Special Report
Inclusion of Women in Clinical Trials -- Policies for Population Subgroups
N Engl J Med 1993; 329:288-292July 22, 1993
- Article
Although the inclusion of women and minorities in medical research is necessary for valid inferences about health and disease in these groups, both women and members of minority groups have been excluded from or underrepresented in many clinical trials. Congress has therefore proposed that the National Institutes of Health (NIH) ensure that all federally funded clinical research include a valid analysis to determine whether the intervention under study affects women or members of minority groups differently from other subgroups. In particular, Section 429B of the NIH Revitalization Act1 provides that the director of the NIH shall ensure that women and members of minority groups are included in each research project. This requirement does not apply if their inclusion is inappropriate with respect to their health, the purpose of the research, or other circumstances that the director of the NIH may designate.
Any NIH-funded clinical trial in which women or members of minority groups are included must be designed and carried out so as to provide a valid analysis of whether the intervention or variables being studied affect women or members of minority groups differently from other subjects in the trial. Cost is not a permissible consideration in determining whether such inclusion is inappropriate. Cost may be considered only when the data regarding women or members of minority groups that would be obtained in the project have been or will be obtained through other means that offer comparable quality. The inclusion of women and minorities may not be required if substantial scientific data demonstrate that there is no significant difference between the effects of the intervention or variables under study on these groups and the effects on the subjects included in the trial.
This new policy would extend the previously stated policies of the NIH and others by requiring subgroup analysis of the relevant intervention or variables2-4.
We are concerned that policies designed to ensure the appropriate inclusion of women and minorities in clinical trials of treatment efficacy might be applied so uncritically as to hamper rather than enhance the advancement of scientific information about these groups. The detection of significant differences among relevant subgroups generally requires clinical trials that are prohibitively large, time consuming, and expensive. Medical researchers may be caught between limited resources and their goal of answering many pressing medical questions. As a result, they may have to sacrifice either statistical power or complete representation of the subgroups of interest. The challenge will be to provide information specific to distinct population groups while collecting accurate information economically. In this article we focus primarily on the consequences of policies mandating subgroup analysis as they pertain to women.
Participation of Women in Clinical Trials
If men and women responded identically to therapy, the issue of representation of women in clinical trials would be less important. Current knowledge on this crucial point is incomplete, however. Excluding women from clinical research therefore prevents physicians from having sufficient information to make informed judgments about the treatment of women. As pointed out by the Council on Ethical and Judicial Affairs of the American Medical Association, the very factors that lead to the exclusion or underrepresentation of women are evidence of the importance of including them5. The questions that require study include the following: whether the fetus is affected by an intervention, whether there is variation in the response of women at different stages of the menstrual cycle, whether women respond differently to therapy before and after menopause, whether oral contraceptives or estrogen-replacement therapy affects the response to other therapies, and whether women and men respond differently to an intervention.
Biologic differences between men and women may reflect genetic, physiologic, lifestyle, cultural, and social differences -- although the mechanisms that explain these differences are to a great extent unknown6-8. In addition, differences between the sexes in responses to drugs and other interventions are being reported more frequently9-12. A major concern expressed by the Council on Ethical and Judicial Affairs is that medical treatments of women are primarily based on clinical experience with men despite the fact that women may react differently from men or that some diseases manifest themselves differently in women. In other words, the results of medical research are generalized to women without sufficient evidence that they in fact apply to women.
Have Women Been Excluded or Underrepresented?
The literature is inconclusive about whether women have been excluded from or importantly underrepresented in clinical trials. Women with childbearing potential have been systematically excluded from most clinical research in the early phases of drug development. The Food and Drug Administration (FDA) has recommended that women with childbearing potential be excluded from the early phases of study of a new drug unless the drug is intended to treat a life-threatening disease or a disease with severe morbidity. The exclusion for childbearing potential has included women using contraception and those not currently sexually active, but not those who have had hysterectomies or tubal ligation. Recently, however, the FDA announced plans to change its policy, as discussed elsewhere in this issue of the Journal13. Many of the largest clinical trials of cardiovascular disease have explicitly excluded women14-18. On the other hand, in trials of the efficacy of new drugs, women have often been included roughly in proportion to the prevalence of disease in men and women19.
Barriers to the Full Participation of Women in Clinical Trials
Women face several barriers to full participation in clinical studies. These barriers result from (1) the responsibility to protect fetuses and the future reproductive capacity of women with childbearing potential; (2) the fear of legal liability if a woman suspects that damage to a fetus or gamete was due to participation in a study; (3) the need for easy recruitment, a compliant population, and subjects who are at high risk for the end points being examined; (4) the availability of identifiable, convenient cohorts (e.g., veterans or army recruits, who are primarily male); and (5) known variations in hormonal status that affect the results of laboratory tests and inferences about treatments20.
