ISSN:0277-6715    

DOI:10.1002/sim.4780100602

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY

ISSN:0277-6715    

DOI:10.1002/sim.4780100603

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY

摘要:

AbstractA compound sampling model, where a unit‐specific parameter is sampled from a prior distribution and then observeds are generated by a sampling distribution depending on the parameter, underlies a wide variety of biopharmaceutical data. For example, in a multi‐centre clinical trial the true treatment effect varies from centre to centre. Observed treatment effects deviate from these true effects through sampling variation. Knowledge of the prior distribution allows use of Bayesian analysis to compute the posterior distribution of clinic‐specific treatment effects (frequently summarized by the posterior mean and variance). More commonly, with the prior not completely specified, observed data can be used to estimate the prior and use it to produce the posterior distribution: an empirical Bayes (or variance component) analysis. In the empirical Bayes model the estimated prior mean gives the typical treatment effect and the estimated prior standard deviation indicates the heterogeneity of treatment effects. In both the Bayes and empirical Bayes approaches, estimated clinic effects are shrunken towards a common value from estimates based on single clinics. This shrinkage produces more efficient estimates. In addition, the compound model helps structure approaches to ranking and selection, provides adjustments for multiplicity, allows estimation of the histogram of clinic‐specific effects, and structures incorporation of external information. This paper outlines the empirical Bayes approach. Coverage will include development and comparison of approaches based on parametric priors (for example, a Gaussian prior with unknown mean and variance) and non‐parametric priors, discussion of the importance of accounting for uncertainty in the estimated prior, comparison of the output and interpretation of fixed and random effects approaches to estimating population values, estimating histograms, and identification of key considerations in the use and interpretation of empirical Baye

ISSN:0277-6715    

DOI:10.1002/sim.4780100604

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY

摘要:

AbstractIn the assessment of adverse drug reactions (ADRs) it is important to establish what is a normal range of values for a given biochemical measurement. Furthermore, such ranges need to be specific to the study population, for example, the elderly. A mixture of Gaussian distributions has been used in the construction of the reference ranges with maximum likelihood used to obtain estimates of the unknown parameters. With this approach, well‐defined reference ranges have been established. Brief details are given of the numbers of ADRs identified in the study population using these range

ISSN:0277-6715    

DOI:10.1002/sim.4780100605

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY

摘要:

AbstractOur purpose is to propose a method of data analysis incorporating historical controls. It consists of three steps of hypothesis testing procedure. The first step is the check of concurrent controls. The second is the significance testing of treatment groups in comparison with historical controls which are composed of negative and positive controls. The third is the significance testing of a linear trend of the dose‐response relationship. The superiority of the proposed procedure is also verifie

ISSN:0277-6715    

DOI:10.1002/sim.4780100606

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY

摘要:

AbstractInterlaboratory studies are conducted to estimate the accuracy of methods of laboratory measurements. The standard parameters used to describe this accuracy are the repeatability and the reproducibility. Usually variance components models are used to estimate these parameters. If the model assumptions are violated the resulting estimates for reproducibility and repeatability may, however, be biased. A new method of residual analysis in variance components models‐developed by the author‐may be used to detect violations of the model assumptions. If the residual analysis indicates that the model assumptions are violated, a simple robust method‐which makes fewer assumptions‐may be used for the estimation of accuracy parameters.The application of this residual analysis is demonstrated using data of an interlaboratory study. Graphical methods play an important role in the evaluation of the residuals. The analysis of the residuals uses methods similar to those used for the analysis of Studentized residuals in the linear model.The estimates obtained by the variance components model and the simple robust method are compared. The results of the residual analysis may be used to decide which of the two estimates can be considered more appropriate.The necessity of residual analysis in the analysis of interlaboratory studies by variance components models is pointed out. Potential hazards inherent to residual analysis in variance components models are discussed. Conclusions for the analysis of interlaboratory studies ar

ISSN:0277-6715    

DOI:10.1002/sim.4780100607

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY

摘要:

AbstractEstimation of the mean and standard deviation of a distribution using linear combinations of the order statistics of a censored sample is discussed. Existing methods are compared with the optimal estimator of this type and new approximations to the optimal estimator are proposed. Their performances in the presence of outliers are assessed by means of simulation.

ISSN:0277-6715    

DOI:10.1002/sim.4780100608

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY

摘要:

AbstractThe basic ideas of multiple testing are outlined and the problem of how to control the probability of erroneous decisions is discussed. The main emphasis is on the concept of the multiple level of significance (controlling the experiment, or family error in the strong sense) which can be achieved by applying the principle of closed tests. Various practical situations encountered in multiple testing in clinical trials are considered: more than one end point; more than two treatments, such as comparisons with a single control, comparisons using ordered alternatives, all pairwise comparisons and contrast methods; and more than one trial. Tests based on global statistics, the union intersection principle and other criteria are discussed. The application of the multiple test concept in sequential sampling is investigated. Finally some comments are made on multiple power, multiple confidence intervals and directed decisions.

ISSN:0277-6715    

DOI:10.1002/sim.4780100609

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY

摘要:

AbstractWhen a residual effect is suspected in a two‐period crossover trial, an analysis of the first‐period data is often chosen instead of the potentially biased crossover analysis. This paper indicates how the usual crossover test has to be interpreted correctly and that its bias has two different consequences, namely a conservative or a liberal test decision if a positive (carryover) or a negative (withdrawal) residual effect exists. A multiple testing procedure is presented allowing for simultaneous crossover and first‐period analysis controlling the experimental error rate. This procedure together with the correct interpretation of the crossover test enables many useful applications of crossover de

ISSN:0277-6715    

DOI:10.1002/sim.4780100610

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY

摘要:

AbstractCrossover studies have been successfully conducted in the case of continuous responses. Existing procedures of analysis for ordinal responses, on the other hand, are rarely satisfactory unless strict, usually unrealistic, assumptions are made. In this paper we investigate a random effects model and show that the model is simple and general. Interpretation of parameters is easy, though with a complicated fitting procedure.

ISSN:0277-6715    

DOI:10.1002/sim.4780100611

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1991

数据来源: WILEY