Global rank tests for multiple, possibly censored, outcomes

Pathway for Development and Validation of Multi-domain Endpoints for Amyloid Light Chain (AL) Amyloidosis

Immunoglobin light chain (AL) amyloidosis is a rare disease in which a plasma cell dyscrasia leads to deposition of insoluble amyloid fibrils in multiple organs. To facilitate development of new therapies for this heterogenous disease, a public-private partnership was formed between the nonprofit Amyloidosis Research Consortium and the US Food and Drug Administration Center for Drug Evaluation and Research. In 2020, the Amyloidosis Forum launched an initiative to identify clinical trial endpoints and analytic strategies across affected organ systems and life impacts via specialized working groups. This review summarizes the proceedings of the Statistical Group and proposes a pathway for development and validation of multi-domain endpoints (MDEs) for potential use in AL amyloidosis clinical trials. Specifically, drawing on candidate domain-specific endpoints recommended by each organ-specific working group, different approaches to constructing MDEs were considered. Future studies were identified to assess the validity, meaningfulness and performance of MDEs through use of natural history and clinical trial data. Ultimately, for drug development, the context of use in a regulatory evaluation, the specific patient population, and the investigational therapeutic mechanism should drive selection of appropriate endpoints. MDEs for AL amyloidosis, once developed and validated, will provide important options for advancing patient-focused drug development in this multi-system disease.

Istore: a project on innovative statistical methodologies to improve rare diseases clinical trials in limited populations

BACKGROUND

The conduct of rare disease clinical trials is still hampered by methodological problems. The number of patients suffering from a rare condition is variable, but may be very small and unfortunately statistical problems for small and finite populations have received less consideration. This paper describes the outline of the iSTORE project, its ambitions, and its methodological approaches.

METHODS

In very small populations, methodological challenges exacerbate. iSTORE's ambition is to develop a comprehensive perspective on natural history course modelling through multiple endpoint methodologies, subgroup similarity identification, and improving level of evidence.

RESULTS

The methodological approaches cover methods for sound scientific modeling of natural history course data, showing similarity between subgroups, defining, and analyzing multiple endpoints and quantifying the level of evidence in multiple endpoint trials that are often hampered by bias.

CONCLUSION

Through its expected results, iSTORE will contribute to the rare diseases research field by providing an approach to better inform about and thus being able to plan a clinical trial. The methodological derivations can be synchronized and transferability will be outlined.

The Effect of Non-Overlapping Somatic Mutations in BRAF, NRAS, NF1, or CKIT on the Incidence and Outcome of Brain Metastases during Immune Checkpoint Inhibitor Therapy of Metastatic Melanoma

Previous studies suggested that somatic BRAF and NRAS mutations in metastatic melanoma increase the risk for brain metastases. The risk related to other non-overlapping "driver" mutations is unknown. We performed a retrospective evaluation of the incidence, timing, and outcome of brain metastases in a population of melanoma patients that underwent uniform next-gen sequencing. All patients were treated with initial checkpoint inhibitor therapy. Seventeen of 88 patients (20.0%) developed brain metastases. Eleven patients had brain metastases at diagnosis (12.9%). These were all patients with BRAF V600 or NF1 mutations. Only six patients with NRAS, NF1, KIT, or BRAF mutations (including fusions/internal rearrangements experienced delayed CNS progression following immunotherapy (7.1%)). No "quadruple negative" patient developed brain metastases. Patients with brain metastases at diagnosis had a better outcome than those with delayed intracranial progression. Current predictive markers, (LDH, tumor mutation burden, and PDL1) were poorly correlated with the development of brain metastases. Treatment with immunotherapy appears to reduce the incidence of brain metastases. Next-gen molecular sequencing of tumors in metastatic melanoma patients was useful in identifying genetic subpopulations with an increased or reduced risk of brain metastases. This may allow eventual personalization of screening strategies.

