RESTORE-IMI 1: A Multicenter, Randomized, Doubleblind Trial Comparing Efficacy and Safety of Imipenem/Relebactam vs Colistin Plus Imipenem in Patients With Imipenem-nonsusceptible Bacterial Infections

Background. The β-lactamase inhibitor relebactam can restore imipenem activity against imipenem-nonsusceptible gram-negative pathogens. We evaluated imipenem/relebactam for treating imipenem-nonsusceptible infections. Methods. Randomized, controlled, double-blind, phase 3 trial. Hospitalized patients with hospital-acquired/ventilatorassociated pneumonia, complicated intraabdominal infection, or complicated urinary tract infection caused by imipenemnonsusceptible (but colistin- and imipenem/relebactam-susceptible) pathogens were randomized 2:1 to 5–21 days imipenem/ relebactam or colistin+imipenem. Primary endpoint: favorable overall response (defined by relevant endpoints for each infection type) in the modified microbiologic intent-to-treat (mMITT) population (qualifying baseline pathogen and ≥1 dose study treatment). Secondary endpoints: clinical response, all-cause mortality, and treatment-emergent nephrotoxicity. Safety analyses included patients with ≥1 dose study treatment. Results. Thirty-one patients received imipenem/relebactam and 16 colistin+imipenem. Among mITT patients (n = 21 imipenem/relebactam, n = 10 colistin+imipenem), 29% had Acute Physiology and Chronic Health Evaluation II scores >15, 23% had creatinine clearance <60 mL/min, and 35% were aged ≥65 years. Qualifying baseline pathogens: Pseudomonas aeruginosa (77%), Klebsiella spp. (16%), other Enterobacteriaceae (6%). Favorable overall response was observed in 71% imipenem/relebactam and 70% colistin+imipenem patients (90% confidence interval [CI] for difference, –27.5, 21.4), day 28 favorable clinical response in 71% and 40% (90% CI, 1.3, 51.5), and 28-day mortality in 10% and 30% (90% CI, –46.4, 6.7), respectively. Serious adverse events (AEs) occurred in 10% of imipenem/relebactam and 31% of colistin+imipenem patients, drug-related AEs in 16% and 31% (no drugrelated deaths), and treatment-emergent nephrotoxicity in 10% and 56% (P = .002), respectively. Conclusions. Imipenem/relebactam is an efficacious and well-tolerated treatment option for carbapenem-nonsusceptible infection

Theory and Methods for Modeling and Fitting Discrete Time Survival Data

Discrete survival data are routinely encountered in many fields of study. There are two common types of discrete survival data. The first type is derived discrete, which is originally continuous but recorded in a discrete version by grouping or rounding into a discrete time. The second type is intrinsically discrete. The dissertation research is motivated by two types of discrete survival data in clinical trials. We develop a class of proportional exponentiated link transformed hazards (ELTH)models and a class of proportional exponentiated link transformed survival (ELTS) models. We examine the role of links in fitting discrete survival data and estimating regression coefficients. We also characterize the conditions for improper survival functions and the conditions for existence of the maximum likelihood estimates under the proposed ELTH models. An extensive simulation study is conducted to examine the empirical performance of the parameter estimates under the Cox proportional hazards model by treating discrete survival times as continuous survival times, and the model comparison criteria, AIC and BIC, in determining links and baseline hazards. A SEER breast cancer dataset is analyzed in details to further demonstrate the proposed methodology. Previous research has shown that outcome misclassification can bias estimation of the survival function under standard survival methods. We develop methods to accurately estimate the survival function when the diagnostic tool used to measure the outcome of disease is not perfectly sensitive and specific. Since the diagnostic tool used to measure disease outcome is not the gold standard, the true outcomes cannot be observed. Our method uses the negative predictive value (NPV) and the positive predictive values (PPV) to construct a bridge between the mismeasured outcomes and the true outcomes. We formulate an exact relationship between the true and the observed survival functions as a formulation of time-varying NPV and PPV. We specify models for the NPV and PPV that depend only on parameters that can be easily estimated from a fraction of the observed data. Furthermore, we extend and conduct an extensive study to accurately estimate the latent survival function based on the assumption that the underlying disease process follow a stochastic process. We further examine the performance of our method by applying it to the VIRAHEP-C data