Measurable changes in health monitored during a clinical trial to assess the efficacy and safety of an intervention. Outcomes can be either positive e. Choice of outcome measure is a critical aspect of clinical trial design.
They must provide a reliable and meaningful measure of the impact of an intervention. Try out PMC Labs and tell us what you think. Learn More. In a randomized trial evaluating the efficacy of a new drug for pulmonary arterial hypertension PAH , patients were randomly assigned to receive the new drug or a placebo. The primary composite outcome was the time to the first PAH-related event worsening of symptoms, initiation of treatment with prostanoids, lung transplantation, or atrial septostomy or to death.
Secondary outcomes included changes in the 6-minute walk distance 6MWD and adverse events. Outcomes also called events or endpoints are variables that are monitored during a study to document the impact that a given intervention or exposure has on the health of a given population. Typical examples of outcomes are cure, clinical worsening, and mortality. The primary outcome is the variable that is the most relevant to answer the research question.
Ideally, it should be patient-centered i. Secondary outcomes are additional outcomes monitored to help interpret the results of the primary outcome: in our example, an increase in the 6MWD is inversely associated with the need for lung transplantation. They can also provide preliminary data for a larger study. Most trials require those AEs that are considered serious to be individually reported to the sponsor and to an ethics review board, to the regulatory agency overseeing the trial, and to an independent DSMB for their careful evaluation during the conduct of the trial, to allow the possibility for the trial to be stopped or modified before its completion if it is suspected that SAEs are associated with the drug, vaccine, or product under investigation.
The choice of the outcome measures in a specific trial largely depends on the purpose of the trial and how relevant, feasible, and acceptable the measures will be in a particular study population. Furthermore, the choice may be constrained by economic, logistic, or ethical considerations. The outcome measures chosen should reflect these objectives as fully as possible, but, when intermediate variables are used, rather than those of main interest, care must be taken to choose variables of direct relevance to the main outcome.
This is not always straightforward. For example, it may be decided to assess the impact of a vaccine by measuring the proportion of individuals who develop antibodies to the vaccine.
This may be reasonable if it is known that there is a high correlation between the development of antibodies and protection from clinical disease. For many diseases, however, this relationship has not been established, and it would not be warranted to base conclusions regarding protection against disease simply on antibody determinations. A health education intervention may be designed to change behaviour to reduce disease risk, but, as discussed in Section 2.
Are individuals responding truthfully? Are they doing what they say they do? Even if behaviour changes, is this associated with a lowering in the incidence of disease? The outcome variable measured should be as close as possible to the outcome of main interest.
While this may seem an obvious suggestion, it may have major impact on the design of a study. For example, if the prevention of death is of prime interest, then, whenever possible, this should be made the endpoint of the trial.
To do so might require an increase in the size of the trial from hundreds to thousands, or even tens of thousands, of individuals. Such a large trial might be difficult to find funding for, and there may never be an adequate test of whether the intermediate variables measured are acceptable surrogates for effects on mortality. To be successful, a trial must be designed to have achievable objectives. A trial which has mortality as the endpoint, but which is too large to be successfully completed, may be of less value than a well-designed smaller trial aimed at assessing the impact on some intermediate endpoint such as severe disease.
There must often be a compromise between relevance and feasibility. It is pointless to set unachievable goals, even if they look attractive in the objectives section of a proposal. Also, it may be of little value to measure the effect of an intervention on an outcome measure which is only distantly related to the measure of prime interest.
The outcome measures selected will be much influenced by the resources available for the trial, the availability of skilled personnel, and the necessary laboratory support to diagnose cases of disease. In many large trials, every individual in the study population may have to be screened for disease or infection in a relatively short time.
With such time constraints, some individuals may be misdiagnosed. The consequences of reductions in diagnostic sensitivity and specificity are discussed in Section 4. The acceptability of the measurement of an outcome variable to the study population is critical to the successful conduct of a trial. For example, the recording of birthweights p. Taking venous blood samples or repeated blood samples is unpopular in many societies.
If the method for measuring the outcome involves pain or inconvenience to the participants, it may be necessary to modify or abandon it. An outcome, of which the assessment involves a long interview with participants at a time when they would otherwise be planting crops or taking care of their household chores, may be unacceptable; it may either have to be abbreviated or carried out at a more convenient time.