A neutral comparison of statistical methods for analyzing longitudinally measured ordinal outcomes in rare diseases

Ordinal data in a repeated measures design of a crossover study for rare diseases usually do not allow for the use of standard parametric methods, and hence, nonparametric methods should be considered instead. However, only limited simulation studies in settings with small sample sizes exist. Therefore, starting from an Epidermolysis Bullosa simplex trial with the above-mentioned design, a rank-based approach using the R package nparLD and different generalized pairwise comparisons (GPC) methods were compared impartially in a simulation study. The results revealed that there was not one single best method for this particular design, because a trade-off exists between achieving high power, accounting for period effects, and for missing data. Specifically, nparLD as well as the unmatched GPC approaches do not address crossover aspects, and the univariate GPC variants partly ignore the longitudinal information. The matched GPC approaches, on the other hand, take the crossover effect into account in the sense of incorporating the within-subject association. Overall, the prioritized unmatched GPC method achieved the highest power in the simulation scenarios, although this may be due to the specified prioritization. The rank-based approach yielded good power even at a sample size ofN = 6 $N=6$ , whereas the matched GPC method could not control the type I error.

Composite endpoints, including patient reported outcomes, in rare diseases

BACKGROUND

When assessing the efficacy of a treatment in any clinical trial, it is recommended by the International Conference on Harmonisation to select a single meaningful endpoint. However, a single endpoint is often not sufficient to reflect the full clinical benefit of a treatment in multifaceted diseases, which is often the case in rare diseases. Therefore, the use of a combination of several clinically meaningful outcomes is preferred. Many methodologies that allow for combining outcomes in a so-called composite endpoint are however limited in a number of ways, not in the least in the number and type of outcomes that can be combined and in the poor small-sample properties. Moreover, patient reported outcomes, such as quality of life, often cannot be integrated in a composite analysis, in spite of their intrinsic value.

RESULTS

Recently, a class of non-parametric generalized pairwise comparisons tests have been proposed, which members do allow for any number and type of outcomes, including patient reported outcomes. The class enjoys good small-sample properties. Moreover, this very flexible class of methods allows for prioritizing the outcomes by clinical severity, allows for matched designs and for adding a threshold of clinical relevance. Our aim is to introduce the generalized pairwise comparison ideas and concepts for rare disease clinical trial analysis, and demonstrate their benefit in a post-hoc analysis of a small-sample trial in epidermolysis bullosa. More precisely, we will include a patient relevant outcome (Quality of life), in a composite endpoint. This publication is part of the European Joint Programme on Rare Diseases (EJP RD) series on innovative methodologies for rare diseases clinical trials, which is based on the webinars presented within the educational activity of EJP RD. This publication covers the webinar topic on composite endpoints in rare diseases and includes participants' response to a questionnaire on this topic.

CONCLUSIONS

Generalized pairwise comparisons is a promising statistical methodology for evaluating any type of composite endpoints in rare disease trials and may allow a better evaluation of therapy efficacy including patients reported outcomes in addition to outcomes related to the diseases signs and symptoms.

On the use of extreme value tail modeling for generalized pairwise comparisons with censored outcomes

In randomized clinical trials, methods of pairwise comparisons such as the 'Net Benefit' or the 'win ratio' have recently gained much attention when interests lies in assessing the effect of a treatment as compared to a standard of care. Among other advantages, these methods are usually praised for delivering a treatment measure that can easily handle multiple outcomes of different nature, while keeping a meaningful interpretation for patients and clinicians. For time-to-event outcomes, a recent suggestion emerged in the literature for estimating these treatment measures by providing a natural handling of censored outcomes. However, this estimation procedure may lead to biased estimates when tails of survival functions cannot be reliably estimated using Kaplan-Meier estimators. The problem then extrapolates to the other outcomes incorporated in the pairwise comparison construction. In this work, we suggest to extend the procedure by the consideration of a hybrid survival function estimator that relies on an extreme value tail model through the Generalized Pareto distribution. We provide an estimator of treatment effect measures that notably improves on bias and remains easily apprehended for practical implementation. This is illustrated in an extensive simulation study as well as in an actual trial of a new cancer immunotherapy.

Impact of correlations between prioritized outcomes on the net benefit and its estimate by generalized pairwise comparisons

Benefit-risk balance is gaining interest in clinical trials. For the comprehensive assessment of benefits and risks, generalized pairwise comparisons are increasingly used to estimate the net benefit based on multiple prioritized outcomes. Although previous research has demonstrated that the correlations between the outcomes impact the net benefit and its estimate, the direction and magnitude of this impact remain unclear. In this study, we investigated the impact of correlations between two binary or Gaussian variables on the true net benefit values via theoretical and numerical analyses. We also explored the impact of correlations between survival and categorical variables on the net benefit estimates based on four existing methods (Gehan, Péron, Gehan with correction, and Péron with correction) in the presence of right censoring via simulation and application to actual oncology clinical trial data. Our theoretical and numerical analyses revealed that the true net benefit values were impacted by the correlations in various directions depending on the outcome distributions. With binary endpoints, this direction was governed by a simple rule with a threshold of 50% for a favorable outcome. Our simulation showed that the net benefit estimates based on Gehan's or Péron's scoring rule could be substantially biased in the presence of right censoring, and that the direction and magnitude of this bias were associated with the outcome correlations. The recently proposed correction method greatly reduced this bias, even in the presence of strong outcome correlations. The impact of correlations should be carefully considered when interpreting the net benefit and its estimate.

