Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference.

Putative surrogate endpoints must undergo a rigorous statistical evaluation before they can be used in clinical trials. Numerous frameworks have been introduced for this purpose. In this study, we extend the scope of the information-theoretic causal-inference approach to encompass scenarios where both outcomes are time-to-event endpoints, using the flexibility provided by D-vine copulas. We evaluate the quality of the putative surrogate using the individual causal association (ICA)-a measure based on the mutual information between the individual causal treatment effects. However, in spite of its appealing mathematical properties, the ICA may be ill defined for composite endpoints. Therefore, we also propose an alternative rank-based metric for assessing the ICA. Due to the fundamental problem of causal inference, the joint distribution of all potential outcomes is only partially identifiable and, consequently, the ICA cannot be estimated without strong unverifiable assumptions. This is addressed by a formal sensitivity analysis that is summarized by the so-called intervals of ignorance and uncertainty. The frequentist properties of these intervals are discussed in detail. Finally, the proposed methods are illustrated with an analysis of pooled data from two advanced colorectal cancer trials. The newly developed techniques have been implemented in the R package Surrogate.

Unraveling the impact of the COVID-19 pandemic on the mortality trends in Belgium between 2020-2022.

BACKGROUND: Over the past four years, the COVID-19 pandemic has exerted a profound impact on public health, including on mortality trends. This study investigates mortality patterns in Belgium by examining all-cause mortality, excess mortality, and cause-specific mortality. METHODS: We retrieved all-cause mortality data from January 1, 2009, to December 31, 2022, stratified by age group and sex. A linear mixed model, informed by all-cause mortality from 2009 to 2019, was used to predict non-pandemic all-cause mortality rates in 2020-2022 and estimate excess mortality. Further, we also analyzed trends in cause-specific and premature mortality. RESULTS: Different all-cause mortality patterns could be observed between the younger (<45 years) and older age groups. The impact of the COVID-19 pandemic was particularly evident among older age groups. The highest excess mortality occurred in 2020, while a reversal in this trend was evident in 2022. We observed a notable effect of COVID-19 on cause-specific and premature mortality patterns over the three-year period. CONCLUSIONS: Despite a consistent decline in COVID-19 reported mortality over this three-year period, it remains imperative to meticulously monitor mortality trends in the years ahead.

The effectiveness and cost-effectiveness of attachment-based family therapy for young adults with high suicidal ideation: protocol of a randomized controlled trial.

BACKGROUND: Young adult suicidality is worldwide a prevalent mental health problem and the number one cause of death, with devastating consequences for individuals and their families, and substantial economic costs. However, psychological and pharmacological treatments currently recommended in guidelines for treatment of high-risk youth for fatal suicide have limited effect. In line with the World Health Organization's (WHO) recommendation to involve the family in treatment of these youth, attachment-based family therapy (ABFT) was developed, a 16-week attachment and emotion-focused treatment, implemented in mental health care settings across various European countries in the past years, and becoming increasingly popular among therapists. However, the (cost-)effectiveness of ABFT has not been studied in emerging adults. In the proposed pragmatic randomized controlled trial (RCT), we aim to evaluate the effectiveness and cost-effectiveness of ABFT compared to treatment as usual (TAU) on suicidality, as delivered in daily practice. METHODS: This pragmatic multicenter study in the Netherlands and Belgium includes 13 participating sites. Participants are suicidal young adults (≥ 31 SIQ-JR score) between 16 and 30 years old who seek mental health treatment (n = 142) and their caregivers. The primary outcome is suicidality (SIQ-JR), with assessments at baseline, post-intervention (5 months after baseline), 3, 6, and 12 months after intervention. We predict that, compared to TAU, ABFT will lead to a stronger reduction in suicidality and will be more cost-effective, over the course of all time points. We also expect stronger decreases in depressive symptoms, given that suicidality is very common in individuals with depressive disorder, as well as more improvement in family functioning, autonomy, entrapment, and young adult attachment, in the ABFT condition. DISCUSSION: This study can contribute to improving the care for suicidal youngsters with high mortality risk. Treatment of suicidal emerging adults is understudied. The results will inform clinical guidelines and policy makers and improve treatment of suicidal emerging adults. TRIAL REGISTRATION: This trial is registered on ClinicalTrials.gov (NCT05965622, first posted on July 28, 2023).

