Words to know - glossary

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Adherence: The extent to which a person’s behaviour – taking medication, following a diet, and/or executing lifestyle changes – corresponds with the agreed recommendations from a healthcare provider.

Association: Association between two variables means the values of one variable relate in some way to the values of the other. An example of association could be the amount of consultation a person has with an HCP and the number of scripts the person has.

Bias: In a scientific research study or clinical trial, a flaw in the study design or the method of collecting or interpreting information. Biases can lead to incorrect conclusions about what the study or clinical trial showed.

Case-control study: In a case-control study, patients who have developed a disease are identified and their past exposure to suspected aetiological factors are compared with that of controls or referents who do not have the disease.

Causality Also referred to as causation, or cause and effect is influence by which one event, process, state or object (a cause) contributes to the production of another event, process, state or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.

Claims data: Claims databases collect information on doctors’ appointments, bills, insurance information, and other patient-provider communications. Claims data, like other medical records, come directly from notes made by the health care provider, and the information is recorded at the time patient sees the doctor.

Clinical study: Performed in people, aimed at evaluating a medical, surgical, or behavioral intervention and are the primary way that researchers find out if a new treatment is safe and effective in people.

Cohort: A group of individuals who share a characteristic at some specific time and who are then followed forward in time, with data being collected at one or more suitable intervals. Examples for defining a cohort could be date of diagnosis, treatment for a certain disease or treatment with a certain product.

Cohort study: See prospective cohort study and retrospective cohort study.

Compliance: The extent to which a patient acts in accordance with the prescribed interval and dose of dosing regimen i.e. the day-to-day taking of medication.

Confounder: A confounding variable can have a hidden effect on the outcome of the study. An example could be comparing the outcome of two interventions A and B. The group receiving A turns out to be younger that the group receiving B. The outcome of the study might not be result of the intervention alone but could be confounded (have been affected) by age.

Correlation: Measures the degree to which two variables move in relation to each other. Correlation measures association, but doesn’t show if x causes y or vice versa, or if the association is caused by a third, perhaps unseen, factor.

Cross-sectional study: Measures the prevalence of health outcomes or determinants of health, or both, in a population at a point in time or over a short period. In cross-sectional studies associations must be interpreted with caution as bias may arise because of selection into or out of the study population.

Effectiveness: An intervention’s performance in real world everyday clinical setting.

Efficacy: An intervention’s performance under ideal and controlled circumstances, such as randomized clinical trials.

Electronic medical records (EMR): Digital equivalent of paper records, or charts at a clinician’s office. EMRs typically contain general information such as treatment and medical history about a patient as it is collected by the individual medical practice.

External validity: The extent to which you can generalize the findings of a study to other situations, people, settings, and measures. In other words, can you apply the findings of your study to a broader context? Real world studies are often referred to as having External Validity.

Hazard ratio: Comparison between the probability of events in a treatment group, compared to the probability of events in a control group. A Hazard Ration of 1 means that both groups experience an equal number of events at any point in time.

Health related quality of Life (HRQoL): A multi-dimensional concept that includes domains related to physical, mental, emotional, and social functioning. HRQoL goes beyond the direct measures of population health, life expectancy, and causes of death, and focuses on the impact the health status has on a person’s quality of life.

Index date: Defines the time the subject enters into the cohort, e.g. time of diagnosis, time of prescription.

Internal validity: the extent to which you can be confident that the causal relationship established in your experiment cannot be explained by other factors. Randomized clinical studies are often referred to as having high Internal Validity.

ISPOR: International Society for Pharmacoeconomics and Outcomes Research, www.ispor.org. The Society’s mission is to promote health economics and outcomes research (HEOR) excellence to improve decision making for health globally.

Kaplan-Meier curves: A visual representation of the probability of an event (for example, asthma exacerbation) is at a certain time interval.

Odds ratio:  An odds ratio (OR) is a measure of association between an exposure and an outcome. The OR represents the odds that an outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure. OR=1 Exposure does not affect odds of outcome, OR>1 Exposure associated with higher odds of outcome, OR<1 Exposure associated with lower odds of outcome.

Patient preference: The relative desirability or acceptability to patients of specified alternatives or choices among outcomes or other attributes that differ among alternative health interventions.

Persistence: The duration of time from initiation to discontinuation of therapy. Analyses of persistence must consider the maximum permissible treatment gap allowed for an individual to still be defined as persistent. The permissible treatment gap should take the pharmacological properties of the drug into account.

Pragmatic trial: Pragmatic trials are essentially combining real-world evidence and randomization. Pragmatic trials are conducted in real-world clinical practice settings, with typical patients and qualified health care professionals. In addition to testing a particular intervention, pragmatic trials can also illustrate the differences in how the intervention works in different health care settings, such as hospitals, clinics, or physician practices.

Primary data: Data generated by the researcher himself/herself, surveys, interviews, experiments, specially designed for understanding and solving the research problem at hand.

Propensity Score Matching: To do propensity score matching you need first to create a propensity score for each subject. A propensity score is the probability that a subject with certain characteristics will be assigned to the treatment group (as opposed to the control group). The propensity scores can be used to reduce or eliminate selection bias in observational studies by balancing covariates (i.e. the characteristics of subjects) between treated and control groups. The propensity score is then matched and used to creates sets of participants for the treatment and the control group. A matched set consists of at least one participant in the treatment group and one in the control group with similar propensity scores. The goal is to approximate a random experiment, eliminating many of the challenges that come with observational data analysis.

Prospective cohort study/Non-interventional study:  Studies, where participants are enrolled into the study before they develop the disease or outcome in question, e.g. allergic asthma. Once the participants are enrolled, they are followed for a period of time to see who gets the outcome in question (and who doesn’t). Usually, the research is conducted with a goal in mind and participants are periodically checked for progress, using the same data collection methods and questions for each person in the study. The opposite of a prospective study is a retrospective study where participants are enrolled after they have developed the disease or outcome being investigated.

Quality of life (QoL): See HRQoL

Real-word data (RWD): Data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources. RWD can come from a number of sources, for example: electronic health records (EHRs), claims and billing activities, product and disease registries, patient-generated data including in home-use settings, data gathered from other sources that can inform on health status, such as mobile devices, etc.

Real-world evidence (RWE): Clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of real-world data. RWE can be generated by different study designs or analyses, including but not limited to; large simple trials, pragmatic trials, and observational studies (prospective and/or retrospective).

Regression to the mean (RTM): A statistical phenomenon that can make natural variation in repeated data look like real change. It happens when unusually large or small measurements tend to be followed by measurements that are closer to the mean.  RTM is a ubiquitous phenomenon in repeated data and should always be considered as a possible cause of an observed change. Its effect can be alleviated through better study design and use of suitable statistical methods.

Retrospective cohort study: An observational study that enrolls participants who already have a disease or condition. In other words, all cases have already happened before the study begins. Researchers then look back in time, using questionnaires, medical records and other methods. The goal is to find out what potential risk factors or other associations and relationships the group has in common. The opposite of a retrospective study is a prospective study where participants are enrolled before they develop the disease or outcome being investigated.

Secondary data: Existing data generated by for example large government institutions (e.g. national statistics), healthcare facilities etc. as part of organizational record keeping. As oppose to primary data, secondary data is not collected specifically to answer the research question at hand.

Survey: Means of collecting health and social science information from a sample of people in a standardized way. There are many methods used to conduct surveys, including questionnaires and in-depth interviews via phone, mail, email, and in-person.