The time‐dependent “cure‐death” model investigating two equally important endpoints simultaneously in trials treating high‐risk patients with resistant pathogens

Abstract

A variety of primary endpoints are used in clinical trials treating patients with severe infectious diseases, and existing guidelines do not provide a consistent recommendation. We propose to study simultaneously two primary endpoints, cure and death, in a comprehensive multistate cure-death model as starting point for a treatment comparison. This technique enables us to study the temporal dynamic of the patient-relevant probability to be cured and alive. We describe and compare traditional and innovative methods suitable for a treatment comparison based on this model. Traditional analyses using risk differences focus on one prespecified timepoint only. A restricted logrank-based test of treatment effect is sensitive to ordered categories of responses and integrates information on duration of response. The pseudo-value regression provides a direct regression model for examination of treatment effect via difference in transition probabilities. Applied to a topical real data example and simulation scenarios, we demonstrate advantages and limitations and provide an insight into how these methods can handle different kinds of treatment imbalances. The cure-death model provides a suitable framework to gain a better understanding of how a new treatment influences the time-dynamic cure and death process. This might help the future planning of randomised clinical trials, sample size calculations, and data analyses.

Optimizing the Design and Analysis of Clinical Trials for Antibacterials Against Multidrug-resistant Organisms: A White Paper From COMBACTE’s STAT-Net

“…If cure and death end points are of particular interest, both measures of clinical benefit can be simultaneously accounted for in a multistate framework using the coprimary end point “get cured and stay alive over time.” The application of this type of analysis has been illustrated by using data from a recently published trial on patients with hospital- or ventilator-associated pneumonia [ 15 ] and can be adapted to more complex disease histories, for example in patients with Clostridium difficile infection [ 14 ]. The application of such multistate models has the advantage of avoiding common survival biases (which occur if one deletes or censors death outcomes when studying cure), because ignoring time dependency may lead to overestimated or underestimated efficacy.…”

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

“…In order to suitably account for the time-dynamic pattern of cure and death after randomization, we focus on the "illness-death model without recovery" embedded in the flexible and powerful multistate model framework (9,10). Since the context here is hospital-acquired infection, we call this model "cure-death model" (19) (Fig. 1), with state 0 defined as initial, state 1 as cured and alive, and state 2 as absorbing death.…”

“…The joint analysis of nonmortal endpoints like cure and the competing endpoint, death over time, can be captured by the flexible multistate framework (9,10), which found its way into applied infection control literature (11)(12)(13)(14)(15)(16)(17)(18). For that purpose, it has recently been suggested to simultaneously study the two coprimary endpoints, cure and death (2,19), in order to understand how the new treatment influences the whole etiological cure process, even beyond the TOC. This is achieved by a multistate "cure-death" model (19), which accounts for the time dependency of cure and death, the presence of competing risks, and various follow-up times.…”

“…For that purpose, it has recently been suggested to simultaneously study the two coprimary endpoints, cure and death (2,19), in order to understand how the new treatment influences the whole etiological cure process, even beyond the TOC. This is achieved by a multistate "cure-death" model (19), which accounts for the time dependency of cure and death, the presence of competing risks, and various follow-up times. The patient-relevant outcome of interest is to be cured and alive over time.…”