Applying Clinical Trial Results.
Part B: Guidelines for Determining Whether a Drug is Exerting (More Than) a Class Effect
Finlay A. McAlister, Andreas Laupacis, George A. Wells, David L. Sackett, for the Evidence-Based Medicine Working Group
Based on the Users' Guides to Evidence-based Medicine and reproduced with permission from JAMA. (1999;282(14):1371-1377). Copyright 1999, American Medical Association.
- Clinical Scenarios
- Introduction
- The Risks of Assuming a Class Effect
- Levels of Evidence
- Other Considerations
- Resolution of Scenarios
- Conclusion
- Acknowledgements
- References
Most classes of drugs include multiple compounds, and the interests of clinicians, manufacturers, and purchasers may conflict around questions of whether a particular drug is more efficacious, safer, or more cost effective than others in its class [1]. In this article, we review the types of evidence commonly cited to support the use of one drug over another of the same class and provide a hierarchy for grading studies which compare one drug with another from the same class, expanding upon our discussion in Part A of this User’s Guide.
Clinical Scenarios
The Clinician
As a busy clinician, you care for many patients with elevated serum cholesterol levels. A speaker at a recent continuing medical education event reviewed the benefits of cholesterol lowering therapy, particularly with hydroxymethylglutaryl-Coenzyme A reductase inhibitors (statins), in the primary and secondary prevention of ischemic heart disease, but did not recommend a particular statin. You decide to consider statin therapy for all of your patients with elevated cholesterol, but are uncertain which of the six statins on the market is the best. You ask your local general internist, cardiologist, and endocrinologist for their opinions and each suggests a different statin, citing different reasons. You contact your local pharmaceutical representatives to provide you with the evidence that their statin is better than that of their competitors. Although you use the JAMA series on "Users guides to the medical literature" to assess the validity of published studies, faced with a variety of competing claims, you realize that you need a framework for grading the strength of these studies.
The Policy Maker
Your colleague, a purchaser for a large Health Maintenance Organization (HMO), is faced with a similar dilemma when she is asked to consider replacing the statin on her HMO’s formulary with a newer one. She wonders whether there is enough evidence to support the contention that the new statin is as good as, or better than, the one currently on formulary. While this statin is cheaper, it has only been evaluated in short-term trials with cholesterol lowering as the solitary endpoint.
Introduction
Although there is no uniformly accepted definition of a drug class, and some argue that it can’t be defined at all, drugs are generally said to belong to the same class for one of three reasons [Table 1]. In this paper, we will define a drug class as those drugs which share a similar structure and mechanism of action. Most classes of drugs include multiple compounds, and because of their similar mechanisms of action, they are generally thought to confer similar pharmacologic effects and clinical outcomes ("class effects"). This assumption is a key medical heuristic [2] and underlies clinical practice guidelines in which evidence from studies involving one or more drugs within a class is extrapolated to other drugs of the same class. For example, it is recommended that “beta-blockers” be prescribed to survivors of myocardial infarction (MI) or “angiotensin converting enzyme (ACE) inhibitors” to patients with heart failure. In this circumstance, clinicians are likely to be interested in the drug within each class with the most attractive efficacy/safety ratio; purchasers the most cost-effective drug from a class; and manufacturers in ensuring that their drug is prescribed as much as possible.
