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Related Concept Videos

Sample Size Calculation01:19

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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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Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
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Blind Procedures02:07

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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Related Experiment Video

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Sample Size Re-Estimation without Breaking the Blind in Clinical Trial.

Chien-Hua Wu1

  • 1a Department of Applied Mathematics , Chung-Yuan Christian University , Chung-Li , Taiwan.

Journal of Biopharmaceutical Statistics
|January 29, 2015
PubMed
Summary

This study proposes a blinded sample size recalculation method for clinical trials with continuous endpoints. Unblinded estimators generally show better performance, but blinded methods are crucial for maintaining study integrity.

Keywords:
BlindCPMPConvex combinationSample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Regulatory requirements mandate maintaining blinding during sample size recalculations in clinical trials.
  • Existing methods for sample size adjustments may compromise participant and researcher blinding.

Purpose of the Study:

  • To propose and evaluate a novel method for sample size recalculation that preserves blinding in clinical trials with continuous endpoints.
  • To extend the work of Shih and Zhao (1997) for blinded sample size adjustments.

Main Methods:

  • Developing estimators for treatment means using convex combinations of stratum means.
  • Utilizing a linear model for stratum responses to estimate treatment means.
  • Conducting simulation experiments to compare blinded and unblinded estimators.

Main Results:

  • Unblinded estimators for population mean and variance generally outperform their blinded counterparts in terms of bias and mean square error.
  • The accuracy of blinded estimators is influenced by treatment proportions within each stratum.
  • The proposed method provides a viable approach for sample size determination during interim analyses.

Conclusions:

  • The proposed sample size recalculation method effectively maintains blinding, meeting regulatory standards like those from the Committee for Proprietary Medical Products.
  • Blinded estimators are essential for preserving study integrity, despite potential performance differences compared to unblinded methods.
  • The study provides practical guidance for implementing blinded sample size adjustments in ongoing clinical trials.