Trials for neurodegenerative diseases:time to innovate

The remarkable progress in our understanding of the mechanisms underlying neurodegenerative diseases heralds an era when neurologists would be at the vanguard of regenerative medicine, instead of chroniclers of decline. To capitalise on these advances that are identifying ever more therapeutic candidates, whether repurposed or entirely new, there is an urgent need for refined methods to test these putative medicines in clinical trials. Our field has the opportunity to learn from innovations in trial design, particularly those pioneered in oncology

The DURATIONS randomised trial design: estimation targets, analysis methods and operating characteristics

Background. Designing trials to reduce treatment duration is important in several therapeutic areas, including TB and antibiotics. We recently proposed a new randomised trial design to overcome some of the limitations of standard two-arm non-inferiority trials. This DURATIONS design involves randomising patients to a number of duration arms, and modelling the so-called duration-response curve. This article investigates the operating characteristics (type-1 and type-2 errors) of different statistical methods of drawing inference from the estimated curve. Methods. Our first estimation target is the shortest duration non-inferior to the control (maximum) duration within a specific risk difference margin. We compare different methods of estimating this quantity, including using model confidence bands, the delta method and bootstrap. We then explore the generalisability of results to estimation targets which focus on absolute event rates, risk ratio and gradient of the curve. Results. We show through simulations that, in most scenarios and for most of the estimation targets, using the bootstrap to estimate variability around the target duration leads to good results for DURATIONS design-appropriate quantities analogous to power and type-1 error. Using model confidence bands is not recommended, while the delta method leads to inflated type-1 error in some scenarios, particularly when the optimal duration is very close to one of the randomised durations. Conclusions. Using the bootstrap to estimate the optimal duration in a DURATIONS design has good operating characteristics in a wide range of scenarios, and can be used with confidence by researchers wishing to design a DURATIONS trial to reduce treatment duration. Uncertainty around several different targets can be estimated with this bootstrap approach.Comment: 4 figures, 1 table + additional materia

Comparison of aggregate and individual participant data approaches to meta-analysis of randomised trials : An observational study

BACKGROUND: It remains unclear when standard systematic reviews and meta-analyses that rely on published aggregate data (AD) can provide robust clinical conclusions. We aimed to compare the results from a large cohort of systematic reviews and meta-analyses based on individual participant data (IPD) with meta-analyses of published AD, to establish when the latter are most likely to be reliable and when the IPD approach might be required. METHODS AND FINDINGS: We used 18 cancer systematic reviews that included IPD meta-analyses: all of those completed and published by the Meta-analysis Group of the MRC Clinical Trials Unit from 1991 to 2010. We extracted or estimated hazard ratios (HRs) and standard errors (SEs) for survival from trial reports and compared these with IPD equivalents at both the trial and meta-analysis level. We also extracted or estimated the number of events. We used paired t tests to assess whether HRs and SEs from published AD differed on average from those from IPD. We assessed agreement, and whether this was associated with trial or meta-analysis characteristics, using the approach of Bland and Altman. The 18 systematic reviews comprised 238 unique trials or trial comparisons, including 37,082 participants. A HR and SE could be generated for 127 trials, representing 53% of the trials and approximately 79% of eligible participants. On average, trial HRs derived from published AD were slightly more in favour of the research interventions than those from IPD (HRAD to HRIPD ratio = 0.95, p = 0.007), but the limits of agreement show that for individual trials, the HRs could deviate substantially. These limits narrowed with an increasing number of participants (p < 0.001) or a greater number (p < 0.001) or proportion (p < 0.001) of events in the AD. On average, meta-analysis HRs from published AD slightly tended to favour the research interventions whether based on fixed-effect (HRAD to HRIPD ratio = 0.97, p = 0.088) or random-effects (HRAD to HRIPD ratio = 0.96, p = 0.044) models, but the limits of agreement show that for individual meta-analyses, agreement was much more variable. These limits tended to narrow with an increasing number (p = 0.077) or proportion of events (p = 0.11) in the AD. However, even when the information size of the AD was large, individual meta-analysis HRs could still differ from their IPD equivalents by a relative 10% in favour of the research intervention to 5% in favour of control. We utilised the results to construct a decision tree for assessing whether an AD meta-analysis includes sufficient information, and when estimates of effects are most likely to be reliable. A lack of power at the meta-analysis level may have prevented us identifying additional factors associated with the reliability of AD meta-analyses, and we cannot be sure that our results are generalisable to all outcomes and effect measures. CONCLUSIONS: In this study we found that HRs from published AD were most likely to agree with those from IPD when the information size was large. Based on these findings, we provide guidance for determining systematically when standard AD meta-analysis will likely generate robust clinical conclusions, and when the IPD approach will add considerable value

Neoadjuvant chemotherapy versus debulking surgery in advanced tubo-ovarian cancers: pooled analysis of individual patient data from the EORTC 55971 and CHORUS trials