Table 1: Different definitions of drug classes
ACE inhibitors= angiotensin-converting enzyme inhibitors |
The absolute treatment effects seen with a drug (defined by the absolute risk reduction [ARR] or number needed to treat [NNT]) are influenced by the baseline risk or control event rate (CER) of those patients in whom it is used. Thus, the ARR varies considerably among different groups of patients. On the other hand, the relative treatment effect of a drug (defined by the relative risk reduction[RRR]) is often (but not always [3]) similar irrespective of the baseline risk of trial participants [4] [5]. If two drugs are tested in separate placebo-controlled trials, only proportional effects such as the RRR seen with each drug can be compared (and then only under the assumption of constant RRR over different CERs). Although the point estimates of effect size vary with the play of chance, a class effect is considered to be present when drugs with similar mechanisms of action generate RRRs (or odds ratios [ORs]) that are similar in direction and magnitude. For example, the Collaborative Group on ACE Inhibitor Trials [6] suggested that there is a class effect for ACE inhibitors in patients with symptomatic heart failure despite the fact that the OR point estimates for effects on total mortality ranged from 0.14 (95% confidence intervals [CI] 0 to 7.6) for perindopril (one trial, 125 patients) to 0.78 (95% CI 0.67 to 0.91) for enalapril (seven trials, 3381 patients). Our confidence in this class effect stems from the recognition that the overall OR in 32 trials involving 7105 patients was 0.77 (CI 0.67-0.88), the confidence intervals for each of the ACE inhibitors overlapped, and there was no statistical heterogeneity between trials of different agents.
The Risks of Assuming a Class Effect
Although drugs of the same class typically exhibit similar pharmacological effects and clinical outcomes, this may not always be the case (witness the current controversy over the safety of sotalol in MI survivors with CHF after the publication of the SWORD Trial [7] which suggested an increase in mortality with sotalol, in contrast to the decrease in mortality with other beta blockers). However, in this context, it is useful to recall a previous controversy over the efficacy of beta-blockers with intrinsic sympathetic activity (ISA) in patients with MI: while an earlier meta-analysis [8] suggested that the treatment effect was larger with non-ISA beta-blockers than those with ISA, subsequent trials [9] failed to confirm this and the totality of the evidence [10] suggests there is little difference between beta-blocker subgroups. Thus, it would seem reasonable to accept a priori that drugs within the same class exert similar effects, unless there is clear evidence of important differences.
However, this assumption can lead to two important errors in extrapolation with major clinical consequences. Firstly, when a class of drugs (such as the thiazide diuretics) all produce similar pharmacological effects (blood pressure lowering) and similar clinical effects (stroke reduction), a second class of drugs (for example, the calcium-channel blockers) that produce the same pharmacological effect might be assumed to produce the same clinical benefit. In the absence of randomized trials verifying that final step, this type of extrapolation may be in error: for example, consider the issue raised in Part A of this Guide- some calcium channel blockers have unfavourable effects on total mortality [11]. Secondly, even within the same class, individual drugs may have different physiologic effects other than the mechanism of action which defined them as being from the same class. To the extent that this is so, it may be inaccurate to extrapolate the clinical outcomes shown in randomized trials of one drug in a class to another member in that class that has not been subjected to similar outcome-centered trials. For example, some authors have argued that, although all of the statins act on the HMG-CoA reductase enzyme, they may have different non-lipid effects on the atherothrombotic process which may influence their clinical efficacy [12].
In order to reduce the risk of faulty extrapolation and to maximize the optimal selection of treatments within a class of drugs, we believe it may be useful to develop and apply a hierarchy of evidence when making decisions about the comparative clinical efficacy and safety of drugs within a class. As pointed out in Part A of this User’s Guide, no matter how strong the pathophysiologic rationale or indirect evidence, the efficacy and safety of a new drug must be established in clinical outcome studies that test more than just biological plausibility.
Levels of Evidence
Levels of evidence are increasingly used by groups which make recommendations about patient-care [13] [14] [15], and we have used some of them as a starting point to develop guidelines for comparing one drug against other drugs within the same class [Table 2]. This comparison should occur as part of a systematic review of all the relevant evidence on the effects of a treatment, identified and assessed by sound and transparent methods such as those employed in the Cochrane Collaboration [The Cochrane Library, Issue 2, 1998. Oxford: Update Software]. We will describe each level in turn, using the choice of statin drugs as an example to illustrate their use (the relevant statin trials are summarized in Table 3).
