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Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Sample Size Calculation01:19

Sample Size Calculation

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...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

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Related Experiment Video

Updated: Jun 16, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Blinded sample size reestimation with count data: methods and applications in multiple sclerosis.

Tim Friede1, Heinz Schmidli

  • 1Universitätsmedizin Göttingen, Abteilung Medizinische Statistik, Göttingen, Germany. tim.friede@med.uni-goettingen.de

Statistics in Medicine
|February 11, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces blinded sample size reestimation for clinical trials with count data. This method adjusts sample size using blinded trial data, maintaining statistical power without inflating Type I error rates.

Related Experiment Videos

Last Updated: Jun 16, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Medical Imaging Analysis

Background:

  • Clinical trial planning relies on accurate estimation of nuisance parameters, often derived from prior studies.
  • Nuisance parameters like variances and event rates are critical for sample size calculation.
  • Existing methods require precise pre-trial estimations, which can introduce uncertainty.

Purpose of the Study:

  • To develop and evaluate blinded sample size reestimation for clinical trials utilizing count data endpoints.
  • To provide sample size adjustment formulas for both Poisson and overdispersed Poisson distributions.
  • To assess the performance of this methodology through simulations.

Main Methods:

  • Developed blinded sample size reestimation for count data endpoints.
  • Derived formulas for Poisson and overdispersed Poisson distributions.
  • Conducted simulations to evaluate operational characteristics and recommend internal pilot study sizes.

Main Results:

  • The proposed methodology effectively reestimates nuisance parameters using blinded data from ongoing trials.
  • Formulas were derived for Poisson and overdispersed Poisson count data, accounting for heterogeneity.
  • Simulations confirmed that blinded sample size reestimation maintains trial power and avoids Type I error inflation.

Conclusions:

  • Blinded sample size reestimation is a viable and effective method for clinical trials with count data.
  • This approach enhances the efficiency and reliability of sample size determination in trials involving endpoints like lesion or relapse counts.
  • The methodology is particularly relevant for studies in multiple sclerosis and other conditions where count data are primary outcomes.