Background: Individual patient data from two randomised trials comparing neoadjuvant chemotherapy with upfront debulking surgery in advanced tubo-ovarian cancer were analysed to examine long-term outcomes for patients and to identify any preferable therapeutic approaches for subgroup populations. Methods: We did a per-protocol pooled analysis of individual patient data from the European Organisation for Research and Treatment of Cancer (EORTC) 55971 trial (NCT00003636) and the Medical Research Council Chemotherapy Or Upfront Surgery (CHORUS) trial (ISRCTN74802813). In the EORTC trial, eligible women had biopsy-proven International Federation of Gynecology and Obstetrics (FIGO) stage IIIC or IV invasive epithelial tubo-ovarian carcinoma. In the CHORUS trial, inclusion criteria were similar to those of the EORTC trial, and women with apparent FIGO stage IIIA and IIIB disease were also eligible. The main aim of the pooled analysis was to show non-inferiority in overall survival with neoadjuvant chemotherapy compared with upfront debulking surgery, using the reverse Kaplan-Meier method. Tests for heterogeneity were based on Cochran's Q heterogeneity statistic. Findings: Data for 1220 women were included in the pooled analysis, 670 from the EORTC trial and 550 from the CHORUS trial. 612 women were randomly allocated to receive upfront debulking surgery and 608 to receive neoadjuvant chemotherapy. Median follow-up was 7·6 years (IQR 6·0–9·6; EORTC, 9·2 years [IQR 7·3–10·4]; CHORUS, 5·9 years [IQR 4·3–7·4]). Median age was 63 years (IQR 56–71) and median size of the largest metastatic tumour at diagnosis was 8 cm (IQR 4·8–13·0). 55 (5%) women had FIGO stage II–IIIB disease, 831 (68%) had stage IIIC disease, and 230 (19%) had stage IV disease, with staging data missing for 104 (9%) women. In the entire population, no difference in median overall survival was noted between patients who underwent neoadjuvant chemotherapy and upfront debulking surgery (27·6 months [IQR 14·1–51·3] and 26·9 months [12·7–50·1], respectively; hazard ratio [HR] 0·97, 95% CI 0·86–1·09; p=0·586). Median overall survival for EORTC and CHORUS patients was significantly different at 30·2 months (IQR 15·7–53·7) and 23·6 months (10·5–46·9), respectively (HR 1·20, 95% CI 1·06–1·36; p=0·004), but was not heterogeneous (Cochran's Q, p=0·17). Women with stage IV disease had significantly better outcomes with neoadjuvant chemotherapy compared with upfront debulking surgery (median overall survival 24·3 months [IQR 14·1–47·6] and 21·2 months [10·0–36·4], respectively; HR 0·76, 95% CI 0·58–1·00; p=0·048; median progression-free survival 10·6 months [7·9–15·0] and 9·7 months [5·2–13·2], respectively; HR 0·77, 95% CI 0·59–1·00; p=0·049). Interpretation: Long-term follow-up data substantiate previous results showing that neoadjuvant chemotherapy and upfront debulking surgery result in similar overall survival in advanced tubo-ovarian cancer, with better survival in women with stage IV disease with neoadjuvant chemotherapy. This pooled analysis, with long-term follow-up, shows that neoadjuvant chemotherapy is a valuable treatment option for patients with stage IIIC–IV tubo-ovarian cancer, particularly in patients with a high tumour burden at presentation or poor performance status

Observations with the High Altitude GAmma-Ray (HAGAR) telescope array in the Indian Himalayas

The High Altitude GAmma-Ray (HAGAR) array is a wavefront sampling array of 7 telescopes, set-up at Hanle, at 4270 m amsl, in the Ladakh region of the Himalayas (Northern India). It constitutes the first phase of the HImalayan Gamma-Ray Observatory (HIGRO) project. HAGAR is the first array of atmospheric Cherenkov telescopes established at a so high altitude, and was designed to reach a relatively low threshold (currently around 200 GeV) with quite a low mirror area (31 m2). Regular source observations are running since September 2008. Estimation of the sensitivity of the experiment is undergoing using several hours of data from the direction of Crab nebula, the standard candle source of TeV gamma-ray astronomy, and from dark regions. Data were acquired using the On-source/Off-source tracking mode, and by comparing these sky regions the strength of the gamma-ray signal could be estimated. Gamma-ray events arrive close to telescope axis direction while the cosmic-ray background events arrive from the whole field of view. We discuss our analysis procedures for the estimate of arrival direction, estimate of gamma ray flux from Crab nebula, and the sensitivity of the HAGAR system, in this paper

Statistical analysis plan for the motor neuron disease systematic multi-arm adaptive randomised trial (MND-SMART)

