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In dose-response studies, the dose level can be treated either as a classification variable in an ANOVA-type model or as a continuous variable in a regression model. There is a fun little bridge between these two seemingly different approaches, for example, when the underlying dose-response relationship can be represented using generalized linear models (GLMs). This post illustrates that bridge in the specific context of Gaussian linear models. It should be noted that scaling

I recently attended a leadership training session where a colleague shared an experience involving a physician on a Data Monitoring Committee (DMC) who questioned the qualifications of the committee statistician. The physician was concerned because the statistician often remained quiet during routine DMC meetings, which typically focus on safety issues. My colleague, however, was confident that the statistician was fully qualified for the role. This raises an interesting que

The Conditional Error Principle (CEP) is the foundation of many frequentist adaptive designs. It asserts that any new statistical test chosen after an adaptation must have a conditional error rate no greater than that of the original, pre-specified test. This allows for flexible mid-course modifications to a trial, such as sample size increase, while still controlling the overall experimental-wise type I error rate. Formally, the CEP framework can be described as follows. Sta

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 frequently subject to regulatory scrutiny. Blinded SSR is generally more acceptable to regulators, but it typically focuses on re-estimating nuisance parameters such as the overall variance, response rate, or event rate, while assuming that the treatment effect is known - a questionable assumption,

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...