Clinical Trial Essentials

The clinical trial protocol is a critical document that dictates every detail of the conduct of the study and ensures that the study is conducted the same way at each site. The clinical trial protocol includes the following:

If the drug or device is approved by regulatory bodies for clinical use, the details included above determine the prescribing information (also called package insert or label) for approved use indications; the aim is to provide information that helps clinicians make evidence-based decisions.

Ideally, results can be extrapolated to populations broader than the one in the clinical trial. However, trial participants may not be the same as everyone who has the disease under investigation. Socioeconomic status, ability to drive, literacy, and proximity to the study site are issues that limit the applicability of trial results to everyone with the disease. In addition, inclusion and exclusion criteria may limit the population enrolled in the study. The best clinical trials often need to make significant efforts to recruit a racially diverse pool of participants. Diversity in clinical trials helps ensure that the study sample reflects real world diversity, which includes many ethnic and racial populations. In the US, racial and ethnic minorities make up almost 40% of the population. A sample that lacks diversity could miss some clinically important racial differences. For example, a deficiency of the enzyme G6PD is more common in men of African, Asian, or Mediterranean descent, and certain drugs can trigger hemolytic anemia in people with G6PD deficiency. Also, for some drugs,the race and genetic background of a person may influence the effectiveness of that drug. Evidence from clinical trials that include people from diverse backgrounds can more confidently be extrapolated to the wider population.

A statistical analysis plan (data analysis plan) defines how the data will be analyzed. This includes a calculation of the minimum number of patients required to detect a clinically meaningful effect (sample size, or power calculation). The statistical analysis plan also defines the primary endpoint and what, if any, secondary endpoints will be analyzed. Study endpoints are ideally a patient-centered outcome, such as mortality, morbidity, and symptom duration. Surrogate endpoints such as laboratory results and changes in imaging study findings are sometimes used. Trial participants are often assessed at baseline and again after treatment, and the change (if any) is then statistically analyzed for the whole group of patients in the study. The statistical analysis plan is included as part of the clinical trial protocol. In a randomized, blinded trial, the statistical analysis plan must be finalized before the blind is broken and the analysis is done. This ensures that study results cannot bias the way data are analyzed. Furthermore, the statistical analysis plan ensures against the possibility that various statistical methods were tried and only the most favorable analysis was reported. Endpoints in clinical trials are commonly analyzed by one of the following methods:

Clinical trials are typically sponsored by a company with an interest in the development or better understanding of the intervention, such as a pharmaceutical company or a company that manufactures devices. The company that sponsors a study in development of an intervention (see phases I to III of Phases of Drug Development Drug Development Promising compounds can be identified by mass screening of hundreds or thousands of molecules for biologic activity. In other cases, knowledge of the specific molecular pathophysiology of various... read more ) typically develops the clinical trial protocol and data analysis plan in close discussion with the regulatory authority, such as the FDA as well as principal investigators. The performance and management of the study is often a major undertaking that includes the following tasks:

A company may do all of these tasks internally or may outsource some or all tasks. A small company may need to outsource tasks, but even a large company may decide to outsource. Some or all tasks may be outsourced to a clinical research organization (CRO) or an academic research organization (ARO), which are companies or academic centers that specialize in coordinating many aspects of clinical trials, including providing input into design, site selection, participant recruitment, and trial monitoring.

Clinical trials compare the intervention being investigated to another treatment, either a placebo or an active comparator. A placebo is an inert preparation (or sham procedure). In a blinded study for an oral drug, the placebo is made to look identical to the drug being studied. If that is not possible, both the study drug and the placebo may be placed in capsules that are identical. Even patients receiving placebo can have beneficial effects (called the placebo effect) or report adverse effects (called the nocebo effect). Without a placebo comparator, both positive and negative effects could be attributed to the study drug. An active comparator is often a commonly prescribed treatment that has a well-established efficacy and safety profile. Active comparators are also used to compare relative efficacies for the defined endpoint, safety (ie, adverse effects), and ease of administration (eg, pills versus injection, fewer injections).

Clinical trials are often double-blinded, meaning that both the patient and the investigator (and everyone involved in the trial who comes in contact with the patient) do not know if the patient is receiving the study drug or placebo. The trial is completed double-blinded to eliminate possible biases that may otherwise enter into the analysis of the trial. Without double-blind design, biases may occur. Bias may be conscious but can also be unconscious and cannot be avoided; therefore, double-blinding is the best way to assess a new intervention.

A randomized, double-blind, placebo-controlled trial is the most common study design used to demonstrate efficacy and safety of an intervention. In these trials, patients are randomly assigned to one of 2 or more groups, those that will receive the placebo and those that will receive the intervention being investigated. Sometimes more than one intervention is studied, such as different doses of a drug or more than one type of interventional procedure. The results for the groups are compared with regard to the primary endpoint. If the intervention group is statistically significantly superior to the placebo group, the difference is assumed to be a true population difference (eg, that it is not due to random variation). If the difference results in a clinically meaningful improvement, the intervention is considered efficacious. (Especially in large studies, there may be a statistically significant difference between intervention and placebo groups that may not be of great clinical significance.) Often 2 large, randomized, controlled trials are required to show efficacy before it is accepted by a regulatory agency (such as the FDA). Combining trial results also contributes to evaluation of the safety of the intervention, because important safety data, such as rare adverse events, may occur only infrequently. Therefore, establishing safety requires data on much larger numbers of patients. These trials are usually done as a parallel design such that the 2 groups receive either the study intervention or placebo treatment, and the results of these 2 arms are compared at the end of the trial for efficacy and safety. Often the improvement from baseline is the endpoint that is compared between the 2 groups, and the differences between the changes are also compared.

