AIM:To present statistical tools to model and optimize the cost of a randomized clinical trial as a function of the stringency of patient inclusion criteria.METHODS: We consider a two treatment, dichotomous outcome tr...AIM:To present statistical tools to model and optimize the cost of a randomized clinical trial as a function of the stringency of patient inclusion criteria.METHODS: We consider a two treatment, dichotomous outcome trial that includes a proportion of patients who are strong responders to the tested intervention. Patients are screened for inclusion using an arbitrary number of test results that are combined into an aggregate suitability score. The screening score is regarded as a diagnostic test for the responsive phenotype, having a specific cutoff value for inclusion and a particular sensitivity and specificity. The cutoff is a measure of stringency of inclusion criteria. Total cost is modeled as a function of the cutoff value, number of patients screened, the number of patients included, the case occurrence rate, response probabilities for control and experimental treatments, and the trial duration required to produce a statistically significant result with a specified power. Regression methods are developed to estimate relevant model parameters from pilot data in an adaptive trial design. RESULTS: The patient numbers and total cost are strongly related to the choice of the cutoff for inclusion. Clear cost minimums exist between 5.6 and 6.1 on arepresentative 10-point scale of exclusiveness. Potential cost savings for typical trial scenarios range in millions of dollars. As the response rate for controls approaches 50%, the proper choice of inclusion criteria can mean the difference between a successful trial and a failed trial. CONCLUSION: Early formal estimation of optimal inclusion criteria allows planning of clinical trials to avoid high costs, excessive delays, and moral hazards of Type II errors.展开更多
In sensitivity experiments, the response is binary and each experimental unit has a critical stimulus level that cannot be observed directly. It is often of interest to estimate extreme quantiles of the distribution o...In sensitivity experiments, the response is binary and each experimental unit has a critical stimulus level that cannot be observed directly. It is often of interest to estimate extreme quantiles of the distribution of these critical stimulus levels over the tested products. For this purpose a new sequential scheme is proposed with some commonly used models. By using the bootstrap repeated-sampling principle, reasonable prior distributions based on a historic data set are specified. Then, a Bayesian strategy for the sequential procedure is provided and the estimator is given. Further, a high order approximation for such an estimator is explored and its consistency is proven. A simulation study shows that the proposed method gives superior performances over the existing methods.展开更多
Computer experiments are constructed to simulate the behavior of complex physical systems. Uniform designs have good performance in computer experiments from several aspects. In practical use, the experimenter needs t...Computer experiments are constructed to simulate the behavior of complex physical systems. Uniform designs have good performance in computer experiments from several aspects. In practical use, the experimenter needs to choose a small size uniform design at the beginning of an experiment due to a limit of time, budget, resources, and so on, and later conduct a follow up experiment to obtain precious information about the system, that is, a sequential experiment. The Lee distance has been widely used in coding theory and its corresponding discrepancy is an important measure for constructing uniform designs. This paper proves that all the follow up designs of a uniform design are uniform and at least two of them can be used as optimal follow up experimental designs. Thus, it is not necessary that the union of any two uniform designs yields a uniform sequential design. Therefore, this article presents a theoretical justification for choosing the best follow up design of a uniform design to construct a uniform sequential design that involves a mixture of ω≥ 1 factors with β_k ≥ 2, 1 ≤ k ≤ωlevels. For illustration of the usage of the proposed results, a closer look is given at using these results for the most extensively used six particular cases, three symmetric and three asymmetric designs, which are often met in practice.展开更多
文摘AIM:To present statistical tools to model and optimize the cost of a randomized clinical trial as a function of the stringency of patient inclusion criteria.METHODS: We consider a two treatment, dichotomous outcome trial that includes a proportion of patients who are strong responders to the tested intervention. Patients are screened for inclusion using an arbitrary number of test results that are combined into an aggregate suitability score. The screening score is regarded as a diagnostic test for the responsive phenotype, having a specific cutoff value for inclusion and a particular sensitivity and specificity. The cutoff is a measure of stringency of inclusion criteria. Total cost is modeled as a function of the cutoff value, number of patients screened, the number of patients included, the case occurrence rate, response probabilities for control and experimental treatments, and the trial duration required to produce a statistically significant result with a specified power. Regression methods are developed to estimate relevant model parameters from pilot data in an adaptive trial design. RESULTS: The patient numbers and total cost are strongly related to the choice of the cutoff for inclusion. Clear cost minimums exist between 5.6 and 6.1 on arepresentative 10-point scale of exclusiveness. Potential cost savings for typical trial scenarios range in millions of dollars. As the response rate for controls approaches 50%, the proper choice of inclusion criteria can mean the difference between a successful trial and a failed trial. CONCLUSION: Early formal estimation of optimal inclusion criteria allows planning of clinical trials to avoid high costs, excessive delays, and moral hazards of Type II errors.
文摘In sensitivity experiments, the response is binary and each experimental unit has a critical stimulus level that cannot be observed directly. It is often of interest to estimate extreme quantiles of the distribution of these critical stimulus levels over the tested products. For this purpose a new sequential scheme is proposed with some commonly used models. By using the bootstrap repeated-sampling principle, reasonable prior distributions based on a historic data set are specified. Then, a Bayesian strategy for the sequential procedure is provided and the estimator is given. Further, a high order approximation for such an estimator is explored and its consistency is proven. A simulation study shows that the proposed method gives superior performances over the existing methods.
基金supported by the Beijing Normal University-Hong Kong Baptist University United International College under Grant Nos.R201409,R201712,and R201810the Zhuhai Premier Discipline Grant
文摘Computer experiments are constructed to simulate the behavior of complex physical systems. Uniform designs have good performance in computer experiments from several aspects. In practical use, the experimenter needs to choose a small size uniform design at the beginning of an experiment due to a limit of time, budget, resources, and so on, and later conduct a follow up experiment to obtain precious information about the system, that is, a sequential experiment. The Lee distance has been widely used in coding theory and its corresponding discrepancy is an important measure for constructing uniform designs. This paper proves that all the follow up designs of a uniform design are uniform and at least two of them can be used as optimal follow up experimental designs. Thus, it is not necessary that the union of any two uniform designs yields a uniform sequential design. Therefore, this article presents a theoretical justification for choosing the best follow up design of a uniform design to construct a uniform sequential design that involves a mixture of ω≥ 1 factors with β_k ≥ 2, 1 ≤ k ≤ωlevels. For illustration of the usage of the proposed results, a closer look is given at using these results for the most extensively used six particular cases, three symmetric and three asymmetric designs, which are often met in practice.
