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Class effect

 

When can we say that drugs have a "class effect"?

Class (noun); "any set of people or things grouped together or differentiated from others". An increasingly asked question is that of whether a set of drugs forms a class, and whether there is a class effect. Class effect is usually taken to mean similar therapeutic effects and similar adverse effects, both in nature and extent. If such a class effect exists, then it makes decision-making easy: you choose the cheapest.

Criteria for drugs to be grouped together as a class involve some or all of the following:

Declaring a class effect requires a bit of thought, though. How much thought, and of what type, has been considered in one of that brilliant JAMA series on users guides to the medical literature [1]. No one should declare a class effect and choose the cheapest without reference to the rules of evidence set out in this paper.

LEVELS OF EVIDENCE FOR EFFICACY

These are shown in Table 1, though if it comes down to levels 3 and 4 evidence for efficacy, the ground is pretty shaky. Level 1 evidence is what we always want and almost always never get, the large randomised head to head comparison. By the time there are enough compounds around to form a class, there is almost no organisation interested in funding expensive, new, trials to test whether A is truly better than B.

Table 1: Levels of evidence for efficacy for class effect

Level
Comparison
Patients
Outcomes
Criteria for validity
1
RCT direct comparison Identical Clinically important Randomisation concealment
Complete follow up
Double-blinding
Outcome assessment must be sound
2
RCT direct comparison Identical Valid surrogate Level 1 plus
Validity of surrogate outcome
2
Indirect comparison with placebo from RCTs Similar or different in disease severity or risk Clinically important or valid surrogate Level 1 plus
Differences in methodological quality
End points
Compliance
Baseline risk
       
3
Subgroup analyses from indirect comparisons of RCTs with placebo Similar or different in disease severity or risk Clinically important or valid surrogate Level 1 plus
Multiple comparisons, post hoc data dredging
Underpowered subgroups
Misclassification into subgroups
3
Indirect comparison with placebo from RCTs Similar or different in disease severity or risk Unvalidated surrogate Surrogate outcomes may not capture all good or bad effects of treatment
4
Indirect comparison of nonrandomised studies Similar or different in disease severity or risk Clinically important Confounding by indication, compliance, or time
Unknown or unmeasured confounders
Measurement error
Limited database, or coding systems not suitable for research

 

Most of the time we will be dealing with randomised trials of A versus placebo or standard treatment and B versus placebo or standard treatment. This will be level 2 evidence based on clinically important outcomes (a healing event) or validated surrogate outcomes (reduction of cholesterol with a statin). So establishing a class effect will likely involve quality systematic review or meta-analysis of quality randomised trials.

What constitutes quality in general is captured in Table 1, though there will be some situation dependent factors. The one thing missing from consideration in Table 1 is size. There probably needs to be some prior estimate of how many patients or events constitutes a reasonable number for analysis.

LEVELS OF EVIDENCE FOR SAFETY

These are shown in Table 2. There are always going to be problems concerning rare, but serious, adverse events. The inverse rule of three tells us that if we have seen no serious adverse events in 1500 exposed patients, then we can be 95% sure that they do not occur more frequently than 1 in 500 patients.

Table 2: Levels of evidence for safety in class effect

 

Level
Type of study
Advantages
Criteria for validity
1 RCT Only design that permits detection of adverse effects when the adverse effect is similar to the event the treatment is trying to prevent Underpowered for detecting adverse events unless specifically designed to do so
2 Cohort Prospective data collection, defined cohort Critically depends on follow up, classification and measurement accuracy
3 Case-control Cheap and usually fast to perform Selection and recall bias may provide problems, and temporal relationships may not be clear.
4 Phase 4 studies Can detect rare but serious adverse events if large enough No control or unmatched control
Critically depends on follow up, classification and measurement accuracy
5 Case series Cheap and usually fast Often small sample size, selection bias may be a problem, no control group
6 Case report(s) Cheap and usually fast Often small sample size, selection bias may be a problem, no control group

 

Randomised trials of efficacy will usually be underpowered to detect rate, serious adverse events, and we will usually have to use other study designs. In practice the difficulty will be that soon after new treatments are introduced there will be a paucity of data for these other types of study. Only rarely will randomised trials powered to detect rare adverse events be conducted.

Most new treatments are introduced after being tests on perhaps a few thousand patients in controlled trials. Caution in treatments for chronic conditions are especially difficult if trials are only short-term, and where other diseases and treatments are likely.

Reference

  1. FA McAlister et al. Users' guides to the medical literature XIX Applying clinical trial results B. Guidelines for determining whether a drug is exerting (more than) a class effect. JAMA 1999 282: 1371-1377.