Propensity Score Estimation to Address Calendar Time-Specific Channeling in Comparative Effectiveness Research of Second Generation Antipsychotics

<div><p>Background</p><p>Channeling occurs when a medication and its potential comparators are selectively prescribed based on differences in underlying patient characteristics. Drug safety advisories can provide new information regarding the relative safety or effectiveness of a drug product which might increase selective prescribing. In particular, when reported adverse effects vary among drugs within a therapeutic class, clinicians may channel patients toward or away from a drug based on the patient's underlying risk for an adverse outcome. If channeling is not identified and appropriately managed it might lead to confounding in observational comparative effectiveness studies.</p><p>Objective</p><p>To demonstrate channeling among new users of second generation antipsychotics following a Food and Drug Administration safety advisory and to evaluate the impact of channeling on cardiovascular risk estimates over time.</p><p>Data Source</p><p>Florida Medicaid data from 2001–2006.</p><p>Study Design</p><p>Retrospective cohort of adults initiating second generation antipsychotics. We used propensity scores to match olanzapine initiators with other second generation antipsychotic initiators. To evaluate channeling away from olanzapine following an FDA safety advisory, we estimated calendar time-specific propensity scores. We compare the performance of these calendar time-specific propensity scores with conventionally-estimated propensity scores on estimates of cardiovascular risk.</p><p>Principal Findings</p><p>Increased channeling away from olanzapine was evident for some, but not all, cardiovascular risk factors and corresponded with the timing of the FDA advisory. Covariate balance was optimized within period and across all periods when using the calendar time-specific propensity score. Hazard ratio estimates for cardiovascular outcomes did not differ across models (Conventional PS: 0.97, 95%CI: 0.81–3.18 versus calendar time-specific PS: 0.93, 95%CI: 0.77–3.04).</p><p>Conclusions</p><p>Changes in channeling over time was evident for several covariates but had limited impact on cardiovascular risk estimates, possibly due to unmeasured confounding. Although calendar time-specific propensity scores appear to improve covariate balance, the impact on comparative effectiveness results is limited in this setting.</p></div

Covariate Balance within and Across Periods for the Top Predictors of Coronary Artery Disease.

<p>Covariate Balance within and Across Periods for the Top Predictors of Coronary Artery Disease.</p

Baseline Characteristics by Sample: 2001–2005.

<p>Baseline Characteristics by Sample: 2001–2005.</p

Hazard Ratio Estimates for Adverse Cardiovascular Outcomes by Model: As Treated Analysis.

<p>Hazard Ratio Estimates for Adverse Cardiovascular Outcomes by Model: As Treated Analysis.</p

Beneficiary Baseline Characteristics By Study Period.

<p>SD, Standard deviation; NE, Northeast; NC, North Central</p><p>^a Mean monthly percentage of population with medical encounters in the 3-months prior</p><p>Beneficiary Baseline Characteristics By Study Period.</p

Overall Population Trends: Incidence Trends per 100 000 Youth.

<p>Overall Population Trends: Incidence Trends per 100 000 Youth.</p

Population Trends by Predictor of Initiation- Gender Specific Trends: Incidence per 100 000 Youth.

<p>Population Trends by Predictor of Initiation- Gender Specific Trends: Incidence per 100 000 Youth.</p

Monthly Mean Incidence (95% CI) Per 100 000 Commercially Insured Children: Overall and by Predictor Subgroups

<p>CI, Confidence Interval; NE, Northeastern; NC, North Central</p><p>^a Based on medical encounters in the 3-months prior to index drug date</p><p>^b Patients who visited “neither” specialties were omitted from table</p><p>Monthly Mean Incidence (95% CI) Per 100 000 Commercially Insured Children: Overall and by Predictor Subgroups</p

Population Trends by Predictor of Initiation- Physician Specialty Trends: Incidence per 100 000 Youth.

<p>Population Trends by Predictor of Initiation- Physician Specialty Trends: Incidence per 100 000 Youth.</p

Population Trends by Predictor of Initiation- Age Group Specific Trends: Incidence per 100 000 Youth.

<p>Population Trends by Predictor of Initiation- Age Group Specific Trends: Incidence per 100 000 Youth.</p