Assessing the Operational Characteristics of the Individual Causal Association as a Metric of Surrogacy in the Binary Continuous Setting.

In a causal inference framework, a new metric has been proposed to quantify surrogacy for a continuous putative surrogate and a binary true endpoint, based on information theory. The proposed metric, termed the individual causal association (ICA), was quantified using a joint causal inference model for the corresponding potential outcomes. Due to the non-identifiability inherent in this type of models, a sensitivity analysis was introduced to study the behavior of the ICA as a function of the non-identifiable parameters characterizing the aforementioned model. In this scenario, to reduce uncertainty, several plausible yet untestable assumptions like monotonicity, independence, conditional independence or homogeneous variance-covariance, are often incorporated into the analysis. We assess the robustness of the methodology regarding these simplifying assumptions via simulation. The practical implications of the findings are demonstrated in the analysis of a randomized clinical trial evaluating an inactivated quadrivalent influenza vaccine.

Evaluating the direct effect of vaccination and non-pharmaceutical interventions during the COVID-19 pandemic in Europe.

BACKGROUND: Across Europe, countries have responded to the COVID-19 pandemic with a combination of non-pharmaceutical interventions and vaccination. Evaluating the effectiveness of such interventions is of particular relevance to policy-makers. METHODS: We leverage almost three years of available data across 38 European countries to evaluate the effectiveness of governmental responses in controlling the pandemic. We developed a Bayesian hierarchical model that flexibly relates daily COVID-19 incidence to past levels of vaccination and non-pharmaceutical interventions as summarised in the Stringency Index. Specifically, we use a distributed lag approach to temporally weight past intervention values, a tensor-product smooth to capture non-linearities and interactions between both types of interventions, and a hierarchical approach to parsimoniously address heterogeneity across countries. RESULTS: We identify a pronounced negative association between daily incidence and the strength of non-pharmaceutical interventions, along with substantial heterogeneity in effectiveness among European countries. Similarly, we observe a strong but more consistent negative association with vaccination levels. Our results show that non-linear interactions shape the effectiveness of interventions, with non-pharmaceutical interventions becoming less effective under high vaccination levels. Finally, our results indicate that the effects of interventions on daily incidence are most pronounced at a lag of 14 days after being in place. CONCLUSIONS: Our Bayesian hierarchical modelling approach reveals clear negative and lagged effects of non-pharmaceutical interventions and vaccination on confirmed COVID-19 cases across European countries.

As soon as COVID-19 hit Europe in early 2020, non-pharmaceutical interventions such as movement restrictions and social distancing were employed to contain the pandemic. Towards the end of 2020, vaccination was available and promoted as an additional defence. We analysed almost three years of public COVID-19 data to determine how effective both types of strategies were in containing the pandemic across 38 European countries. We developed a statistical model to relate confirmed cases to how strict non-pharmaceutical interventions were and to vaccination levels. Both non-pharmaceutical interventions and vaccination resulted in decreased confirmed cases, although variation exists among countries. When an intervention is applied, the effect on number of confirmed cases could be seen most about fourteen days after implementation.

Large vessel vasculitis is a risk factor for relapse only in giant cell arteritis patients without polymyalgia rheumatica.

