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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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Distributions to Estimate Population Parameter01:26

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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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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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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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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Sample size re-estimation incorporating prior information on a nuisance parameter.

Tobias Mütze1, Heinz Schmidli2, Tim Friede1,3

  • 1Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.

Pharmaceutical Statistics
|November 29, 2017
PubMed
Summary
This summary is machine-generated.

Incorporating prior information into clinical trial sample size re-estimation can improve trial power and efficiency when data aligns. However, traditional methods may be superior if prior and current trial data conflict.

Keywords:
internal pilot studymeta-analysismeta-analytic-predictive priorsnuisance parametersample size re-estimation

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Area of Science:

  • Biostatistics
  • Clinical Trial Design

Background:

  • Prior information is often informally used in clinical trial planning.
  • Sample size re-estimation with internal pilot studies is crucial for efficient trial design.
  • Nuisance parameters, like outcome variance for continuous endpoints, require careful estimation.

Purpose of the Study:

  • To present a formal approach for incorporating prior information into sample size re-estimation.
  • To enhance sample size re-estimation in trials with internal pilot studies.
  • To evaluate the impact of prior information on trial operating characteristics.

Main Methods:

  • Utilized frequentist methods for trial planning and analysis.
  • Employed the Bayesian meta-analytic-predictive approach to summarize external variance data.
  • Proposed updating the meta-analytic-predictive prior with internal pilot study results for re-estimation.

Main Results:

  • The proposed method improves operating characteristics (e.g., power) when prior and current trial data (variance) do not conflict.
  • Traditional sample size re-estimation using pooled variance is often superior when a prior-data conflict exists.
  • Robustifying prior information did not consistently overcome prior-data conflicts.

Conclusions:

  • Incorporating external information into sample size re-estimation can be beneficial but requires careful consideration of potential prior-data conflicts.
  • The decision to include prior information should balance potential gains against risks, especially in the presence of conflicting data.
  • Further research may explore methods to mitigate the negative impact of prior-data conflicts.