Core outcome set for studies evaluating interventions to prevent or treat delirium in long-term care older residents: international key stakeholder informed consensus study.

BACKGROUND: Trials of interventions to prevent or treat delirium in older adults resident in long-term care settings (LTC) report heterogenous outcomes, hampering the identification of effective management strategies for this important condition. Our objective was to develop international consensus among key stakeholders for a core outcome set (COS) for future trials of interventions to prevent and/or treat delirium in this population. METHODS: We used a rigorous COS development process including qualitative interviews with family members and staff with experience of delirium in LTC; a modified two-round Delphi survey; and virtual consensus meetings using nominal group technique. The study was registered with the Core Outcome Measures in Effectiveness Trials (COMET) initiative (https://www.comet-initiative.org/studies/details/796). RESULTS: Item generation identified 22 delirium-specific outcomes and 32 other outcomes from 18 qualitative interviews. When combined with outcomes identified in our earlier systematic review, and following an item reduction step, this gave 43 outcomes that advanced to the formal consensus processes. These involved 169 participants from 12 countries, and included healthcare professionals (121, 72%), researchers (24, 14%), and family members/people with experience of delirium (24, 14%). Six outcomes were identified as essential to include in all trials of interventions for delirium in LTC, and were therefore included in the COS. These are: 'delirium occurrence'; 'delirium related distress'; 'delirium severity'; 'cognition including memory', 'admission to hospital' and 'mortality'. CONCLUSIONS: This COS, endorsed by the American Delirium Society and the European and Australasian Delirium Associations, is recommended for use in future clinical trials evaluating delirium prevention or treatment interventions for older adults residing in LTC.

Leveraging information from secondary endpoints to enhance dynamic borrowing across subpopulations.

Randomized trials seek efficient treatment effect estimation within target populations, yet scientific interest often also centers on subpopulations. Although there are typically too few subjects within each subpopulation to efficiently estimate these subpopulation treatment effects, one can gain precision by borrowing strength across subpopulations, as is the case in a basket trial. While dynamic borrowing has been proposed as an efficient approach to estimating subpopulation treatment effects on primary endpoints, additional efficiency could be gained by leveraging the information found in secondary endpoints. We propose a multisource exchangeability model (MEM) that incorporates secondary endpoints to more efficiently assess subpopulation exchangeability. Across simulation studies, our proposed model almost uniformly reduces the mean squared error when compared to the standard MEM that only considers data from the primary endpoint by gaining efficiency when subpopulations respond similarly to the treatment and reducing the magnitude of bias when the subpopulations are heterogeneous. We illustrate our model's feasibility using data from a recently completed trial of very low nicotine content cigarettes to estimate the effect on abstinence from smoking within three priority subpopulations. Our proposed model led to increases in the effective sample size two to four times greater than under the standard MEM.

Confirmatory Adaptive Designs for Clinical Trials With Multiple Time-to-Event Outcomes in Multi-state Markov Models.

The analysis of multiple time-to-event outcomes in a randomized controlled clinical trial can be accomplished with existing methods. However, depending on the characteristics of the disease under investigation and the circumstances in which the study is planned, it may be of interest to conduct interim analyses and adapt the study design if necessary. Due to the expected dependency of the endpoints, the full available information on the involved endpoints may not be used for this purpose. We suggest a solution to this problem by embedding the endpoints in a multistate model. If this model is Markovian, it is possible to take the disease history of the patients into account and allow for data-dependent design adaptations. To this end, we introduce a flexible test procedure for a variety of applications, but are particularly concerned with the simultaneous consideration of progression-free survival (PFS) and overall survival (OS). This setting is of key interest in oncological trials. We conduct simulation studies to determine the properties for small sample sizes and demonstrate an application based on data from the NB2004-HR study.

Design considerations for Factorial Adaptive Multi-Arm Multi-Stage (FAST) clinical trials.

