CLRM-09. INCORPORATING EXTERNAL CONTROL ARM IN MDNA55 RECURRENT GLIOBLASTOMA REGISTRATION TRIAL

BACKGROUND Drug development in recurrent glioblastoma multiforme (rGBM) is challenging. For randomized controlled trials (RCTs) short survival horizons and limited life-prolonging treatment options may delay accrual and introduce bias through differential dropout of control patients. Comparing results of a single-arm Phase 2b trial of intratumoral delivery of MDNA55 (an interleukin-4 receptor targeted fusion protein) to an external control arm, we sought early efficacy insights and consideration by the FDA of incorporating an ECA in a Phase 3 registrational trial. METHODS Using propensity score weighting, we compared rGBM patients from the Phase 2b trial (NCT02858895) (2017-2019) to patients from rGBM registries who had received standard of care therapies (2011-2019) and met eligibility requirements. Propensity scores were estimated using a logistic regression model with 11 covariates. We compared the propensity score weighted groups according to demographic and disease attributes before and after weighting and compared overall survival between the two groups. RESULTS Through propensity score weighting, 43 (98%, 43/44) MDNA55 patients and 40.80 weighted ECA patients (from 62 unweighted registry patients) were identified for comparison. MDNA55 and ECA patients were balanced on all baseline characteristics (i.e., standardized mean difference ≤ 0.15). Compared to ECA patients, MDNA55 patients had a 37% lower hazard of death (hazard ratio 0.63, 95% confidence interval: 0.39,1.02). CONCLUSION In advance of a Phase 3 trial, comparison of Phase 2b trial results to an ECA suggests that MDNA55 may be efficacious in rGBM. In view of the known challenges associated with drug development for rGBM, these results provided a proof-of-concept for the design of a novel hybrid Phase 3 trial. This planned Phase 3 trial incorporates propensity score weighting to create a composite hybrid randomized and external control arm, an approach preferred by the FDA over full replacement of a randomized control with an external control.

1216Combining propensity score-weighting and multiple imputation is not a trivial task

Background Propensity score (PS)-weighting and multiple imputation are two widely used statistical methods. Combining the two is not trivial and has received little attention in theory and practice. We present our considerations for their combination with application to a study of long-distance migration and post-traumatic stress disorder. We elaborate on the assumptions underlying the methods and discuss the methodological and practical implications of our choices and alternatives. Methods We made a number of choices a priori: to use logistic regression-based PS to produce “standardised mortality ratio”-weights and SMC-FCS to multiply impute missing data. We present a methodology to combine the methods by choosing the PS model based on covariate balance, using this model as the substantive model in the multiple imputation, producing and averaging the point estimates from each multiply imputed data set to give the estimate of association and computing the percentile confidence interval by bootstrapping. Results In our application, a simple PS model was chosen as the substantive model for imputing 10 data sets with 40 iterations and repeating the entirety 999 times to obtain a bootstrap confidence interval. Computing time was approximately 36 hours. Conclusions Our structured approach is demanding in both work-load and computational time. We do not consider the prior a draw-back: it makes some of the underlying assumptions explicit and the latter may be a nuisance that diminishes with time. Key messages Combining propensity score-weighting and multiple imputation is not a trivial task.

Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach

Background: Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials. Methods: We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate–subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups. Results: Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model. Conclusion: Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate–subgroup interactions in the propensity score model.

Association of Body Mass Index With Clinical Outcomes in Patients With Heart Failure With Reduced Ejection Fraction Treated With Sacubitril/Valsartan

Background: Only limited data are available that address the association between body mass index (BMI) and clinical outcomes in patients with heart failure with reduced ejection fraction who are receiving sacubitril/valsartan. Methods: We performed a retrospective multi-center cohort study in which we compared 3 body mass index groups (normal, overweight and obese groups) in patients with heart failure with reduced ejection fraction receiving sacubitril/valsartan. The follow-up period was at least 1 year. Propensity score weighting was performed. The primary outcomes were hospitalization for heart failure and all-cause mortality. Results: Of the 721 patients in the original cohort, propensity score weighting generated a cohort of 540 patients in 3 groups: normal weight (n = 78), overweight (n = 181), and obese (n = 281). All baseline characteristics were well-balanced between 3 groups after propensity score weighting. Among our results, we found no significant differences in hospitalization for heart failure (normal weight versus overweight: average hazard ratio [AHR] 1.29, 95% confidence interval [CI] = 0.76-2.20, P = 0.35; normal weight versus obese: AHR 1.04, 95% CI = 0.63-1.70, P = 0.88; overweight versus obese groups: AHR 0.81, 95% CI = 0.54-1.20, P = 0.29) or all-cause mortality (normal weight versus overweight: AHR 0.99, 95% CI = 0.59-1.67, P = 0.97; normal weight versus obese: AHR 0.87, 95% CI = 0.53-1.42, P = 0.57; overweight versus obese: AHR 0.87, 95% CI = 0.58-1.32, P = 0.52). Conclusion: We identified no significant associations between BMI and clinical outcomes in patients diagnosed with heart failure with a reduced ejection fraction who were treated with sacubitril/valsartan. A large-scale study should be performed to verify these results.

A National Analysis of Music Coursetaking, Social-Emotional Learning, and Academic Achievement Using Propensity Scores

The purpose of this study was to examine the association between curricular high school music participation, academic achievement, and social-emotional learning. The analysis involved a “doubly robust” approach combining propensity score weighting and nested multiple regression using data from the nationally representative High School Longitudinal Study of 2009. Results of the study were mixed. Preliminary tests revealed many significant differences between the choral and instrumental students and the control group, but the propensity score weighting moderated almost all of these effects to nonsignificance. The only unambiguously positive finding was that instrumental music students had higher reading scores than comparable students who did not enroll in music. Yet, subpopulation effects emerged for certain categories of music students based on factors such as race, sex, and prior school achievement. Although the lack of widespread main effects in this study coheres with prior research, the results for certain subpopulations suggest intriguing future directions for research on potential extramusical benefits of music education.