OBJECTIVES: To evaluate differences in presentation and outcome of giant cell arteritis (GCA) patients with and without large vessel vasculitis (LVV) and according to the extent and severity of LVV. METHODS: Consecutive patients diagnosed with GCA between 2003 and 2020 who have had FDG PET imaging at diagnosis ≤3 days after initiation of glucocorticoids and followed for ≥12 months at the University Hospitals Leuven (Belgium), were included retrospectively. PET scans were visually scored (0-3) in 7 vascular areas and a total vascular score (TVS) was calculated. LVV was defined as FDG uptake ≥2 in any large vessel. RESULTS: We included 238 GCA patients, of which 169 (71%) had LVV. LVV patients were younger (69 vs 74 years, p< 0.001) and more frequently female (72% vs 49%, p= 0.001). In patients without PMR symptoms, the presence of LVV was associated with relapse (aOR 3.05 [95%CI 1.32-7.43], p= 0.011) and with a lower probability of stopping glucocorticoids (aHR 0.59 [95%CI 0.37-0.94], p= 0.025). However, in those with PMR symptoms, there was no difference in relapse risk (aOR 1.20 [95%CI 0.53-2.66], p= 0.657) and in the probability of stopping glucocorticoids (aHR 1.25 [95%CI 0.75-2.09], p= 0.394) between patients with and without LVV. A higher TVS was associated with an increased risk of relapse (aOR 1.09 [95%CI 1.04-1.15], p= 0.001] in patients without PMR symptoms, but not in those with PMR symptoms (aOR 1.01 [95%CI 0.96-1.07], p= 0.693). CONCLUSION: LVV is a risk factor for relapse in GCA patients without PMR symptoms with a higher relapse risk in those with higher TVS.

Innovative approaches for vaccine trials as a key component of pandemic preparedness - a white paper.

BACKGROUND: WHO postulates the application of adaptive design features in the global clinical trial ecosystem. However, the adaptive platform trial (APT) methodology has not been widely adopted in clinical research on vaccines. METHODS: The VACCELERATE Consortium organized a two-day workshop to discuss the applicability of APT methodology in vaccine trials under non-pandemic as well as pandemic conditions. Core aspects of the discussions are summarized in this article. RESULTS: An "ever-warm" APT appears ideally suited to improve efficiency and speed of vaccine research. Continuous learning based on accumulating APT trial data allows for pre-planned adaptations during its course. Given the relative design complexity, alignment of all stakeholders at all stages of an APT is central. Vaccine trial modelling is crucial, both before and in a pandemic emergency. Various inferential paradigms are possible (frequentist, likelihood, or Bayesian). The focus in the interpandemic interval may be on research gaps left by industry trials. For activation in emergency, template Disease X protocols of syndromal design for pathogens yet unknown need to be stockpiled and updated regularly. Governance of a vaccine APT should be fully integrated into supranational pandemic response mechanisms. DISCUSSION: A broad range of adaptive features can be applied in platform trials on vaccines. Faster knowledge generation comes with increased complexity of trial design. Design complexity should not preclude simple execution at trial sites. Continuously generated evidence represents a return on investment that will garner societal support for sustainable funding. Adaptive design features will naturally find their way into platform trials on vaccines.

Polymyalgia rheumatica is a risk factor for more recalcitrant disease in giant cell arteritis: A retrospective cohort study.

OBJECTIVES: To evaluate differences in presentation and outcome of giant cell arteritis (GCA) patients with and without polymyalgia rheumatica (PMR) symptoms. METHODS: Consecutive patients diagnosed with GCA between 2000 and 2020 and followed for ≥12 months at the University Hospitals Leuven (Belgium), were included retrospectively. RESULTS: We included 398 GCA patients, of which 181 (45%) with PMR symptoms. Patients with PMR symptoms had a longer symptom duration (11 vs 6 weeks, p < 0.001). They less frequently reported fever (19% vs 28%, p = 0.030) and fatigue (52% vs 64%, p = 0.015) and tended to have less permanent vision loss (12% vs 19%, p = 0.052). There was no difference in the cumulative oral GC dose at 2 years (4.4 vs 4.3 g methylprednisolone, p = 0.571). However, those with PMR symptoms were treated with higher GC doses during subsequent follow-up (p < 0.05 from 38 months after diagnosis) and had a lower probability of stopping GC (62% vs 71%, HR 0.74 [95%CI 0.58-0.94], p = 0.018) with a longer median duration of GC treatment (29 vs 23 months, p = 0.021). In addition, presence of PMR symptoms was associated with an increased risk of relapse (64% vs 51%, HR 1.38 [95%CI 1.06-1.79], p = 0.017) with a higher number of relapses (1.47 [95%CI 1.30-1.65] vs 1.16 relapses [95%CI 1.02-1.31], p = 0.007). Patients with PMR symptoms less frequently developed thoracic aortic aneurysms during follow-up (3% vs 11%, p = 0.005). CONCLUSION: GCA patients with PMR symptoms had more recalcitrant disease with a higher risk of relapse and longer duration of GC treatment with need for higher GC doses.

