Introduction
Hierarchical composite endpoints (HCE) are complex endpoints combining events of different clinical importance into an ordinal outcome that prioritize the most severe event of a patient. Up to now, one of the difficulties in interpreting HCEs has been the lack of proper tools for visualizing the treatment effect captured by HCE.
Introduction
Suppose data consisting of i.i.d. observations \(X^n=(X_1,X_2,\cdots,X_n)\) are available from a distribution \(F(x,\theta),\,\theta\in\Theta\subset\mathbf{R}.\) The exact value \(\theta\) corresponding to the distribution that generated the observations is unknown. The problem is, using the available data \(X^n,\) construct tests for making decisions on the possible value of unknown parameter \(\theta\).
Continuous distributions
Mann-Whitney estimate for the win probability
Consider two independent, continuous RVs (random variables) \(\xi\) and \(\eta\). The following probability is called the win probability of RV \(\eta\) against the RV \(\xi\)
Introduction
This is a short reminder of some simple properties of exponential distributions.
The continuous random variable (RV) \(\xi\) has an exponential distribution with the rate \(\lambda>0\) if its CMD (cumulative distribution function) has the following form
Introduction
Abstract
In clinical research the dose–response relationship is often non-linear, therefore advanced fitting models are needed to capture their behavior. The talk will concentrate on fitting non-linear parametric models to the dose–response data and will explain some specificities of this problem.