Generalized pairwise comparisons for censored data: An overview

The method of generalized pairwise comparisons (GPC) is an extension of the well-known nonparametric Wilcoxon-Mann-Whitney test for comparing two groups of observations. Multiple generalizations of Wilcoxon-Mann-Whitney test and other GPC methods have been proposed over the years to handle censored data. These methods apply different approaches to handling loss of information due to censoring: ignoring noninformative pairwise comparisons due to censoring (Gehan, Harrell, and Buyse); imputation using estimates of the survival distribution (Efron, Péron, and Latta); or inverse probability of censoring weighting (IPCW, Datta and Dong). Based on the GPC statistic, a measure of treatment effect, the "net benefit," can be defined. It quantifies the difference between the probabilities that a randomly selected individual from one group is doing better than an individual from the other group. This paper aims at evaluating GPC methods for censored data, both in the context of hypothesis testing and estimation, and providing recommendations related to their choice in various situations. The methods that ignore uninformative pairs have comparable power to more complex and computationally demanding methods in situations of low censoring, and are slightly superior for high proportions (>40%) of censoring. If one is interested in estimation of the net benefit, Harrell's c index is an unbiased estimator if the proportional hazards assumption holds. Otherwise, the imputation (Efron or Peron) or IPCW (Datta, Dong) methods provide unbiased estimators in case of proportions of drop-out censoring up to 60%.

Individualized Net Benefit estimation and meta-analysis using generalized pairwise comparisons in N-of-1 trials

BACKGROUND

The Net Benefit (Δ) is a measure of the benefit-risk balance in clinical trials, based on generalized pairwise comparisons (GPC) using several prioritized outcomes and thresholds of clinical relevance. We extended Δ to N-of-1 trials, with a focus on patient-level and population-level Δ.

METHODS

We developed a Δ estimator at the individual level as an extension of the stratum-specific Δ, and at the population-level as an extension of the stratified Δ. We performed a simulation study mimicking PROFIL, a series of 38 N-of-1 trials testing sildenafil in Raynaud's phenomenon, to assess the power for such an analysis with realistic data. We then reanalyzed PROFIL using GPC. This reanalysis was finally interpreted in the context of the main analysis of PROFIL which used Bayesian individual probabilities of efficacy.

RESULTS

Simulations under the null showed good size of the test for both individual and population levels. The test lacked power when being simulated from the true PROFIL data, even when increasing the number of repetitions up to 140 days per patient. PROFIL individual-level estimated Δ were well correlated with the probabilities of efficacy from the Bayesian analysis while showing similarly wide confidence intervals. Population-level estimated Δ was not significantly different from zero, consistently with the previous Bayesian analysis.

CONCLUSION

GPC can be used to estimate individual Δ which can then be aggregated in a meta-analytic way in N-of-1 trials. GPC ability to easily incorporate patient preferences allow for more personalized treatment evaluation, while needing much less computing time than Bayesian modeling.

Evaluating Treatment Efficacy by Combining Multiple Measures in Clinical Trial Applications

A variety of clinical and laboratory measures can be used in clinical trials to assess the benefit of a new treatment over the standard of care. Data from clinical studies are often analyzed by combining individual outcomes into one primary outcome. That primary outcome is then referred to as a composite endpoint or a combined endpoint. We propose an analysis on the composite endpoint with Gehan's (1965) ranking approach where each subject in the treatment group is compared with each subject in the control group in a pair-wise manner. Our approach reduces computational time and complexity to construct a subject-level pairwise composite score. We develop a statistical testing procedure for the analysis of composite endpoints when using the hierarchical scores. In this article, we propose two tests (a parametric test and a non-parametric bootstrap procedure) for evaluating the effect of treatment. The proposed parametric test has an asymptotic F-distribution based on standard statistical assumptions. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods and to compare them with an existing method. We illustrate the methods using publicly available data from two clinical studies.

