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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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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.
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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
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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.
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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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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.
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Sample size re-estimation in clinical trials.

Peijin Wang1, Shein-Chung Chow1

  • 1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.

Statistics in Medicine
|August 25, 2021
PubMed
Summary
This summary is machine-generated.

New methods for clinical trial sample size re-estimation, adjusted effect size (AES) and iterated expectation/variance (IEV), improve reliability by accounting for variability. IEV generally offers better performance, especially with large effect sizes.

Keywords:
adjusted effect size approachadjusted statistical powercontrol of type I error rateiterated expectation/variance approach

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Statistical Methodology

Background:

  • Sample size re-estimation in clinical trials is crucial for ensuring study objectives are met.
  • Traditional conditional power analysis for sample size re-estimation overlooks variability in observed treatment effects and their estimates.
  • This oversight can lead to unreliable and non-robust re-estimated sample sizes.

Purpose of the Study:

  • To propose and evaluate novel methods for sample size re-estimation that account for variabilities in observed treatment effects and associated estimates.
  • To compare the performance of the proposed adjusted effect size (AES) and iterated expectation/variance (IEV) approaches against traditional methods.
  • To assess the impact of these methods on type I error rate control and statistical power.

Main Methods:

  • Development of the adjusted effect size (AES) approach to incorporate variability in effect size estimates.
  • Development of the iterated expectation/variance (IEV) approach to account for variability in both effect size and its variance.
  • Evaluation of statistical properties, including type I error rate and power, through simulations and analysis.

Main Results:

  • The traditional approach demonstrates poor control of type I error rate inflation.
  • The iterated expectation/variance (IEV) approach shows the best performance in controlling type I error and maintaining statistical power.
  • Both AES and IEV approaches maintain statistical power above 80%, with IEV exceeding 95% power when using an adjusted significance level.
  • IEV may require larger sample size increases for smaller effect sizes; AES is more suitable for controlling type I error with reasonable sample sizes when effect sizes are not large.

Conclusions:

  • The proposed AES and IEV methods offer more robust and reliable sample size re-estimations compared to traditional approaches.
  • The IEV approach is generally superior, particularly for large effect sizes, in maintaining statistical power and controlling errors.
  • The AES approach provides a suitable alternative for scenarios requiring strict type I error control and moderate sample size adjustments.