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The average treatment effect (ATE) is a measure used to compare treatments (or interventions) in randomized experiments, evaluation of policy interventions, and medical trials. The ATE measures the difference in mean (average) outcomes between units assigned to the treatment and units assigned to the control.
An estimand is a quantity that is to be estimated in a statistical analysis. [1] The term is used to distinguish the target of inference from the method used to obtain an approximation of this target (i.e., the estimator ) and the specific value obtained from a given method and dataset (i.e., the estimate ). [ 2 ]
Randomized clinical trials analyzed by the intention-to-treat (ITT) approach provide unbiased comparisons among the treatment groups. Intention to treat analyses are done to avoid the effects of crossover and dropout, which may break the random assignment to the treatment groups in a study. ITT analysis provides information about the potential ...
In the presence of non-compliance, the ATE can no longer be recovered. Instead, what is recovered is the average treatment effect for a certain subpopulation known as the compliers, which is the LATE. When there may exist heterogeneous treatment effects across groups, the LATE is unlikely to be equivalent to the ATE.
This analysis can be restricted to only the participants who fulfill the protocol in terms of the eligibility, adherence to the intervention, and outcome assessment. This analysis is known as an "on-treatment" or "per protocol" analysis. A per-protocol analysis represents a "best-case scenario" to reveal the effect of the drug being studied.
The estimation of differences between treatment effects can be made with greater reliability than the estimation of absolute treatment effects. Confounding: A confounding design is one where some treatment effects (main or interactions) are estimated by the same linear combination of the experimental observations as some blocking effects. In ...
Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results. [1]
With a binary post-treatment covariate (e.g. attrition) and a binary treatment (e.g. "treatment" and "control") there are four possible strata in which subjects could be: those who always stay in the study regardless of which treatment they were assigned; those who would always drop-out of the study regardless of which treatment they were assigned