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

EasyChair Preprint 427

Abstract

The paper talks about 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 drugs approval date that the patient's treatment (new or standard) began. Second, we included a covariate in the model for the number of days since approval that treatment (new or standard) started. 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.

Keyphrases: causal inference, drug effectiveness study, secular trend bias

@booklet{EasyChair:427,
  author    = {Sharon Hensley Alford and Piyush Madan and Shilpa Mahatma and Italo Buleje and Yanyan Han and Fang Lu},
  title     = {Effect of secular trend in drug effectiveness study in real world data},
  doi       = {10.29007/699v},
  howpublished = {EasyChair Preprint 427},
  year      = {EasyChair, 2018}}