Elranatamab efficacy in MagnetisMM-3 compared with real-world control arms in triple-class refractory multiple myeloma.

Elranatamab efficacy in the single-arm, registrational MagnetisMM-3 trial (NCT04649359) was compared with that of physician's choice of treatment (PCT) for triple-class refractory multiple myeloma. MagnestisMM-3 eligibility criteria were applied to two USA-based oncology electronic health record databases, COTA and Flatiron Health (FH), to identify cohorts for this study (NCT05932290). Applied statistical techniques accounted for cohort imbalances. MagnetisMM-3 (BCMA-naive; n = 123) outcomes were compared with those from COTA (n = 239) and FH (n = 152). Elranatamab was associated with a significantly higher objective response rate (risk ratios, 1.88-2.25), significantly longer progression-free survival (hazard ratios [HRs], 0.37-0.57), and, across most analyses, significantly longer overall survival (HRs, 0.46-0.66) versus PCT. BCMA-naive patients who were treated with elranatamab exhibited significantly better outcomes than patients treated in real-world clinical practice.

Elranatamab is a new medicine for the treatment of people with multiple myeloma. In the ongoing clinical trial MagnetisMM-3, most people had fewer myeloma cells when treated with elranatamab. However, MagnetisMM-3 only looks at the effects of elranatamab without comparing it to other myeloma treatments. Therefore, a new study was designed to compare the effectiveness of elranatamab in the MagnetisMM-3 study with other treatments used in real-world clinical practice (not in a clinical trial). Data from people in MagnetisMM-3 was compared with data from two US databases (COTA and Flatiron Health) containing health records of patients treated for multiple myeloma in real-life clinical practice. The same criteria used to select patients for the MagnetisMM-3 trial (123 people) were used to identify people with similar characteristics in COTA (239 people) and Flatiron Health (152 people). More people treated with elranatamab had fewer myeloma cells in their bodies after treatment than people who received their doctor's choice of treatment in clinical practice. In fact, six out of ten people treated with elranatamab had fewer myeloma cells versus about three in ten people from each real-world database. People treated with elranatamab versus physician's choice of treatment lived longer without their disease getting worse and lived longer overall. In conclusion, this study found that more people treated with elranatamab responded to treatment and lived longer than similar people from the COTA and Flatiron Health databases who were given treatments available in a real-world clinical setting.Clinical Trial Registration: NCT05932290 (ClinicalTrials.gov).

Evaluating Flexible Modeling of Continuous Covariates in Inverse-Weighted Estimators.

Correct specification of the exposure model is essential for unbiased estimation in marginal structural models with inverse-probability-of-treatment weights. However, although flexible modeling is commonplace when estimating effects of continuous covariates in outcome models, its use is less frequent in estimation of inverse probability weights. Using simulations, we assess the accuracy of the treatment effect estimates and covariate balance obtained with different exposure model specifications when the true relationship between a continuous, possibly time-varying covariate Lt and the logit of the probability of exposure is nonlinear. Specifically, we compare 4 approaches to modeling the effect of Lt when estimating inverse probability weights: a linear function, the covariate-balancing propensity score, and 2 easy-to-implement flexible methods that relax the assumption of linearity: cubic regression splines and fractional polynomials. Using data from 2 empirical studies, we compare linear exposure models with flexible exposure models to estimate the effect of sustained virological response to hepatitis C virus treatment on the progression of liver fibrosis. Our simulation results demonstrate that ignoring important nonlinear relationships when fitting the exposure model may provide poorer covariate balance and induce substantial bias in the estimated exposure-outcome associations. Analysts should routinely consider flexible modeling of continuous covariates when estimating inverse-probability-of-treatment weights.

Health-selective migration among patients with rheumatoid arthritis in Québec: a cohort study using administrative data.

Little is known about how rheumatoid arthritis (RA) affects an individual's ability to relocate. The current literature suggests the relationship between health and migration is often disease-specific. We sought to estimate the impact of RA diagnosis on migration within a Canadian province, comparing migration rates in residents before and after RA diagnosis. We identified a cohort of 81,181 individuals diagnosed with RA between 1998 and 2009 using Québec administrative databases. A migration was defined as a change in the first three characters of the postal code. We categorized migrations as urban or rural depending upon an individual's origin and destination. We estimated the association between RA diagnosis and migration by fitting marginal models using a generalized estimating equations approach, adjusting for age, sex, and population level socioeconomic status indicators. The vast majority of moves after RA diagnosis were within urban areas. RA diagnosis was associated with increased migration except for people around age 50 moving within urban areas. Although RA was associated with increased inter-urban migration in many demographic groups, the net result did not translate to higher rates of rural-to-urban migration after RA diagnosis. Our results suggest fairly complex associations between RA diagnosis and migration. Both age and location (urban or rural) modify this effect. Overall, we did not see a greater movement from rural-to-urban areas after RA diagnosis. This is of interest for studies of regional environmental effects on chronic disease patterns.

Correcting for Measurement Error in Time-Varying Covariates in Marginal Structural Models.

Unbiased estimation of causal parameters from marginal structural models (MSMs) requires a fundamental assumption of no unmeasured confounding. Unfortunately, the time-varying covariates used to obtain inverse probability weights are often error-prone. Although substantial measurement error in important confounders is known to undermine control of confounders in conventional unweighted regression models, this issue has received comparatively limited attention in the MSM literature. Here we propose a novel application of the simulation-extrapolation (SIMEX) procedure to address measurement error in time-varying covariates, and we compare 2 approaches. The direct approach to SIMEX-based correction targets outcome model parameters, while the indirect approach corrects the weights estimated using the exposure model. We assess the performance of the proposed methods in simulations under different clinically plausible assumptions. The simulations demonstrate that measurement errors in time-dependent covariates may induce substantial bias in MSM estimators of causal effects of time-varying exposures, and that both proposed SIMEX approaches yield practically unbiased estimates in scenarios featuring low-to-moderate degrees of error. We illustrate the proposed approach in a simple analysis of the relationship between sustained virological response and liver fibrosis progression among persons infected with hepatitis C virus, while accounting for measurement error in γ-glutamyltransferase, using data collected in the Canadian Co-infection Cohort Study from 2003 to 2014.