Homogeneity versus Heterogeneity
A clinical trial must balance conflicting desires for homogeneity and heterogeneity. Ideally, the study cohort is homogeneous enough to yield a high probability of learning whether a therapy is safe and effective but heterogeneous enough to ensure that the observed results are not applicable only to a narrowly defined subgroup. No rules provide reliable guidance for planning the composition of the study cohort for a single study or for structuring a series of studies to investigate a therapy in different populations. The rational choice of cohorts in a clinical trial depends on the disease under investigation and the questions being asked, as well as on the conviction that the results can be generalized to a more diverse population.
The members of a study cohort generally differ from the population about which inferences are to be made in several important ways. Unlike a randomly selected sample of the population, the study subjects are volunteers. Furthermore, the inclusion and exclusion criteria of the study produce a more narrowly defined set of subjects than the group that may be eligible for a treatment. To minimize the number of subjects who drop out, for instance, clinical trials often exclude people who plan to change residence during the follow-up period, people likely to die soon from a disease other than the one being investigated, and people the investigators believe will not follow the requirements of the protocol. In making inferences from a study's results, a crucial assumption is that the characteristics that differentiate the study cohort from the target population are not likely to translate into important physiologic differences that lead to distinct responses to therapy. Most clinical studies cannot rely on classic statistical inference, extrapolating from a random sample to the population from which the sample is drawn. Instead, the formal basis of inference rests on the principle of random assignment (for randomized clinical trials) and on the belief that various subgroups of a population are likely to respond similarly to interventions21. Thus, asking whether there is a biologically plausible reason to expect different responses to an intervention among population subgroups is a crucial step in the design of clinical studies. When a homogeneous response cannot be assumed for specific subgroups of the population, it is essential that enough members of the relevant subgroups be included so that a differential response can be detected and measured.
Exclusion of a given subgroup from a study precludes formal inferences about the expected results for that subgroup. Therefore, a strategy that is commonly recommended is to design studies in which the subgroup composition of the study cohort mirrors that of the general population that would eventually receive the treatment. To make inferences applicable to women from such a trial, one might ask a number of questions: Is the therapy effective overall (main effect)? Is the therapy effective for women (subgroup effect)? Is the therapy effective in both men and women but is the magnitude of the effect different (quantitative interaction)? Is the therapy effective in one sex but ineffective or harmful in the other (qualitative interaction)?
For the population at large, valid answers to the questions about main effect and qualitative interaction are crucial. The answers help in formulating general policy about treatment. The questions about subgroup effect and quantitative interaction are important for understanding the biologic mechanism of the disease and its treatment, selecting the dose or otherwise modifying the administration of the therapy, designing new therapies, and determining sex-specific costs and benefits.
Interpretation of an Unexpected Difference between Men and Women
When a study is designed to detect the main effect of treatment and there is no reasonable hypothesis of important differences between subgroups, investigators must be cautious in interpreting surprising findings of differences. Clinical trials are rarely large enough to test a treatment effect within a subgroup of the population reliably. Most clinical trials, however, report their results both for the entire study cohort and for subgroups of interest. For example, investigators typically report data from clinical studies separately for men and women even when the sample size is inadequate. If the sample size is chosen to show an overall treatment effect, an equivalent response in men and women can masquerade as a differential response, and vice versa22.
Some post hoc analyses of different results for men and women are difficult to interpret. Fisher et al.23. found that for postoperative treatment of rectal cancer a combined chemotherapeutic regimen, as compared with surgery alone, resulted in an overall improvement in disease-free survival (P = 0.006) and in survival (P = 0.05). When evaluated according to sex, however, the benefit of chemotherapy at five years, both in disease-free survival and overall survival, was restricted to men.
Extremely large samples may be needed to detect significant differences among subgroups (for example, subgroups with different hormonal statuses). A recent overview of 133 randomized clinical trials of treatment of early breast cancer included 75,000 women24. This very large sample demonstrated that different subgroups of women have different responses to therapy. The high cost and complexity of large-group trials prohibit their performance except in common diseases. Moreover, in only a few cases will there be a sufficient number of relevant randomized clinical trials to allow the pooling of data on very large numbers of people across trials.
When study groups are small, how should an observed difference in therapeutic response between men and women be interpreted? Some recommended approaches for differentiating between true subgroup effects and chance occurrences include empirical Bayes methodology,25 methods to correct for multiplicity, and consideration of structured hypotheses26. As in all clinical research, biologic plausibility should be examined.
Conclusions
This analysis reveals a series of conundrums. In the absence of plausible scientific hypotheses concerning possible differences between the sexes in response to therapy, time and money are wasted if clinical trials must be large enough to detect differences between men and women. The situation is even more difficult when women and men in minority groups are considered. If the sample is too small, however, researchers may miss true effects or overstate apparent effects. When women are excluded entirely, researchers cannot gain even a hint about differential response. In fact, a hypothesis that men and women respond similarly to an intervention logically implies that sex should not be a criterion for participation in a clinical trial.