Some trials offer the opportunity to measure outcomes that are not directly related to the objectives of the original study itself. These opportunities can be exploited by researchers to answer questions with minimal additional funding. For example, a diarrhoeal surveillance study might be carried out within a clinical trial in which a cohort of healthy children is being followed over time. However, it is very important that the add-on study does not interfere with the original study outcome measure.
Such additions should be considered at the beginning of the study and should have a separate study protocol. It is also important to inform sponsors, participants, and all stakeholders of the original trial of the coexistence of the proposed add-on study. Such investigations will usually require separate ethical approval and informed consent. The extent to which different observers will make the same diagnoses or assessments on a participant and to which observers are consistent in their classifications between participants may have an important influence on the results of a trial.
Clearly, it is desirable to choose outcome measures for which there is substantial reproducibility and agreement among observers, with respect to the classification of participants in the trial. For objective outcome measures, variations between observers, or by the same observer at different times, may be small and unlikely to influence the results of a study. For outcome measures requiring some degree of subjective assessment, however, such variations may be substantial. The likely degree of such variations will influence the choice of outcome measures, as it will be preferable to select those measures that have the smallest inter- and intra-observer variations, yet still give valid measures of the impact of the intervention.
Variation among observers is often much greater than expected, for example, in the reading of a chest X-ray to assess whether there is evidence of pneumonia. If a study involves several observers, pilot studies should be conducted, in order to measure the extent of the variation and then to seek to standardize the assessment methods to minimize the variation.
With suitable training, it is usually possible to reduce the variation between observers substantially. For some outcomes, independent assessment by two observers should be routine, with a third being called in to resolve disagreements. It may be costly to screen the p.
Sometimes, it is possible to have the observer examine the same individual twice, but these examinations may not be independent, unless the survey is large and the observer does not remember the result of the first assessment. It is important to make every effort to reduce variability to the maximum extent possible. The purpose of trials is usually to demonstrate the effect of an intervention or to compare differences between interventions.
Knowledge of the inherent variability in diagnostic procedures is essential for this demonstration, and the best way of assessing this is through replicate measures. It is especially important to take account of between-observer differences when communities are the units of randomization in a field trial.
Differences between observers may produce biases if different observers are used in different communities. In such situations, it is better to organize the fieldwork so that the workload within each community is split among different observers and differences between the observers are not confounded with the effect of the intervention.
Sensitivity is defined as the proportion of true cases that are classified as cases in the study. Specificity is the proportion of non-cases that are classified as non-cases in the study. A low sensitivity is associated with a reduction in the measured incidence of the disease. This decreases the likelihood of observing a significant difference between two groups in a trial of a given size. In statistical terms, it reduces the power of the study see Chapter 5 , Section 2.
If the incidence of the disease in both the intervention group and the comparison group will be affected proportionately in the same way, as is often the case, it does not bias the estimate of the relative disease incidence in the two groups, though the absolute magnitude of the difference will be less than the true difference.
Thus, in the context of a vaccine trial, because protective efficacy is assessed, in terms of relative differences in incidence between groups, the estimate of protective efficacy will not be biased, but the confidence limits on the estimate will be wider than they would be using a more sensitive case definition. In theory, the reduction in power associated with low sensitivity can be compensated for by increasing the trial size.
In general, a low specificity of diagnosis is a more serious problem than a low sensitivity in intervention trials. A low specificity results in the disease incidence rates being estimated to be higher than they really are, as some participants without the disease under study are classified incorrectly as cases. Generally, the levels of inflation in the rates will be similar, in absolute terms, in the intervention and comparison groups, and thus the ratio of the measured rates in the two groups will be less than the true ratio, though the difference in the rates should be unbiased.
Thus, in vaccine trials, for example, the vaccine efficacy estimate will be biased towards zero, though the absolute p. Increasing the trial size will not compensate for the bias in the estimate of vaccine efficacy. Sometimes, this is not possible, and, even if definitive diagnostic procedures exist, it may be necessary to use imperfect procedures in a field trial for reasons of cost or logistics. In this situation, if an assessment is made of sensitivity and specificity, it is possible to evaluate the consequences for the results of a field trial, and possible even to correct for biases in efficacy estimates due to the use of a non-specific diagnostic test.
