Assessing Noninferiority in Treatment Trials for Severe Infectious Diseases: an Extension to the Entire Follow-Up Period Using a Cure-Death Multistate Model

Abstract

In current and former clinical trials for the development of antibacterial drugs, various primary endpoints have been used, and treatment effects are evaluated mostly in noninferiority analyses at the end of follow-up, which varies between studies. A more convincing and highly patient-relevant statement would be a noninferiority assessment over the entire follow-up period with cure and death as coprimary endpoints, while preserving the desired alpha level for statistical testing. To account for the time-dynamic pattern of cure and death, we apply a cure-death multistate model. The endpoint of interest is "get cured and stay alive over time." Noninferiority between treatments over the entire follow-up period is studied by means of one-sided confidence bands provided by a flexible resampling technique. We illustrate the technique by applying it to a recently published study and establish noninferiority in being cured and alive over a time frame of interest for the entire population, patients with hospital-acquired pneumonia, but not for the subset of patients with ventilator-associated pneumonia. Our analysis improves the original results in the sense that our endpoint is more patient benefiting, a stronger noninferiority statement is demonstrated, and the time dependency of cure and death, competing events, and different follow-up times is captured. Multistate methodology combined with confidence bands adds a valuable statistical tool for clinical trials in the context of infection control. The framework is not restricted to the cure-death model but can be adapted to more complex multistate endpoints and equivalence or superiority analyses.

Keywords: endpoints; hospital-acquired infection; multistate model; noninferiority.

FIG 1

The cure-death model for comparing two antimicrobial therapies with an initial infection/randomization state, a cured and alive state, and a death state. The direction of arrows illustrates the potential transitions between the states determined by transition hazards. They can be interpreted as the momentary forces that pull a subject out of one state into another.

FIG 2

Transition probabilities and differences in probabilities derived from the Aalen-Johansen estimator for subgroups in the ceftobiprole trial (26). (Top) Probability to be cured and alive. (Bottom) Estimated difference of probabilities, PCAD, with the 95% one-sided simultaneous confidence band (CB), the corresponding boundary value, q, and the protocol-defined noninferiority (NI) margin of −15% on the time interval [0, 47].