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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.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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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: Equal Sample Sizes01:15

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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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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Evidence-based sample size estimation based upon an updated meta-regression analysis.

Michael A Rotondi1, Allan Donner2, John J Koval2

  • 1School of Kinesiology and Health Science, York University, Toronto, Ontario, M3J 1P3, Canada. mrotondi@yorku.ca.

Research Synthesis Methods
|June 9, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to assess how much future trials can strengthen evidence for a covariate explaining treatment effect variations. It helps determine if planned studies have enough power to confirm or deny a covariate

Keywords:
evidence synthesisheterogeneitysample size estimationstudy design

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • Meta-analysis traditionally pools treatment effects.
  • Meta-regression explores heterogeneity sources using study-level covariates.
  • Existing meta-regressions may lack power to confirm covariate influence.

Purpose of the Study:

  • To develop a quantitative method for evaluating the potential support a new trial can offer to a meta-regression hypothesis.
  • To assess the feasibility and sample size requirements for planned studies investigating covariate effects on treatment efficacy.

Main Methods:

  • An empirical algorithm is proposed to estimate the evidential support from a future trial.
  • The algorithm quantifies the impact of a planned study on the statistical power of a meta-regression model.
  • Sample size estimation is demonstrated using a real-world example.

Main Results:

  • The approach provides quantitative insight into the value of proposed research.
  • It enables examination of planned study sizes for hypothesis testing.
  • The method aids in determining if a covariate is a statistically significant source of variation.

Conclusions:

  • The proposed algorithm facilitates informed decisions about conducting new trials in meta-regression.
  • It offers a framework for sample size planning to confirm or refute covariate effects.
  • This method is applicable to various study designs, including individually and cluster randomized trials.