Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study

Sung, Lillian; Hayden, Jill A.; Greenberg, Mark; Koren, Gideon; Feldman, Brian M.; Tomlinson, George

doi:10.1016/j.jclinepi.2004.08.010

Cited by 132 publications

(97 citation statements)

References 49 publications

(16 reference statements)

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“…Therefore, the number of patients fitted within the range of permissible error was obtained based on the following formula: 2 2 × (normal distribution) 2 × power of test × (1-power of test) ÷ (permissible error) 2 . 12,13) Because the power of test, significance level and permissible error were given as 0.80, 0.95 and 0.10, respectively, a normal distribution of sample size was determined by 1.96.…”

Section: Methodsmentioning

confidence: 99%

Six-Minute Walk Distance Is an Independent Predictor of Hospital Readmission in Patients With Chronic Heart Failure

et al. 2014

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SummaryPatients with chronic heart failure (CHF) are frequently readmitted to the hospital due to disease progression. Although a shorter 6-minute walk distance (6MWD) is correlated with poor prognosis, 6MWD is not considered a clinical indicator for predicting hospital readmission.We investigated whether 6MWD measured at the time of hospital discharge predicted readmission due to heart failure in CHF patients.Patients admitted to the hospital for the first time due to heart failure were enrolled. After 6MWD was measured at discharge, patients were followed-up for 3 years. Clinical characteristics, 6MWD and readmission due to heart failure were evaluated in 252 patients (68.5 ± 11.8 years old, 162 males). Significant factors that affected readmission were extracted and cut-off values were determined using multivariate logistic regression analysis and receiver operating characteristic curves.Of 252 CHF patients, 103 were readmitted within 3 years. 6MWD at the time of discharge was significantly shorter in readmitted patients than non-readmitted patients (P < 0.001) and was a significant predictor of readmission (P < 0.001). The odds ratio for readmission was 1.22 (P < 0.001) with each 10-meter decrease in 6MWD. The 6MWD cut-off value was determined to be 390 meters, with a sensitivity of 0.75 and a specificity of 0.77.6MWD measured at the time of discharge is an independent predictor of hospital readmission in CHF patients, with a cut-off value of 390 meters. (Int Heart J 2014; 55: 331-336)

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Section: Methodsmentioning

confidence: 99%

Six-Minute Walk Distance Is an Independent Predictor of Hospital Readmission in Patients With Chronic Heart Failure

et al. 2014

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“…77 Guidelines for reporting Bayesian analyses already exist (e.g. BayesWatch 32 and ROBUST 39 ), but their recommendations were rarely adhered to by the articles included in this review. The ROBUST and BayesWatch frameworks stipulate that articles should report prior distributions, statistical models, computational techniques, and posterior distributions.…”

Section: Discussionmentioning

confidence: 99%

“…37,38 BayesWatch and Reporting Of Bayes Used in clinical Studies (ROBUST) are two checklists which have been developed for the reporting of Bayesian analyses for health technology assessment. 32,39 The aim of our article was to perform a critical systematic review of clinical trials using Bayesian survival analyses, to describe what methods and priors are used, and to assess the quality of reporting. We will conclude by making some suggestions for how reporting checklists could be tailored to Bayesian survival trials.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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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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“…A number of guidelines for the components of a Bayesian analysis that should be reported have been published. These include ROBUST (Reporting of Bayes Used in Clinical Studies) 11 , BaSiS (Baysian Standards in Science) 12 , and BayesWatch 13 . Reporting of the components recommended by these guidelines will help the reader interpret the analysis, and allow the reader to have confidence that the approach was appropriate 11 .…”

Section: Reading a Bayesian Study With A Critical Eyementioning

confidence: 99%

“…These include ROBUST (Reporting of Bayes Used in Clinical Studies) 11 , BaSiS (Baysian Standards in Science) 12 , and BayesWatch 13 . Reporting of the components recommended by these guidelines will help the reader interpret the analysis, and allow the reader to have confidence that the approach was appropriate 11 . At the minimum, Bayesian studies should include some description of the prior distribution (which may include justification or rationale for this choice), the analysis conducted (analytic model, analytic technique), and the results (measures of central tendency and dispersion).…”

Section: Reading a Bayesian Study With A Critical Eyementioning

confidence: 99%

Bayesian Inference: Statistical Gimmick or Added Value?

JOHNSON

2011

J Rheumatol

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Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study

Cited by 132 publications

References 49 publications

Six-Minute Walk Distance Is an Independent Predictor of Hospital Readmission in Patients With Chronic Heart Failure

Six-Minute Walk Distance Is an Independent Predictor of Hospital Readmission in Patients With Chronic Heart Failure

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

Bayesian Inference: Statistical Gimmick or Added Value?

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