Proportion of treatment effect explained: An overview of interpretations.

The selection of the primary endpoint in a clinical trial plays a critical role in determining the trial's success. Ideally, the primary endpoint is the clinically most relevant outcome, also termed the true endpoint. However, practical considerations, like extended follow-up, may complicate this choice, prompting the proposal to replace the true endpoint with so-called surrogate endpoints. Evaluating the validity of these surrogate endpoints is crucial, and a popular evaluation framework is based on the proportion of treatment effect explained (PTE). While methodological advancements in this area have focused primarily on estimation methods, interpretation remains a challenge hindering the practical use of the PTE. We review various ways to interpret the PTE. These interpretations-two causal and one non-causal-reveal connections between the PTE principal surrogacy, causal mediation analysis, and the prediction of trial-level treatment effects. A common limitation across these interpretations is the reliance on unverifiable assumptions. As such, we argue that the PTE is only meaningful when researchers are willing to make very strong assumptions. These challenges are also illustrated in an analysis of three hypothetical vaccine trials.

Trends in cardiovascular disease incidence among 22 million people in the UK over 20 years: population based study.

OBJECTIVE: To investigate the incidence of cardiovascular disease (CVD) overall and by age, sex, and socioeconomic status, and its variation over time, in the UK during 2000-19. DESIGN: Population based study. SETTING: UK. PARTICIPANTS: 1 650 052 individuals registered with a general practice contributing to Clinical Practice Research Datalink and newly diagnosed with at least one CVD from 1 January 2000 to 30 June 2019. MAIN OUTCOME MEASURES: The primary outcome was incident diagnosis of CVD, comprising acute coronary syndrome, aortic aneurysm, aortic stenosis, atrial fibrillation or flutter, chronic ischaemic heart disease, heart failure, peripheral artery disease, second or third degree heart block, stroke (ischaemic, haemorrhagic, and unspecified), and venous thromboembolism (deep vein thrombosis or pulmonary embolism). Disease incidence rates were calculated individually and as a composite outcome of all 10 CVDs combined and were standardised for age and sex using the 2013 European standard population. Negative binomial regression models investigated temporal trends and variation by age, sex, and socioeconomic status. RESULTS: The mean age of the population was 70.5 years and 47.6% (n=784 904) were women. The age and sex standardised incidence of all 10 prespecified CVDs declined by 19% during 2000-19 (incidence rate ratio 2017-19 v 2000-02: 0.80, 95% confidence interval 0.73 to 0.88). The incidence of coronary heart disease and stroke decreased by about 30% (incidence rate ratios for acute coronary syndrome, chronic ischaemic heart disease, and stroke were 0.70 (0.69 to 0.70), 0.67 (0.66 to 0.67), and 0.75 (0.67 to 0.83), respectively). In parallel, an increasing number of diagnoses of cardiac arrhythmias, valve disease, and thromboembolic diseases were observed. As a result, the overall incidence of CVDs across the 10 conditions remained relatively stable from the mid-2000s. Age stratified analyses further showed that the observed decline in coronary heart disease incidence was largely restricted to age groups older than 60 years, with little or no improvement in younger age groups. Trends were generally similar between men and women. A socioeconomic gradient was observed for almost every CVD investigated. The gradient did not decrease over time and was most noticeable for peripheral artery disease (incidence rate ratio most deprived v least deprived: 1.98 (1.87 to 2.09)), acute coronary syndrome (1.55 (1.54 to 1.57)), and heart failure (1.50 (1.41 to 1.59)). CONCLUSIONS: Despite substantial improvements in the prevention of atherosclerotic diseases in the UK, the overall burden of CVDs remained high during 2000-19. For CVDs to decrease further, future prevention strategies might need to consider a broader spectrum of conditions, including arrhythmias, valve diseases, and thromboembolism, and examine the specific needs of younger age groups and socioeconomically deprived populations.

Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic.

OBJECTIVES: Belgium experienced multiple COVID-19 waves that hit various groups in the population, which changed the mortality pattern compared to periods before the pandemic. In this study, we investigated the geographical excess mortality trend in Belgium during the first year of the COVID-19 pandemic. METHODS: We retrieved the number of deaths and population data in 2020 based on gender, age, and municipality of residence, and we made a comparison with the mortality data in 2017-2019 using a spatially discrete model. RESULTS: Excess mortality was significantly associated with age, gender, and COVID-19 incidence, with larger effects in the second half of 2020. Most municipalities had higher risks of mortality with a number of exceptions in the northeastern part of Belgium. Some discrepancies in excess mortality were observed between the north and south regions. CONCLUSIONS: This study offers useful insight into excess mortality and will aid local and regional authorities in monitoring mortality trends.

Correction: The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic.

[This corrects the article DOI: 10.1371/journal.pgph.0002601.].

A joint normal-ordinal (probit) model for ordinal and continuous longitudinal data.

In biomedical studies, continuous and ordinal longitudinal variables are frequently encountered. In many of these studies it is of interest to estimate the effect of one of these longitudinal variables on the other. Time-dependent covariates have, however, several limitations; they can, for example, not be included when the data is not collected at fixed intervals. The issues can be circumvented by implementing joint models, where two or more longitudinal variables are treated as a response and modeled with a correlated random effect. Next, by conditioning on these response(s), we can study the effect of one or more longitudinal variables on another. We propose a normal-ordinal(probit) joint model. First, we derive closed-form formulas to estimate the model-based correlations between the responses on their original scale. In addition, we derive the marginal model, where the interpretation is no longer conditional on the random effects. As a consequence, we can make predictions for a subvector of one response conditional on the other response and potentially a subvector of the history of the response. Next, we extend the approach to a high-dimensional case with more than two ordinal and/or continuous longitudinal variables. The methodology is applied to a case study where, among others, a longitudinal ordinal response is predicted with a longitudinal continuous variable.

A joint penalized spline smoothing model for the number of positive and negative COVID-19 tests.

One of the key tools to understand and reduce the spread of the SARS-CoV-2 virus is testing. The total number of tests, the number of positive tests, the number of negative tests, and the positivity rate are interconnected indicators and vary with time. To better understand the relationship between these indicators, against the background of an evolving pandemic, the association between the number of positive tests and the number of negative tests is studied using a joint modeling approach. All countries in the European Union, Switzerland, the United Kingdom, and Norway are included in the analysis. We propose a joint penalized spline model in which the penalized spline is reparameterized as a linear mixed model. The model allows for flexible trajectories by smoothing the country-specific deviations from the overall penalized spline and accounts for heteroscedasticity by allowing the autocorrelation parameters and residual variances to vary among countries. The association between the number of positive tests and the number of negative tests is derived from the joint distribution for the random intercepts and slopes. The correlation between the random intercepts and the correlation between the random slopes were both positive. This suggests that, when countries increase their testing capacity, both the number of positive tests and negative tests will increase. A significant correlation was found between the random intercepts, but the correlation between the random slopes was not significant due to a wide credible interval.

Translating the COVID-19 epidemiological situation into policies and measures: the Belgian experience.

