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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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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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Estimation after blinded sample size reassessment.

Martin Posch1, Florian Klinglmueller1,2, Franz König1

  • 11 Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

Statistical Methods in Medical Research
|October 5, 2016
PubMed
Summary
This summary is machine-generated.

Blinded sample size reassessment in clinical trials can impact treatment effect estimates. This study analyzes how blinded interim data affects bias and confidence intervals in superiority trials.

Keywords:
Adaptive designinterim analysisinternal pilot studypoint estimatesample size reassessment

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Statistical Inference

Background:

  • Blinded sample size reassessment is used in clinical trials when planning data is uncertain.
  • It aims to maintain statistical power by adjusting sample size based on interim data.
  • Reliable information on nuisance parameters is often unavailable during the planning phase.

Purpose of the Study:

  • To investigate the impact of blinded sample size reassessment on statistical properties.
  • To evaluate effects on point estimates and confidence intervals in superiority trials.
  • To analyze bias in treatment effect estimates under different reassessment rules.

Main Methods:

  • Analysis of parallel group superiority trials with a normal endpoint.
  • Evaluation of two standard reassessment rules based on the z-test sample size formula.
  • Derivation of a worst-case reassessment rule to maximize mean bias.
  • Calculation of an upper bound for the mean bias of the treatment effect estimate.

Main Results:

  • Blinded sample size reassessment can influence the properties of point estimates.
  • The study quantifies the potential bias introduced in treatment effect estimates.
  • An upper bound for mean bias was derived for treatment effect estimation.

Conclusions:

  • Understanding the impact of blinded sample size reassessment is crucial for accurate clinical trial interpretation.
  • The findings provide insights into bias control and estimation properties.
  • This research aids in optimizing statistical methods for clinical trial design and analysis.