Semiparametric regression modeling of the global percentile outcome

When no single outcome is sufficient to capture the multidimensional impairments of a disease, investigators often rely on multiple outcomes for comprehensive assessment of global disease status. Methods for assessing covariate effects on global disease status include the composite outcome and global test procedures. One global test procedure is the O'Brien's rank-sum test, which combines information from multiple outcomes using a global rank-sum score. However, existing methods for the global rank-sum do not lend themselves to regression modeling. We consider sensible regression strategies for the global percentile outcome (GPO), under the transformed linear model and the monotonic index model. Posing minimal assumptions, we develop estimation and inference procedures that account for the special features of the GPO. Asymptotics are established using U-statistic and U-process techniques. We illustrate the practical utilities of the proposed methods via extensive simulations and application to a Parkinson's disease study.

Sample size formula for general win ratio analysis

Originally proposed for the analysis of prioritized composite endpoints, the win ratio has now expanded into a broad class of methodology based on general pairwise comparisons. Complicated by the non-i.i.d. structure of the test statistic, however, sample size estimation for the win ratio has lagged behind. In this article, we develop general and easy-to-use formulas to calculate sample size for win ratio analysis of different outcome types. In a nonparametric setting, the null variance of the test statistic is derived using U-statistic theory in terms of a dispersion parameter called the standard rank deviation, an intrinsic characteristic of the null outcome distribution and the user-defined rule of comparison. The effect size can be hypothesized either on the original scale of the population win ratio, or on the scale of a "usual" effect size suited to the outcome type. The latter approach allows one to measure the effect size by, for example, odds/continuation ratio for totally/partially ordered outcomes and hazard ratios for composite time-to-event outcomes. Simulation studies show that the derived formulas provide accurate estimates for the required sample size across different settings. As illustration, real data from two clinical studies of hepatic and cardiovascular diseases are used as pilot data to calculate sample sizes for future trials.

Calculating power for the Finkelstein and Schoenfeld test statistic for a composite endpoint with two components

The Finkelstein and Schoenfeld (FS) test is a popular generalized pairwise comparison approach to analyze prioritized composite endpoints (eg, components are assessed in order of clinical importance). Power and sample size estimation for the FS test, however, are generally done via simulation studies. This simulation approach can be extremely computationally burdensome, compounded by increasing number of composite endpoints and with increasing sample size. Here we propose an analytical solution to calculate power and sample size for commonly encountered two-component hierarchical composite endpoints. The power formulas are derived assuming underlying distributions in each of the component outcomes on the population level, which provide a computationally efficient and practical alternative to the standard simulation approach. Monte Carlo simulation results demonstrate that performance of the proposed power formulas are consistent with that of the simulation approach, and have generally desirable objective properties including robustness to mis-specified distributional assumptions. We demonstrate the application of the proposed formulas by calculating power and sample size for the Transthyretin Amyloidosis Cardiomyopathy Clinical Trial.

Event-specific win ratios for inference with terminal and non-terminal events

For semi-competing risks data involving a non-terminal event and a terminal event we derive the asymptotic distributions of the event-specific win ratios under proportional hazards (PH) assumptions for the relevant cause-specific hazard functions of the non-terminal and terminal event, respectively. The win ratios converge to the respective hazard ratios under the PH assumptions and therefore are censoring-free, whether or not the censoring distributions in the two treatment arms are the same. With the asymptotic bivariate normal distributions of the win ratios, confidence intervals and testing procedures are obtained. Through extensive simulation studies and data analysis, we identified proper transformations of the win ratios that yield good control of the type one error rate for various testing procedures while maintaining competitive power. The confidence intervals also have good coverage probabilities. Furthermore, a test for the PH assumptions and a test of equal hazard ratios are developed. The new procedures are illustrated in the clinical trial Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function, which evaluated the effects of spironolactone in patients with heart failure and a preserved left ventricular ejection fraction.

Constraint-Induced Movement Therapy for Cerebral Palsy: A Randomized Trial

OBJECTIVES

With the Children with Hemiparesis Arm and Hand Movement Project (CHAMP) multisite factorial randomized controlled trial, we compared 2 doses and 2 constraint types of constraint-induced movement therapy (CIMT) to usual customary treatment (UCT).

