Appendix Table. Glossary of Terms.

Terms	Description
Overall treatment effect	A comparison of response between 2 groups that comprise the entire study sample, where each group is exposed to a different treatment.
Bayesian inference versus frequentist inference	Frequentist inference is a statistical framework that evaluates the population parameters by imagining repeated samples from an appropriate model. The population parameters are assumed to be fixed, but unknown. Bayesian inference is a framework that uses prior beliefs or information and updates those beliefs based on the observed data to derive probabilistic statements about unknown population parameters, using an appropriate model for the data-generating process. Here, the population parameters are random and unknown. Both frequentist and Bayesian frameworks require a data-generating model, but the Bayesian framework also requires a prior distribution for population parameters. In the frequentist framework, the parameters are fixed but the data are random, whereas in the Bayesian framework, the data are fixed and the parameters are random.
Conditional average treatment effect (CATE)	A model-based estimate of the individual treatment effect where a model depicting the relationship between the outcome, treatment, and covariates is fitted. Then, CATE is calculated for each individual in a study sample as a contrast of their model-estimated response under 2 treatments.
Effect modification	A measure of how the treatment effect varies according to different values of a covariate. Effect modification is commonly assessed by including a treatment by covariate product term in a regression model. For example, the coefficient of age-treatment product term is a measure of how the treatment effect varies as age varies.
Effectiveness	The performance of an intervention in the setting in which it is usually used in practice.
Efficacy	The performance of an intervention under ideal and controlled circumstances.
Heterogeneity of treatment effect (HTE)	The explainable (nonrandom) variation in treatment response that can be attributed to differences in patient characteristics.
Individual treatment effect	A comparison of an individual’s response under 2 different treatments. This is often unobservable because any individual can only be exposed to 1 treatment (unless the condition being treated is acute).
Individualized treatment effect	See conditional average treatment effect.
Interaction	Same as effect modification in terms of statistical description, but quite different conceptually. Interaction is said to exist between 2 manipulable variables, whereas effect modification measures how 1 manipulable variable varies as a function of a fixed covariate. Interaction can be synergistic or antagonistic.
Posterior distribution	A probability distribution that reflects the researcher’s belief about a population parameter of interest after observing the data.
Prior distribution	A probability distribution that reflects the researcher’s belief about a population parameter of interest before observing the data.
Qualitative HTE	A variation in treatment effect, of the opposite direction, according to levels of covariate. For example, men have a beneficial effect from the treatment, but women have a harmful effect.
Quantitative HTE	A variation in treatment effect, of the same direction, according to levels of a covariate. For example, men and women both have a beneficial effect from the treatment, but the magnitude of benefit is significantly different.
Real-world data (RWD)	Data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources.
Real-world evidence	Clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.
Shrinkage estimation	Treatment effect in a subgroup is estimated as a compromise between the "raw" or "observed" treatment effect in that group and the overall (average) treatment effect. The degree of compromise depends on the size of the subgroup and the shrinkage method. The smaller the subgroup the greater the compromise.
Subgroup analysis	The most popular way of examining HTE, in which the entire study sample is divided into mutually exclusive groups and the treatment effect is estimated in each group-for example, the treatment effect in men and in women.
Generalizability/ Transportability	Pertains to whether the evidence on benefits and risks of an intervention obtained from a controlled clinical trial is valid when applied to patients in the real world.
Applicability	Pertains to whether the evidence on benefits and risks of an intervention obtained from a controlled clinical trial is relevant and valid for a particular subpopulation of at-risk individuals. The distinction between applicability and generalizability is that applicability requires that we define a specific subpopulation, for example, Hispanic, women, older than 70 years, with diabetes.
Treatment effect scale	The scale in which treatment effect is measured. For example, this could be a ratio of average response under treatment to the average response without treatment (relative scale), or it could be the difference in average response under treatment to the average response without treatment (absolute scale).