Table 2: Levels of Evidence for comparing the efficacy of drugs within the same class
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Clinically Important Outcomes refer to long-term efficacy data and the particular endpoints depend on the condition being treated. For statins used to prevent/treat atherosclerotic disease, clinically important outcomes would include all-cause mortality, myocardial infarction, and stroke.
Surrogate Outcomes are considered “Validated” only when the relationship between the surrogate outcome and clinically important outcomes has been firmly established in long-term RCTs.
Table 3: Features of randomized, placebo-controlled, statin trials designed to detect differences in clinically important endpoints
Legend: ‡ point estimates only * since all-cause mortality was non-significantly increased in active treatment arm, results are presented as number needed to treat to cause one death AMI = acute myocardial infarction
4S = Scandinavian Simvastatin Survival Study
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LEVEL 1: (randomized clinical trials providing head-to-head comparisons of the drug of interest with other drugs of the same class for their effects on clinically important outcomes): This would generate the strongest evidence for the decision maker; however, there are potential threats to validity [Table 2] and several methodologic issues unique to these trials which the reader must consider. Firstly, at least one of the drugs should have been shown to have a clinically important impact versus placebo in previous trials carried out in a similar population to the current trial. Secondly, the choice of appropriate dose for each drug is a complicated issue as this will affect the outcomes and safety profiles for both drugs. Finally, one must carefully consider the trial size and methods before concluding equivalence of two drugs- equivalence trials require much larger sample sizes than standard trials [16] and any laxity in trial conduct or patient compliance will tend to mask any real differences between drugs.
The choice of clinically important outcomes for level 1 studies depends upon the target intervention. In the case of therapies designed to prevent or arrest atherosclerosis (such as statins), this implies long-term efficacy data on events such as myocardial infarction, stroke, and all-cause mortality. On the other hand, for interventions designed to treat symptomatic diseases (such as gastroesophageal reflux disease), clinically important outcomes could include symptom scores and other quality of life measures.
Although there are examples of level 1 evidence in other branches of medicine (such as head-to-head trials of the selective 5-hydroxytryptamine type 3 receptor antagonists for postoperative nausea and vomiting [17] [18]), they are rare in the cardiovascular literature. Our literature search failed to find any level 1 evidence for statins.
LEVEL 2: (randomized clinical trials providing head-to-head comparisons of the drug of interest with other drugs of the same class for their effects on validated surrogate outcomes OR comparisons across two or more placebo-controlled trials for effects on clinically important outcomes or validated surrogate outcomes): Part A of this User’s Guide discussed criteria for deciding whether to accept results of trials based on surrogate outcomes. Ecologic studies, cohort studies, and RCTs with pre-statin lipid lowering agents were supportive of the lipid-lowering hypothesis[19] (that lowering LDL cholesterol lowers the risk of atherosclerotic heart disease); however, it was not until the publication of the large scale statin trials [20] [21] [22] [23] [24] (see Table 3 for full description of trials and acronyms used in this article) consistently linking reductions in LDL-cholesterol to reductions in morbidity and mortality that we felt comfortable in accepting the surrogate endpoint of LDL-cholesterol lowering as a proxy for clinically important outcomes. Thus, in order to accept head-to-head comparisons for surrogate outcomes as level 2 evidence, at least one of the comparators must have demonstrated efficacy in long-term trials with clinically important outcomes.
While a randomized trial [25] comparing four statins for their effects on LDL-cholesterol, HDL-cholesterol, and triglyceride over an eight week period would be an example of level 2 evidence, it is important to also incorporate considerations of the size and duration of trials in the decision-making process (as discussed later).
