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Related Concept Videos

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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
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Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Related Experiment Video

Updated: Jun 26, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Upstrapping to determine futility: predicting future outcomes nonparametrically from past data.

Jessica L Wild1, Adit A Ginde2, Christopher J Lindsell3

  • 1Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. jessica.wild@cuanschutz.edu.

Trials
|May 9, 2024
PubMed
Summary

The upstrap method, a new nonparametric approach, shows promise for clinical trial futility monitoring. This resampling technique offers comparable performance to traditional methods with potential reductions in sample size.

Keywords:
Alpha-spendingConditional powerFutility monitoringInterim monitoringNonparametricUpstrap

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Interim monitoring is crucial in clinical trials to assess futility.
  • Existing methods like alpha-spending and conditional power have limitations for complex designs.
  • The upstrap, a nonparametric resampling method, is proposed for interim monitoring.

Purpose of the Study:

  • To evaluate the utility of the upstrap method for interim futility monitoring in clinical trials.
  • To compare the performance of upstrapping with traditional futility monitoring methods.
  • To assess different calibration strategies for the upstrap method.

Main Methods:

  • A simulation study was conducted using the upstrap method, which involves resampling interim data to simulate complete trials.
  • P-values were calculated for each simulated trial, and compared against a decision threshold.
  • Performance was evaluated against alpha-spending and conditional power methods using various sample sizes and calibration strategies.

Main Results:

  • Upstrapping demonstrated a higher likelihood of detecting futility in null scenarios compared to alternative scenarios.
  • Compared to O'Brien-Fleming methods, upstrapped approaches showed minimal differences in type I error rates (≤1.7%).
  • Upstrapping resulted in lower expected sample sizes (2-22% in null, 0-15% in alternative) and variable power compared to traditional methods.

Conclusions:

  • The upstrap method is a viable resampling-based approach for clinical trial futility monitoring.
  • Calibration allows for adjustable aggressiveness in futility monitoring.
  • Upstrapping offers performance similarities to established methods like alpha-spending and conditional power.