1.1 - What is the role of statistics in clinical research?1.1 - What is the role of statistics in clinical research?
What is the role of statistics in clinical research?
Clinical research involves investigating proposed medical treatments, assessing the relative benefits of competing therapies, and establishing optimal treatment combinations. Clinical research attempts to answer questions such as “should a man with prostate cancer undergo radical prostatectomy or radiation or watchfully wait?” and “is the incidence of serious adverse effects among patients receiving a new pain-relieving therapy greater than the incidence of serious adverse effects in patients receiving the standard therapy?”
Before the widespread use of experimental trials, clinicians attempted to answer such questions by generalizing from the experiences of individual patients to the population at large. Clinical judgement and reasoning were applied to reports of interesting cases. The concepts of variability among individuals and its sources were not formally addressed.
As the field of statistics, the “theoretical science or formal study of the inferential process, especially the planning and analysis of experiments, surveys, and observational studies.” (Piantadosi 2005). has developed in the twentieth century, clinical research has utilized statistical methods to provide formal accounting for sources of variability in patients’responses to treatment. The use of statistics allows clinical researchers to draw reasonable and accurate inferences from collected information and to make sound decisions in the presence of uncertainty. Mastery of statistical concepts can prevent numerous errors and biases in medical research.
Statistical reasoning is characterized by the following:
- Establishing an objective framework for conducting an investigation
- Placing data and theory on an equal scientific footing
- Designing data production through experimentation
- Quantifying the influence of chance
- Estimating systematic and random effects
- Combining theory and data using formal methods
Carter, Scheaffer, and Marks (1986) stated that:
Clinical and statistical reasoning are both crucial to progress in medicine. Clinical researchers must generalize from the few to many and combine empirical evidence with theory. In both medical and statistical sciences, empirical knowledge is generated from observations and data. Medical theory is based upon established biology and hypotheses. Statistical theory is derived from mathematical and probabilistic models. (Piantadosi 2005), To establish a hypothesis requires both a theoretical basis in biology and statistical support for the hypothesis, based on the observed data and the theoretical statistical model.
What constitutes a ‘clinical trial’?
An experiment is a series of observations made under conditions controlled by the scientist.
A clinical trial actually is an experiment testing medical treatments on human subjects. The clinical investigator controls factors that contribute to variability and bias such as the selection of subjects, application of the treatment, evaluation of outcome, and methods of analysis. The distinction of a clinical trial from other types of medical studies is the experimental nature of the trial and its occurrence in humans.
Design is the process or structure that isolates the factors of interest. Although the researcher designs a trial to control variability due to factors other than the treatment of interest, there is inherently larger variability in research involving humans than in a controlled laboratory situation.
The term “clinical trial” is preferred over “clinical experiment” because the latter may connote disrespect for the value of human life.
In what contexts are clinical trials used?
Clinical trials are used to develop and test interventions in nearly all areas of medicine and public health. In many countries, approval for marketing new drugs hinges on efficacy and safety results from clinical trials. Similar requirements exist for the marketing of vaccines. The U.S. Food and Drug Administration (FDA) now requires manufacturers of new or high-risk medical devices to provide data demonstrating clinical safety and effectiveness. (Scott 2004). Surgical interventions pose unique challenges since surgical approaches are typically undertaken for patients with a good prognosis and may not be amenable to randomization or masking investigators and patients to the intervention, all conditions which can lead to biases. Clinical trials are useful for demonstrating efficacy and safety of various medical therapies, preventative measures and diagnostic procedures if the treatment can be applied uniformly and potential biases controlled.
In addition to testing novel therapies, clinical trials frequently are used to confirm findings from earlier studies. When the results of a study are surprising or contradict biological theory, a confirmatory trial may follow. Medical practice generally does not change based upon the results of one study. Design flaws, methodological errors, problems with study conduct, or analysis and reporting mistakes can render a clinical trial suspect. Hence, confirmation of results in a replicative study, or a trial extending the use of the therapy to a different population, is often warranted.
Clinical trials are time-consuming, labor-intensive, and expensive and require the cooperative effort of physicians, patients, nurses, data managers, methodologists, and statisticians. Patient recruitment can be difficult. Some multi-center (across institutions) clinical trials cost up to hundreds of million of dollars and take five years or more to complete. Prevention trials, conducted in healthy subjects to determine if treatments prevent the onset of disease, are important but the most cumbersome, lengthy, and expensive to conduct.
Many studies have a “window of opportunity” during which they are most feasible and will have the greatest impact on clinical practice. For comparative trials, the window usually exists relatively early in the development of a new therapy. If the treatment becomes widely accepted or discounted based on anecdotal experience, it may become impossible to formally test the efficacy of the procedure. Even when clinicians remain unconvinced of efficacy or relative safety, patient recruitment can become problematic.
Some important medical advances have been made without the formal methods of controlled clinical trials, i.e., without randomization, statistical design, and analysis. Examples include the use of vitamins, insulin, some antibiotics, and some vaccines.
Piantadosi (2005) gives the following requirements for a study based on a non-experimental comparative design to provide valid and convincing evidence:
- The treatment of interest must occur naturally
- The study subjects have to provide valid observations for the biological question
- The natural history of the disease with standard therapy, or in the absence of therapy, must be known
- The effect of the treatment must be large enough to overshadow random error and bias
- Evidence of efficacy must be consistent with biological knowledge
Examples of non-experimental designs that can yield convincing evidence of treatment efficacy can be found among epidemiological studies, historically-controlled trials, and from data mining.