Hierarchical network meta-analysis models to address sparsity of events and differing treatment classifications with regard to adverse outcomes

This is the accepted version of the article, which has been published in final form at DOI: 10.1002/sim.6131.Meta-analysis for adverse events resulting from medical interventions has many challenges, in part due to small numbers of such events within primary studies. Furthermore, variability in drug dose, potential differences between drugs within the same pharmaceutical class and multiple indications for a specific treatment can all add to the complexity of the evidence base. This paper explores the use of synthesis methods, incorporating mixed treatment comparisons, to estimate the risk of adverse events for a medical intervention, while acknowledging and modelling the complexity of the structure of the evidence base. The motivating example was the effect on malignancy of three anti-tumour necrosis factor (anti-TNF) drugs (etanercept, adalimumab and infliximab) indicated to treat rheumatoid arthritis. Using data derived from 13 primary studies, a series of meta-analysis models of increasing complexity were applied. Models ranged from a straightforward comparison of anti-TNF against non-anti-TNF controls, to more complex models in which a treatment was defined by individual drug and its dose. Hierarchical models to allow 'borrowing strength' across treatment classes and dose levels, and models involving constraints on the impact of dose level, are described. These models provide a flexible approach to estimating sparse, often adverse, outcomes associated with interventions. Each model makes its own set of assumptions, and approaches to assessing goodness of fit of the various models will usually be extremely limited in their effectiveness, due to the sparse nature of the data. Both methodological and clinical considerations are required to fit realistically complex models in this area and to evaluate their appropriateness.Partially supported by a National Institute for Health Research Senior Investigator Awar

A novel approach to bivariate meta-analysis of binary outcomes and its application in the context of surrogate endpoints

Bivariate meta-analysis provides a useful framework for combining information across related studies and has been widely utilised to combine evidence from clinical studies in order to evaluate treatment efficacy. Bivariate meta-analysis has also been used to investigate surrogacy patterns between treatment effects on the surrogate and the final outcome. Surrogate endpoints play an important role in drug development when they can be used to measure treatment effect early compared to the final clinical outcome and to predict clinical benefit or harm. The standard bivariate meta-analytic approach models the observed treatment effects on the surrogate and final outcomes jointly, at both the within-study and between-studies levels, using a bivariate normal distribution. For binomial data a normal approximation can be used on log odds ratio scale, however, this method may lead to biased results when the proportions of events are close to one or zero, affecting the validation of surrogate endpoints. In this paper, two Bayesian meta-analytic approaches are introduced which allow for modelling the within-study variability using binomial data directly. The first uses independent binomial likelihoods to model the within-study variability avoiding to approximate the observed treatment effects, however, ignores the within-study association. The second, models the summarised events in each arm jointly using a bivariate copula with binomial marginals. This allows the model to take into account the within-study association through the copula dependence parameter. We applied the methods to an illustrative example in chronic myeloid leukemia to investigate the surrogate relationship between complete cytogenetic response (CCyR) and event-free-survival (EFS).Comment: 20 pages, 6 figure

Assessing the effectiveness of primary angioplasty compared with thrombolysis and its relationship to time delay: a Bayesian evidence synthesis

Background: Meta-analyses of trials have shown greater benefits from angioplasty than thrombolysis after an acute myocardial infarction, but the time delay in initiating angioplasty needs to be considered. Objective: To extend earlier meta-analyses by considering 1- and 6-month outcome data for both forms of reperfusion. To use Bayesian statistical methods to quantify the uncertainty associated with the estimated relationships. Methods: A systematic review and meta-analysis published in 2003 was updated. Data on key clinical outcomes and the difference between time-to-balloon and time-to-needle were independently extracted by two researchers. Bayesian statistical methods were used to synthesise evidence despite differences between reported follow-up times and outcomes. Outcomes are presented as absolute probabilities of specific events and odds ratios (ORs; with 95% credible intervals (Crl)) as a function of the additional time delay associated with angioplasty. \ Results: 22 studies were included in the meta-analysis, with 3760 and 3758 patients randomised to primary angioplasty and thrombolysis, respectively. The mean ( SE) angioplasty-related time delay ( over and above time to thrombolysis) was 54.3 (2.2) minutes. For this delay, mean event probabilities were lower for primary angioplasty for all outcomes. Mortality within 1 month was 4.5% after angioplasty and 6.4% after thrombolysis ( OR = 0.68 ( 95% Crl 0.46 to 1.01)). For non-fatal reinfarction, OR = 0.32 ( 95% Crl 0.20 to 0.51); for non-fatal stroke OR = 0.24 ( 95% Crl 0.11 to 0.50). For all outcomes, the benefit of angioplasty decreased with longer delay from initiation. Conclusions: The benefit of primary angioplasty, over thrombolysis, depends on the former's additional time delay. For delays of 30-90 minutes, angioplasty is superior for 1- month fatal and non-fatal outcomes. For delays of around 90 minutes thrombolysis may be the preferred option as assessed by 6-month mortality; there is considerable uncertainty for longer time delays

Bivariate network meta-analysis for surrogate endpoint evaluation

Surrogate endpoints are very important in regulatory decision-making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on the pairwise methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods which combine data on treatment effects on the surrogate and final outcomes, from trials investigating heterogeneous treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the surrogacy patterns across multiple trials (different populations) within a treatment contrast and across treatment contrasts, thus enabling predictions of the treatment effect on the final outcome for a new study in a new population or investigating a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using simulated data and an illustrative example in advanced colorectal cancer. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatments for which surrogacy holds, thus leading to better predictions