Background: MND-SMART is a platform, multi-arm, multi-stage, multi-centre, randomised controlled trial recruiting people with motor neuron disease. Initially, the treatments memantine and trazodone will each be compared against placebo, but other investigational treatments will be introduced into the trial later. The co-primary outcomes are the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALS-FRS-R) functional outcome, which is assessed longitudinally, and overall survival. Methods: Initially in MND-SMART, participants are randomised 1:1:1 via a minimisation algorithm to receive placebo or one of the two investigational treatments with up to 531 to be randomised in total. The comparisons between each research arm and placebo will be conducted in four stages, with the opportunity to cease further randomisations to poorly performing research arms at the end of stages 1 or 2. The final ALS-FRS-R analysis will be at the end of stage 3 and final survival analysis at the end of stage 4. The estimands for the co-primary outcomes are described in detail. The primary analysis of ALS-FRS-R at the end of stages 1 to 3 will involve fitting a normal linear mixed model to the data to calculate a mean difference in rate of ALS-FRS-R change between each research treatment and placebo. The pairwise type 1 error rate will be controlled, because each treatment comparison will generate its own distinct and separate interpretation. This publication is based on a formal statistical analysis plan document that was finalised and signed on 18 May 2022. Discussion: In developing the statistical analysis plan, we had to carefully consider several issues such as multiple testing, estimand specification, interim analyses, and statistical analysis of the repeated measurements of ALS-FRS-R. This analysis plan attempts to balance multiple factors, including minimisation of bias, maximising power and precision, and deriving clinically interpretable summaries of treatment effects. Trial registration: EudraCT Number, 2019–000099-41. Registered 2 October 2019, https://www.clinicaltrialsregister.eu/ctr-search/search?query=mnd-smart ClinicalTrials.gov, NCT04302870. Registered 10 March 2020

Designs for clinical trials with time-to-event outcomes based on stopping guidelines for lack of benefit

<p>Abstract</p> <p>background</p> <p>The pace of novel medical treatments and approaches to therapy has accelerated in recent years. Unfortunately, many potential therapeutic advances do not fulfil their promise when subjected to randomized controlled trials. It is therefore highly desirable to speed up the process of evaluating new treatment options, particularly in phase II and phase III trials. To help realize such an aim, in 2003, Royston and colleagues proposed a class of multi-arm, two-stage trial designs intended to eliminate poorly performing contenders at a first stage (point in time). Only treatments showing a predefined degree of advantage against a control treatment were allowed through to a second stage. Arms that survived the first-stage comparison on an intermediate outcome measure entered a second stage of patient accrual, culminating in comparisons against control on the definitive outcome measure. The intermediate outcome is typically on the causal pathway to the definitive outcome (i.e. the features that cause an intermediate event also tend to cause a definitive event), an example in cancer being progression-free and overall survival. Although the 2003 paper alluded to multi-arm trials, most of the essential design features concerned only two-arm trials. Here, we extend the two-arm designs to allow an arbitrary number of stages, thereby increasing flexibility by building in several 'looks' at the accumulating data. Such trials can terminate at any of the intermediate stages or the final stage.</p> <p>Methods</p> <p>We describe the trial design and the mathematics required to obtain the timing of the 'looks' and the overall significance level and power of the design. We support our results by extensive simulation studies. As an example, we discuss the design of the STAMPEDE trial in prostate cancer.</p> <p>Results</p> <p>The mathematical results on significance level and power are confirmed by the computer simulations. Our approach compares favourably with methodology based on beta spending functions and on monitoring only a primary outcome measure for lack of benefit of the new treatment.</p> <p>Conclusions</p> <p>The new designs are practical and are supported by theory. They hold considerable promise for speeding up the evaluation of new treatments in phase II and III trials.</p

Type I error rates of multi-arm multi-stage clinical trials: strong control and impact of intermediate outcomes

BACKGROUND: The multi-arm multi-stage (MAMS) design described by Royston et al. [Stat Med. 2003;22(14):2239-56 and Trials. 2011;12:81] can accelerate treatment evaluation by comparing multiple treatments with a control in a single trial and stopping recruitment to arms not showing sufficient promise during the course of the study. To increase efficiency further, interim assessments can be based on an intermediate outcome (I) that is observed earlier than the definitive outcome (D) of the study. Two measures of type I error rate are often of interest in a MAMS trial. Pairwise type I error rate (PWER) is the probability of recommending an ineffective treatment at the end of the study regardless of other experimental arms in the trial. Familywise type I error rate (FWER) is the probability of recommending at least one ineffective treatment and is often of greater interest in a study with more than one experimental arm. METHODS: We demonstrate how to calculate the PWER and FWER when the I and D outcomes in a MAMS design differ. We explore how each measure varies with respect to the underlying treatment effect on I and show how to control the type I error rate under any scenario. We conclude by applying the methods to estimate the maximum type I error rate of an ongoing MAMS study and show how the design might have looked had it controlled the FWER under any scenario. RESULTS: The PWER and FWER converge to their maximum values as the effectiveness of the experimental arms on I increases. We show that both measures can be controlled under any scenario by setting the pairwise significance level in the final stage of the study to the target level. In an example, controlling the FWER is shown to increase considerably the size of the trial although it remains substantially more efficient than evaluating each new treatment in separate trials. CONCLUSIONS: The proposed methods allow the PWER and FWER to be controlled in various MAMS designs, potentially increasing the uptake of the MAMS design in practice. The methods are also applicable in cases where the I and D outcomes are identical