Ein Lineares Modell für die Randomisierungspläne von Zelen

Ihm, Peter

doi:10.1007/978-3-642-81753-3_21

Cited by 3 publications

(10 citation statements)

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“…With regard to the ethical problems of clinical therapy studies, numerous proposals have been made about how to weaken the restictive demand for randomization so that a statistical evaluation of the design becomes possible. In this paper we will examine more closely the method of "pre randomization" which leads to the well known prerandomized designs (PRD) [1][2][3][4][5].…”

Section: Introduction -Results -Recommendations For Practicementioning

confidence: 99%

“…This is why the distributions of the statistics can only be given asymptotically, even under the assumption of normality. The behaviour of the statistics for small sample sizes is examined by means of simulations when it turns out that the statistic proposed in [2] may lead to anticonservative decisions.…”

Section: Introduction -Results -Recommendations For Practicementioning

confidence: 99%

“…As T z and Tr are formally the statistics of the t-test using the Welch approximation, these statistics are compared with the respective t-distribution. Further on, we analyse the behaviour of the statistic T] suggested by Ihm [2] for the therapy effect. Using our notation, one obtains for T] from the formulas (4.8) and (5.4) in In particular, we choose, ml = mz = m = 10(5)25; PI = 0.1,0.3,0.5,0.8, 0.9, d = 0 and f = 0,1,2; the number of simulations was 2,000 for each situation.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Verteilung der Teststatistiken nur asymptotisch angegeben werden, auch wenn man die Normalverteilung voraussetzt. Das Verhalten der Statistiken fUr kleine Stichprobenumfange wird mit Hilfe von Simulationen untersucht, wobei die in dieser Arbeit betrachteten Statistiken mit den von Ihm [2] vorgeschlagenen Statistiken verglichen werden. Es stellt sich dabei heraus, daB die in [2] vorgeschlagenen Statistiken zu stark antikonservativen Entscheidungen fiihren k6nnen, wiihrend die »kanonischen Statistiken«, die von Zelen [4] vorgeschlagen und in dieser Arbeit betrachtet werden, das Niveau gut einhalten bzw.…”

Section: On the Mathematical Basis Of Zelen's Prerandomized Designs Zunclassified

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On the Mathematical Basis of Zelen’s Prerandomized Designs

Brunner¹,

Neumann²

1985

Methods Inf Med

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SummaryThe mathematical basis of Zelen’s suggestion [4] of pre randomizing patients in a clinical trial and then asking them for their consent is investigated. The first problem is to estimate the therapy and selection effects. In the simple prerandomized design (PRD) this is possible without any problems. Similar observations have been made by Anbar [1] and McHugh [3]. However, for the double PRD additional assumptions are needed in order to render therapy and selection effects estimable. The second problem is to determine the distribution of the statistics. It has to be taken into consideration that the sample sizes are random variables in the PRDs. This is why the distribution of the statistics can only be determined asymptotically, even under the assumption of normal distribution. The behaviour of the statistics for small samples is investigated by means of simulations, where the statistics considered in the present paper are compared with the statistics suggested by Ihm [2]. It turns out that the statistics suggested in [2] may lead to anticonservative decisions, whereas the “canonical statistics” suggested by Zelen [4] and considered in the present paper keep the level quite well or may lead to slightly conservative decisions, if there are considerable selection effects.

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Section: Introduction -Results -Recommendations For Practicementioning

confidence: 99%

Section: Introduction -Results -Recommendations For Practicementioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

Section: On the Mathematical Basis Of Zelen's Prerandomized Designs Zunclassified

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On the Mathematical Basis of Zelen’s Prerandomized Designs

Brunner¹,

Neumann²

1985

Methods Inf Med

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“…Although they obtained identical estimates of efficacy as from a model with subject types A, C, I, and N, Newcombe 13 (building on Ihm 14 ) and Sato 15 used a different linear models framework. Permutt and Hebel 16 proposed a potential outcomes model involving types A, C, I, N with Assumptions 1 and 2 but did not present explicit formulas.…”

Section: Historical Overviewmentioning

confidence: 99%

Simple maximum likelihood estimates of efficacy in randomized trials and before-and-after studies, with implications for meta-analysis

Baker

Kramer²

2005

Stat Methods Med Res

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Efficacy, which we define as the effect of receiving intervention on health outcomes among a group of subjects, is the quantity of interest for many investigators. In contrast, intent-to-treat analyses in randomized trials and their analogue for observational before-and-after studies compare outcomes between randomization groups or before-and-after time periods. When there is switching of interventions, estimates based on intent-to-treat are biased for estimating efficacy. By constructing a model based on potential outcomes, one can make reasonable assumptions to estimate efficacy under 'all-or-none' switching of interventions in which switching occurs immediately after randomization or at the start of the time period. This paper reviews the basic methodology, with emphasis on simple maximum likelihood estimates that arise with completely observed outcomes, partially missing binary outcomes, and discrete-time survival outcomes. Particular attention is paid to estimating efficacy in meta-analysis, where the interpretation is much more straightforward than with intent-to-treat analyses.

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Analysis of Survival Data from a Randomized Trial with All-or-None Compliance: Estimating the Cost-Effectiveness of a Cancer Screening Program

Baker

1998

Journal of the American Statistical Association

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The purpose of a diagnostic efficacy study is to evaluate and compare the sensitivities and specificities of several diagnostic tests. Usually the diagnostic tests are correlated conditional on disease status, and the reference test is subject to error. In the Chlamydia trachomatis study, five screening tests for detecting chlamydia in endocervical specimens were compared. The five tests are correlated, and the reference test (the cell culture test) has less than 100% sensitivity. The conventional method ignores both the correlations between the tests and the misclassification of the reference test and thus cannot provide a valid analysis. We propose a model to evaluate and compare the sensitivities and specificities of correlated diagnostic tests when there is either an imperfect reference test or even no reference test. The model also can estimate the effects of covariates. It is a generalized linear mixed model with two unobserved variables, one continuous and one dichotomous. We use a hybrid algorithm, which consists of the EM algorithm and the Newton-Raphson method, for obtaining its maximum likelihood estimation. Methods for model checking and for estimating and comparing both subject-specific and population-averaged sensitivities and specificities are given.

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Ein Lineares Modell für die Randomisierungspläne von Zelen

Cited by 3 publications

References 1 publication

On the Mathematical Basis of Zelen’s Prerandomized Designs

On the Mathematical Basis of Zelen’s Prerandomized Designs

Simple maximum likelihood estimates of efficacy in randomized trials and before-and-after studies, with implications for meta-analysis

Analysis of Survival Data from a Randomized Trial with All-or-None Compliance: Estimating the Cost-Effectiveness of a Cancer Screening Program

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