METHODS

CHAMP randomly assigned 118 2- to 8-year-olds with hemiparetic cerebral palsy to one of 5 treatments with assessments at baseline, end of treatment, and 6 months posttreatment. Primary blinded outcomes were the assisting hand assessment; Peabody Motor Development Scales, Second Edition, Visual Motor Integration; and Quality of Upper Extremity Skills Test Dissociated Movement. Parents rated functioning on the Pediatric Evaluation of Disabilities Inventory-Computer Adaptive Test Daily Activities and Child Motor Activity Log How Often scale. Analyses were focused on blinded and parent-report outcomes and rank-order gains across all measures.

RESULTS

Findings varied in statistical significance when analyzing individual blinded outcomes. parent reports, and rank-order gains. Consistently, high-dose CIMT, regardless of constraint type, produced a pattern of greatest short- and long-term gains (1.7% probability of occurring by chance alone) and significant gains on visual motor integration and dissociated movement at 6 months. O'Brien's rank-order analyses revealed high-dose CIMT produced significantly greater improvement than a moderate dose or UCT. All CIMT groups improved significantly more in parent-reported functioning, compared with that of UCT. Children with UCT also revealed objective gains (eg, 48% exceeded the smallest-detectable assisting hand assessment change, compared with 71% high-dose CIMT at the end of treatment).

CONCLUSIONS

CHAMP provides novel albeit complex findings: although most individual blinded outcomes fell below statistical significance for group differences, high-dose CIMT consistently produced the largest improvements at both time points. An unexpected finding concerns shifts in UCT toward higher dosages, with improved outcomes compared with previous reports.

Unbiasedness and efficiency of non-parametric and UMVUE estimators of the probabilistic index and related statistics

In reliability theory, diagnostic accuracy, and clinical trials, the quantity P(X>Y)+1/2P(X=Y), also known as the Probabilistic Index (PI), is a common treatment effect measure when comparing two groups of observations. The quantity P(X>Y)-P(Y>X), a linear transformation of PI known as the net benefit, has also been advocated as an intuitively appealing treatment effect measure. Parametric estimation of PI has received a lot of attention in the past 40 years, with the formulation of the Uniformly Minimum-Variance Unbiased Estimator (UMVUE) for many distributions. However, the non-parametric Mann-Whitney estimator of the PI is also known to be UMVUE in some situations. To understand this seeming contradiction, in this paper a systematic comparison is performed between the non-parametric estimator for the PI and parametric UMVUE estimators in various settings. We show that the Mann-Whitney estimator is always an unbiased estimator of the PI with univariate, completely observed data, while the parametric UMVUE is not when the distribution is misspecified. Additionally, the Mann-Whitney estimator is the UMVUE when observations belong to an unrestricted family. When observations come from a more restrictive family of distributions, the loss in efficiency for the non-parametric estimator is limited in realistic clinical scenarios. In conclusion, the Mann-Whitney estimator is simple to use and is a reliable estimator for the PI and net benefit in realistic clinical scenarios.

Event-specific win ratios and testing with terminal and non-terminal events

BACKGROUND/AIMS

In clinical trials, the primary outcome is often a composite endpoint defined as time to the first occurrence of either death or certain non-fatal events. Thus, a portion of available data would be omitted. In the win ratio approach, priorities are given to the clinically more important events, and more data are used. However, its power may be low if the treatment effect is predominantly on the non-terminal event.

METHODS

We propose event-specific win ratios obtained separately on the terminal and non-terminal events. They can then be used to form global tests such as a linear combination test, the maximum test, or a χ2 test.

RESULTS

In simulations, these tests often improve the power of the original win ratio test. Furthermore, when the terminal and non-terminal events experience differential treatment effects, the new tests are often more powerful than the log-rank test for the composite outcome. Whether the treatment effect is primarily on the terminal events or not, the new tests based on the event-specific win ratios can be useful when different types of events are present. The new tests can reject the null hypothesis of no difference in the event distributions in the two treatment arms with the terminal event showing detrimental effect and the non-terminal event showing beneficial effect. The maximum test and the χ2 test do not have test-estimation coherency, but the maximum test has the coherency that the global null is rejected if and only if the null for one of the event types is rejected. When applied to data from the trial Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function (TOPCAT), the new tests all reject the null hypothesis of no treatment effect while both the log-rank test used in TOPCAT and the original win ratio approach show non-significant p-values.

CONCLUSION

Whether the treatment effect is primarily on the terminal events or the non-terminal events, the maximum test based on the event-specific win ratios can be a useful alternative for testing treatment effect in clinical trials with time-to-event outcomes when different types of events are present.