On the other hand, a number of level 2 comparisons can be made between various statins- for example, one can compare the treatment effects seen with simvastatin versus pravastatin in secondary prevention trials (such as the 4S20 and LIPID24 studies, see Table 3). While consistency of effects in such comparisons would be strong evidence for the presence of a class effect, these comparisons are less useful in determining whether one drug is more efficacious than another since the advantages of randomization are lost and the comparison is essentially that between two or more cohorts. In addition to the potential biases outlined in Table 2, there is also the real possibility of confounding between a subject’s risk/responsiveness and exposure to a particular treatment in those situations where the subjects from different trials have different risk status- for example, if one were to compare the statin used in a primary prevention trial (such as lovastatin in AFCAPS/TexCAPS [23]) with another statin tested in a secondary prevention trial (such as simvastatin in 4S). In fact, comparisons such as these would only be valid when the drug efficacy is known to be independent of baseline risk, an assumption which appears valid in some situations (such as antiplatelet [5] or antihypertensive [4] therapy) but has been questioned for the statins [26] [27] [28] [29] [30] [31].
It is theoretically possible to compare the efficacy of two drugs tested in separate placebo-controlled trials. As outlined by Bucher et al [32], an indirect estimate of the association between drugs A and B can be obtained by comparing the odds ratio (or relative risk) from studies of drug A versus placebo (p) and from studies comparing drug B versus placebo: ORA vs. B= ORA vs. p / ORB vs. p . However, this assumes that none of the potential biases outlined in Table 2 are operative and that an intervention’s treatment effect is consistent across different patient subgroups. Furthermore, these indirect estimates may provide substantially different effect size estimates than direct comparisons of drug A against drug B. For example, a systematic overview of strategies to prevent Pneumocystis carinii pneumonia in HIV positive patients documented that the indirect comparison of trimethoprim-sulphamethoxazole (T-S) versus dapsone/pyrimethamine (D/P) suggested a much larger effect size from T-S (OR 0.37, 95% CI 0.21-0.65) than was seen in the direct comparisons (overall OR 0.64 in the nine trials of T-S versus D/P, 95% CI 0.45-0.90) [32]. Thus, the strength of inference from indirect comparisons is limited.
Level 3 and 4 studies have numerous flaws as outlined below and are best viewed as exercises in hypothesis generation.
LEVEL 3: (comparisons across subgroups from different placebo-controlled trials OR comparisons across placebo-controlled trials in which outcomes are restricted to unvalidated surrogate markers): In addition to the biases that affect higher level studies, comparisons based on subgroup analysis are potentially flawed [Table 2]. Both simple statistics and experience have taught us that many initial subgroup conclusions (especially when they are the result of “data-dredging”) are subsequently shown to be wrong [33] [34]. An example of such a comparison would be looking at the efficacy of simvastatin in the 4S subgroup with the lowest lipid levels (241 patients with total cholesterol 5.5 to 6.24 mmol/L) [27] versus the efficacy of pravastatin in the CARE subgroup with comparable lipid profiles (2087 patients with total cholesterol 5.4 to 6.21 mmol/L) [22].
Level 3 evidence may also include the use of surrogate markers which, although they may lie along a recognized pathogenetic pathway from mechanisms of action to important clinical outcomes, have not been validated in long-term randomized clinical trials. Using an example from Part A of this User’s Guide, this would involve making inferences about reduction in fractures from the effects on bone density of two different bisphosphanates in two independent randomized trials.