Adjusting for treatment switching in the METRIC study shows further improved overall survival with trametinib compared with chemotherapy

Trametinib, a selective inhibitor of mitogen-activated protein kinase kinase 1 (MEK1) and MEK2, significantly improves progression-free survival compared with chemotherapy in patients with BRAF V600E/K mutation–positive advanced or metastatic melanoma (MM). However, the pivotal clinical trial permitted randomized chemotherapy control group patients to switch to trametinib after disease progression, which confounded estimates of the overall survival (OS) advantage of trametinib. Our purpose was to estimate the switching-adjusted treatment effect of trametinib for OS and assess the suitability of each adjustment method in the primary efficacy population. Of the patients randomized to chemotherapy, 67.4% switched to trametinib. We applied the rank-preserving structural failure time model, inverse probability of censoring weights, and a two-stage accelerated failure time model to obtain estimates of the relative treatment effect adjusted for switching. The intent-to-treat (ITT) analysis estimated a 28% reduction in the hazard of death with trametinib treatment (hazard ratio [HR], 0.72; 95% CI, 0.52–0.98) for patients in the primary efficacy population (data cut May 20, 2013). Adjustment analyses deemed plausible provided OS HR point estimates ranging from 0.48 to 0.53. Similar reductions in the HR were estimated for the first-line metastatic subgroup. Treatment with trametinib, compared with chemotherapy, significantly reduced the risk of death and risk of disease progression in patients with BRAF V600E/K mutation–positive advanced melanoma or MM. Adjusting for switching resulted in lower HRs than those obtained from standard ITT analyses. However, CI are wide and results are sensitive to the assumptions associated with each adjustment method

The Prevalence of Depression in White-European and South-Asian People with Impaired Glucose Regulation and Screen-Detected Type 2 Diabetes Mellitus

Background There is a clear relationship between depression and diabetes. However, the directionality of the relationship remains unclear and very little research has considered a multi-ethnic population. The aim of this study was to determine the prevalence of depression in a White-European (WE) and South-Asian (SA) population attending a community diabetes screening programme, and to explore the association of depression with screen-detected Type 2 diabetes mellitus (T2DM) and impaired glucose regulation (IGR). Methodology/Principal Findings Participants were recruited from general practices in Leicestershire (United Kingdom) between August 2004 and December 2007. 4682 WE (40–75 years) and 1327 SA participants (25–75 years) underwent an Oral Glucose Tolerance Test, detailed history, anthropometric measurements and completed the World Health Organisation-Five (WHO-5) Wellbeing Index. Depression was defined by a WHO-5 wellbeing score ≤13. Unadjusted prevalence of depression for people in the total sample with T2DM and IGR was 21.3% (21.6% in WE, 20.6% in SA, p = 0.75) and 26.0% (25.3% in WE, 28.9% in SA, p = 0.65) respectively. For people with normal glucose tolerance, the prevalence was 25.1% (24.9% in WE, 26.4% in SA, p = 0.86). Age-adjusted prevalences were higher for females than males. Odds ratios adjusted for age, gender, and ethnicity, showed no significant increase in prevalent depression for people with T2DM (OR = 0.95, 95%CI 0.62 to 1.45) or IGR (OR = 1.17, 95%CI 0.96 to1.42). Conclusions Prior to the knowledge of diagnosis, depression was not significantly more prevalent in people with screen detected T2DM or IGR. Differences in prevalent depression between WE and SA people were also not identified. In this multi-ethnic population, female gender was significantly associated with depression

Life expectancy in Duchenne Muscular Dystrophy : reproduced individual patient data meta-analysis

Objective: Duchenne Muscular Dystrophy (DMD) is a rare progressive disease, which is often diagnosed in early childhood, and leads to considerably reduced life-expectancy; due to its rarity, research literature and patient numbers are limited. To fully characterise the natural history, it is crucial to obtain appropriate estimates of the life-expectancy and mortality rates of patients with DMD. Methods: A systematic review of the published literature on mortality in DMD up until July 2020 was undertaken, specifically focusing on publications in which Kaplan-Meier (KM) survival curves with age as a time-scale were presented. These were digitised and individual patient data (IPD) reconstructed. The pooled IPD were analysed using the Kaplan-Meier estimator and parametric survival analysis models. Estimates were also stratified by birth cohort. Results: Of 1177 articles identified, 14 publications met the inclusion criteria and provided data on 2283 patients, of whom 1049 had died. Median life-expectancy was 22.0 years (95% CI: 21.2, 22.4). Analyses stratifying by three time-periods in which patients were born showed markedly increased life-expectancy in more recent patient populations; patients born after 1990 have a median life-expectancy of 28.1 years (95% CI 25.1, 30.3). Conclusions: This paper presents a full overview of mortality across the lifetime of a patient with DMD, and highlights recent improvements in survival. In the absence of large-scale prospective cohort studies or trials reporting mortality data for patients with DMD, extraction of IPD from the literature provides a viable alternative to estimating life-expectancy for this patient population

Novel methods to deal with publication biases: secondary analysis of antidepressant trials in the FDA trial registry database and related journal publications

Objective To assess the performance of novel contour enhanced funnel plots and a regression based adjustment method to detect and adjust for publication biases