For example, there is no universally agreed definition of a case of clinical malaria. Most would agree that the presence of parasites in the blood is necessary unless a potential case has taken treatment before presenting to the study clinic , and many would agree that the presence of fever associated with parasitaemia increases the likelihood of the disease being clinical malaria, but it is also possible that the fever is due to other causes, rather than the parasitaemia being the cause of the fever.
The bias induced by a low specificity of diagnosis is most severe for diseases that have a low incidence. A good example of this is provided by leprosy, which is both difficult to diagnose in the early stages and also of low incidence. The power of the study is reduced, however. If the vaccine has no effect on the progression of their disease and they are detected as cases later in the trial, a false low estimate of efficacy will result.
Conversely, once individuals have been screened for entry into the trial and they are being followed for the development of disease, a highly specific test is required to avoid the bias illustrated in the preceding paragraph. In situations where there may be no clear-cut definitions of a case for example, early leprosy or childhood TB , studies of intra- and inter-observer variation may be undertaken, using various definitions of the disease.
If an intermediate surrogate endpoint is proposed, provide justification why the main disease outcome of interest is not being used, and that the intermediate endpoint reflects the expected pathway of the effect of treatment on the main outcome of interest. Outcomes should be able to be measured directly or via proxy from data sources proposed for study. The instrument chosen should reflect the hypothesized effect of treatment on specific aspects of disease symptoms or treatment, or quality of life, if known.
Propose use of a standard instrument that has been validated for use in population representative of the study population, when possible. Have the instrument validated for use in translation to other specific languages if it is intended to be used in those languages for study, when possible. Have the instrument validated for the intended mode of administration, when possible.
Describe potential issues of bias, misclassification, and missing data that may be expected to occur with the proposed outcomes, including those specific to PRO data. Provide a plan for minimization of potential bias, misclassification, and missing data issues identified. Proposed analytic methods should correspond to the nature of the outcome measure e.
Plan sensitivity analyses relating to expected questions that arise around the study outcomes. Propose sensitivity analyses that address different relevant definitions of the study outcome s or multiple related outcomes e.
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The product may not be sold for profit or incorporated into any profitmaking venture without the expressed written permission of AHRQ. Turn recording back on. National Center for Biotechnology Information , U. Search term. Introduction The selection of outcomes to include in observational comparative effectiveness research CER studies involves the consideration of multiple stakeholder viewpoints provider, patient, payer, regulatory, industry, academic and societal and the intended use for decisionmaking of resulting evidence.
Conceptual Models of Health Outcomes In considering the range of health outcomes that may be of interest to patients, health care providers, and other decisionmakers, key areas of focus are medical conditions, impact on health-related or general quality of life, and resource utilization.
Table 6. Figure 6. Outcome Measurement Properties The properties of outcome measures that are an integral part of an investigator's evaluation and selection of appropriate measures include reliability, validity, and variability.
Clinical Outcomes Clinical outcomes are perhaps the most common category of outcome to be considered in CER studies. Definitions of Clinical Outcomes Temporal Aspects The nature of the disease state to be treated, the mechanism, and the intended effect of the treatment under study determine whether the clinical outcomes to be identified are incident a first or new diagnosis of the condition of interest , prevalent existing disease , or recurrent new occurrence or exacerbation of disease in a patient who has a previous diagnosis of that condition.
Subjective Versus Objective Assessments Most clinical outcomes involve a diagnosis or assessment by a health care provider. PROs are recorded without amendment or interpretation of the patient's response by a clinician or other observer.
A PRO measurement can be recorded by the patient directly, or recorded by an interviewer, provided that the interviewer records the patient's response exactly. Observer-reported outcome ObsRO assessment : An assessment that is determined by an observer who does not have a background of professional training that is relevant to the measurement being made, i. This type of assessment is often used when the patient is unable to self-report e.
An ObsRO assessment should only be used in the reporting of observable concepts e. Clinician-reported outcome ClinRO assessment : An assessment that is determined by an observer with some recognized professional training that is relevant to the measurement being made. Composite Endpoints Some clinical outcomes are composed of a series of items, and are referred to as composite endpoints.
Intermediate Endpoints The use of an intermediate or surrogate endpoint is more common in clinical trials than in observational studies. Selection of Clinical Outcome Measures Identification of a suitable measure of a clinical outcome for an observational CER study is a process in which various aspects of the nature of the disease or condition under study should be considered along with sources of information by which the required information may be feasibly and reliably obtained.