LEVEL 4: (comparisons involving or confined to non-randomized evidence): This type of evidence is only possible for conditions such as hypertension or hyperlipidemia in which there are a large number of potential treatments and they are commonly used by practitioners. Non-randomized evidence can include cohort or case control studies, modelling studies (using risk prediction equations such as that derived from the Framingham data [35]), and/or outcomes research employing administrative databases. Although these type of analyses can provide useful insights (particularly with respect to dose-response relationships) [36], we suggest that they are best viewed as exercises in hypothesis-generation. In particular, outcomes research studies, originally developed to determine whether the efficacy of interventions proven in randomized trials have their anticipated impacts at a population level, have sometimes been used to pursue the primary determination of efficacy- a purpose for which they were not intended. When used for this latter purpose, in addition to the limitations common with the other observational data listed in Table 2, they present unique problems in interpretation that restrict the validity of inferences drawn from them about the relative efficacy of medications from the same class.[37]
An example of level 4 evidence is a recent re-analysis of the WOSCOPS database in which the observed coronary event rates in pravastatin-treated patients were compared to those predicted from the Framingham coronary risk equation (using the constellation of risk factors and mean on-treatment cholesterol levels observed in the trial) to determine whether the treatment benefit with pravastatin exceeded that expected from the degree of cholesterol lowering achieved (leading to inferences about whether pravastatin’s efficacy exceeds that expected of other statins) [28].
Other Considerations
Amount of Efficacy Evidence
While we have thus far focused on the validity of the evidence, it is important to also recognize that the number, size, and duration of studies are essential factors to be considered in the decision-making process. Certainly, the superiority of one drug within a class can only be definitively established with level 1 evidence. However, while level 1 evidence would be ideal for establishing that a group of drugs exert a class effect (by showing narrow confidence limits around the difference between drugs), we recognize that this is rarely available and is unlikely to ever be available for many classes of drugs (due to the difficulties in funding and conducting such large trials that are unlikely to appeal to researchers, manufacturers, or funders). In this situation, the amount of level 2 evidence becomes important. For instance, one would feel more comfortable in concluding a class effect if there were a number of placebo-controlled trials demonstrating that various drugs from the same class had similar treatment effects. However, our intent here is not to set a single level that must be achieved before a drug can be claimed to be superior to others in its class or before a class effect can be established. These are decisions which individual clinicians or policy makers must make, taking into account their local circumstances and individual comfort levels.
Safety
In the past decade, there have been numerous examples of drugs within the same class that have been shown to have different safety profiles (for example, practolol causes sclerosing peritonitis and keratoconjunctivitis while other beta-blockers do not; ticlopidine causes more neutropenia than clopidogrel; phenylbutazone causes agranulocytosis while other non-steroidal antiinflammatory agents do not). Although not the primary focus of this paper, considerations of drug safety are part of any treatment or purchasing decision, and we therefore offer a set of levels of evidence for determining safety in Table 4. The first tests of a drug in humans (phase I studies) are designed to determine the maximally tolerated dose, and clinical trials are generally designed to determine the efficacy of the drug. As such, the sample sizes of neither are adequate to detect uncommon adverse effects. The reader’s attention is drawn to the inverse rule of 3: to be 95% sure of seeing at least 1 adverse drug reaction that occurs once in every x patients, you need to follow 3x patients [38]. Given the size and duration of most clinical trials, adverse effects that occur in less than 1 in 1000 participants or take more than six months to appear will generally remain undetected [2]. However, randomized clinical trials are still the strongest design for detecting real differences in adverse effects (such as the different rates of intracranial bleeding with different thrombolytic agents [39] [40]) and meta-analysis of such trials can give an unbiased estimate of excess hazards. In the absence of clinical trials, we believe that premarketing safety data must be considered preliminary and large phase IV studies or systematic post-marketing surveillance data are necessary to confirm the safety of new drugs.
Table 4: Levels of evidence for comparing the safety of drugs within the same class
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Convenience/compliance
While once-a-day medications are more convenient and usually have higher compliance rates, evidence on drug compliance derived from trials may translate poorly into clincial practice. For instance, while compliance with the various statins during the course of the trials described in Table 3 ranged from 90% to 94%, analyses of administrative databases in Canada and the United States [41] revealed that only half of statin-treated patients were still taking their medication one year after it was prescribed.
