Employing a latent variable framework to improve efficiency in composite endpoint analysis

Extracorporeal photopheresis (ECP) in the treatment of chronic lung allograft dysfunction (CLAD): a prospective, multicentre, open-label, randomised controlled trial studying the addition of ECP to standard care in the treatment of bilateral lung transplant patients with CLAD (E-CLAD UK)

BACKGROUND

Long-term survival after lung transplantation is limited compared with other organ transplants. The main cause is development of progressive immune-mediated damage to the lung allograft. This damage, which can develop via multiple immune pathways, is captured under the umbrella term chronic lung allograft dysfunction (CLAD). Despite the availability of powerful immunosuppressive drugs, there are presently no treatments proven to reverse or reliably halt the loss of lung function caused by CLAD. The aim of the E-CLAD UK trial is to determine whether the addition of immunomodulatory therapy, in the form of extracorporeal photopheresis (ECP), to standard care is more efficacious at stabilising lung function in CLAD compared with standard care alone.

METHODS AND ANALYSIS

E-CLAD UK is a Phase II clinical trial of an investigational medicinal product (Methoxsalen) delivered to a buffy coat prepared via an enclosed ECP circuit. Target recruitment is 90 bilateral lung transplant patients identified as having CLAD and being treated at one of the five UK adult lung transplant centres. Participants will be randomised 1:1 to intervention plus standard of care, or standard of care alone. Intervention will comprise nine ECP cycles spread over 20 weeks, each course involving two treatments of ECP on consecutive days. All participants will be followed up for a period of 24 weeks.The primary outcome is lung function stabilisation derived from change in forced expiratory volume in one second and forced vital capacity at 12 and 24 weeks compared with baseline at study entry. Other parameters include change in exercise capacity, health-related quality of life and safety. A mechanistic study will seek to identify molecular or cellular markers linked to treatment response and qualitative interviews will explore patient experiences of CLAD and the ECP treatment.A patient and public advisory group is integral to the trial from design to implementation, developing material to support the consent process and interview materials.

ETHICS AND DISSEMINATION

The East Midlands-Derby Research Ethics Committee has provided ethical approval (REC 22/EM/0218). Dissemination will be via publications, patient-friendly summaries and presentation at scientific meetings.

TRIAL REGISTRATION NUMBER

EudraCT number 2022-002659-20; ISRCTN 10615985.

Association of Neighborhood Deprivation With Healthcare Utilization Among Persons With Human Immunodeficiency Virus: A Latent Class Analysis

Background

We previously identified 3 latent classes of healthcare utilization among people with human immunodeficiency virus (PWH): adherent, nonadherent, and sick. Although membership in the "nonadherent" group was associated with subsequent disengagement from human immunodeficiency virus (HIV) care, socioeconomic predictors of class membership remain unexplored.

Methods

We validated our healthcare utilization-based latent class model of PWH receiving care at Duke University (Durham, North Carolina) using patient-level data from 2015 to 2018. SDI scores were assigned to cohort members based on residential addresses. Associations of patient-level covariates with class membership were estimated using multivariable logistic regression and movement between classes was estimated using latent transition analysis.

Results

A total of 1443 unique patients (median age of 50 years, 28% female sex at birth, 57% Black) were included in the analysis. PWH in the most disadvantaged (highest) SDI decile were more likely to be in the "nonadherent" class than the remainder of the cohort (odds ratio [OR], 1.58 [95% confidence interval {CI}, .95-2.63]) and were significantly more likely to be in the "sick" class (OR, 2.65 [95% CI, 2.13-3.30]). PWH in the highest SDI decile were also more likely to transition into and less likely to transition out of the "sick" class.

Conclusions

PWH who resided in neighborhoods with high levels of social deprivation were more likely to have latent class membership in suboptimal healthcare utilization groupings, and membership persisted over time. Risk stratification models based on healthcare utilization may be useful tools in the early identification of persons at risk for suboptimal HIV care engagement.

Sample size estimation using a latent variable model for mixed outcome co-primary, multiple primary and composite endpoints

Mixed outcome endpoints that combine multiple continuous and discrete components are often employed as primary outcome measures in clinical trials. These may be in the form of co-primary endpoints, which conclude effectiveness overall if an effect occurs in all of the components, or multiple primary endpoints, which require an effect in at least one of the components. Alternatively, they may be combined to form composite endpoints, which reduce the outcomes to a one-dimensional endpoint. There are many advantages to joint modeling the individual outcomes, however in order to do this in practice we require techniques for sample size estimation. In this article we show how the latent variable model can be used to estimate the joint endpoints and propose hypotheses, power calculations and sample size estimation methods for each. We illustrate the techniques using a numerical example based on a four-dimensional endpoint and find that the sample size required for the co-primary endpoint is larger than that required for the individual endpoint with the smallest effect size. Conversely, the sample size required in the multiple primary case is similar to that needed for the outcome with the largest effect size. We show that the empirical power is achieved for each endpoint and that the FWER can be sufficiently controlled using a Bonferroni correction if the correlations between endpoints are less than 0.5. Otherwise, less conservative adjustments may be needed. We further illustrate empirically the efficiency gains that may be achieved in the composite endpoint setting.

