Research Team Members
Components of a Clinical Trial
Using Statistics to Interpret Results

Learning Objectives Define key members of the research team Review key components of a clinical trial Describe the purpose of the randomization, stratification, and blinding in clinical trial protocols Name common statistical methods used to interpret clinical trial results

Clinical trials follow strict scientific guidelines that dictate how a study is designed and who participates in it. The reasons for these guidelines may not be immediately clear to a person urgently seeking treatment, but they protect people and provide scientifically sound results that can lead to truly effective therapies and techniques.

Research Team Members

Designing and implementing a clinical trial requires the many talents of a multidisciplinary research team. Each team may be set up differently, depending on an institution's policy and resources. Typical team members and their responsibilities include:

• Principal investigator - oversees all aspects of a clinical trial, specifically, concept development, protocol writing, protocol submission for institutional review board (IRB) approval, participant recruitment, informed consent, and data collection, analysis, interpretation, and presentation.

• Research nurse - coordinates the clinical trial and educates staff, participants, and referring health care providers. This nurse acts as an information conduit from the clinical setting to the principal investigator and vice versa, and assists the principal investigator with toxicity and response monitoring, quality assurance, audits, and data management and analysis.

• Data manager - handles the management of clinical trial data, including electronic data entry. Collaborates with the principal investigator and research nurse to identify what participant data will be tracked. The data manager also provides data to monitoring agencies and prepares summaries for interim and final data analysis.

• Staff physicians and nurses - administer treatment to participants as specified in the protocol; assess and record toxicity, drug tolerance, and adverse events; collaborate with the principal investigator and research nurse in observing and reporting clinical trends; and provide clinical management and participant education.

Components of a Clinical Trial

Protocol

Every trial has a written, detailed action plan, called a protocol. The protocol provides the background, specifies the objectives, and describes the design and organization of the trial. Every site participating in the trial uses the same protocol, ensuring consistency of procedures and enhancing communication. This uniformity ensures that results from all sites can be combined and compared.

The clinical trial protocol answers the following questions:

• What is the scientific rationale or basis for conducting the trial?

• What are the objectives?

• How many participants will be in the trial?

• Who is eligible to participate? (This is determined on the basis of factors such as age and disease status.)

• What is the intervention, and what is its duration or schedule?

• What side effects might there be?

• What medical tests or followup visits will participants have? How often?

• What information will be gathered about participants?

• What are the endpoints of the trial?

The following FDA-required protocol elements help investigators answer the questions above and assist participants and health care professionals in understanding the goals of a clinical trial:

• General information

• Background information (with relevant references from the scientific literature)

• Trial objectives and purpose

• Trial design

• Participant selection and withdrawal

• Participant treatment

• Efficacy assessment

• Safety assessment

• Statistics

• Direct access to source data and documents

• Quality control and quality assurance

• Ethics

• Data handling and record keeping

• Financing and insurance

• Publication policy

• Supplements

Eligibility Criteria

Participant eligibility criteria can range from general (age, sex, type of cancer) to specific (prior treatment, tumor characteristics, blood cell counts, organ function). Eligibility criteria may also vary with trial phase. In phase 1 and 2 trials, the criteria often focus on making sure that people who might be harmed because of abnormal organ function or other factors are not put at risk. Phase 2 and 3 trials often add criteria regarding disease type and stage, and number of prior treatments.

Eligibility criteria might be very detailed if researchers think that a drug will work best on a specific type of cancer or population. Trials with narrow eligibility criteria might be complicated to conduct and might produce less widely applicable results.

Researchers therefore attempt to include as many types of people as possible in a clinical trial without making the study population too diverse to tell whether the treatment might be as effective on a more narrowly defined population. The more diverse the trial's population, the more useful the results could be to the general population, particularly in phase 3 trials. Results of phase 3 trials should be as generally applicable as possible in order to benefit the maximum number of people.

The trend today is toward broadening eligibility criteria for phase 3 clinical trials. Less restrictive criteria may enable more researchers and people with cancer to participate in these trials. With more participants, the disadvantages of having a more diverse population will be outweighed by the results applying more generally to the population.

Endpoints

An endpoint is a measurable outcome that indicates an intervention's effectiveness. Endpoints differ depending on the phase and type of trial. For instance, a treatment trial endpoint could be tumor response or participant survival. Quality-of-life or supportive care trial endpoints could include participants' welfare and control of symptoms.

