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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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Sample Proportion and Population Proportion01:20

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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...
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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A complete procedure for testing a claim about a population proportion is provided here.
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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.
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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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Predictively consistent prior effective sample sizes.

Beat Neuenschwander1, Sebastian Weber1, Heinz Schmidli1

  • 1Novartis Pharma AG, Basel, Switzerland.

Biometrics
|March 7, 2020
PubMed
Summary
This summary is machine-generated.

Determining effective sample size (ESS) for prior information in clinical trials is complex. A new predictive consistency criterion and the local-information-ratio ESS method are introduced to accurately quantify prior information, improving trial design.

Keywords:
Fisher informationco-datahistorical datameta-analytic-predictive prior distributionprior predictive distribution

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Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trial Design

Background:

  • Incorporating prior information via prior distributions is crucial for efficient randomized clinical trials, particularly for reducing control group sizes.
  • Quantifying prior information using effective sample size (ESS) aids in trial design, but current methods yield inconsistent results for non-conjugate priors.

Purpose of the Study:

  • To introduce a predictive consistency criterion for evaluating methods of calculating prior ESS.
  • To propose a new, predictively consistent method for calculating prior ESS, termed the local-information-ratio ESS.
  • To demonstrate the application of the new method in scenarios with non-conjugate priors and in clinical trial design.

Main Methods:

  • Evaluation of existing ESS calculation methods against a proposed predictive consistency criterion.
  • Development and theoretical justification of the local-information-ratio ESS.
  • Application of the local-information-ratio ESS to specific statistical models (e.g., normal data with Student-t prior, exponential data with generalized Gamma prior).

Main Results:

  • Current methods for calculating prior ESS do not satisfy the predictive consistency criterion.
  • The proposed local-information-ratio ESS method is shown to be predictively consistent.
  • The new method provides corrected ESS values for non-conjugate settings, illustrated with examples.

Conclusions:

  • A fundamental flaw in existing prior ESS calculation methods is identified through the predictive consistency criterion.
  • The local-information-ratio ESS offers a reliable approach for quantifying prior information in complex statistical models.
  • This advancement has direct implications for designing randomized clinical trials using historical data and for subgroup analyses.