# Application of Association Rules Analysis in Mining Adverse Drug Reaction Signals
## 5 Discussion
### Advantages over the Traditional MHRA  
Compared with the traditional DPA method, (1) the advantage of ARA lies in the higher adaptability to datasets with different characteristics. Taking the MHRA method as an example, this method has fixed index combination thresholds. If the data for the dataset were increased, the sensitivity would decrease. While the ARA method's index thresholds change dynamically, the thresholds can be determined by simulation experiments based on the specific characteristics of the dataset. Similarly, due to the limitation of the combination of metrics of the MHRA method, the amount of data in the dataset using this method cannot be too small. Otherwise, it will cause few positive signals to be detected, which leads to no research values. All metrics of the ARA method are expressed in the form of proportions, so the result is relatively more stable even if the amount of data is small. (2) The ARA method has better detection performance for rare ADR signals. We have verified this in the above experiment. At the theoretical level, due to the different data characteristics of each dataset and some unavoidable interference factors, such as individual selective reporting of ADRs and over-reporting of ADRs, these may cause some real and rare ADRs to be masked. If we used the MHRA method, the positive ADR signal might be ignored due to the fact that the number of records of specific ADRs is small, or the frequency of ADRs caused by specific drugs is relatively lower in the same ADR range. The ARA method is more accurate and stable for detecting ADR signals because of its metrics' proportional representation and dynamics. From the perspective of formulas, The MHRA method requires that the ADR combination completely meets a \(\geq\) 3, PRR \(\geq\) 2, and x\({}^{2}\)\(\geq\) 4, then it is determined as an ADR signal. The ARA method requires that the ADR combination completely meets Confidence \(\geq\) 0.007 and Lift \(\geq\) 1, then it is determined as an ADR signal. The three formulas of MHRA method can be explained as  
1. The number of target ADR caused by the target drug \(\geq\)3;
2. The probability of target ADR caused by the target drug \(\geq\) the probability of target ADR caused by all other drugs \(\times\)2;
3. The Chi-square value of the ADR \(\geq\)4, which means the drug is related to the ADR. The two formulas of the ARA method can be explained as:  
1. The number of cases of target drug producing target ADR accounted for the number of cases of target drug producing all ADRs \(\geq\) 0.007 (the probability of target ADR occurring when the target drug is used \(\geq\) 0.007);
2. When using the target drug, the probability of the target ADR is greater than the probability of using all the drugs to produce the target ADR. At the same time, Lift also reflects the correlation between the drug and the ADR. By analyzing the definitions of the formulas of the two methods and their meaning of expressions, we divided the variable 'a' in the MHRA method and Formula (4) in the ARA method into a group for comparison, at the same time, we divided Formulas (1) and (2) in the MHRA method and Formula (5) in the ARA method into a group for comparison. Formula (5) in the ARA method is more comprehensive Formulas (1) and (2) in the MHRA method. The function of Lift's formula expression is similar to that of the PRR's formula expression in MHRA, but because it also has the implication of \"correlation\", the Chi-square value is added to MHRA to compensate for the implication of \"correlation\". For the PRR's expression in the MHRA method and the Lift's expression in the ARA method, the values of their denominators c/c + d and a + c/a + b + c + d tend to behave in the same way when the amount of data in the dataset is large enough, so the requirements needed to achieve the PRR's formula are more stringent, but this also reduces the ability of the MHRA method to capture rare ADR signals.