A survey of two stage tests of hypotheses: theory and application

Hewett, John E.; Spurrier, John D.

doi:10.1080/03610928308828608

Cited by 35 publications

(14 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the combined sample the statistic J is computed, where "1 +"2 / $(a,b) is as given in (2.5), and the sum X is over all Note that the proposed t w o s t a g e procedure is different from the usual Stein-type two-stage procedure. The relative advantage of each is discussed in Hewett and Spurrier (1983). In particular, unlike Stein's procedure, one may not have to take the second sample if the statistic at the first test leads to acceptance or rejection.…”

Section: The Two-stage Testmentioning

confidence: 98%

Two-stage testing whether new is better than used

Alam

Basu

1990

Sequential Analysis

View full text Add to dashboard Cite

A two-stage test is proposed for testing the hypothesis Ho: F is exponential versus HI: F is NBU and not exponential, on the basis of a random sample from F. To compare the performance of the two-stage test with the corresponding one sample test, powers and expected sample sizes are computed by simulation for various alternatives. It is shown that, for the two-stage test with approximately the same power, the expected sample size is considerably smaller. Critical values are tabulated to permit application of the test. Finally, an illustrative example is given.

show abstract

Section: The Two-stage Testmentioning

confidence: 98%

Two-stage testing whether new is better than used

Alam

Basu

1990

Sequential Analysis

View full text Add to dashboard Cite

show abstract

“…He looked at critical values for the case in which: (i) the sizes of the two samples were ÿxed; (ii) the results of the ÿrst sample determined only whether one would take a second sample; (iii) interim hypothesis testing was allowed, and (iv) the results of the ÿrst sample in no way a ected the size of the second sample. Hewett and Spurrier 7 described the use of two-sample tests as a compromise between a ÿxed sample size approach and purely sequential methods. The two-sample methods o er many of the beneÿts of pure sequential tests while minimizing the associated practical and theoretical problems.…”

Section: Steinmentioning

confidence: 99%

Exact test size and power of a Gaussian error linear model for an internal pilot study

Coffey

Muller

1999

Statist. Med.

View full text Add to dashboard Cite

Wittes and Brittain recommended using an 'internal pilot study' to adjust sample size. The approach involves five steps in testing a general linear hypothesis for a general linear univariate model, with Gaussian errors. First, specify the design, hypothesis, desired test size, power, a smallest 'clinically meaningful' effect, and a speculated error variance. Second, conduct a power analysis to choose provisionally a planned sample size. Third, collect a specified proportion of the planned sample as the internal pilot sample, and estimate the variance (but do not test the hypothesis). Fourth, update the power analysis with the variance estimate to adjust the total sample size. Fifth, finish the study and test the hypothesis with all data. We describe methods for computing exact test size and power under this scenario. Our analytic results agree with simulations of Wittes and Brittain. Furthermore, our exact results apply to any general linear univariate model with fixed predictors, which is much more general than the two-sample t-test considered by Wittes and Brittain. In addition, our results allow for examination of the impact on test size of internal pilot studies for more complicated designs in the framework of the general linear model. We examine the impact of (i) small samples, (ii) allowing the planned sample size to decrease, (iii) the choice of internal pilot sample size, and (iv) the maximum allowable size of the second sample. All affect test size, power and expected total sample size. We present a number of examples including one that uses an internal pilot study in a three-group analysis of variance.

show abstract

“…That is, under normality, both the probability o f a Type I error and power can be simultaneously controlled. Other approaches to controlling power are reviewed by Hewett and Spurrier (1983).…”

Section: Desired Amount O F Power As Is Evident Power Is Not An Issmentioning

confidence: 99%

Power analyses when comparing trimmed means

Wilcox¹,

Keselman²

2002

J. Mod. App. Stat. Meth.

View full text Add to dashboard Cite

Given a random sample from each o f two independent groups, this article takes up the problem o f estimating power, as well as a power curve, when comparing 20% trimmed means with a percentile bootstrap method. Many methods were considered, but only one was found to be satisfactory in terms o f obtaining both a point estimate o f power as well as a (one-sided) confidence interval. The method is illustrated with data from a reading study where theory suggests two groups should differ but nonsignificant results were obtained.

show abstract

A survey of two stage tests of hypotheses: theory and application

Cited by 35 publications

References 52 publications

Two-stage testing whether new is better than used

Two-stage testing whether new is better than used

Exact test size and power of a Gaussian error linear model for an internal pilot study

Power analyses when comparing trimmed means

Contact Info

Product

Resources

About