Response to “inclusion of women and minorities in clinical trials and the NIH Revitalization Act of 1993 — The perspective of NIH clinical trialists”

Current status of inclusion of black subjects in neuropsychological studies: A scoping review and call to action

In recognition that insufficient diversity in research impedes the generalizability of findings and negatively impacts clinical outcomes, the 1993 National Institutes of Health (NIH) Revitalization Act required NIH-funded clinical trials to include and assess outcomes for women and minority participants. Since that time, the American Psychological Association (APA) and the American Academy of Clinical Neuropsychology (AACN) have also acknowledged the reporting of this information as an essential element of research, and they have established similar aspirational goals. Nevertheless, Black communities remain disproportionately underrepresented in neuropsychology research. The objective of this study was to investigate current levels of inclusion and reporting of Black research participation in neuropsychological studies.

Publications from high impact neuropsychology journals between 2019-2020 were selected via established methodologies. Studies were analyzed to determine the rates of demographic inclusion and reporting of minority, particularly Black, participants.

A total of 1,764 articles were reviewed across seven neuropsychology journals. Of the 653 studies not excluded for other reasons, 43% neglected to include sufficient information about participants' race/ethnicity. Of the subset of eligible studies that did include racial/ethnic demographic information (n = 349), only 61% included any Black participants at all. Only 34.1% of them included enough Black participants equal to or greater than the proportion of Black individuals within the United States.

Setting a standard of routinely reporting and analytically reflecting on demographic information is necessary to make valid inferences regarding disease sequelae, treatment, and public health strategies. The authors offer specific recommendations to improve the inclusion and reporting of Black research participation, ensure compliance with established policies, and improve the quality of neuropsychological research.

Towards personalized medicine: exploring the consequences of the effect model-based approach

Although personalized medicine has been a subject of research and debate in recent years, it has been underused in medical practice, except in some cancers. We believe that the main reason for the gap between the potential of personalized medicine and its use in daily medical practice can be explained by the lack of an appropriate tool to facilitate the use of biomarker values in a doctor's decision-making process. We propose that the effect model could form the basis of such a tool.

[Critical of the additive model of the randomized controlled trial]

Randomized, double-blind, placebo-controlled clinical trials are currently the best way to demonstrate the clinical effectiveness of drugs. Its methodology relies on the method of difference (John Stuart Mill), through which the observed difference between two groups (drug vs placebo) can be attributed to the pharmacological effect of the drug being tested. However, this additive model can be questioned in the event of statistical interactions between the pharmacological and the placebo effects. Evidence in different domains has shown that the placebo effect can influence the effect of the active principle. This article evaluates the methodological, clinical and epistemological consequences of this phenomenon. Topics treated include extrapolating results, accounting for heterogeneous results, demonstrating the existence of several factors in the placebo effect, the necessity to take these factors into account for given symptoms or pathologies, as well as the problem of the "specific" effect.

New insights on the relation between untreated and treated outcomes for a given therapy effect model is not necessarily linear

BACKGROUND AND OBJECTIVES

A relation between the size of treatment efficacy and severity of the disease has been postulated and observed as linear for a few therapies. We have called this relation the effect model. Our objectives were to demonstrate that the relation is general and not necessarily linear.

STUDY DESIGN AND SETTING

We extend the number of observed effect model. Then we established three numerical models of treatment activity corresponding to the three modes of action we have identified. Using these models, we simulated the relation.

RESULTS

Empirical evidence confirms the effect model and suggests that it may be linear over a short range of event frequency. However, it provides an incomplete understanding of the phenomenon because of the inescapable limitations of data from randomized clinical trials. Numerical modeling and simulation show that the real effect model is likely to be more complicated. It is probably linear only in rare instances. The effect model is general. It depends on factors related to the individual, disease and outcome.

CONCLUSION

Contrarily to common, assumption, since the effect model is often curvilinear, the relative risk cannot be granted as constant. The effect model should be taken into account when discovering and developing new therapies, when making, health care policy decisions or adjusting clinical decisions to the patient risk profile.

Planning of clinical trials

This article focuses on steps of planning clinical trials most relevant to the question the clinician asks and how this question is properly transformed in a design and a protocol. All steps are important for the data quality or the validity of the results. A clinical trial is an experiment aimed at testing an hypothesis regarding the efficacy of a given intervention on an event, symptom or impaired quality of life in patients with a defined condition and a particular profile. As such, it should meet the fundamentals of scientific discovery that guarantee causality between the observed difference and the intervention. All the planning components are thought according to these fundamentals.

Interpretations of 'appropriate' minority inclusion in clinical research

The range of possible interpretations of the phrase "appropriate representation" has left investigators struggling with the practical application of the National Institutes of Health guidelines on the inclusion of minorities in research. At least three goals might be reached by including minorities in clinical research: to test specific hypotheses about differences by race and ethnicity; to generate hypotheses about possible differences by race and ethnicity; and to ensure the just distribution of the benefits and burdens of participation in research, regardless of whether there are expected differences in outcome by race or ethnicity. In this paper, we describe possible interpretations of "appropriate representation," as well as provide a general approach that investigators might use to address this issue. To expand scientific knowledge about the health of minority populations, investigators should be expected to state which goal they have selected and why that goal is appropriate as compared with other possible goals.

