Estimating the safety impacts in before–after studies using the Naïve Adjustment Method

The before–after study is the most popular approach for estimating the safety impacts of an intervention or treatment. Recent research, however, has shown that the most common before–after approaches can still provide a biased estimate when an entry criterion is used and when the characteristics of the treatment and control groups are dissimilar. Recently, a new simple method, referred to as the Naïve Adjustment Method (NAM), has been proposed to mitigate the limitations identified above. Unfortunately, the effectiveness of the NAM using ‘real’ data has not yet been properly investigated. Hence, this paper examined the accuracy of the NAM when the treatment group contains sites that have different mean values. Simulated and two observed datasets were used. The results show that the NAM outperforms the Naïve, the Control Group, and the empirical Bayesian methods. Furthermore, it can be used as a simpler alternative for adjusting the Naïve estimators documented in previous studies.