Basics of Biomedical and Clinical Research

Natural phenomenon while important in shaping clinical decisions or biomedical thinking remains subjective and non- generalizable knowledge. Therefore, to improve the care of future patients we must apply valid research methodology to natural phenomenon observation in order to obtain reliable inference that will guide practice beyond the sample of patients we studied.

—L. Holmes, Jr., Santa Monica, California, March 2013

Highlights: Clinical and biomedical research rationale, research question and hypothesis, sampling, sample size and power estimations, generalizability, screening test, NNT, NNH

INTRODUCTION

Biomedical and clinical research remain tools to understanding disease pathways, treatment modalities, and outcomes of care. While knowledge of biomedical sciences and clinical medicine is significant for advances in this field, the generation of such knowledge requires solid and reliable design processes as well as adequate statistical techniques. Biomedical and clinical research is conducted primarily to enhance therapeutics, implying the in- tent to improve patients' care. The application of this concept in biomedical sciences, public health, and clinical medicine signaled a departure from nihilism, which claimed that disease improved without therapy. The scientific medical discoveries on pellagra, diabetes mellitus, and antibiotics like penicillin and sulfonamide provide reliable data on medicinal benefits in therapeutics. Today, with biomedical and clinical research, clinical investigators applying reliable and valid research methodologies can demonstrate the efficacy and effectiveness of agents and devices, competing therapies, combination treatments, comparative effectiveness, and diagnostic and screening criteria for most diseases.

In claiming the advantage of therapeutics in medicine (complementary versus traditional), there is a need to understand the biological theories and the complexities of disease among clinical investigators, who may be expert physicians, as well as other health-care providers and those who are indeed researchers. While understanding the biological and clinical importance of a disease is essential in formulating the research question, the clinician is also expected to acquire statistical reasoning. The combination of these two models enhances the analysis and the interpretation of the data from clinical research. Therefore, in clinical research, there is an investigator (clinical) who examines the formal hypothesis or establishes the biology based on work in the clinical settings (experience, observation, and data), as well as another investigator (biostatistician/epidemiologist) whose contribution is to generalize observations from sample to target population, as well as combine empirical (observation and data) and theory-based knowledge (probability and determinism) with the understanding of the results of the study. Despite these distinctions, effective clinical and biomedical research involves the understanding of these two models of thinking or reasoning by the investigators, clinicians, and epidemiologists/biostatisticians. Without this integration, our effort toward the design and interpretation of research findings is limited, since making reasonable, accurate, and reliable inferences from data in the presence of uncertainty remains the cornerstone of clinical research results utilization for improving the health care of future patients. In stressing the essence of this integration, one is not claiming the relevance of statistical reasoning over biological and clinical importance, since clinical research thinking is fundamentally biologic, clinical, and statistical.

The approach to biomedical and clinical research involves research conceptualization, the design process, and statistical inference. In biomedical sciences, for example, the research conceptualization may involve therapeutics in mice or rats with cancer, and because of the similarities to human malignancies, these findings would be translational, and hence generalization (biologic) to human malignancies without a formal statistical model can be made. The design process may involve treated and untreated mice, with a follow-up time to determine the survival difference in the two groups. The statistical inference, given that adequate numbers (sample size and power estimations) of mice were studied, involves the use of Kaplan-Meir's survival estimates, as well as the log rank test for the equality of survival in these two groups. Finally, statistical stability is examined in ruling out random variation using p value (significance level) and 95 percent confidence interval (precision). A similar approach is utilized in clinical research that involves human subjects or patients in clinical settings. The research conceptualization in this context involves the clinical investigator or clinician utilizing his or her experience in the management of patients with malignancy (leukemia, for example), observation, and data to formulate hypotheses regarding therapeutics. A case-comparison/control design could be applied here in which the treated-group (cases) are placed on the new drug X, while comparison (control) are placed on a standard care drug Y and both are followed for the assessment of outcome (death or biochemical failure). The statistical inference and the interpretation of the results are similar to the example with mice and malignancy therapeutics. There are excellent books in study designs, including one by the authors of this book.

Historically, central to clinical research and therapeutics is the concept of disease screening and diagnostic testing. We can view the disease diagnosis as well as the diagnostic test as key elements in the ascertainment of subjects for clinical research. Inappropriate patient ascertainment may result in selection, information, and misclassification biases (discussed in subsequent chapters). This historical concept remains valid in research conduct and is the main material elaborated in this chapter. The sensitivity, specificity, predictive values, and likelihood ratios are described with examples. Thus, the validity of the results obtained in clinical research depend on how adequately the subjects were...