Related Experiment Video
Updated: Jan 30, 2026

Creating Transient Cell Membrane Pores Using a Standard Inkjet Printer
Published on: March 16, 2012
Creating historical controls using data from a previous line of treatment - Two non-standard approaches
Anthony J Hatswell1,2, William G Sullivan3,4
1BresMed Health Solutions, Steel City House, West Street, Sheffield, UK.
Abstract:
Where medical interventions are licensed based on only uncontrolled study data (for example a single-arm trial), a common approach for estimating the incremental benefit is to compare the treatment to a 'historical control'; data collected from patients who did not receive the intervention. We illustrate with motivating examples two methods for the creation of historical controls where disease progression and overall survival are typically the key clinically meaningful endpoints. The first method utilises information routinely collected in a clinical trial programme: patients' time to disease progression on their previous line of treatment against which outcomes can be compared. The second uses published clinical outcomes for the prior line of treatment which can be extrapolated to estimate outcomes at the next line. As examples we use two pharmaceuticals licensed on the basis of uncontrolled clinical studies - idelalisib for double-refractory follicular lymphoma and ofatumumab for double-refractory chronic lymphocytic leukemia. Although subject to limitations that should be considered on a case-by-case basis, the methods may be appropriate when trying to quantify the clinical benefit of treatment based on limited and uncontrolled trial data. As a result, the methods can be used to inform health technology adoption decisions.
Related Concept Videos
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Model Approaches for Pharmacokinetic Data: Compartment Models
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Model Approaches for Pharmacokinetic Data: Physiological Models
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