A framework for considering the risk-benefit trade-off in designing noninferiority trials using composite outcome approaches

When a new treatment regimen is expected to have comparable or slightly worse efficacy to that of the control regimen but has benefits in other domains such as safety and tolerability, a noninferiority (NI) trial may be appropriate but is fraught with difficulty in justifying an acceptable NI margin that is based on both clinical and statistical input. To overcome this, we propose to utilize composite risk-benefit outcomes that combine elements from domains of importance (eg, efficacy, safety, and tolerability). The composite outcome itself may be analyzed using a superiority framework, or it can be used as a tool at the design stage of a NI trial for selecting an NI margin for efficacy that balances changes in risks and benefits. In the latter case, the choice of NI margin may be based on a novel quantity called the maximum allowable decrease in efficacy (MADE), defined as the marginal difference in efficacy between arms that would yield a null treatment effect for the composite outcome given an assumed distribution for the composite outcome. We observe that MADE: (1) is larger when the safety improvement for the experimental arm is larger, (2) depends on the association between the efficacy and safety outcomes, and (3) depends on the control arm efficacy rate. We use a numerical example for power comparisons between a superiority test for the composite outcome vs a noninferiority test for efficacy using the MADE as the NI margin, and apply the methods to a TB treatment trial.

Aspirin versus low-molecular-weight heparin for venous thromboembolism prophylaxis in orthopaedic trauma patients: A patient-centered randomized controlled trial

Background

Emerging evidence suggests aspirin may be an effective venous thromboembolism (VTE) prophylaxis for orthopaedic trauma patients, with fewer bleeding complications. We used a patient-centered weighted composite outcome to globally evaluate aspirin versus low-molecular-weight heparin (LMWH) for VTE prevention in fracture patients.

Methods

We conducted an open-label randomized clinical trial of adult patients admitted to an academic trauma center with an operative extremity fracture, or a pelvis or acetabular fracture. Patients were randomized to receive LMWH (enoxaparin 30-mg) twice daily (n = 164) or aspirin 81-mg twice daily (n = 165). The primary outcome was a composite endpoint of bleeding complications, deep surgical site infection, deep vein thrombosis, pulmonary embolism, and death within 90 days of injury. A Global Rank test and weighted time to event analysis were used to determine the probability of treatment superiority for LMWH, given a 9% patient preference margin for oral administration over skin injections.

Results

Overall, 18 different combinations of outcomes were experienced by patients in the study. Ninety-nine patients in the aspirin group (59.9%) and 98 patients in the LMWH group (59.4%) were event-free within 90 days of injury. Using a Global Rank test, the LMWH had a 50.4% (95% CI, 47.7–53.2%, p = 0.73) probability of treatment superiority over aspirin. In the time to event analysis, LMWH had a 60.5% probability of treatment superiority over aspirin with considerable uncertainty (95% CI, 24.3–88.0%, p = 0.59).

Conclusion

The findings of the Global Rank test suggest no evidence of superiority between LMWH or aspirin for VTE prevention in fracture patients. LMWH demonstrated a 60.5% VTE prevention benefit in the weighted time to event analysis. However, this difference did not reach statistical significance and was similar to the elicited patient preferences for aspirin.

Repeated 5-day cycles of low dose aldesleukin in amyotrophic lateral sclerosis (IMODALS): A phase 2a randomised, double-blind, placebo-controlled trial

Background

Low-dose interleukin-2 (ld-IL-2) enhances regulatory T-cell (Treg) function in auto-inflammatory conditions. Neuroinflammation being a pathogenic feature of amyotrophic lateral sclerosis (ALS), we evaluated the pharmacodynamics and safety of ld-IL-2 in ALS subjects.

Methods

We performed a single centre, parallel three-arm, randomised, double-blind, placebo-controlled study. Eligibility criteria included age < 75 years, disease duration < 5 years, riluzole treatment > 3 months, and a slow vital capacity ≥ 70% of normal. Patients were randomised (1:1:1) to aldesleukin 2 MIU, 1 MIU, or placebo once daily for 5 days every 4 weeks for 3 cycles. Primary outcome was change from baseline in Treg percentage of CD4⁺ T cells (%Tregs) following a first cycle. Secondary laboratory outcomes included: %Treg and Treg number following repeated cycles, and plasma CCL2 and neurofilament light chain protein (NFL) concentrations as surrogate markers of efficacy. Safety outcomes included motor-function (ALSFRS-R), slow vital capacity (SVC), and adverse event reports. This trial is registered with ClinicalTrials.gov, NCT02059759.

