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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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Contaminants and Errors01:16

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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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Testing a Claim about Standard Deviation01:19

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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.
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Testing a Claim about Population Proportion01:24

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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.
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Estimating Population Mean with Unknown Standard Deviation01:22

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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.
William S. Gosset (1876–1937) of the...
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Confidence Interval for Estimating Population Mean01:25

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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On sample size estimation and re-estimation adjusting for variability in confirmatory trials.

Pei-Shien Wu1,2, Min Lin2, Shein-Chung Chow1

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

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|September 18, 2015
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Summary

Clinical trial planning requires accurate sample size estimation (SSE). This study introduces a sample size re-estimation (SSR) method to improve reliability by controlling nuisance parameter variability, ensuring more stable and ethical study designs.

Keywords:
Achieving conditional powercontrolling variabilitymaintaining effect sizereproducibility probability

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Sample size estimation (SSE) is critical for clinical study planning, balancing statistical power with ethical and budgetary constraints.
  • Traditional SSE relies on pre-specified parameters, but estimated nuisance parameters (like variance) from pilot studies can be unstable.
  • Sample size re-estimation (SSR) at interim analyses offers a way to adjust sample size based on accrued data, addressing uncertainties.

Purpose of the Study:

  • To evaluate a novel sample size re-estimation (SSR) method designed to control the variability of nuisance parameters.
  • To assess the performance of the proposed SSR method in maintaining the type I error rate.

Main Methods:

  • A new SSR method focusing on controlling nuisance parameter variability is proposed.
  • A numerical study was conducted to evaluate the method's performance regarding type I error control.

Main Results:

  • The proposed SSR method demonstrates effectiveness in controlling the type I error rate.
  • The study provides a foundation for extending SSR to other criteria like effect size and conditional power.

Conclusions:

  • The proposed SSR method offers a more robust approach to sample size adjustments in clinical trials.
  • Controlling nuisance parameter variability is a key factor in developing reliable SSR strategies.
  • This work can be extended to enhance various aspects of clinical trial design and execution.