Bias due to interval censored outcomes in a study of flare risk after hydroxychloroquine taper/cessation in systemic lupus erythematosus

Objective: This study attempted to quantify the bias expected due to partly interval censored (IC) outcomes in the estimated association between hydroxychloroquine (HCQ) taper/cessation and time to disease flare among individuals with systemic lupus erythematosus (SLE).

Methods: Using data-driven simulations, we estimated bias expected due to IC using real-world data from the Systemic Lupus International Collaborating Clinics inception cohort. The time-varying exposure of interest was a binary indicator of hydroxychloroquine tapering/cessation. The composite outcome was lupus flare, defined as lupus hospitalizations or increases in disease activity or medication dose. The two latter components were IC, as they were recorded only at annual assessment, without a precise date.

For the unknown IC event times, a “true” event time was randomly generated from a uniform distribution of the time between two assessments. Each simulated sample was analyzed separately imputing unknown event times (for IC outcomes) either at the midpoint or endpoint of the interval between the two adjacent yearly assessments. Results of multivariable Cox proportional hazards models, adjusted for demographics, drugs, and clinical variables, using either “true” or imputed IC event times were compared.

Results: The 1,543 SLE patients were followed for a median of 42.2 months. During follow-up, 396 participants tapered/stopped HCQ and 1,187 experienced a flare. The adjusted uncorrected hazard ratio (HR) was 1.51 (95%CI: 1.30, 1.75) and 1.40 (95%CI: 1.21, 1.62) for midpoint and endpoint imputations, respectively. Data-driven simulations showed that imputation of IC event times resulted in a small but systematic bias toward the null that was consistently larger for endpoint than for midpoint imputation.

Conclusion: IC events induced bias toward the null in the estimated association between HCQ taper/cessation and lupus flares. Data-driven simulations are useful for quantitative bias analyses in complex situations, as they allow accounting for relevant characteristics of a particular real-world dataset.