Interactions With the Health Care System For any medical condition, one should first determine the source of reporting or detection that may lead to initial contact with the medical system. Humanistic Outcomes While outcomes of interest to patients generally include those of interest to physicians, payers, regulators, and others, they are often differentiated by two characteristics: 1 they are clinically meaningful with practical implications for disease recognition and management i. The FDA defines HRQoL as follows: HRQL is a multidomain concept that represents the patient's general perception of the effect of illness and treatment on physical, psychological, and social aspects of life.
Patient-Reported Outcomes Patient-reported outcomes PROs include any outcomes that are based on data provided by patients or by people who can report on their behalf proxies , as opposed to data from other sources.
Types of Humanistic Outcome Measures Generic Measures Generic PRO questionnaires are measurement instruments designed to be used across different subgroups of individuals, and contain common domains that are relevant to almost all populations.
Descriptive Versus Preference Format Descriptive questionnaires ask about general or common domains and complaints, and usually provide multiple scores. Content Validity Content validity is the extent to which a PRO instrument covers the breadth and depth of salient issues for the intended group of patients.
Responsiveness and Minimally Important Difference Responsiveness is a measure of a PRO instrument's sensitivity to changes in health status or other outcome being measured.
Floor and Ceiling Effects Poor content validity can also lead to a mismatch between the distribution of responses and the true distribution of the concept of interest in the population. Population It is important to understand the target population that will be completing the PRO assessment. Burden It is important to match the respondent burden created by a PRO instrument to the requirements of the population being studied.
Cost and Copyright Another practical consideration is the copyright status of a PRO being considered for use. Mode and Format of Administration As noted above, there are various options for how a questionnaire should be administered and how the data should be captured, each method having both advantages and disadvantages. Static Versus Dynamic Questionnaires Static forms are the type of questionnaire that employs a fixed-format set of questions and response options.
Economic and Utilization Outcomes While clinical outcomes represent the provider and professional perspective, and humanistic outcomes represent the patient perspective, economic outcomes, including measures of health resource utilization, represent the payer and societal perspective.
Health Resource Utilization Measures of health resource utilization, such as number of inpatient or outpatient visits, total days of hospitalization in a given year, or number of days treated with IV antibiotics, are often used as efficient and easily interpretable proxies for measuring cost, since actual costs are dependent on numerous factors e.
Selection of Resource Utilization and Cost Measures The selection of measures of resource utilization or costs should correspond to the primary hypothesis in terms of the impact of an intervention.
Study Design and Analysis Considerations Study Period and Length of Followup In designing a study, the required study period and length of followup are determined by the expected time frame within which an intervention may be expected to impact the outcome of interest.
Avoidance of Bias in Study Design Misclassification The role of the researcher is to understand the extent and sources of misclassification in outcome measurement, and to try to reduce these as much as possible. Analytic Considerations Form of Outcome Measure and Analysis Approach To a large extent, the form of the primary outcome of interest—that is, whether the outcome is measured and expressed as a dichotomous or polytomous categorical variable or a continuous variable, and whether it is to be measured at a single time point, measured repeatedly at fixed intervals, or measured repeatedly at varying time intervals—determines the appropriate statistical methods that may be applied in analysis.
Sensitivity Analysis One of the key factors to address in planned sensitivity analyses for an observational CER study is how varying definitions of the study outcome or related outcomes will affect the measures of association from the study. Conclusion Future Directions Increased use of EHRs as a source of data for observational research, including registries, other types of observational studies, and specifically for CER, has prompted initiatives to develop standardized definitions of key outcomes and other data elements that would be used across health systems and different EHR platforms to facilitate comparisons between studies and pooling of data.
Summary This chapter has provided an overview of considerations in development of outcome definitions for observational CER studies; has described implications of the nature of the proposed outcomes for the study design; and has enumerated issues of bias that may arise in incorporating the ascertainment of outcomes into observational research.
Checklist: Guidance and key considerations for outcome selection and measurement for an observational CER protocol View in own window Guidance Key Considerations Check Propose primary and secondary outcomes that directly correspond to research questions. References 1. Linking clinical variables with health-related quality of life.
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