BACKGROUND: Multi-Arm, Multi-Stage (MAMS) clinical trial designs allow for multiple therapies to be compared across a spectrum of clinical trial phases. MAMS designs fall under several overarching design groups, including adaptive designs (AD) and multi-arm (MA) designs. Factorial clinical trials designs represent a combination of factorial and MAMS trial designs and can provide increased efficiency relative to fixed, traditional designs. We explore design choices associated with Factorial Adaptive Multi-Arm Multi-Stage (FAST) designs, which represent the combination of factorial and MAMS designs. METHODS: Simulation studies were conducted to assess the impact of the type of analyses, the timing of analyses, and the effect size observed across multiple outcomes on trial operating characteristics for a FAST design. Given multiple outcomes types assessed within the hypothetical trial, the primary analysis approach for each assessment varied depending on data type. RESULTS: The simulation studies demonstrate that the proposed class of FAST trial designs can offer a framework to potentially provide improvements relative to other trial designs, such as a MAMS or factorial trial. Further, we note that the design implementation decisions, such as the timing and type of analyses conducted throughout trial, can have a great impact on trial operating characteristics. CONCLUSIONS: Motivated by a trial currently under design, our work shows that the FAST category of trial can potentially offer benefits similar to both MAMS and factorial designs; however, the chosen design aspects which can be included in a FAST trial need to be thoroughly explored during the planning phase.

Informing Endpoints for Clinical Trials of Geographic Atrophy.

Geographic atrophy (GA), the non-neovascular advanced form of age-related macular degeneration, remains an important disease area in which treatment needs are currently unmet. Recent clinical trials using drugs that target the complement pathway have shown modest yet consistent reductions in GA expansion but without commensurate changes in measures of visual function. In this review, we summarize information from the wide range of studies describing the characteristics of GA morphology and enumerate the factors influencing the growth rates of lesions and the directionality of expansion. In addition, we review the relationship between GA growth and the various measures of vision that reflect changes in function. We consider the reasons for the discordance between the anatomical and functional endpoints in current use and discuss methods to align these key outcomes.

Clinical trial designs and endpoints.

Trials should be designed with consideration of the individual disease context and research question. Many different approaches may be justified. In this chapter, we therefore consider some of the principal components of trial design in general and within the context of the emerging field of gene and cell therapies. Many aspects of developing a trial protocol require striking a balance between scientific rigor and practicalities for which the voice of patients and their families should form an integral part. We outline the importance of the acceptability of trial designs to participants, the determination of a target population and eligibility criteria, stratification methods that ensure a balanced control of variance across the trial, adequate controls to answer research questions including considerations of placebo allocation, blinding, and endpoints.

Proxy endpoints - bridging clinical trials and real world data.

OBJECTIVE: Disease severity scores, or endpoints, are routinely measured during Randomized Controlled Trials (RCTs) to closely monitor the effect of treatment. In real-world clinical practice, although a larger set of patients is observed, the specific RCT endpoints are often not captured, which makes it hard to utilize real-world data (RWD) to evaluate drug efficacy in larger populations. METHODS: To overcome this challenge, we developed an ensemble technique which learns proxy models of disease endpoints in RWD. Using a multi-stage learning framework applied to RCT data, we first identify features considered significant drivers of disease available within RWD. To create endpoint proxy models, we use Explainable Boosting Machines (EBMs) which allow for both end-user interpretability and modeling of non-linear relationships. RESULTS: We demonstrate our approach on two diseases, rheumatoid arthritis (RA) and atopic dermatitis (AD). As we show, our combined feature selection and prediction method achieves good results for both disease areas, improving upon prior methods proposed for predictive disease severity scoring. CONCLUSION: Having disease severity over time for a patient is important to further disease understanding and management. Our results open the door to more use cases in the space of RA and AD such as treatment effect estimates or prognostic scoring on RWD. Our framework may be extended beyond RA and AD to other diseases where the severity score is not well measured in electronic health records.

The impact of allocation bias on test decisions in clinical trials with multiple endpoints using multiple testing strategies.

