Sequential conditional probability ratio tests for normalized test statistic on information time.

Xiaoping Xiong; Ming Tan; James Boyett

doi:10.1111/1541-0420.00072

Home/Archive/PubMed

PubMed · 14601763

Sequential conditional probability ratio tests for normalized test statistic on information time.

Xiaoping Xiong·Ming Tan·James Boyett

View original source ↗Export metadata ↓

Abstract

Sequential conditional probability ratio tests (SCPRTs) provide monitoring procedures with a unique property that a decision reached at early stopping is unlikely to be reversed should the trial continue to the planned end (Xiong, X., 1995, Journal of the American Statistical Association 90, 1463-1473; Tan, M., Xiong, X., and Kutner, M. H., 1998, Biometrics 54, 682-95). It actually provides a probability statement of a conclusion reversal, should the proposed interim analysis plan be used. To broaden its scope of applications, in this article we develop the SCPRT in terms of Brownian motion, which is applicable to most clinical trials with various endpoints. In addition, we utilize the unique structure of the SCPRT to derive a class of adaptive sequential tests that retain the significance level and power in the presence of a nuisance parameter. We illustrate the proposed methods with examples in clinical trials.

Explore related subjects

Biometry Clinical Trials as Topic Humans Models, Statistical Probability Theory Survival Analysis Time Factors Comparative Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Xiaoping Xiong, Ming Tan, James Boyett. 2003. Sequential conditional probability ratio tests for normalized test statistic on information time.. https://doi.org/10.1111/1541-0420.00072

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2005

Interval estimation and hypothesis testing of intraclass correlation coefficients: the generalized variable approach.

In this paper, we propose a novel approach using the concept of generalized variable (GV) for the confidence interval estimation of the difference of two intraclass correlation coefficients under unequal family sizes. This approach can also easily provide P-values for hypothesis testing. Simulation results show that the GV approach can provide confidence intervals with good coverage properties and perform hypothesis testing with satisfactory type-I error control. Furthermore, the confidence intervals and P-values by GV approach can be easily obtained by simulation. Therefore the GV approach is a suitable candidate for making inference concerning two intraclass correlation coefficients.

Biometry↗

PubMed2005

Simple fitting of subject-specific curves for longitudinal data.

We present a simple semiparametric model for fitting subject-specific curves for longitudinal data. Individual curves are modelled as penalized splines with random coefficients. This model has a mixed model representation, and it is easily implemented in standard statistical software. We conduct an analysis of the long-term effect of radiation therapy on the height of children suffering from acute lymphoblastic leukaemia using penalized splines in the framework of semiparametric mixed effects models. The analysis revealed significant differences between therapies and showed that the growth rate of girls in the study cannot be fully explained by the group-average curve and that individual curves are necessary to reflect the individual response to treatment. We also show how to implement these models in S-PLUS and R in the appendix.

Biometry↗

PubMed2005

Summarizing data through a piecewise linear growth curve model.

Most of the research in clinical trials is based on longitudinal designs, which involve repeated measurements of a variable of interest. Such designs are very powerful, both statistically and scientifically. Recent advances in statistical theory and software development incorporate the covariance structures such as unstructured, compound symmetry, auto-regressive and random effects, etc., for analysing longitudinal data. Hathaway et al. propose a technique for summarizing longitudinal data using linear growth curve model and establish that the number of summary statistics is fixed as four irrespective of the length of study. In this paper, we develop a procedure for analysing the longitudinal data through a piecewise linear growth curve model on the lines of Hathaway et al. Under different covariance structures, the linear model is fitted for Leprosy data and the residual sum of squares computed. Goodness of fit has also been considered for various models. In order to prove that the proposed method is robust and better than the others in terms of goodness of fit, simulation studies are carried out and the results presented.

Biometry↗