Introduction Comparisons of healthcare utilization and expenditure among multiple sclerosis (MS) disease-modifying therapies (DMTs) are limited. Methods In this retrospective cohort study using commercial insurance claims data of a US population (2010–2019), we compared healthcare utilization and costs in MS across different DMTs. We assigned patients to different treatment arms: no DMT (ND), high-efficacy (HE) DMT (alemtuzumab, B cell depletion, cladribine, and natalizumab), and standard-efficacy (SE) DMT (dimethyl fumarate, glatiramer acetate, interferon beta, sphingosine-1-phosphate receptor modulator, and teriflunomide). We obtained healthcare costs and occurrences of healthcare services: outpatient visits, emergency room visits, hospitalizations, MS-related magnetic resonance imaging (MRI). We quantified relapses (based on MS-related hospitalizations, as well as outpatient visits with prescription of high-dose steroids) and medical complexity (based on unique drug classes of prescriptions). We calculated covariate-adjusted incidence rate ratio of healthcare services using negative binomial regression with ND as reference and covariate-adjusted mean cumulative healthcare costs using a generalized linear model with log-link function and gamma distribution. Results Among the 25,932 patients with MS (mean age 52.8 years, 75.2% women), both HE (mean age 54.0 years, 76.2% women) and SE (mean age 43.9 years, 75.6% women) groups had more non-pharmacy healthcare utilization than ND (mean age 57.6 years, 75.4% women), including overall outpatient doctor visits, neurology visits, and MS-related MRIs as well as relapses and medical complexities. Relative to ND, both HE and SE groups had higher pharmacy costs and overall healthcare costs 12 months after treatment initiation, despite having lower or equivalent non-pharmacy medical costs. In patients on DMT, pharmacy costs accounted for up to 65% of overall healthcare costs with over 85% of pharmacy costs attributable to DMT costs. Conclusion DMT cost is a key driver of the overall healthcare expenditure in MS. Future comparative and cost-effectiveness studies integrating claims and electronic health records data with better balancing of patient characteristics are warranted. Supplementary Information The online version contains supplementary material available at 10.1007/s40120-022-00358-4.

Background The HEALing (Helping to End Addiction Long-termSM) Communities Study (HCS) is a multi-site parallel group cluster randomized wait-list comparison trial designed to evaluate the effect of the Communities That Heal (CTH) intervention compared to usual care on opioid overdose deaths. Covariate-constrained randomization (CCR) was applied to balance the community-level baseline covariates in the HCS. The purpose of this paper is to evaluate the performance of model-based tests and permutation tests in the HCS setting. We conducted a simulation study to evaluate type I error rates and power for model-based and permutation tests for the multi-site HCS as well as for a subgroup analysis of a single state (Massachusetts). We also investigated whether the maximum degree of imbalance in the CCR design has an impact on the performance of the tests. Methods The primary outcome, the number of opioid overdose deaths, is count data assessed at the community level that will be analyzed using a negative binomial regression model. We conducted a simulation study to evaluate the type I error rates and power for 3 tests: (1) Wald-type t-test with small-sample corrected empirical standard error estimates, (2) Wald-type z-test with model-based standard error estimates, and (3) permutation test with test statistics calculated by the difference in average residuals for the two groups. Results Our simulation results demonstrated that Wald-type t-tests with small-sample corrected empirical standard error estimates from the negative binomial regression model maintained proper type I error. Wald-type z-tests with model-based standard error estimates were anti-conservative. Permutation tests preserved type I error rates if the constrained space was not too small. For all tests, the power was high to detect the hypothesized 40% reduction in opioid overdose deaths for the intervention vs. comparison group both for the overall HCS and the subgroup analysis of Massachusetts (MA). Conclusions Based on the results of our simulation study, the Wald-type t-test with small-sample corrected empirical standard error estimates from a negative binomial regression model is a valid and appropriate approach for analyzing cluster-level count data from the HEALing Communities Study. Trial registration ClinicalTrials.gov http://www.clinicaltrials.gov; Identifier: NCT04111939

Background The HEALing (Helping to End Addiction Long-termSM) Communities Study (HCS) is a multi-site parallel group cluster randomized wait-list comparison trial designed to evaluate the effect of the Communities That Heal (CTH) intervention compared to usual care on opioid overdose deaths. Covariate constrained randomization (CCR) was applied to balance the community-level baseline covariates in the HCS. The purpose of this paper is to evaluate the performance of model-based tests and permutation tests in the HCS setting. We conducted a simulation study to evaluate Type I error rates and power for model-based and permutation tests for the multi-site HCS as well as for a subgroup analysis of a single state (Massachusetts). We also investigated whether the maximum degree of imbalance in the CCR design has an impact on the performance of the tests. Methods The primary outcome, number of opioid overdose deaths, is count data assessed at the community-level that will be analyzed using a negative binomial regression model. We conducted a simulation study to evaluate the Type I error rates and power for 3 tests: 1) Wald-type t-test with small-sample corrected empirical standard error estimates; 2) Wald-type z-test with model-based standard error estimates; and 3) permutation test with test statistics calculated by the difference in average residuals for the two groups. Results Our simulation results demonstrated that Wald-type t-tests with small-sample corrected empirical standard error estimates from the negative binomial regression model maintained proper Type I error. Wald-type z-tests with model-based standard error estimates were anti-conservative. Permutation tests preserved Type I error rates if the constrained space was not too small. For all tests, power was high to detect the hypothesized 40% reduction in opioid overdose deaths for the intervention vs. comparison group both for the overall HCS and the subgroup analysis of Massachusetts (MA). Conclusions Based on the results of our simulation study, the Wald-type t-test with small-sample corrected empirical standard error estimates from a negative binomial regression model is a valid and appropriate approach for analyzing cluster-level count data from the HEALing Communities Study. Trial registration: ClinicalTrials.gov http://www.clinicaltrials.gov; Identifier: NCT04111939

Network meta-analysis (NMA) is essential for clinical decision-making. NMA enables inference for all pair-wise comparisons between interventions available for the same indication, by using both direct evidence and indirect evidence. In randomized trials with time-to event outcome data, such as lung cancer data, conventional NMA methods rely on the hazard ratio and the proportional hazards assumption, and ignore the varying follow-up durations across trials. We introduce a novel multivariate NMA model for the difference in restricted mean survival times (RMST). Our model synthesizes all the available evidence from multiple time points simultaneously and borrows information across time points through within-study covariance and between-study covariance for the differences in RMST. We propose an estimator of the within-study covariance and we then assume it to be known. We estimate the model under the Bayesian framework. We evaluated our model by conducting a simulation study. Our multiple-time-point model yields lower mean squared error over the conventional single-time-point model at all time points, especially when the availability of evidence decreases. We illustrated the model on a network of randomized trials of second-line treatments of advanced non-small-cell lung cancer. Our multiple-time-point model yielded increased precision and detected evidence of benefit at earlier time points as compared to the single-time-point model.Our model has the advantage of providing clinically interpretable measures of treatment effects.