Estimation is the process of finding an estimate or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable.
- estimand (trial objective) is the target of estimation (for example, the treatment difference in change from baseline to Week 26 in FEV1)
- estimator is the method of estimation (such as ANCOVA, Logistic regression, Cox regression)
- estimate is the numerical result (such as LS mean difference, odds ratio, hazard ratio and their 95% confidence interval).
The primary estimand (a precise description of the treatment effect reflecting the objective of the clinical trial) assessed effects regardless of treatment discontinuation or rescue interventions.
In a paper by Little and Lewis (JAMA, 2021) Estimands, Estimators, and Estimates, the estimated and the estimator were compared:
- The estimand compares outcomes that capture the main benefits and risks of treatments
- Estimators should summarize the causal effects of treatments in the sample of individuals in the study
- Estimands should summarize the causal effects of treatments in the target population
- The estimator should provide a valid and unbiased estimate of the study estimand
The primary estimand is the intent-to-treat estimand, the treatment difference between dupilumab and control in the mean percentage reduction of OCS dose at Week 24 while maintaining asthma control of all patients in the ITT population no matter whether the patients discontinue treatment before Week 24 or not. To estimate the estimand, data of patients who permanently discontinue treatment will be incorporated in the primary analysis, and missing data due to patients dropping out from study will be handled by approaches specified in the missing data handling section below.
The primary efficacy endpoint will be analyzed using an analysis of covariance (ANCOVA) model. The model will include the percentage reduction of OCS dose at Week 24 as the response variable, and the treatment groups, optimized OCS dose at baseline, regions (pooled countries), and baseline eosinophil level subgroups (less than 0.15 Giga/L, greater than or equal to 0.15 Giga/L) as covariates. The treatment difference will be tested at the 2-sided significance level of alpha=0.05. Descriptive statistics for the primary efficacy endpoint will be provided, including the number of patients, means, standard errors, and least squares (LS) means by the treatment groups, as well as the difference in LS means and the corresponding 95% confidence interval (CI). The missing data (missing measures at Week 24) will be imputed using pattern mixture model by multiple imputation.
In a paper by Harvard researchers, "An Applied Researcher’s Guide to Estimating Effects From Multisite Individually Randomized Trials: Estimands, Estimators, and Estimates", the following conclusions are given:
"Defining an estimand is critical to the design, analysis, and interpretation of a multisite RCT. Even when one is interested in estimating an average treatment effect, careful consideration must be given to defining the target of inference. For example, the choice of estimand influences what formula is appropriate when conducting power calculations - yet many write-ups of power calculation are ambiguous or silent with regards to the estimand chosen. relatedly, registering studies and creating analysis plans are becoming the norm when conducting RCTs, yet even newly created registries do not require an estimand be defined. Consequently, readers of an analysis plan are left to assume an implied estimand based on the power calculation formula used and/or estimator selected. Similarly, many scholarly articles and reports do not state a target of inference, making it difficult to assess whether the chosen estimator is appropriate and obscuring the goal of the research. We recommend defining the estimand(s) early in the research process and clearly stating them in important products."