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Biomedical subjects

Joachim Röhmel

Publications and source records attributed to Joachim Röhmel.

8 recordsLinked to original sources
PubMed2005

Problems with existing procedures to calculate exact unconditional P-values for non-inferiority/superiority and confidence intervals for two binomials and how to resolve them.

Recently several papers have been published that deal with the construction of exact unconditional tests for non-inferiority and confidence intervals based on the approximative unconditional restricted maximum likelihood test for two binomial random variables. Soon after the papers have been published the commercially available software for exact tests StatXact has incorporated the new methods. There are however gaps in the proofs which since have not been resolved adequately. Further it turned out that the methods for testing non-inferiority are not coherent and test for non-inferiority can easily come to different conclusions compared to the confidence interval inclusion rule. In this paper, a proposal is made how to resolve the open problems. Berger and Boos (1994) developed the confidence interval method for testing equality of two proportions. StatXact (Version 5) has extended this method for shifted hypotheses. It is shown that at least for unbalanced designs (i.e. largely different sample sizes) the Berger and Boos method can lead to controversial results.

Biopharmaceutics↗
PubMed2005

On confidence bounds for the ratio of net differences in the "gold standard" design with reference, experimental, and placebo treatment.

The three-arm clinical study including a placebo has been recommended to be the preferred study design for the comparison of an experimental treatment relative to a reference treatment. In a confirmatory three-arm study multiplicity issues arise that are not present in two-arm studies. In the past a successful demonstration of superiority of the reference over placebo has been regarded a prerequisite validation step for the demonstration of superiority of the experimental treatment over placebo. However, for an investigator this last comparison is the most critical one. In a clinical study the demonstration of superiority of the experimental treatment over placebo is a result of its own value and this should therefore not be made dependent on tests that are of higher priority in a hierarchical test procedure. This can be achieved through a symmetrical formulation of Fieller's method for constructing confidence intervals for ratio of the expected values from normally distributed variables. In the symmetrical formulation the different meanings of nominator and denominator disappear, and simultaneous statements for comparisons between the experimental treatment and placebo, reference treatment and placebo, and reference and experimental treatment can be made. This is accomplished by moving the discussion on confidence sets from the line of real numbers to the unit circle which allows representing confidence sectors always as connected sets, gives always simple geometrical interpretations, and is easy to be transformed back to the real numbers if the traditional calculations behave well. The proposed procedures provides additional insight in existing methods but does obviously not answer the clinical question whether or not the demonstration of superiority of the reference treatment over placebo is a necessary validation step for further comparisons.

Algorithms↗
PubMed2004

Power and sample size determination when assessing the clinical relevance of trial results by 'responder analyses'.

A fundamental issue in regulatory decision making is the assessment of the benefit/risk profile of a compound. In order to do this, establishing the existence of a treatment effect by a significance test is not sufficient, but the clinical relevance of a potential benefit must also be taken into account. A number of regulatory guidelines propose that clinical relevance should be assessed by considering the rate of responders, i.e. the proportion of patients who are observed to achieve an apparently meaningful benefit. In this paper, we present methods for planning clinical trials that aim at demonstrating both statistical and clinical significance in superiority trials. Procedures based on analytical calculations are derived for normally distributed data and the case of a single endpoint as well as multiple primary outcomes. A bootstrap procedure is proposed that can be applied to non-normal data. Application is illustrated by a clinical trial in Alzheimer's disease.

Activities of Daily Living↗
PubMed2004

Hypothesis testing in the "gold standard" design for proving the efficacy of an experimental treatment relative to placebo and a reference.

This article reviews the most important reasons to include a placebo and a reference treatment group in a study to investigate the efficacy of a new experimental treatment. We argue that as a general rule the regulatory requirement is the proven superiority of the experimental treatment over placebo and the proven noninferiority of the experimental treatment as compared to the reference treatment. Whether or not the reference treatment can be shown to be superior to placebo may impact the formulation of the indication but should not, per se, question the usefulness of the experimental treatment or the credibility of the principal proof of efficacy. We argue that a mandatory requirement for the reference treatment to be superior to placebo is ill founded and especially difficult to justify in the situation where the experimental treatment can also prove its superiority over the reference treatment. For this latter situation, it is shown that no adjustment for multiple hypothesis testing is needed, if at the same time superiority of the reference over placebo and superiority of the experimental treatment over the reference are investigated.

Placebo Effect↗
PubMed2003

Assessing non-inferiority of a new treatment in a three-arm clinical trial including a placebo.

In non-inferiority trials, where non-inferiority of a new experimental drug compared to an active control has to be shown, it may be advisable to use an additional placebo group for internal validation if ethically justifiable. The focus of this paper is on such designs. Assuming normality and homogeneity of variances we will derive a statistical test procedure which turns out to be equivalent to the assessment based on Fieller's confidence interval. Based on the power function of this test, sample size calculations are carried out to achieve a given power. Additionally, the optimal allocation of the total sample size is derived. As an alternative to this parametric procedure, the bootstrap percentile interval is discussed and finally compared with Fieller's confidence interval in a study on mildly asthmatic patients.

Anti-Asthmatic Agents↗