#### How we are studying it

We will quantify the extent to which confounding by “indication” is observable in claims and EHR data. To do so, we developing a directed acyclic graph (DAG) that will guide our choice of metrics to identify confounding by “indication.” We will make these public, using Daggity.

Longitudinal data necessitate accounting for time-varying exposures and confounding to fall within a causal framework. As new ADFs come to market, patients may be switched between ADFs or comparators, creating methodological complications. We are evaluating the relative importance of applying advanced epidemiologic methods for assessing time-varying causation in claims-based studies similar to those found in post-marketing requirements. To accomplish this, we will first use claims-linked-mortality-data to ascertain whether ADF patients have different risk profiles than non-ADF opioid patients. Then, we will apply statistical methods to account for time-varying confounding.

A critical conceptual component of this Task is that a patient’s risk profile will change over time, as new behaviors emerge and are documented in the medical record. To account for these time-varying issues we will adjust for observable time-fixed and time- varying confounding by utilizing two approaches: (1) g- estimation of structural nested cumulative failure time models (SNCFTM); and (2) inverse probability weighted marginal structural models (MSM). Both of these methods utilize the counterfactual or potential outcomes framework. G-estimation of SNCFTM allows comparison of counterfactual outcomes within the categories of observed baseline and time-varying covariates (structural nested models), thereby allowing a direct contrast of counterfactual outcomes for the observed time-varying exposure categories. These models can be fit for both categorical and continuous time- varying exposures. On the other hand, inverse probability weighted MSM account for time-varying confounding by comparing counterfactual outcomes for the entire observed population (i.e., potential outcomes if the entire population had a certain defined level of exposure compared to if the same entire population had another defined level of exposure). Both methods can be readily implemented in standard statistical software packages.

Controlling for time-varying confounding using traditional regression methods is inappropriate because such methods block causal effects of the exposure on the outcome and introduce collider-stratification (selection) bias. This fact has been overlooked in much of the opioids research published to date.There are paradoxical pitfalls in ADF evaluation using longitudinal data which pose a threat to validity of PMR studies. For example, it may appear intuitive to adjust for pain severity to control for differences between ADF recipients and comparators. Collider stratification bias arises because of time-varyingness: exposure X1 (at time 1) affects outcome Y1 (at time 1), and both X and Y are affected by the confounder pain symptoms (C1). All three of these have an effect on the exposure at time 2 (X2), and the pain symptoms at time 2 (C2). All of which also affect the outcome Y2. If we were to adjust on pain scores (C1 and C2), we actually open up many of the collider pathways between C1, X1, and Y1, thereby causing selection bias.