Merely including some women in a study is not sufficient to learn how to treat women. Similarly, including women in clinical trials in a ratio consistent with the prevalence of disease in both sexes may not provide reliable information about treatment. Oversampling of women may not be sufficient unless the samples of men and women are large. Failure to see an effect does not necessarily mean that no effect is present. Instead, it may reflect inadequate statistical power. Introducing men and women from minority groups intensifies the problem, since multiple examination of subgroups may lead to the observation of effects that are not truly present.
We suggest some approaches to these issues. First, to help researchers (and study sections reviewing clinical research) develop appropriate samples, a set of guidelines based on the best available scientific evidence is needed (as specified in the NIH Revitalization Act)1. Second, even if the investigators in a clinical trial do not intend to analyze data separately for men and women, it would be useful to collect data on certain variables (e.g., the hormonal status of women, weight, and adiposity) to allow eventual analyses should suggestive trends be found or should hypotheses arise from other studies.
Third, it is important not to shy away from extrapolating from the experience in one sex when the conclusion have biologic plausibility in the other. It is also important to analyze according to sex if there is scientific reason to hypothesize that the responses of men and women differ substantially. Only by formulating scientifically meaningful questions and then testing them in rigorous studies can we expect to learn how women and men respond to therapy and how best to prevent and treat diseases.
Finally, avenues other than the clinical trial are available to help formulate hypotheses regarding the differential response of men and women to therapy. Case histories have provided insight into subgroups. An unusual cluster of events may spark suspicion that a therapy is harmful to a subgroup of people. For example, the surprising occurrence of vaginal cancer in a group of young women led to the implication of diethylstilbestrol as the causative agent in the daughters of women who had taken the drug during pregnancy27.
More formal approaches to observational studies include many epidemiologic methods, the phase IV postmarketing study, and outcomes research28. The pharmaceutical industry is currently studying new approaches designed to expedite and improve the results of drug-development activities. Such approaches include pharmacokinetic screens and the use of surrogate end points for pharmacodynamic measures. Another approach is meta-analysis, a set of techniques for combining data from various studies29. Data from various demographic subgroups can be pooled across many studies to provide more information on treatment effect than is available from a single trial.
An armamentarium of methods to learn about subgroup responses to therapy is available and must be used. Research designs and analytic techniques must be appropriate to the specific questions being asked, and data must be collected that will ultimately be useful in sorting out the relations between sex, hormonal status, and response to therapy. A global solution, such as the one proposed in the NIH Revitalization Act, cannot provide the answers to complex and varied questions about the effects of therapy on women.
In summary, neither adherence to quotas in the composition of a study cohort nor the irrational exclusion of a subgroup of people can be supported scientifically. Determining the number of women to be included in a trial should reflect reasonable hypotheses about the relation of treatment efficacy to sex, not global rules about the composition of study cohorts.
Supported by interest earned on the endowment funds given to the Institute of Medicine by the Howard Hughes Medical Institute.
This article is a synopsis of a white paper written by the Board on Health Sciences Policy. Copies of the white paper are available from the Division of Health Sciences Policy, Institute of Medicine, National Academy of Sciences, 2101 Constitution Ave., Washington, DC 20418.
Source Information
Address reprint requests to Dr. Ruth Ellen Bulger at the Division of Health Sciences Policy, Institute of Medicine, National Academy of Sciences, 2101 Constitution Ave., FO 3018, Washington, DC 20418.
Appendix
The members of the Board on Health Sciences Policy are as follows: J.C. Bennett, University of Alabama School of Medicine, Birmingham (chairman); E. Daddario, Washington, D.C.; W.N. Hubbard, Jr., Hickory Corners, Mich.; T. Inui, Harvard Medical School, Boston; R.J. Johns, Johns Hopkins University School of Medicine, Baltimore; E.R. Kandel, Columbia University College of Physicians and Surgeons, New York; P. King, Georgetown University Law School, Washington, D.C.; S.S. King, Cedars-Sinai Medical Center, Los Angeles; E.L. Larson, Georgetown University School of Nursing, Washington, D.C.; J. Lederberg, Rockefeller University, New York; R.I. Levy, Wyeth-Ayerst Research, Philadelphia; M.L. Polan, Stanford University, Stanford, Calif.; J.D. Stobo, Johns Hopkins University School of Medicine, Baltimore; P.D. Stolley, University of Maryland School of Medicine, Baltimore; J.E. Wennberg, Dartmouth Medical School, Hanover, N.H.; and J.D. Wilson, Southwestern Medical Center, Dallas. Study director: J. Townsend. Consultant: J. Wittes. Division director: R.E. Bulger.
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