Clinical Trials Flashcards
Define Superiority Trials
Superiority Trials test whether a new drug is better than a comparator (e.g. placebo or current best treatment)
Define Equivalence Trials
Equivalence Trials test whether a new drug is the same as an existing treatment
Define non-inferiority Trials
Non-inferiority Trials test whether a new treatment is no worse than an existing treatment
Clinical equivalence trials
Tests whether clinical outcomes for a new drug are the same as for an existing treatment
Bioequivalence trials
Tests whether PK parameters e.g. blood concentrations or receptor occupancy for a new drug are the same as for an exisitng treatment
Compare and contrast Superiority, equivalence and non-inferiority trial designs
Superiority:
Most clinical trials
Treatment advance (comparison with placebo or existing best practice)
Equivalence:
Treatment change, comparison with original
(change in drug delivery or manufacture…generic drugs or biosimilars)
Non-Inferiority:
Current effective treatments, where placebo would be unethical
(new treatments may have secondary advantages e.g. better safety, acceptability, cost)
What are factors that determine an ‘ideal clinical trial’?
The ideal clinical trial determines the safety and efficacy of a new drug
-Clinically meaningful (represents a meaningful advance in healthcare/patient outcomes)
-Reliable (Results can be reproducible if trial is repeated)
-Valid:
Internal: observed differences can be correctly attributed to the intervention
External: trial results can be generalised to the real-world population
State the null and alternative hypothesis for superiority trials
Null hypothesis: Treatment is not better than or the same as the comparator
Alternative hypothesis: Treatment is better than comparator
State the null and alternative hypothesis for Equivalence trials
Null hypothesis: Treatment is not similar to comparator
Alternative hypothesis: Treatment is similar to comparator
State the null and alternative hypothesis for non-inferiority trials
Null hypothesis: Treatment is worse than comparator
Alternative hypothesis: Treatment is not worse than (is the same or better than comparator)
What is a one-sided test?
Tests for the possibility of a relationship in one direction.
One-sided tests provides more power to detect an effect.
Risk of missing an effect in the other direction
What is a two-sided test
Tests for the possibility of a relationship in both directions
Define Bias
Systematic distortion of the results of a clinical study away from the truth
List some causes of bias in clinical trials
Caused by inadequacies in design, conduct or analysis of the trial or
publication of its results
Reduces validity of study results
Per protocol analysis
Determines maximum potential effectiveness of treatment
Don’t have to worry about drop outs
Intention to treat
Preserves randomisation
* Minimises bias
* Deals with dropouts
* Mirrors real life
* Allows interim analysis
* Practical and pragmatic
* Might miss a real effect
Define confounders
A parameter that is related to both the study treatment and the outcome, but is not caused by the treatment. Confounders can interfere with the ability of a trial to produce a true result and should be controlled for in study design by randomisation
How can confounders be minimised?
Randomisation
Stratification
Minimisation
Define randomisation
A process of allocating participants to study groups that gives everyone an equal chance of being allocated to any group
Define stratification
Randomisation within subgroups of key confounders
Define minimisation
Dynamic method of randomisation that assigns participants to treatment groups in an order that minimises overall imbalance between groups for a number of selected confounding factors
Define Type 1 error
Rejecting the null hypothesis when it is true, false positive result
Designated as alpha, usually set at 0.05 (5%)
Define Type 2 error
Failing to reject the null hypothesis when it is false, false negative result
Designated as beta, usually set at 20%
How is the risk of random error reduced?
Reduced by large sample
and less variance
