# Clinical Trials

## 4. Endpoints

### Endpoints

A clinical trial endpoint is defined as a measure that allows us to decide whether the null hypothesis of a clinical trial should be accepted or rejected (Bakhai et al., 2006a). In a clinical trial, the null hypothesis states that there is no statistically significant difference between two treatments or strategies being compared with respect to the endpoint measure chosen.

Clinical trial endpoints can be classified as primary or secondary.

Primary endpoints measure outcomes that will answer the primary (or most important) question being asked by a trial, such as whether a new treatment is better at preventing disease-related death than the standard therapy. In this case, the primary endpoint would be based on the occurrence of disease-related deaths during the duration of the trial. The size of a trial is determined by the power needed to detect a difference in this primary endpoint.

Secondary endpoints ask other relevant questions about the same study; for example, whether there is also a reduction in disease measures other than death, or whether the new treatment reduces the overall cost of treating patients. When secondary endpoints are also important the trial must be powered sufficiently to detect a difference in both endpoints, and expert statistical and design advice may be needed.

### Types of Endpoints

An endpoint could take different forms:

• A quantitative (or continuous or numerical) measurement representing a specific measure or count (e.g., quality of life, blood pressure, or heart rate). These endpoints can be summarized by means and medians (Wang et al., 2006f).
• A binary clinical outcome indicating whether an event has occurred (e.g., death from any cause, the occurrence of disease signs or symptoms, the relief of symptoms). The proportions, odds ratios and risk ratios can be used to compare these endpoints (Wang et al., 2006d).
• The time to occurrence of an event of interest or survival time (e.g., the time from randomization of patient to death). Kaplan-Meier plot is often used to compare the survival experience graphically and Cox model is frequently used to estimate the treatment effect (Cox, 1984; Wang et al., 2006b).
• The use of healthcare resources (e.g. the number of hospital admissions).

Ideally, a trial should have a single endpoint based on just one outcome measure. However, as the art of trial design has evolved, most large trials have a primary (composite) endpoint consisting of multiple outcome measures. An endpoint can also be the time taken for an event to occur. For such an endpoint, the events of interest for which a time is to be recorded—such as stroke or heart attack—must be predefined. Trial endpoints can also be a quantitative measurement of a biochemical or socioeconomic parameter such as cholesterol level or quality-of-life.

Bakhai A, Chhabra A, Wang D. (2006a) Endpoints. In: D Wang & A Bakhai, (Ed.s). Clinical Trials: A practical guide to design, analysis and reporting. London: Remedica. 37-45.
Wang D, Clemens F, Clayton T. (2006f) Comparison of Means. In: D Wang & A Bakhai, (Ed.s). Clinical trials: A practical guide to design, analysis and reporting. London: Remedica. 197-216.
Wang D, Clayton T, Clemens F. (2006d) Comparison of Proportions. In: D Wang & A Bakhai, (Ed.s). Clinical trials: A practical guide to design, analysis and reporting. London: Remedica. 217-234.
Cox DR, Oakes D. (1984). Analysis of survival data. London: Chapman and Hall.
Wang D, Clayton T, Bakhai A. (2006b) Analysis of Survival Data. In: D Wang & A Bakhai, (Ed.s). Clinical trials: A practical guide to design, analysis and reporting. London: Remedica. 235-254.