A Swapping Method for Average Treatment Effect Estimation

A Swapping Method for Average Treatment Effect Estimation

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Accurate estimate of treatment effect is vital to clinical studies. A randomized controlled trial (RCT) is the gold standard to reduce bias when testing a new treatment. The people participating in the trial are randomly allocated to either the group receiving the treatment or to a group receiving the control. Such a randomization process minimises the selection bias and therefore allows the researchers to determine any effect differences between the treatment and the control groups. However, the imbalance in covariates such as age, gender, and laboratory measurements can exist in flawed randomized trials due to non-random dropouts and interruption, and non-randomized studies such as open label or single-blind designs. Although methods such as propensity score based methods can remedy the issue, the imbalance continues to be one of major issues in clinical studies.

We propose a swapping method to achieve the balance. We classify all patients into subclasses based on discrete covariates such as gender. Within each subclass, we then estimate the average treatment effect based on the principle of choosing a better model between the treatment and baseline groups. The simulation studies indicate that our method provides more stable estimates than traditional methods. The proposed datasets are for non-randomized studies. They are helpful to illustrate the public the importance of correcting the bias and therefore providing more reliable and accurate estimate of treatment effects in clinical studies. We can provide a powerful tool that will be published and distributed to the community.

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https://www.tci-thaijo.org/index.php/thaistat/article/view/163194/117937