Examples of endpoints include:

• Tumor response rate - the proportion of trial participants whose tumor was reduced in size by a specific amount, usually described as a percentage. If 7 of 10 patients responded, the response rate is 70 percent.

• Disease-free survival - the amount of time a participant survives without cancer occurring or recurring, usually measured in months.

• Overall survival - the amount of time a participant lives, typically measured from the beginning of the clinical trial until the time of death.

Tumor response rate is a typical endpoint in a phase 2 treatment trial. However, even if a treatment reduces the size of a participant's tumor and lengthens the period of disease-free survival, it may not lengthen overall survival. In such a case, side effects and failure to extend overall survival might outweigh the benefit of longer disease-free survival. Alternatively, the participant's improved quality of life during the tumor-free interval might outweigh other factors.

Because tumor response rates are often temporary and may not translate into long-term survival benefits for the participant, response rate is a reasonable measure of a treatment's effectiveness in a phase 2 trial, whereas participant survival and quality of life are better endpoints in a phase 3 trial.

Randomization

In phase 3 trials (and some phase 2 trials) participants are assigned to either the investigational or control group by chance, via a computer program or table of random numbers. This process, called randomization, gives each person the same chance of being assigned to either group. Randomization ensures that unknown factors do not influence the trial results.

Randomization is a method used to prevent bias in research. A computer or a table of random numbers generates treatment assignments, and participants have an equal chance to be assigned to one of two or more groups (e.g., the control group or the investigational group).

If physicians or participants themselves chose the group, assignments might be biased. Physicians, for instance, might unconsciously assign participants with a more hopeful prognosis to the experimental group, thus making the new therapy seem more effective than it really is. Conversely, participants with a less hopeful prognosis might pick the experimental treatment, leading it to look less effective than it really is.

Randomization tends to produce comparable groups in terms of factors affecting prognosis and other participant characteristics. In this way, randomization guarantees the validity of the conclusion concerning the effectiveness of the treatment.

Stratification

Stratification is used in randomized trials when factors that can influence the intervention's success are known. For instance, participants whose cancer has spread from the original tumor site can be separated, or stratified, from those whose cancer has not spread. Assignment of interventions within the two groups is then randomized. Stratification enables researchers to look at factors in both groups.

Stratification is a process used in randomized trials when factors that can influence the intervention's success are known. Assignment of interventions within the two groups is then randomized. Stratification enables researchers to look in separate subgroups to see whether differences exist.

Blinding

Trials set so that participants do not know which intervention they are receiving are known as single-blinded trials. Those in which neither researchers nor participants know who is in the investigational or control group are called double-blinded trials. Double-blinded trials ensure that people assessing the outcome will not be influenced by knowing which intervention a participant is receiving and also that ancillary followup treatment will be the same.

Data Collection and Management Tools

Most research teams use standardized and newly created tools to collect, process, analyze, and audit data. Tools vary in format from visual analog scales to open-ended questionnaires. Examples of tools for participants to use to self-report data include diaries, calendars, logs, and surveys.

The case report form is the basic tool of data abstraction. Many reports use a Web-based format, others are paper-based. NCI is constructing an informatics system that will reduce the extensive paperwork often associated with clinical trials. For example, the Common Toxicity Criteria (CTC), a Web-based, interactive application, uses standardized language to identify and grade adverse events in cancer clinical trials. Forms are also available for rapid reporting of adverse events, electronically or by telephone, to alert researchers to potential safety issues. The Adverse Event Expedited Reporting System (AdEERS) is a Web-based program that enables researchers using NCI-sponsored investigational agents to expedite the reporting of serious and/or unexpected adverse events directly to NCI and FDA.

Using Statistics to Interpret Results

Researchers use statistical methods to determine whether an effect observed in a clinical trial is real (statistically significant) or caused by chance (not statistically significant). Although the examples included here use terminology and illustrations from treatment trials, these statistical techniques apply to all types of clinical trials.

Key Terms

Familiarity with the following terms is useful in understanding how researchers use statistics to interpret clinical trial results:

• p-values reflect the likelihood that the results of a clinical trial are because of chance rather than due to a real difference between the tested treatments. The smaller the value of p, the greater the likelihood that the results are not because of chance. A p-value of 0.05 (that is, 1 in 20) or smaller is widely accepted as an indication that the results are statistically significant.

