful techniques and easy to use softwares, are available in literature. A Bayesian analysis provides a more intuitive statement of probability that hypothesis is true. Bayesian approach uses all available information and can give answers to complex questions more accurately. This means that Bayesian methods include prior information. In Bayesian analysis, no relevant information is excluded as prior represents all the available information apart from data itself. Bayesian techniques are specifically used for decision making. Uncertainty is the main hurdle in making decisions. Due to lack of information about relevant parameters, there is uncertainty about given decision. Bayesian methods measure these uncertainties by using probability. In this study, selected techniques of biostatistical Bayesian inference (the probability based inferencing approach, to identify uncertainty in databases) are discussed. To show the efficiency of a Hybrid technique, its application on two distinct data sets is presented in a novel way.

Bayesian Analysis of a 2 × 2 Contingency Table with Both Completely and Partially Cross-Classified Data

1985 ◽  
Vol 10 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Philip J. Smith ◽  
Sung C Choi ◽  
Erdogan Gunel
Keyword(s):  
Experimental Design ◽  
Bayesian Analysis ◽  
Bayesian Methods ◽  
Incomplete Data ◽  
Prior Distribution ◽  
Contingency Table ◽  
Response Variable ◽  
Testing Hypotheses ◽  
Test Conditions ◽  
First Response

A frequently used experimental design is one in which the experimental units are measured twice (e.g., under different test conditions). When the response variable is dichotomous, the equality of the two proportions is usually assessed by a test due to McNemar (1947) . However, in addition to obtaining this complete data where two responses are available for each unit, incomplete data may be available also: In this case observations are available on the first response alone for some units and additional observations are available on the second response alone for other units. In this paper Bayesian methods are presented for estimating and testing hypotheses regarding the two success probabilities in light of both the complete and incomplete data. A method by which the prior distribution may be assessed is sketched and a numerical example to illustrate the method is presented.

Bayesian survival analysis in clinical trials: What methods are used in practice?

2016 ◽  
Vol 14 (1) ◽  
pp. 78-87 ◽  
Author(s):  
Caroline Brard ◽  
Gwénaël Le Teuff ◽  
Marie-Cécile Le Deley ◽  
Lisa V Hampson
Keyword(s):  
Clinical Trials ◽  
Survival Analysis ◽  
Bayesian Methods ◽  
Regression Models ◽  
Final Analysis ◽  
Bayesian Regression ◽  
Phase Iii ◽  
Informative Priors ◽  
Primary Analysis ◽  
Survival Analyses

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

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