Interpreting Clinical Trial Data
An MSL is a scientific expert that has the ability to interpret and present complex data. If you’ve done a basic science PhD you will be an expert at analysing experimental data…….but what about clinical trial data?
Do you know the difference between the ITT and mITT population?
Can you explain what LOCF means?
Can you easily identify limitations of a study?
Check out some commonly used clinical trial terms below to help you navigate this important aspect of being an MSL. For a more detailed explanation of critically appraising clinical paper check out our clinical paper workshop.
ITT
Intention to Treat.
The ITT population in a clinical trial includes every patient who is randomized to treatment or placebo (even if they drop out after randomization or do not take the study drug as directed).
Analysis of the ITT population has some potential issues: It ignores noncompliance, protocol deviations, withdrawal/drop out from the study, and anything that happens after randomization.
Some clinical trials also analyse a modified ITT population which allows the exclusion of some randomized subjects in a justified way.
LOCF
· Last observation carried forward.
· This is a method to account for missing data in a clinical trial.
· If a patient data point is missing (for example if a study is 52 weeks in duration and a patient drops out after 34 weeks), the patient's previous data point is imputed for all subsequent, scheduled, but missing data points.
· The combination of the observed and imputed data is then analyzed as though there were no missing data.
· Commonly used in trials with long treatment regimes and high rates of drop out.
Safety Population
· The safety population includes all patients who received at least one dose of a study treatment (even if it is a placebo).
· The safety population is analysed to understand safety implications of a drug, including adverse events and toxicity.
Non-inferiority
· Non-inferiority trials test whether a new treatment (e.g. Drug X) is less efficacious than an active control treatment already in use. In other words, non-inferiority trials aim to answer the question “Is Drug X less effective than the current standard of care?”
· If non-inferiority is to be proved, a non-inferiority margin has to be specified in the protocol of the study.
· For example, if the non-inferiority margin is specified at 10%, If Drug X is within a 10% margin of the active control treatment, then you conclude that Drug X is not worse than the active control treatment i.e. it is non-inferior.
