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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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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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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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Discrimination-based sample size calculations for multivariable prognostic models for time-to-event data.

Rachel C Jinks1, Patrick Royston2, Mahesh K B Parmar3

  • 1MRC Clinical Trials Unit at UCL, Aviation House, 125 Kingsway, London, WC2B 6NH, UK. rcjinks@gmail.com.

BMC Medical Research Methodology
|October 14, 2015
PubMed
Summary
This summary is machine-generated.

This study provides new sample size recommendations for survival models, focusing on prognostic ability rather than coefficient significance. These methods help researchers determine appropriate sample sizes for developing accurate multivariable prognostic models.

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Medical Statistics

Background:

  • Prognostic studies for time-to-event data are common but often lack pre-analysis sample size considerations.
  • Existing sample size rules (events per variable) are not ideal for prognostic model development.
  • Retrospective studies dominate, with few addressing sample size for predictive accuracy.

Purpose of the Study:

  • To develop evidence-based sample size recommendations for multivariable prognostic models using time-to-event data.
  • To base these recommendations on the prognostic ability of the models, specifically using a measure of discrimination (D).
  • To enhance the practical application of these sample size calculations in clinical research.

Main Methods:

  • Derived formulae for sample size based on discrimination measure D (Royston and Sauerbrei).
  • Utilized two approaches: significance of D versus a previous estimate and precision of D (confidence interval width).
  • Validated methods with simulation studies and conducted a literature review for published D values.

Main Results:

  • Developed sample size calculation formulae for prognostic models.
  • Illustrated methods with a liver cancer prognostic study, noting potentially large sample sizes.
  • Provided a method to convert Harrell's c-index to D and a decision-making flowchart.

Conclusions:

  • Introduced sample size calculations grounded in prognostic ability, not just coefficient significance.
  • Focused on practical utility for contemporary clinical research.
  • Offered recommendations for applying these new sample size methods.