Effect of secular trend in drug effectiveness study in real world data

TL;DR

This study identifies and attempts to correct secular trend bias in real-world drug effectiveness data, highlighting the importance of bias detection for accurate causal inference in AI models.

Contribution

The paper demonstrates the presence of secular trend bias in drug effectiveness studies and evaluates methods to mitigate this bias, emphasizing the need for bias awareness in AI healthcare models.

Findings

Secular trend bias was detected in the data.

Standard adjustment methods did not fully eliminate the bias.

Residual bias remained due to unmeasured patient characteristics.

Abstract

We discovered secular trend bias in a drug effectiveness study for a recently approved drug. We compared treatment outcomes between patients who received the newly approved drug and patients exposed to the standard treatment. All patients diagnosed after the new drug's approval date were considered. We built a machine learning causal inference model to determine patient subpopulations likely to respond better to the newly approved drug. After identifying the presence of secular trend bias in our data, we attempted to adjust for the bias in two different ways. First, we matched patients on the number of days from the new drug's approval date that the patient's treatment (new or standard) began. Second, we included a covariate in the model for the number of days between the date of approval of the new drug and the treatment (new or standard) start date. Neither approach completely mitigated the bias. Residual bias we attribute to differences in patient disease severity or other unmeasured patient characteristics. Had we not identified the secular trend bias in our data, the causal inference model would have been interpreted without consideration for this underlying bias. Being aware of, testing for, and handling potential bias in the data is essential to diminish the uncertainty in AI modeling.