Recurrent time-to-event models with ordinal outcomes

Pressure injury treatment by intermittent electrical stimulation (PROTECT-2): protocol for a multicenter randomized clinical trial

BACKGROUND

Pressure ulcers account for a substantial fraction of hospital-acquired pathology, with consequent morbidity and economic cost. Treatments are largely focused on preventing further injury, whereas interventions that facilitate healing remain limited. Intermittent electrical stimulation (IES) increases local blood flow and redistributes pressure from muscle-bone interfaces, thus potentially reducing ulcer progression and facilitating healing.

METHODS

The Pressure Injury Treatment by Intermittent Electrical Stimulation (PROTECT-2) trial will be a parallel-arm multicenter randomized trial to test the hypothesis that IES combined with routine care reduces sacral and ischial pressure injury over time compared to routine care alone. We plan to enroll 548 patients across various centers. Hospitalized patients with stage 1 or stage 2 sacral or ischial pressure injuries will be randomized to IES and routine care or routine care alone. Wound stage will be followed until death, discharge, or the development of an exclusion criteria for up to 3 months. The primary endpoint will be pressure injury score measured over time.

DISCUSSION

Sacral and ischial pressure injuries present a burden to hospitalized patients with both clinical and economic consequences. The PROTECT-2 trial will evaluate whether IES is an effective intervention and thus reduces progression of stage 1 and stage 2 sacral and ischial pressure injuries.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05085288 Registered October 20, 2021.

An Overview of Regression Models for Adverse Events Analysis

Over the last few years, several review articles described the adverse events analysis as sub-optimal in clinical trials. Indeed, the context surrounding adverse events analyses often imply an overwhelming number of events, a lack of power to find associations, but also a lack of specific training regarding those complex data. In randomized controlled trials or in observational studies, comparing the occurrence of adverse events according to a covariable of interest (e.g., treatment) is a recurrent question in the analysis of drug safety data, and adjusting other important factors is often relevant. This article is an overview of the existing regression models that may be considered to compare adverse events and to discuss model choice regarding the characteristics of the adverse events of interest. Many dimensions may be relevant to compare the adverse events between patients, (e.g., timing, recurrence, and severity). Recent efforts have been made to cover all of them. For chronic treatments, the occurrence of intercurrent events during the patient follow-up usually needs the modeling approach to be adapted (at least with regard to their interpretation). Moreover, analysis based on regression models should not be limited to the estimation of relative effects. Indeed, absolute risks stemming from the model should be presented systematically to help the interpretation, to validate the model, and to encourage comparison of studies.

Toward Incorporating Health-Related Quality of Life as Coprimary End Points in Clinical Trials: Time to Achieve Clinical Important Differences and QoL Profiles

PURPOSE

Besides morbidity and mortality, quality of life (QoL) is a key outcome of cancer treatments. Trials on the basis of clinical outcomes have expectations that QoL outcomes can be either tolerated or improved. Simultaneously considering QoL and clinical outcomes is challenging with lack of suitable metrics allowing incorporation of QoL as coprimary end points in clinical trial design and utilization of hierarchical hypothesis testing.

METHOD

We propose combining time to achieving a minimal clinically important difference (MCID) and probabilities of a MCID occurring in each QoL domain to provide QoL metrics analogous to those used for clinical end points. For QoL domains of interest, these yield QoL profiles, time to MCID, and number needed to treat. Incorporation of QoL as coprimary end points in clinical trial designs through hierarchical hypothesis testing can easily be achieved. The noninferiority designed Laparoscopic Approach to Carcinoma of the Endometrium trial, evaluating laparoscopic versus open abdominal surgery for endometrial cancer with Functional Assessment of Cancer Therapy-General QoL domains, is used to illustrate the usefulness of these metrics.

RESULTS

This analysis revealed that laparoscopic surgery had a significant shorter time to MCID for physical and functional well-being QoL domains (physical mean: 1.5 months, 95% CI, 0.5 to 2.6; P = .002; and functional mean: 1.4 months; 95% CI, 0.4 to 2.4; P = .003) than abdominal surgery, but little difference between the two approaches for psychologic social and emotion well-being. Probability profile plots show a consistent > 2-fold higher chance of attaining a MCID for physical and functional well-being over time for laparoscopic compared with abdominal surgery.

CONCLUSION

This analysis reinforces the potential value of novel MCID metrics and their usefulness in raising the profile of QoL outcomes to complement clinical end points. The methods will allow health professionals to counsel patients about QoL outcomes and clinical outcomes simultaneously.

Using recurrent time-to-event models with multinomial outcomes to generate toxicity profiles

Most clinical studies, which investigate the impact of therapy simultaneously, record the frequency of adverse events in order to monitor safety of the intervention. Study reports typically summarise adverse event data by tabulating the frequencies of the worst grade experienced but provide no details of the temporal profiles of specific types of adverse events. Such 'toxicity profiles' are potentially important tools in disease management and in the assessment of newer therapies including targeted treatments and immunotherapy where different types of toxicity may be more common at various times during long-term drug exposure. Toxicity profiles of commonly experienced adverse events occurring due to exposure to long-term treatment could assist in evaluating the costs of the health care benefits of therapy. We show how to generate toxicity profiles using an adaptation of the ordinal time-to-event model comprising of a two-step process, involving estimation of the multinomial response probabilities using multinomial logistic regression and combining these with recurrent time to event hazard estimates to produce cumulative event probabilities for each of the multinomial adverse event response categories. Such a model permits the simultaneous assessment of the risk of events over time and provides cumulative risk probabilities for each type of adverse event response. The method can be applied more generally by using different models to estimate outcome/response probabilities. The method is illustrated by developing toxicity profiles for three distinct types of adverse events associated with two treatment regimens for patients with advanced breast cancer.