Is there evidence of implicit exclusion criteria for elderly subjects in randomized trials? Evidence from the GUSTO-1 study

BACKGROUND

Some experts have raised concerns about the ability to generalize randomized trials, emphasizing that patients who participate in these studies are often not representative of those seen in clinical practice, particularly in the case of elderly patients. To determine the effect of implicit exclusion criteria on a trial study sample, we compared data from the Global Utilization of Streptokinase and t-PA for Occluded Coronary Arteries (GUSTO) trial with data from a retrospective registry from selected hospitals, the National Registry of Myocardial Infarction (NRMI), and a nationally representative study of myocardial infarction care, the Cooperative Cardiovascular Project (CCP).

METHODS

We compared GUSTO subjects aged 65 years and older who were enrolled in the United States with similarily aged patients in the 2 observational studies who met the trial's eligibility criteria. We examined baseline characteristics, clinical presentation, treatments, procedures, clinical events, and in-hospital mortality rates.

RESULTS

We found modest, although significant, differences between patients in NRMI, CCP, and GUSTO in demographic and clinical characteristics, treatment, and outcome. For example, GUSTO patients were significantly younger (73.1 +/- 5.7 vs 74.7 +/- 6.8 for NRMI and 75.8 +/- 7.2 for CCP), less likely to have Killip class III/IV at presentation (3.1% vs 6.2% for NRMI and 32.7% for CCP), and more likely to receive aspirin (95.5% vs 86.3% for NRMI and 86.5% for CCP) and beta-blockers (71.9% vs 43.5% for NRMI and 52.7% for CCP). Overall, NRMI and CCP patients had a lower risk of 30-day mortality after adjustment for demographic, clinical, and hospital characteristics than patients in GUSTO (odds ratio, 0.79; 95% CI, 0.73-0.86 for NRMI; odds ratio, 0.65; 95% CI, 0.59-0.71 for CCP).

CONCLUSIONS

Older patients enrolled in a randomized trial without an age restriction had many similarities compared with patients seen in clinical practice. The higher mortality rate of the GUSTO patients does not support the hypothesis that the trial enrolled a healthier cohort than is seen in practice.

Pharmacogenetics and responders to a therapy: theoretical background and practical problems

In a narrow meaning, responders to a therapy are all those who will react as expected following the administration of this therapy. However, a wider definition is worth considering: all those for whom the administration of the therapy will be beneficial. Innovative therapies are increasingly expensive and hazardous, and limiting prescriptions to responders is both economically and ethically compulsory. The theoretical basis for such an approach exists. The process of defining the profile of responders consists of identifying the characteristics of the patients that interact with the size of the effect and integrating them quantitatively in a predictive model. The effect model, which is the relation between the risks of the event with and without the treatment, can be used for the prediction. It can integrate interactions of the efficacy with risk factors and/or genes. The data to be used to achieve both the identification of the interactions and the building of the predictive model are those from the studied population, the set of patients enrolled in clinical trials. Hence, the process of defining the therapy is an extrapolation from the studied population. To carry out the extrapolation process one can use various available techniques, of which none fully fits the purpose. No method is currently both fully adequate and validated. Finally, the predictive models, which we need to identify responders, do not exist in practice. Fortunately, new research approaches have been developed recently.

Participation in the Ischemic Optic Neuropathy Decompression Trial: sex, race, and age

BACKGROUND

The Ischemic Optic Neuropathy Decompression Trial (IONDT) is a randomized, single masked, multicenter trial designed to assess the safety and efficacy of optic nerve decompression surgery compared with careful follow-up in patients 50 years or older with non-arteritic anterior ischemic neuropathy (NAION).

OBJECTIVES

To examine and evaluate the sex, race, and age distributions of the screened, and subsequently enrolled, IONDT population, especially the proportions of female, minority, and elderly patients, and demographic characteristics of clinical center investigators, the geographical location of IONDT Clinical Centers, and the referral patterns of local physicians.

SETTING

Twenty-five U.S. clinical centers.

PARTICIPANTS

There were 1,681 referrals to the Clinical Centers; an Eligibility Screening Form providing demographic information was completed for 1,152.

FINDINGS

Forty-three percent (495/1,152) of screened cases were women. Seven percent (85/1,152) were minorities: 33 African-Americans, 34 Hispanics, 17 Asians, and 1 Native-American. The average age was 66 +/- 10 years with a range of 22-92 years of age. Of the 1,152 referred patients, 305 were eligible for randomization; 258 (85%) of these agreed to participate in the trial. The demographic makeup of the randomized IONDT patients was similar to that of the screened population.

CONCLUSIONS

Women and the elderly are well represented in the IONDT. Because the number of participating minorities was low, we cannot reliably assess their level of participation. Both a low incidence of NAION in minorities and a low referral rate of minorities to clinical trials are plausible explanations for our findings.