Cost
Faced with a decision as to whether a new drug from a class should be offered to eligible patients within the population, clinicians and policy makers have different (although not mutually exclusive) perspectives. For clinicians, this decision usually hinges on the efficacy, safety, convenience/compliance, and cost of the new drug versus the old, and the applicability of the trial evidence to their patient [42]. However, for policy makers these issues will form only one piece of the puzzle and they must also evaluate the efficiency ("the effects or end results achieved in relation to the effort expended in terms of money, resources, and time") [43], affordability, and opportunity costs of any new drugs. The efficiency of any intervention is determined by formal economic analyses, and the Users’ Guide series has offered criteria for evaluating their methodological quality [44]. Although cost-minimization analysis is the simplest and least controversial of the economic analysis techniques, it requires proof that the outcomes with both alternatives are the same. As this rarely exists, the policy maker must rely on other types of analyses (cost-effectiveness, cost-benefit, or cost-utility analyses) which involve varying degrees of assumption and guesswork. As pointed out by Naylor and colleagues [45], economic analyses should be viewed as "promising, clearly helpful, still in need of refinement and open, like any new technology, to both wise use and well-intentioned abuse."
Further hampering the policy-makers’ task, the decision as to whether a new drug is efficient enough to warrant its adoption depends critically on the social, political, and economic realities of their particular health care setting. Thus, attempts to establish universal cut-points (using cost/QALY ratios) have been largely unsuccessful [46]. While there are occasions where there is compelling evidence for adoption (the new drug is as effective or more effective than others of its class and is less costly) or rejection (the new drug is less effective than others of its class and is more costly) of a new drug, much of the time the policy maker is operating in a cost- utility grey zone between these two extremes[45].
Resolution of Scenarios
The Clinician
Given the qualitative consistency of the RRR for acute myocardial infarction in patients treated with three of the statins in large trials with clinically important outcomes [Table 3] and the convincing nature of LDL-cholesterol lowering as a surrogate outcome [19] [29] [47] [48] [49], our clinician concludes that there is a class effect of statin drugs on the occurrence of ischemic heart disease. In the apparent absence of differences in safety or compliance profile between the various statins, he decides to pursue a cost-minimization strategy. While the newer statin has only been evaluated for cholesterol-lowering efficacy in a short-term trial (less than six months), he decides to prescribe it as it is the cheapest statin in his local setting.
The Policy Maker
The policy maker agrees with the clinician that the statins appear to exert a class effect in terms of efficacy. However, she is concerned that the efficacy of the newer statin has not been evaluated in long-term trials with clinically important outcomes or validated surrogate outcomes. Thus, she decides to keep the older (and more expensive) statin on her formulary until level 1 or long-term level 2 evidence is available proving that the newer statin is as good as, or better, than the currently provided statin.
Conclusion
While it would be preferable that every drug in each class (and indeed every dose and every formulation) be evaluated in RCTs with active comparators from the same class for their effects on clinically important outcomes, this has not been accomplished for several important classes of drugs. We believe that advocates of newer drugs within a class must provide evidence of equivalence (or superiority) to the older agents and "randomized comparative trials … remain the preferred evidentiary standard" [50]. Recognizing that this gold standard is not always attainable (in the case of the statins, such RCTs would require very large sample sizes and long follow-up to detect significant differences in myocardial infarction or death between two different statins), we suggest that discussions about class effects will benefit from citing the levels of evidence behind the arguments and recognizing the strengths and weaknesses inherent in each study design.
Acknowledgements
FAM is supported by the Medical Research Council of Canada and the Alberta Heritage Foundation for Medical Research, AL is supported by the Medical Research Council of Canada, and DLS is supported by the Research and Development Programme of the National Health Service, United Kingdom. The authors gratefully acknowledge the input of various members of the University of Ottawa Clinical Epidemiology Unit and attendees of the EQUINOX symposium in earlier discussions of this topic, Dr. J. Glennie in providing information on the approaches of regulators to these issues, and Drs. I. Chalmers and F. Lawson in reviewing earlier versions of this manuscript. The authors also thank the anonymous reviewers for their helpful input.
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