Findings

All randomised patients (12 per group), recruited from October 2015 to December 2015, were alive at the end of follow-up and included in the intent-to-treat (ITT) analysis. No drug-related serious adverse event was observed. Non-serious adverse events occurred more frequently with the 1 and 2 MIU IL-2 doses compared to placebo, including injection site reactions and flu-like symptoms. Primary outcome analysis showed a significant increase (p < 0·0001) in %Tregs in the 2 MIU and 1 MIU arms (mean [SD]: 2 MIU: +6·2% [2·2]; 1 MIU: +3·9% [1·2]) as compared to placebo (mean [SD]: -0·49% [1·3]). Effect sizes (ES) were large in treated groups: 2 MIU ES=3·7 (IC95%: 2·3–4·9) and 1 MIU ES=3·5 (IC95%: 2·1–4·6). Secondary outcomes showed a significant increase in %Tregs following repeated cycles (p < 0·0001) as compared to placebo, and a dose-dependent decrease in plasma CCL2 (p = 0·0049). There were no significant differences amongst the three groups on plasma NFL levels.

Interpretation

Ld-IL-2 is well tolerated and immunologically effective in subjects with ALS. These results warrant further investigation into their eventual therapeutic impact on slowing ALS disease progression.

Funding

: The French Health Ministry (PHRC-I-14-056), EU H2020 (grant #633413), and the Association pour la Recherche sur la SLA (ARSLA).

Applying a Risk-benefit Analysis to Outcomes in Tuberculosis Clinical Trials

Although it is common to analyze efficacy and safety separately in clinical trials, this could yield a misleading study conclusion if an increase in efficacy is accompanied by a decrease in safety. A risk-benefit analysis is a systematic approach to examine safety and efficacy jointly. Both the "rank-based" and "partial-credit" methods described in this paper allow researchers to create a single, composite outcome incorporating efficacy, safety, and other factors. The first approach compares the distribution of rankings between arms. In the second approach, a score can be assigned to each outcome category, considering its severity and comparing the mean or median scores of arms. The methods were applied to the A5279/Brief Rifapentine-Isoniazid Efficacy for TB Prevention study, and design considerations for future clinical trials are discussed, including the challenge of arriving at a consensus on rankings/scorings. If well designed, a risk-benefit analysis may allow for a superiority comparison and, therefore, avoid setting a noninferiority margin. Clinical Trials Registration. NCT01404312 (A5279).

Multistate modeling and simulation of patient trajectories after allogeneic hematopoietic stem cell transplantation to inform drug development

We present a case study for developing clinical trial scenarios in a complex progressive disease with multiple events of interest. The idea is to first capture the course of the disease in a multistate Markov model, and then to simulate clinical trials from this model, including a variety of hypothesized drug effects. This case study focuses on the prevention of graft-versus-host disease (GvHD) after allogeneic hematopoietic stem cell transplantation (HSCT). The patient trajectory after HSCT is characterized by a complex interplay of various events of interest, and there is no established best method of measuring and/or analyzing treatment benefits. We characterized patient trajectories by means of multistate models that we fitted to a subset of the Center for International Blood and Marrow Transplant Research (CIBMTR) database. Events of interest included acute GvHD of grade III or IV, severe chronic GvHD, relapse of the underlying disease, and death. The transition probability matrix was estimated using the Aalen-Johansen estimator, and patient characteristics were identified that were associated with different transition rates. In a second step, clinical trial scenarios were simulated from the model assuming various drug effects on the background transition rates, and the operating characteristics of different endpoints and analysis strategies were compared in these scenarios. This helped devise a drug development strategy in GvHD prevention after allogeneic HSCT. More generally, multistate models provide a rich framework for exploring complex progressive diseases, and the availability of a corresponding simulation machinery provides great flexibility for clinical trial planning.

Aortic Stenosis and Heart Failure: Disease Ascertainment and Statistical Considerations for Clinical Trials

Aortic stenosis is a progressive disease that develops over decades, and once symptomatic and untreated, is associated with poor survival. Transcatheter aortic valve replacement has evolved significantly in the past decade and has expanded its indication from surgically inoperable and high-risk patients to patients with intermediate risk. Assessment of heart failure-related outcomes include the use of functional assessments, disease-specific quality of life surveys and standardised ascertainment of events, such as hospitalisations. Multiple statistical approaches are currently being tested to account for recurrent events such as hospitalisations for heart failure or to combine binary and continuous outcomes, both intended to assess the holistic burden of the disease, as opposed to the traditional analysis of time to first event.

