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Group Sequential Tests for Delayed Responses (with discussion)

延迟响应的组序贯检验(附讨论)

Summary Group sequential methods are used routinely to monitor clinical trials and to provide early stopping when there is evidence of a treatment effect, a lack of an effect or concerns about patient safety. In many studies, the response of clinical interest is measured some time after the start of treatment and there are subjects at each interim analysis who have been treated but are yet to respond. We formulate a new form of group sequential test which gives a proper…

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An improved method for deriving optimal one-sided group sequential tests

推导最优单侧成组序贯检验的改进方法

SUMMARY We derive one-sided group sequential tests for normal responses which minimize expected sample size; minimization is at a single value of the normal mean or integrated with respect to a normal density. The methods employed are much faster and also numerically more stable than those of Jennison (1987). They provide solutions for cases with as many as 200 groups and, for small numbers of groups, facilitate optimization over the choice of group sizes. We present a new…

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Exact calculations for sequential t , X 2 and F tests

序贯 t、卡方与 F 检验的精确计算

Sequential and group sequential procedures are proposed for monitoring repeated t, X2 or F statistics. These can be used to construct sequential hypothesis tests or repeated confidence intervals when the parameter of interest is a normal mean with unknown variance or a multivariate normal mean with variance matrix known or known up to a scale factor. Exact methods for calculating error probabilities and sample size distributions are described and tables of critical values…

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Interim Analyses: The Repeated Confidence Interval Approach

中期分析:重复置信区间方法

SUMMARY Most medical trials are monitored for early evidence of treatment differences or harmful side-effects and many sequential methods have been proposed for this. Similarly, data collected in a prospective epidemiological study are likely to be reviewed periodically in the course of the study. The repeated confidence interval approach, which combines aspects of sequential estimation and testing, allows a full exploration of the data at each interim analysis and does not…

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Optimal Properties of the Bechhofer-Kulkarni Bernoulli Selection Procedure

Bechhofer–Kulkarni伯努利总体选择程序的最优性质

In a recent article Bechhofer and Kulkarni proposed a class of closed adaptive sequential procedures for selecting that one of $k \geq 2$ Bernoulli populations with the largest single-trial success probability. These sequential procedures which take no more than $n$ observations from any one of the $k$ populations achieve the same probability of a correct selection as does a single-stage procedure which takes exactly $n$ observations from every one of the $k$ populations. In…

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Repeated Confidence Intervals for the Median Survival Time

We describe methods for the construction of a confidence interval for median survival time based on right-censored data. These methods are extended to the construction of repeated confidence intervals for the median, based on accumulating data; here, the overall probability that all intervals contain the true median is guaranteed at a fixed level. The use of repeated confidence intervals for median survival time in post-marketing surveillance is discussed.

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Analysis of Field Experiments by Least Squares Smoothing

用最小二乘平滑分析田间试验

SUMMARY Assuming a smooth trend plus independent error model for the environmental effects in the yields of a field plot experiment, least squares smoothing methods are developed to estimate both the treatment effects and the unknown trend. Treatment estimates are closely related to those resulting from a generalized least squares analysis in which the covariance structure for the environmental effects has a particular form. However, the main emphases are on the accuracy of…

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Repeated confidence intervals for the median survival time

中位生存时间的重复置信区间

We describe methods for the construction of a confidence interval for median survival time based on right-censored data. These methods are extended to the construction of repeated confidence intervals for the median, based on accumulating data; here, the overall probability that all intervals contain the true median is guaranteed at a fixed level. The use of repeated confidence intervals for median survival time in post-marketing surveillance is discussed.

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