Traditionally, prevalence, incidence, and mortality (survival) have been the outcome measures in clinical cancer epidemiology studies. More recently, quality-of-life measures have become increasingly popular. In ophthalmic oncology, visual acuity is also an important outcome measure.

Prevalence refers to the proportion of the study population with the condition of interest. Usually, prevalence is given for a specific moment in time (point prevalence), but sometimes it is estimated for a period of time (e.g., 1-year or lifetime prevalences). For example, the lifetime prevalence of uveal melanoma in a Caucasian population with oculo (dermal) melanocytosis is estimated to be 0.26% [4].

Whereas prevalence relates to existing cases, incidence relates to the proportion of new cases in the study population. It is important that the population under investigation is at risk of developing the condition. For example, persons with bilateral enucleation are no longer at risk of developing uveal melanoma. There are two different measures of incidence: cumulative incidence (CI) and incidence density (ID). CI is the proportion of new cases in a population at risk over a specified period of time. For example, the CI of second malignant neoplasms in hereditary retinoblastoma patients is 17% at the age of 35 years [5]. ID refers to the rate of developing the condition during follow-up, usually expressed as a proportion per person-year at risk.

Cancer is among the leading causes of mortality. In order to understand processes that either hasten or delay this outcome, it is necessary to rigorously define the burden of disease. Clinical epidemiologists have created several concepts of mortality, all with their own definitions, interpretations, and uses. Unfortunately, many of these concepts use similar nomenclature, so it is important to always clarify the definition of mortality at hand. Broadly speaking, mortality rate refers to the incidence of death, and survival rate is its complement, i.e., survival rate = 100—mortality rate.

Population mortality is the chance that a person in the general population will die from a specific disease over a specified time frame. It is a useful concept for measuring the burden of disease in a population. For example, the population mortality for heart disease was 197.2 per 100,000 population per year in 2015. Therefore, it is a measure more important for public health policymakers as opposed to clinicians. This measure is calculated from death certificates, where the cause of death is known [6].

Overall mortality is the chance that a person with a disease will die within a time period after diagnosis. It is important to specify a time period for the mortality statistics—in the long run, mortality is 100% for any condition. Of note, this definition is indifferent to the cause of death. Overall mortality is the most common measure of mortality in the literature and is often used to guide prognosis. This measure is helpful in identifying risk factors for poor prognosis, as well as measuring disparities between populations. The interpretation of this measure is complicated by biases, including lead time, length, and overdiagnosis biases.

Cause-specific mortality is the chance that a person with a disease will die within a time period after diagnosis due to the disease. This is in contrast to overall survival, which does not distinguish between causes of death. Cause-specific mortality most closely measures the “deadliness” of a disease, but similar to overall mortality, it can be affected by lead time, length, and overdiagnosis bias.

Relative mortality is a proportion that compares the overall mortality of people with a disease to that of an unaffected but otherwise identical population. Relative mortality measures the excess mortality associated with a diagnosis compared to the general population. It is a convenient measure to calculate because it does not require the cause of death to be recorded. It is also a helpful measure for understanding the deadliness of a disease that affects the elderly, where patients are at risk of dying from other causes. To calculate relative survival, the overall survival of a cohort of patients is compared to life tables of the general population, matched by age, sex, race, and other important demographic features.

Conditional mortality is another clinically informative measure of mortality, which measures the chance that a person with a disease will die within a specified time period after having already lived with the disease for a certain amount of time. It is a helpful measure to assess how prognosis changes over time. Conditional survival requires a cohort of patients where long-term follow-up is close to complete.

Another class of outcomes that is of interest to clinical epidemiologists is time-to-event. Time to event measures the length of time that elapses from some start point to a defined endpoint, the most common time-to-event measure being survival, which is defined as the time from diagnosis to death (not to be confused with survival rate). Other events can be used as the endpoint, such as relapse or radiographic progression. Time-to-event data is typically graphically presented with a Kaplan–Meier plot, which displays a nonparametric estimation of the rate of survival in a population as a function of time [7].

While overall mortality and cause-specific mortality are the two gold standard measures of mortality when evaluating the efficacy of interventions, other measures of efficacy are being proposed, such as surrogate endpoints, which are biomarkers that are intended to substitute for a clinical endpoint. Of note, these are not clinical quantities of interest but are correlated with them. Examples of surrogate endpoints include evidence of radiographic progression, biochemical markers, and physical signs [8]. These are controversial measures and are not uniformly accepted within the scientific and regulatory community. Proponents of surrogate endpoints argue that they are preferable for reasons of feasibility, cost, length of study, and possible expediency of the drug approval process. Critics of surrogate endpoints point out that the evidence base for surrogate endpoints correlating closely with clinical outcomes is often tenuous.

With increasing survival rates and severe side effects of some treatment modalities, quality-of-life measures have become increasingly important in ophthalmic oncology. These measures encompass symptoms and physical, social, and psychological functioning from a patient’s perspective. Usually, quality of life is assessed with a structured questionnaire, and scores are summarized assuming an interval scale. Several questionnaires have recently been developed for patients with ocular diseases, such as the measure of outcome in ocular disease [MOOD]) [9].