Related Experiment Video
Updated: Mar 10, 2026
07:28
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
Published on: January 21, 2017
7.4K
IMPLICATION OF ALTERNATIVE MINIMAL CLINICALLY IMPORTANT DIFFERENCE THRESHOLD ESTIMATION METHODS ON TECHNOLOGY
Diana Brixner1, Eli O Meltzer2, Kellie Morland3
1Department of Pharmacotherapy,University of Utahdiana.brixner@utah.edu.
International Journal of Technology Assessment in Health Care
|December 7, 2016
Summary
Different methods for estimating the minimal clinically important difference (MCID) in seasonal allergic rhinitis (SAR) yield conflicting results. This impacts health technology assessments for SAR treatments.
Area of Science:
- Allergy and Immunology
- Pharmacoeconomics
- Clinical Trial Design
Background:
- Minimal clinically important difference (MCID) thresholds are crucial for interpreting treatment effects in seasonal allergic rhinitis (SAR).
- Various techniques exist for estimating MCID, potentially leading to divergent conclusions regarding treatment efficacy.
Purpose of the Study:
- To compare the magnitude of different MCID threshold estimates for SAR.
- To evaluate how varying MCID estimates affect health technology assessment (HTA) decisions for SAR treatments.
Main Methods:
- Utilized data on the change from baseline in the reflective Total Nasal Symptom Score (rTNSS) for four intranasal SAR treatments.
- Compared treatment effects against anchor-based MCID thresholds and those from an Agency for Healthcare Research and Quality (AHRQ) panel.
Main Results:
- Observed changes in rTNSS varied across treatments, with significant reductions for azelastine hydrochloride, fluticasone furoate, and MP-AzeFlu.
- Comparing treatment effects to different MCID thresholds led to conflicting interpretations of clinical importance.
- Anchor-based MCID estimates suggested clinical importance more readily than AHRQ panel estimates.
Conclusions:
- The MCID threshold from the AHRQ panel was substantially larger than anchor-based estimates.
- Discrepancies in MCID thresholds create conflicting recommendations on the clinical benefits of SAR treatments.
Related Concept Videos
01:05Difference from Background: Limit of Detection
8.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
8.7K
01:19Testing a Claim about Standard Deviation
3.1K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
3.1K
01:13Accuracy and Errors in Hypothesis Testing
656
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
656
01:04Significance Testing: Overview
12.9K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
12.9K