Journal of biopharmaceutical statistics
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Comparative Study
A Bayesian inference of P(π1 > π2) for two proportions.
The statistical inference concerning the difference between two independent binominal proportions is often discussed in medical and statistical literature. However, such discussions are often based on the frequentist viewpoint rather than the Bayesian viewpoint. ⋯ We also present the results of actual clinical trials to show the utility of θ. Our findings suggest that θ can potentially provide useful information in a clinical trial.
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The main purpose of a Phase I trial of a new antitumor agent is to determine the appropriate dosing regimen and characterize the safety profile of a new molecular or monoclonal antibody. Phase II cancer clinical trials are conducted to assess the efficacy of a new anticancer therapy and to determine whether it has sufficient activity against a specific type of tumor to warrant further development. In this paper, commonly used statistical designs, based on either frequentist approaches or Bayesian methods, for Phase I and Phase II cancer clinical trials are reviewed and discussed. Future directions of designing more efficient trial are explored.
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Two-stage designs are commonly used for Phase II trials. Optimal two-stage designs have the lowest expected sample size for a specific treatment effect, for example, the null value, but can perform poorly if the true treatment effect differs. ⋯ The proposed design performs well for a wider range of treatment effects and so is useful for Phase II trials. We compare the design to a previously used optimal design and show it has superior expected sample size properties.
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In the evaluation of medical products, including drugs, biological products, and medical devices, comparative observational studies could play an important role when properly conducted randomized, well-controlled clinical trials are infeasible due to ethical or practical reasons. However, various biases could be introduced at every stage and into every aspect of the observational study, and consequently the interpretation of the resulting statistical inference would be of concern. ⋯ There are also times when they are implemented in an unscientific manner, such as performing propensity score model selection for a dataset involving outcome data in the same dataset, so that the integrity of observational study design and the interpretability of outcome analysis results could be compromised. In this paper, regulatory considerations on prospective study design using propensity scores are shared and illustrated with hypothetical examples.