Lessons Learned From Recent Cardiovascular Clinical Trials: Part I

Abstract

T his is the first installment in a 4-part series of articles about our experiences with cardiovascular clinical research during the past 2 decades. From our vantage points as statistical and clinical researchers, we derive a series of lessons from recent clinical trials and incorporate them into principles designed to guide the practicing clinician who cares for patients with heart diseases.We are entering an era in which the imperative to understand the rational basis for diagnostic and therapeutic options has become a major force in medical care. Medical products (drugs, devices, and biologics) are proliferating simultaneously with a substantial restructuring of the delivery of health care, with a focus on evidence to support medical interventions. As the texture of evidence to support clinical practice becomes clearer, the imperative to investigate the effectiveness of behavioral interventions becomes more important. When coupled with concern about the rising cost of medical care, this proliferation of technological and behavioral intervention reinforces the view that we cannot afford to offer all possible diagnostic and therapeutic options to all patients. Recent publicity about therapies that were in widespread use and were later found to be detrimental 1-3 has fueled this concern to extend well beyond the cost implications. The advent of genomics, proteomics, combinatorial chemistry, and advanced biomedical engineering will increase the importance of developing rational evidence about therapeutic risks and benefits. This effort to develop a rational basis for diagnostic and therapeutic decisions on the basis of quantitative assessments is a major component of evidencebased medicine.The randomized clinical trial (RCT) has emerged as the principal research tool for developing evidence to inform and influence clinical practice, particularly in the cardiovascular field. The practice of clinical trials is built on an understanding of the biology of disease, the structure of medical care delivery, statistical design and analysis, data management, clinical research quality, and scientific and medical integrity. As clinical trials continue to evolve, investigators will be challenged to find the optimum balance among these factors that best evaluates new interventions and places older approaches in the proper context as new technology and practice patterns emerge.Because the RCT is relatively new as a scientific technique, little empirical experience could be used to test this balance in the past. However, the recent explosive growth of RCTs with their widely discussed methodological approaches has been valuable in guiding current RCT design and deployment. 4,5 In the first 2 parts of this series of articles, we will review some of the lessons we have learned from recent cardiovascular trials about the conduct of trials. In the second two parts, we will examine proposed implications of these lessons for medical practice and focus on applying the concepts in clinical trials to patient care decisions. These concepts...

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“…This suggests that the most reliable estimates of benefit and harm in these subgroups are likely to be the pooled estimates obtained by combining results from all available trials. 73,75 These findings suggest that erythropoiesis-stimulating agents should not be routinely used as an alternative to blood transfusion in patients with chemotherapy-induced anemia unless future studies document safety and clinical benefits in this population.…”

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“…Important data related to drugs, doses, schedule, delays, modifications, and adverse events are often lacking, and it is virtually impossible to capture the reasons that certain patients are treated in a particular fashion. One cannot adjust for potential confounding due to unknown, unmeasured, or ignored variables, and efforts to do so, such as the use of instrumental variables and propensity score analysis, have their own important limitations [10].…”

“…Important data related to drugs, doses, schedule, delays, modifications, and adverse events are often lacking, and it is virtually impossible to capture the reasons that certain patients are treated in a particular fashion. One cannot adjust for potential confounding due to unknown, unmeasured, or ignored variables, and efforts to do so, such as the use of instrumental variables and propensity score analysis, have their own important limitations [10].Statistical oversight of observational studies is important because the methodological challenges can be every bit as challenging or more so as with RCTs. The same rigorous attention to study design, conduct, analysis, and reporting should be applied to observational studies as to high-quality RCTs.…”