Learners who succeed in Clinical Research courses gain mastery in a vital part of healthcare. Results from randomized clinical trials are usually considered the highest level of evidence for determining whether a treatment is effective, because trials incorporate features to ensure that evaluation of the benefits and risks are objective and unbiased. Instructors help learners get to this level by showing them ways to interpret and understand common statistical concepts, as well as solid data management principles critical for any scientific domain.
Our Clinical Research courses explain the basic principles for the design of randomized clinical trials, and how they should be recorded. Learners are introduced to terminology and several common designs used in clinical trials, such as parallel and cross-over designs. Mechanics of clinical trials, such as randomization and binding of treatment, are reviewed. Instruction is available on how to properly understand published clinical research, as well as how to understand common statistical concepts.
Understanding Clinical Data Analysis: Learning Statistical Principles From Published Clinical Resear
A background in health care can be beneficial to studying clinical research. This could be as simple as working as a volunteer or intern at a hospital or clinic or working in an official capacity in a hospital, clinic, doctor's office, or pharmacy. Work or an internship at a large health care company may also help you better understand clinical research, as can working at a clinical research site. Even volunteering as a research subject can give you a better understanding of the topic. Work or an internship at a government agency, like the U.S. Food and Drug Administration, may prepare you to better understand clinical research, or you may have volunteered at a nonprofit that specializes in health care initiatives. Those who study clinical research should also have a good understanding of medical terminology.
People with a passion for science and improving the health of others are well-suited for a role in clinical research. You'll need to be a person who is good at observation as well as someone who is analytical since the field requires plenty of data analysis. Critical thinking and decision-making skills are a must, as are good communication skills, both written and verbal. You must be willing to provide constructive feedback to subjects and colleagues, as well as motivate your subjects. You'll also need to document and record findings. For this reason, computer skills are important as well. Those who work in the clinical research field should be organized, good administrators, multi-taskers, and people who can think quickly on their feet.
Because in clinical research we rely on a sample of the patient population, variance is a key consideration in the evaluation of observed differences. The observed difference between exposed and unexposed groups can be large, but one must consider how it stands next to the variation in the data. Since these parameters are highly quantifiable, the probability that the means are different (or similar) can be calculated. This process takes place in a statistical method called analysis of variance (ANOVA). The details of this process are beyond the scope of this chapter; nevertheless, ANOVA is a fundamental statistical methodology and is found in many texts and is performed by many statistical software packages. In essence, the ANOVA answers the question: are differences between the study groups' mean values substantial relative to the overall variance (all groups together)? It is important to note that even though ANOVA reveals statistically significant differences, the ANOVA does not indicate between which groups the difference exists. Therefore, further analysis with multiple comparison tests must be performed to determine which means are significantly different. Portney and Watkins (19) provide a good overview of these procedures.
The relative risk or risk ratio (RR) is calculated from a cohort study where exposed and non-exposed participants are followed over time and the incidence of disease is observed. Because the hallmark of a cohort study is following a population over time to identify incident cases of disease, the cohort is screened to assure that no participant enrolled in the study has already experienced the outcome or disease event. Then, the cohort is followed for a specific period of time, and the incidence of events for the exposed and unexposed groups is measured. The relative risk can also be used to analyze clinical trial data. The relative risk (RR) is calculated from the labeled 22 table (Table 9) using the formula:
Thus far, we have used examples for analyses from observational studies. Experimental studies or clinical trials are analyzed in much the same manner. In clinical trials, patients are followed until some outcome is observed in the planned study period; these are incidence studies. As incidence studies, the RR will be the measure of association tested for statistical significance. Additionally, many clinical trials lend themselves to straightforward analyses with chi-square tests, ANOVA, or other methods that result only in a p-value. Table 13 summarizes common methods used to analyze healthcare data.
For one example, we review the results of a trial of the beta-blocker, bucindolol, used in patients with advanced chronic heart failure (CHF) (27). While it is accepted that beta-blockers reduce morbidity and mortality in patients with mild to moderate CHF, these investigators enrolled 2708 patients designated as New York Heart Association (NYHA) class III or IV to test the efficacy of the beta-blocker in reducing morbidity and mortality in patients with high baseline severity. The primary outcome of interest was all-cause-mortality, which, being a relatively rare event, drove the sample size requirement to 2800 in order to statistically detect a clinically significant difference of 25%. Once enrolled, patients were randomly assigned to receive either placebo or the beta-blocker, and neither the patient nor the physician knew to which treatment the patient was assigned. This study was stopped after the seventh interim analysis due to the accruing evidence of the usefulness of beta-blockers for CHF patients from other studies. At the time the study was stopped, there was no difference in mortality between the two groups (33% in the placebo group vs. 30% in the beta-blocker group, p=0.16). After the follow-up data was completed, adjustments for varying follow-up time could be made. The adjusted difference in mortality rate was still not significant (p=0.13). However, a sub-analysis of the secondary endpoint of cardiac death did yield a significant hazard ratio (HR) of 0.86 with a 95% CI of 0.74 to 0.99. This HR being less than the value 1 means that the beta-blocker was protective against cardiac death in the follow-up period. The CI not including the value 1 leads to the conclusion that this HR is statistically significant at the level of p
This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings.
The authors have been working and publishing together for 18 years, and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of philosophy, biology, and mathematics.
The goal of clinical research is to develop generalizable knowledge that improves human health or increases understanding of human biology. People who participate in clinical research make it possible to secure that knowledge. The path to finding out if a new drug or treatment is safe or effective, for example, is to test it on patient volunteers. But by placing some people at risk of harm for the good of others, clinical research has the potential to exploit patient volunteers. The purpose of ethical guidelines is both to protect patient volunteers and to preserve the integrity of the science.
A study should be designed in a way that will get an understandable answer to the valuable research question. This includes considering whether the question researchers are asking is answerable, whether the research methods are valid and feasible, and whether the study is designed with a clear scientific objective and using accepted principles, methods, and reliable practices. It is also important that statistical plans be of sufficient power to definitively test the objective, for example, and for data analysis. Invalid research is unethical because it is a waste of resources and exposes people to risk for no purpose
The efficacy and safety of medicinal products should be demonstrated by clinical trials that follow the guidance in E6 Good Clinical Practice: Consolidated Guidance adopted by the ICH, May 1, 1996. The role of statistics in clinical trial design and analysis is acknowledged as essential in that ICH guidance. The proliferation of statistical research in the area of clinical trials coupled with the critical role of clinical research in the drug approval process and health care in general necessitate a succinct document on statistical issues related to clinical trials. This guidance is written primarily to attempt to harmonize the principles of statistical methodology applied toclinical trials for marketing applications submitted in Europe, Japan and the United States.
Our collaborative distance-learning training program in clinical research is offered to participants from Boston and throughout the world. It is designed for individuals who wish to gain basic and advanced training in clinical trials before moving into the field and for those who have experience in this area and aim to broaden their role in the design, management, analysis, and reporting of clinical trials. 2ff7e9595c
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