Related Experiment Video
Updated: Aug 13, 2026
04:53
A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
Published on: September 20, 2019
Methacholine challenge tests: sample sizes required in crossover trials
1Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Essen, Germany. markus.neuhaeuser@medizin.uni-essen.de
International Journal of Clinical Pharmacology and Therapeutics
|January 24, 2006
Summary
Accurate sample size calculations are crucial for methacholine challenge testing in asthma drug studies. This research provides correct sample sizes, revealing significantly larger requirements than previously reported.
Area of Science:
- Pharmacology and Clinical Trial Design
- Respiratory Medicine and Asthma Research
Background:
- Methacholine challenge testing is a standard method for evaluating the pharmacodynamic effects of asthma medications.
- Accurate sample size calculations are vital for the robust design and interpretation of clinical studies.
- Previous literature has reported incorrect sample size estimations for methacholine challenge studies.
Purpose of the Study:
- To present accurate sample size calculations for comparing two treatments using methacholine challenge testing.
- To provide correct sample size requirements for crossover study designs.
- To address and correct erroneous sample size figures previously published.
Main Methods:
- Development of formulas for sample size calculations.
- Presentation of required subject numbers for specified statistical power.
- Inclusion of calculations for difference, equivalence, and non-inferiority study designs.
Main Results:
- Formulas and subject numbers are provided for various study designs.
- The calculations demonstrate the sample size needed to achieve desired statistical power.
Conclusions:
- Methacholine challenge testing necessitates a substantially larger sample size than previously understood.
- The findings underscore the importance of using correct sample size calculations for reliable study outcomes.
Related Concept Videos
01:16Crossover Experiments
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
01:13Multiple Comparison Tests
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
01:23McNemar's Test
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
01:17Cochran's Q Test
Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...
01:23Test for Homogeneity
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
01:24Testing a Claim about Population Proportion
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...