Related Experiment Video
Updated: Dec 24, 2025
06:16
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
737
Sample size calculation and re-estimation based on the prevalence in a single-arm confirmatory diagnostic accuracy
1University Medical Center Hamburg-Eppendorf, Institute of Medical Biometry and Epidemiology, Hamburg, Germany.
Statistical Methods in Medical Research
|April 18, 2020
Summary
Accurate sample size calculation for diagnostic accuracy studies is crucial. A one-time blinded re-estimation design is recommended to ensure optimal sample size, regardless of true prevalence, improving study power and reliability.
Area of Science:
- Biostatistics
- Diagnostic Accuracy Research
- Clinical Trial Design
Background:
- Sensitivity and specificity are co-primary endpoints in diagnostic accuracy studies.
- Accurate sample size calculation requires accounting for target population prevalence.
- Incorrect prevalence assumptions can lead to overpowered or underpowered studies.
Purpose of the Study:
- To propose a method for optimal sample size calculation independent of prevalence.
- To evaluate blinded re-estimation designs for addressing incorrect prevalence assumptions.
- To compare the performance of one-time and repeated blinded re-estimation designs against a fixed design.
Main Methods:
- A simulation study was conducted to evaluate blinded re-estimation designs.
- One-time and repeated blinded re-estimation designs were compared to a fixed sample size design.
- The study assessed type I error rates, empirical power, and prevalence estimation bias.
Main Results:
- Both blinded re-estimation designs maintained desired statistical power and controlled type I error rates.
- Unbiased prevalence estimates were achieved with both re-estimation designs.
- The one-time re-estimation design showed no disadvantage compared to the repeated design; 50% of initial sample size is optimal for the internal pilot study.
Conclusions:
- A one-time blinded re-estimation design is recommended for diagnostic accuracy studies.
- This approach ensures reliable sample size and study power, irrespective of true prevalence.
- Optimal sample size calculation is critical for valid diagnostic accuracy assessments.
Related Concept Videos
01:19Sample Size Calculation
6.0K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
6.0K
01:27Margin of Error
6.7K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
6.7K
01:22Estimating Population Mean with Unknown Standard Deviation
8.7K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
8.7K
01:13Accuracy and Errors in Hypothesis Testing
516
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%...
516
01:16Contaminants and Errors
313
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...
313
01:20Sample Proportion and Population Proportion
6.3K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
6.3K