The COVID-19 pandemic led to sustained surveillance efforts, which made unprecedented volumes and types of data available. In Belgium, these data were used to conduct a targeted and regular assessment of the epidemiological situation. In addition, management tools were developed, incorporating key indicators and thresholds, to define risk levels and offer guidance to policy makers. Categorizing risk into various levels provided a stable framework to monitor the COVID-19 epidemiological situation and allowed for clear communication to authorities. Although translating risk levels into specific public health measures has remained challenging, this experience was foundational for future evaluation of the situation for respiratory infections in general, which, in Belgium, is now based on a management tool combining different data sources.

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 × $\times$ 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi et al. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a 2 × 2 $2\times 2$ crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modeling framework.

Identity Functioning in Patients with an Eating Disorder: Developmental Trajectories throughout Treatment.

Increasing research has indicated a strong association between identity functioning and eating disorder (ED) symptomatology. However, a detailed investigation of identity throughout ED treatment is lacking. The present longitudinal study examined identity in inpatients with an ED and explored its simultaneous change with ED symptomatology throughout treatment. A total of 225 female patients completed questionnaires at admission. From these 225 patients participating at admission (Wave 1), 110 also participated in at least one additional measurement wave, with 43.64% (n = 48) participating at admission and during treatment, 16.36% (n = 18) participating at admission and at discharge, and 40% (n = 44) participating at admission, during treatment and at discharge. Questionnaires on identity synthesis, identity confusion, identity processes, and ED symptomatology were completed. Latent growth curve modeling was used to address the research questions. Throughout treatment, a decrease in identity confusion and an increase in identity synthesis and adaptive identity processes were found. Accordingly, increases in identity synthesis and identification with commitment were related to general decreases in the drive for thinness and body dissatisfaction. Similarly, such decreases in ED symptoms were related to general decreases in identity confusion and ruminative exploration. The present study points to an increase in identity functioning throughout treatment, and longitudinal associations between identity functioning and ED symptomatology were found. Helping patients to decrease their ruminative exploration and to increase their identification with previously made life commitments and treating body/weight concerns could both be helpful in ED treatment.

Prevalence, characteristics, and outcome of subclinical vasculitis in polymyalgia rheumatica: a retrospective cohort study.

OBJECTIVES: Two recent meta-analyses reported subclinical vasculitis in 22-23% of patients with polymyalgia rheumatica (PMR). We aimed to evaluate the prevalence, characteristics, and outcome of subclinical vasculitis among our PMR patients. METHODS: Consecutive patients with GCA/PMR spectrum disease with isolated PMR symptoms who underwent FDG PET imaging between 2003-2020 and who were followed for ≥6 months, were included retrospectively. Vasculitis was defined as FDG uptake ≥ grade 2 in any vessel. RESULTS: We included 337 patients, of whom 31 (9%) with subclinical vasculitis. Among those with subclinical vasculitis, 21 (58%) had isolated large vessel vasculitis, 3 (10%) had isolated cranial vasculitis and 7 (23%) had both cranial and large vessel vasculitis. The glucocorticoid (GC) starting dose and GC doses during follow-up were higher in those with subclinical vasculitis until 12 months after diagnosis (p< 0.001). There was no difference in the duration of GC treatment (25 vs 20 months, p= 0.187). Cox proportional hazard regression analyses showed no difference in the proportion of patients able to stop GC (HR 0.78 [95% CI 0.49-1.25], p= 0.303) and in the proportion of patients with relapse (HR 0.82 [95%CI 0.50-1.36], p= 0.441). CONCLUSION: Only 9% of our PMR patients had subclinical vasculitis with a predilection for large vessel vasculitis. There were no differences in relapse rate and duration of GC treatment, however those with subclinical vasculitis received higher GC doses until 12 months after diagnosis. Prospective interventional trials are needed to evaluate the outcome of PMR patients with and without subclinical vasculitis treated with similar GC protocol.

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.