Integrating Parents in Neonatal and Pediatric Research

BACKGROUND

Parents and their infants are the beneficiaries of neonatal and pediatric research, but in the past they have been excluded from most stages of research projects. As a result, many projects may fail to produce the most worthwhile information for parents and families. Lately, veteran resource parents and patients have been increasingly integrated in research initiatives.

METHODS

Benchmarking of neonatal and pediatric research initiatives where resource parents and/or ex neonatal patients have helped to optimize pediatric research. We review ways in which resource parents/patients can be involved in research, with examples and practical ideas of how to proceed.

RESULTS

Resource parents/patients can be collaborators in research and be integrated in many steps: prioritizing research projects, designing trials, determining the outcomes of interest, ethics review, developing and improving consent procedures, collection and interpretation of data, participation in data safety monitoring committees, publication of results, and presentation to peer groups. Some of the strategies for integration of stakeholders in clinical research are more complex, may involve risk and require more training than others.

CONCLUSION

We suggest that groups wanting to involve parents in their research endeavors start with simpler tasks that entail less risk and develop teams of resource parents who have differing interests and abilities. Quality control of programs is essential, such as frequently giving and obtaining feedback from resource parents/patients and researchers. In the future, integration of resource parents/patients into every step of clinical research will be essential to ensure that parent and family important outcomes are examined.

Methods for the analysis of multiple endpoints in small populations: A review

While current guidelines generally recommend single endpoints for primary analyses of confirmatory clinical trials, it is recognized that certain settings require inference on multiple endpoints for comprehensive conclusions on treatment effects. Furthermore, combining treatment effect estimates from several outcome measures can increase the statistical power of tests. Such an efficient use of resources is of special relevance for trials in small populations. This paper reviews approaches based on a combination of test statistics or measurements across endpoints as well as multiple testing procedures that allow for confirmatory conclusions on individual endpoints. We especially focus on feasibility in trials with small sample sizes and do not solely rely on asymptotic considerations. A systematic literature search in the Scopus database, supplemented by a manual search, was performed to identify research papers on analysis methods for multiple endpoints with relevance to small populations. The identified methods were grouped into approaches that combine endpoints into a single measure to increase the power of statistical tests and methods to investigate differential treatment effects in several individual endpoints by multiple testing.

An optimal Wilcoxon-Mann-Whitney test of mortality and a continuous outcome

We consider a two-group randomized clinical trial, where mortality affects the assessment of a follow-up continuous outcome. Using the worst-rank composite endpoint, we develop a weighted Wilcoxon-Mann-Whitney test statistic to analyze the data. We determine the optimal weights for the Wilcoxon-Mann-Whitney test statistic that maximize its power. We derive a formula for its power and demonstrate its accuracy in simulations. Finally, we apply the method to data from an acute ischemic stroke clinical trial of normobaric oxygen therapy.

Cross-validation of optimized composites for preclinical Alzheimer's disease

Introduction

We discuss optimization and validation of composite end points for presymptomatic Alzheimer's disease clinical trials. Optimized composites offer hope of substantial gains in statistical power or reduction in sample size. But there is tradeoff between optimization and face validity such that optimization should only be considered if there is a convincing rationale. As with statistically derived regions of interest in neuroimaging, validation on independent data sets is essential.

Methods

Using four data sets, we consider the optimized weighting of four components of a cognitive composite which includes measures of (1) global cognition, (2) semantic memory, (3) episodic memory, and (4) executive function. Weights are optimized to either discriminate amyloid positivity or maximize power to detect a treatment effect in an amyloid-positive population. We apply repeated 5 × 3-fold cross-validation to quantify the out-of-sample performance of optimized composite end points.

Results

We found the optimized weights varied greatly across the folds of the cross-validation with either optimization method. Both optimization methods tend to down-weight the measures of global cognition and executive function. However, when these optimized composites were applied to the validation sets, they did not provide consistent improvements in power. In fact, overall, the optimized composites performed worse than those without optimization.

Discussion

We find that component weight optimization does not yield valid improvements in sensitivity of this composite to detect treatment effects.