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

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

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

Randomised controlled trial and health economic evaluation of the impact of diagnostic testing for influenza, respiratory syncytial virus and Streptococcus pneumoniae infection on the management of acute admissions in the elderly and high-risk 18- to 64-year-olds

Please cite the published version which is available via the DOI link in this record.Western industrialised nations face a large increase in the number of older people. People over the age of 60 years account for almost half of the 16.8 million hospital admissions in England from 2009 to 2010. During 2009-10, respiratory infections accounted for approximately 1 in 30 hospital admissions and 1 in 20 of the 51.5 million bed-days.HTA ProgrammeNational Institute for Health Research (NIHR

Assessing methods for dealing with treatment switching in clinical trials: A follow-up simulation study

When patients randomised to the control group of a randomised controlled trial are allowed to switch onto the experimental treatment, intention-to-treat analyses of the treatment effect are confounded because the separation of randomised groups is lost. Previous research has investigated statistical methods that aim to estimate the treatment effect that would have been observed had this treatment switching not occurred and has demonstrated their performance in a limited set of scenarios. Here, we investigate these methods in a new range of realistic scenarios, allowing conclusions to be made based upon a broader evidence base. We simulated randomised controlled trials incorporating prognosis-related treatment switching and investigated the impact of sample size, reduced switching proportions, disease severity, and alternative data-generating models on the performance of adjustment methods, assessed through a comparison of bias, mean squared error, and coverage, related to the estimation of true restricted mean survival in the absence of switching in the control group. Rank preserving structural failure time models, inverse probability of censoring weights, and two-stage methods consistently produced less bias than the intentionto-treat analysis. The switching proportion was confirmed to be a key determinant of bias: sample size and censoring proportion were relatively less important. It is critical to determine the size of the treatment effect in terms of an acceleration factor (rather than a hazard ratio) to provide information on the likely bias associated with rank-preserving structural failure time model adjustments. In general, inverse probability of censoring weight methods are more volatile than other adjustment methods

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

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