Abstract
Prediction provides discipline and pragmatic importance to empirical research. The design with the predictive probability approach provides an excellent alternative for conducting multi-stage phase II trials; it is efficient and flexible and possesses desirable statistical properties. . In this paper we consider the Bayesian predictive procedures within the experimental design, for this, we define indices of satisfaction related to a test as a decreasing function of the p-value and satisfaction is higher than the null hypothesis is rejected wider. This design possesses good frequentist properties and allows early termination of the trial. We treated our applications by simulation and real data in experimental planning and sequential designs with binary outcomes.
Highlights
Ahlam Labdaoui and Hayet Merabet abstract: Prediction provides discipline and pragmatic importance to empirical research
In this paper we consider the Bayesian predictive procedures within the experimental design, for this, we define indices of satisfaction related to a test as a decreasing function of the p-value and satisfaction is higher than the null hypothesis is rejected wider
Evidence-based medicine has a central place for meta-analysis to summarize results from randomized controlled trials; prediction models may summarize the effects of predictors to provide individualized predictions of a diagnostic or prognostic outcome [3]
Summary
Ahlam Labdaoui and Hayet Merabet abstract: Prediction provides discipline and pragmatic importance to empirical research. In this paper we consider the Bayesian predictive procedures within the experimental design, for this, we define indices of satisfaction related to a test as a decreasing function of the p-value and satisfaction is higher than the null hypothesis is rejected wider. This design possesses good frequentist properties and allows early termination of the trial. Evidence-based medicine has a central place for meta-analysis to summarize results from randomized controlled trials; prediction models may summarize the effects of predictors to provide individualized predictions of a diagnostic or prognostic outcome [3]. The computations and the simulation results concern an inferential problem are given by software: Matlab and R
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