Paper doi abstract bibtex

Background—Bayesian predictive probabilities can be used for interim monitoring of clinical trials to estimate the probability of observing a statistically significant treatment effect if the trial were to continue to its predefined maximum sample size. Purpose—We explore settings in which Bayesian predictive probabilities are advantageous for interim monitoring compared to Bayesian posterior probabilities, p-values, conditional power, or group sequential methods. Results—For interim analyses that address prediction hypotheses, such as futility monitoring and efficacy monitoring with lagged outcomes, only predictive probabilities properly account for the amount of data remaining to be observed in a clinical trial and have the flexibility to incorporate additional information via auxiliary variables. Limitations—Computational burdens limit the feasibility of predictive probabilities in many clinical trial settings. The specification of prior distributions brings additional challenges for regulatory approval. Conclusions—The use of Bayesian predictive probabilities enables the choice of logical interim stopping rules that closely align with the clinical decision making process.

@article{saville_utility_2014-1, title = {The utility of {Bayesian} predictive probabilities for interim monitoring of clinical trials}, volume = {11}, issn = {1740-7745, 1740-7753}, url = {http://journals.sagepub.com/doi/10.1177/1740774514531352}, doi = {10.1177/1740774514531352}, abstract = {Background—Bayesian predictive probabilities can be used for interim monitoring of clinical trials to estimate the probability of observing a statistically significant treatment effect if the trial were to continue to its predefined maximum sample size. Purpose—We explore settings in which Bayesian predictive probabilities are advantageous for interim monitoring compared to Bayesian posterior probabilities, p-values, conditional power, or group sequential methods. Results—For interim analyses that address prediction hypotheses, such as futility monitoring and efficacy monitoring with lagged outcomes, only predictive probabilities properly account for the amount of data remaining to be observed in a clinical trial and have the flexibility to incorporate additional information via auxiliary variables. Limitations—Computational burdens limit the feasibility of predictive probabilities in many clinical trial settings. The specification of prior distributions brings additional challenges for regulatory approval. Conclusions—The use of Bayesian predictive probabilities enables the choice of logical interim stopping rules that closely align with the clinical decision making process.}, language = {en}, number = {4}, urldate = {2019-05-02}, journal = {Clinical Trials: Journal of the Society for Clinical Trials}, author = {Saville, Benjamin R and Connor, Jason T and Ayers, Gregory D and Alvarez, JoAnn}, month = aug, year = {2014}, pages = {485--493}, file = {Saville et al. - 2014 - The utility of Bayesian predictive probabilities f.pdf:/Users/neil.hawkins/Zotero/storage/VWBYH9QS/Saville et al. - 2014 - The utility of Bayesian predictive probabilities f.pdf:application/pdf;Saville et al. - 2014 - The utility of Bayesian predictive probabilities f.pdf:/Users/neil.hawkins/Zotero/storage/7NYRPY6P/Saville et al. - 2014 - The utility of Bayesian predictive probabilities f.pdf:application/pdf}, }

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