• Confidence intervals reflect a range of values of the true value that would be obtained if everyone with a particular cancer were treated with the treatment under study. The wider the interval, the more variable the result and the less likely it is to be close to the true value. Confidence intervals are typically thought of as the approximate bounds or limits of the true value. Researchers frequently use either a 95 or a 99 percent confidence interval.

• Sample size is the number of people participating in a trial.

• Statistical power refers to the chance of finding a statistically significant result when there is one. Ideally, statistical power should be 0.80 or 0.90 - reflecting an 80 to 90 percent chance of detecting that the true difference in treatment effectiveness is the smallest size considered medically important to detect.

• Relative risk is the likelihood that cancer will occur within a specific timeframe in one group versus another.

Statistical Significance

The result of a clinical trial can be statistically significant (not due to chance) without being clinically significant (medically important). Suppose, for instance, that a group receiving an experimental treatment has a 2 percent higher survival rate than the group receiving the standard treatment. This difference could be statistically significant, but if participants who survive longer experience serious side effects, it may not be medically important. In this case, the side effects might be worth tolerating only if the experimental treatment group has a 10 percent higher survival rate. Good trial planning and interpretation take into consideration both medical importance and statistical significance.

The results of a trial are usually considered statistically significant when data comparison results in a p-value of 0.05 or smaller. If the p-value is 0.01 or even 0.001, the results are considered even more significant because there is less likelihood that the results are due to chance.

Confidence intervals are often useful data for researchers because they enable researchers to generalize the results of the trial to the population.

For example, in a treatment trial with an investigational and a control group, the mean (average) values of the endpoints (e.g., survival for 5 years after treatment) are calculated separately for each group. Then the standard error - how far the values extend on either side of the mean - is calculated for each group. The less overlap between the confidence interval for the standard treatment group and the experimental treatment group, the more likely the difference between the groups is statistically significant. Research reports typically include confidence intervals, for example:

The rate of 5-year survival for group A was 73% (95% confidence interval, 65.7% to 80.3%). The rate of 5-year survival for group B was 58% (95% confidence interval, 49.8% to 66.2%). p = 0.004.

In this case, the confidence intervals come close to each other - 65.7 percent and 66.2 percent - but do not overlap. The p-value is definitely statistically significant.

Confidence intervals can give an indication of whether the results of small-sized trials that are not statistically significant are nevertheless medically significant. They can be particularly important tools when the trial size is limited because the type of cancer is rare.

Trial Size

The number of participants in a clinical trial greatly influences its statistical significance. With too few participants, a trial does not generate enough information to draw a conclusion, and important results may be missed. On the other hand, by testing more people than needed to obtain statistically significant results, a trial takes longer to produce results and may give ineffective or unsafe therapy to more people than necessary.

When planning a clinical trial, researchers first decide how large a difference between treatment groups is medically important. Next, they calculate sample size, or how many people should be enrolled in the trial. The sample should include enough participants to get a statistically significant result (a p-value of 0.05 or smaller).

Sample size also influences the statistical power of the research and is calculated before the trial begins. As sample size increases, statistical power increases. Ideally, power should be 0.80 or 0.90. Calculating statistical power helps a researcher decide how many people to enroll in a trial.

Relative Risk

Relative risk usually describes the risk of getting cancer based on lifestyle, environmental exposure to cancer-causing agents, or family history of disease. However, when used in cancer clinical trial reports, relative risk usually indicates the likelihood that cancer will occur within a specific timeframe in one group versus another.

Intention to Treat

Phase 3 trials are often analyzed on an intention-to-treat basis - that is, all participants who were initially admitted into the trial and randomized are included in the primary analysis. Intention-to-treat analysis therefore includes people who:

• Did not follow instructions

• Can no longer be located or contacted

• Withdrew from the trial

• Did not receive treatment

Including data from the groups above may weaken the results of a trial, but excluding the data would bias the trial. For instance, if half of the people in a treatment group withdrew because they thought the drug they were taking was ineffective and had severe side effects, and if the other half of the group had a 50 percent response rate, then excluding the data from the participants who withdrew makes the drug appear to be 50 percent effective. The actual response rate is 25 percent. Intention-to-treat analysis typically excludes participants who were ineligible to be included in the trial but were randomized.

Refer to the case study for a review and summary of content covered in this workbook.

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