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In Parts 1 and 2 of this series, we evaluated moment-based approaches for blinded sample size re-estimation (SSR). This part focuses on a likelihood-based alternative - the maximum likelihood estimation (MLE), which leverages the full data likelihood rather than relying solely on sample moments, such as variance or kurtosis. As before, a continuous observation 𝑋 from the pooled data of a two-group parallel study can be viewed as a random variable arising from a two-component

As noted in Part 1 of this series, the combined data from a two-group parallel study with a continuous endpoint can be viewed as a random sample from a two-component Gaussian mixture model with a known mixture proportion. Let 𝑋 denote an observation from the combined data, then where 𝜔 is the mixture proportion, µ₁ and µ₂ are the component means, and σ² is the common variance . Let 𝛿 = µ₁ - µ₂ and 𝜔 = 1/2 , th en the variance of the mixture distribution is given by Eq. (

Sample size re-estimation (SSR) is often performed in clinical trials to address uncertainty in design-stage assumptions (e.g. , effect size) . Unblinded SSR is straightforward but often subject to regulatory scrutiny. Blinded SSR is generally more acceptable to regulators, but it typically requires information about nuisance parameters, such as the variance for continuous outcomes. In this series, I will evaluate the statistical and practical challenges of blinded SSR to enh

Suppose we want to use a two-pan balance (no bias) to weigh four different fruits: an apple, a pearl, an orange, and a banana (see...

Randomized, double-blind, controlled trials (RCTs) are widely considered the gold standard for modern intervention-based clinical...

This post is motivated by a disagreement I recently had with a colleague regarding a statistical issue related to the ICH ( International...

This post continues our previous discussions on statistical issues related to analysis of covariance (ANCOVA), focusing on the impact of...

In statistical literature, the Cauchy distribution is notoriously labeled as "evil" or "pathological" because it does not have...

It's often claimed that analysis of covariance (ANCOVA) is biased or invalid if a nonlinear relationship exists between covariates and...

In his excellent book Statistical Issues in Drug Development  (2021), Professor Stephen Senn provides a comprehensive overview of...