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Related Concept Videos

Confidence Coefficient01:24

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Conditional power and information fraction calculations at an interim analysis for random coefficient models.

Sandra A Lewis1, Kevin J Carroll2, Todd DeVries1

  • 1Chinook Therapeutics, Novartis Company, Seattle, Washington, USA.

Pharmaceutical Statistics
|November 3, 2023
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Summary

This study explains how to calculate conditional power for random coefficient models in clinical trials. These calculations aid in designing trials and supporting surrogate endpoints for accelerated approval, using estimated glomerular filtration rate (eGFR) as an example.

Keywords:
IgA nephropathyconditional powerinformation fractioninterim analysislongitudinal datarandom coefficients model

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Nephrology

Background:

  • Random coefficient (RC) models are vital for analyzing longitudinal data in clinical trials, particularly for estimating change over time.
  • Accelerated approval strategies often rely on surrogate endpoints, necessitating robust confirmatory longitudinal endpoints to demonstrate clinical benefit, as seen in immunoglobulin A nephropathy trials.

Purpose of the Study:

  • To provide practical methods for calculating conditional power (CP) in random coefficient models during interim analyses.
  • To demonstrate the utility of CP and information fraction calculations for optimizing clinical trial design and validating surrogate endpoints for accelerated approval.

Main Methods:

  • The paper details the calculation of conditional power (CP) for random coefficient (RC) models using longitudinal data.
  • Illustrates methods with practical examples, focusing on the rate of change in estimated glomerular filtration rate (eGFR) slope.

Main Results:

  • Demonstrates how conditional power calculations can inform decisions during interim trial analyses.
  • Highlights the importance of these calculations for supporting confirmatory endpoints at the time of accelerated approval.

Conclusions:

  • Understanding conditional power and information fraction in RC models is crucial for effective clinical trial design.
  • These statistical methods offer valuable support for the confirmatory longitudinal endpoint in accelerated approval pathways, particularly in conditions like immunoglobulin A nephropathy.