Sometimes a crossover design is used. In these trials, each group is randomly treated first with a study intervention or placebo in a blinded fashion. However in the crossover design, after the endpoint is assessed, each group then receives the other treatment for the same period of time, again in a blinded fashion. The endpoints are assessed by treatment assignment (whether the treatment was given in the first or the second period). This design has the advantage of allowing each participant to receive both active treatment and placebo, allowing comparison of their effects in the same individual and eliminating the variability between subjects (the largest source of random variability in clinical trials). As a result, the crossover design allows the trial to include fewer patients. However, a crossover design introduces a potentially problematic new variable, the sequence in which interventions are received, and the effect of one treatment on the other. If a treatment in the first period has demonstrable efficacy, then the baseline values in the second period are different for some participants. Such differences can be addressed by appropriate statistical methods. To minimize carryover effects from the first period, some crossover trials have an intervening "washout" period between the treatments. A washout period also helps to ensure that baseline values in the second treatment period are similar to the first.

A randomized, double-blind, active comparator-controlled trial compares the trial intervention to a comparator that is a known treatment for the disorder. Active comparator trials are performed in several situations, such as:

Active comparator trials may be double blinded. When active comparator trials are double blinded, they are sometimes called "double dummy," and each participant gets active drug A and placebo for B or active drug B and placebo for A. Several statistical approaches are used in each of the 3 common scenarios. These approaches may include the opportunity to first demonstrate noninferiority and then test for superiority. A statistical analysis plan must be specified before the start of the study.

If a clinical trial is not blinded, it is called open-label. Open-label studies are used in several situations in which placebos are not appropriate, such as:

Open-label studies are subject to biases that may influence the results. These include investigator bias or participant bias, for example, when there is a preconceived idea that one therapy may be better than the other. Open-label studies may also be done to assess safety in a large number of patients, especially when a placebo controlled trial might be cost prohibitive. Sometimes open-label studies are the only approach to answer a clinical question.

An observational study is one in which there is no experimental intervention. Observational studies can be prospective or retrospective. They are often used when randomization may not be appropriate or may even be unethical, such as when studying the effects of smoking or alcohol use. Observational studies are also used when randomized trials are impractical because an independent variable cannot be easily controlled for, such as family history, education, or socioeconomic status. Observational studies can be used to study rare events that are unlikely to develop in sufficient numbers in controlled prospective trials. Observational studies are not used for pharmaceutical registration trials (ie, trials used as evidence for registration of a drug), but they may be used to assess pharmacovigilance (drug safety) by using large databases to compare adverse events between groups of people who have taken a drug and those who have not.

A cohort study is an observational study in which a group (called a cohort) of people at risk for a disorder are compared to a group of people who are similar except for being at risk for the disorder. In a prospective cohort study, people are followed over time to learn about the onset and progression of the disorder. Cohort studies include, for example, comparing people who are vaccinated against a certain disease to those who are not vaccinated, tracking outcomes such as subsequent infection, hospitalization, and/or mortality. Usually each person in a cohort study is "matched" to one or up to several controls who have similar age, gender, and clinical features except for the risk factors under observation.

Propensity score matching is a statistical technique that attempts to estimate the effect of having received an intervention. Propensity matching works by accounting for the factors (covariates) that predict receiving the treatment or intervention. Propensity scoring, which is often a more effective method than trying to find matched controls, reduces the bias due to confounding variables that could result from comparing unadjusted outcomes among people who received the treatment to those who did not. Propensity scoring can be particularly useful when potentially confounding variables cannot easily be matched or controlled for, but it cannot account for unmeasured or unsuspected variables (eg, clinical "gestalt").

A case-control study is another type of observational study in which patients with an outcome of interest are compared to "controls" who are as similar as possible except for having had the outcome of interest. Case-control studies are particularly useful when outcomes are rare and thus unlikely to occur in sufficient numbers in prospective, controlled clinical trials (which, due to practical limitations, cannot enroll very large numbers of patients).

Retrospective studies use already existing information, such as patients' clinical characteristics and how they were treated. Retrospective studies have the advantage of being an efficient way to obtain information quickly on large numbers of patients. The outcome of interest is defined, and then relevant variables (eg, predictive variables) are analyzed. Data sources for retrospective studies include chart reviews, national data registries such as the Centers for Disease Control and Prevention (CDC) National Health and Nutrition Examination Survey (NHANES) and the United Kingdom's primary care physician database Clinical Practice Research Datalink (CPRD), claims databases from large insurance companies, or other health care databases such as from the U.S. Department of Veterans Affairs (VA). However, absence of uniformity in data collection and recording is a limitation of retrospective studies. Often retrospective studies use matched controls to help control for variability in data acquisition. Propensity score matching is often used in retrospective studies.