BACKGROUND: Considering multiple endpoints in clinical trials provide a more comprehensive understanding of treatment effects and may lead to increased power or reduced sample size, which may be beneficial in rare diseases. Besides the small sample sizes, allocation bias is an issue that affects the validity of these trials. We investigate the impact of allocation bias on testing decisions in clinical trials with multiple endpoints and offer a tool for selecting an appropriate randomization procedure (RP). METHODS: We derive a model for quantifying the effect of allocation bias depending on the RP in the case of two-arm parallel group trials with continuous multiple endpoints. We focus on two approaches to analyze multiple endpoints, either the Sidák procedure to show efficacy in at least one endpoint and the all-or-none procedure to show efficacy in all endpoints. RESULTS: To evaluate the impact of allocation bias on the test decision we propose a biasing policy for multiple endpoints. The impact of allocation on the test decision is measured by the family-wise error rate of the Sidák procedure and the type I error rate of the all-or-none procedure. Using the biasing policy we derive formulas to calculate these error rates. In simulations we show that, for the Sidák procedure as well as for the all-or-none procedure, allocation bias leads to inflation of the mean family-wise error and mean type I error, respectively. The strength of this inflation is affected by the choice of the RP. CONCLUSION: Allocation bias should be considered during the design phase of a trial to increase validity. The developed methodology is useful for selecting an appropriate RP for a clinical trial with multiple endpoints to minimize allocation bias effects.

Establishing Clinical Trial Endpoints in Selecting Patients for RPGR Retinal Gene Therapy.

Purpose: Clinical trials for X-linked retinitis pigmentosa (RP) often assess retinal structure using optical coherence tomography (OCT) and function using microperimetry to evaluate initial eligibility and endpoints. Therefore, we seek to determine which parameters might be most sensitive in screening new patients for enrollment. Methods: Thirty-one patients (62 eyes) with confirmed retinitis pigmentosa GTPase regulator (RPGR) mutations attending Oxford Eye Hospital were included in this retrospective analysis. Outer retinal structure was investigated by measuring the remaining ellipsoid zone (EZ) and external limiting membrane (ELM) on OCT. Visual function was evaluated by using 10-2 microperimetry mean sensitivity. Results: The median age of patients with RPGR was 31 years (interquartile range [IQR] = 22-39 years). For the right and left eyes, respectively, the median EZ length through the foveal section was 921 µm (IQR = 607-1570) and 865 µm (IQR = 508-1442) and median ELM length was 2056 µm (IQR = 1336-2764) and 1860 µm (IQR = 1152-2680). Similarly, the median microperimetry sensitivity (MS) was 2.0 decibel (dB; IQR = 0.4-5.4) and 1.1 dB (IQR = 0.1-5.4). Linear mixed model regression analysis showed that EZ was significantly positively correlated to ELM (P < 0.001, R² = 0.931). EZ and ELM were significantly correlated with the microperimetry sensitivity with exponential relationship (P < 0.001, R² = 0.71 and 0.72, respectively). Using the exponential equation of regression line, EZ below approximately 600 µm (RE = 637 µm, 95% confidence interval [CI] = 397-877, LE = 586 µm, 95% CI = 356-817) results in microperimetry sensitivity of approximately 0 dB. There was high degree of inter-eye symmetry for progression of EZ, ELM, and microperimetry sensitivity. Age was significantly correlated with the analyzed parameters (P < 0.001), although with low R² for each of them. Discussion: EZ may comprise a surrogate biomarker for prediction of visual function in X-linked RP caused by mutations in RPGR and, in turn, identification of appropriate patients for enrollment in clinical trials. As expected, age plays a role in predicting potential biomarkers and visual function, however, it should not be used to preclude patients for gene therapy due to the poor correlation and heterogeneity of disease onset. Translational Relevance: Biomarkers of visual function in RPGR-associated RP may lead to identification of appropriate patients for enrollment in clinical trials.