Improving power in PSA response analyses of metastatic castration-resistant prostate cancer trials

BACKGROUND

To determine how much an augmented analysis approach could improve the efficiency of prostate-specific antigen (PSA) response analyses in clinical practice. PSA response rates are commonly used outcome measures in metastatic castration-resistant prostate cancer (mCRPC) trial reports. PSA response is evaluated by comparing continuous PSA data (e.g., change from baseline) to a threshold (e.g., 50% reduction). Consequently, information in the continuous data is discarded. Recent papers have proposed an augmented approach that retains the conventional response rate, but employs the continuous data to improve precision of estimation.

METHODS

A literature review identified published prostate cancer trials that included a waterfall plot of continuous PSA data. This continuous data was extracted to enable the conventional and augmented approaches to be compared.

RESULTS

Sixty-four articles, reporting results for 78 mCRPC treatment arms, were re-analysed. The median efficiency gain from using the augmented analysis, in terms of the implied increase to the sample size of the original study, was 103.2% (IQR [89.8,190.9%]).

CONCLUSIONS

Augmented PSA response analysis requires no additional data to be collected and can be performed easily using available software. It improves precision of estimation to a degree that is equivalent to a substantial sample size increase. The implication of this work is that prostate cancer trials using PSA response as a primary endpoint could be delivered with fewer participants and, therefore, more rapidly with reduced cost.

Increasing power in the analysis of responder endpoints in rheumatology: a software tutorial

BACKGROUND

Composite responder endpoints feature frequently in rheumatology due to the multifaceted nature of many of these conditions. Current analysis methods used to analyse these endpoints discard much of the data used to classify patients as responders and are therefore highly inefficient, resulting in low power. We highlight a novel augmented methodology that uses more of the information available to improve the precision of reported treatment effects. Since these methods are more challenging to implement, we developed free, user-friendly software available in a web-based interface and as R packages. The software consists of two programs: one that supports the analysis of responder endpoints; the second that facilitates sample size estimation. We demonstrate the use of the software to conduct the analysis with both the augmented and standard analysis method using the MUSE study, a phase IIb trial in patients with systemic lupus erythematosus.

RESULTS

The software outputs similar point estimates with smaller confidence intervals for the odds ratio, risk ratio and risk difference estimators using the augmented approach. The sample size required in each arm for a future trial using the novel approach based on the MUSE data is 50 versus 135 for the standard method, translating to a reduction in required sample size of approximately 63%.

CONCLUSIONS

We encourage trialists to use the software demonstrated to implement the augmented methodology in future studies to improve efficiency.

Innovative trial approaches in immune-mediated inflammatory diseases: current use and future potential

BACKGROUND

Despite progress that has been made in the treatment of many immune-mediated inflammatory diseases (IMIDs), there remains a need for improved treatments. Randomised controlled trials (RCTs) provide the highest form of evidence on the effectiveness of a potential new treatment regimen, but they are extremely expensive and time consuming to conduct. Consequently, much focus has been given in recent years to innovative design and analysis methods that could improve the efficiency of RCTs. In this article, we review the current use and future potential of these methods within the context of IMID trials.

METHODS

We provide a review of several innovative methods that would provide utility in IMID research. These include novel study designs (adaptive trials, Sequential Multi-Assignment Randomised Trials, basket, and umbrella trials) and data analysis methodologies (augmented analyses of composite responder endpoints, using high-dimensional biomarker information to stratify patients, and emulation of RCTs from routinely collected data). IMID trials are now well-placed to embrace innovative methods. For example, well-developed statistical frameworks for adaptive trial design are ready for implementation, whilst the growing availability of historical datasets makes the use of Bayesian methods particularly applicable. To assess whether and how these innovative methods have been used in practice, we conducted a review via PubMed of clinical trials pertaining to any of 51 IMIDs that were published between 2018 and 20 in five high impact factor clinical journals.

RESULTS

Amongst 97 articles included in the review, 19 (19.6%) used an innovative design method, but most of these were relatively straightforward examples of innovative approaches. Only two (2.1%) reported the use of evidence from routinely collected data, cohorts, or biobanks. Eight (9.2%) collected high-dimensional data.

CONCLUSIONS

Application of innovative statistical methodology to IMID trials has the potential to greatly improve efficiency, to generalise and extrapolate trial results, and to further personalise treatment strategies. Currently, such methods are infrequently utilised in practice. New research is required to ensure that IMID trials can benefit from the most suitable methods.