Multidimensional isotonic regression and estimation of the threshold value

No abstract available

Extension of CART using multiple splits under order restrictions

CART was introduced by Breiman et al. (1984) as a classification tool. It divides the whole sample recursively in two subpopulations by finding the best possible split with respect to a optimisation criterion. This method, restricted up to date to binary splits, is extended in this paper for allowing also multiple splits. The main problem with this extension is related to the optimal number of splits and the location of the corresponding cutpoints. In order to reduce the computational effort and enhance parsimony, the reduced isotonic regression was used in order to solve this problem. The extended CART method was tested in a simulation study and was compared with the classical approach in an epidemiological study. In both studies the extended CART turned out to be a useful and reliable alternative

Modelling Under Order Restrictions

Order restricted effects of predictors can be represented in models by the monotonic transformation fitted by the pooled adjacent violators algorithm as described by T. Robertson et al. In the context of multivariate modelling, this paper aims to introduce next to additive monotonic models a multidimensional approach. The corresponding permutations test to assess significance for the predictors is described and some feeble points of the approach are discussed. We also introduce a procedure to improve the parsimony of the model by reducing the resulting level sets. The use of monotonic regression in connection with the threshold value estimation problematic is outlined and two similar approaches to assess it are discussed

Modelling time-varying effects in Cox model under order restrictions

The violation of the proportional hazards assumption in Cox model occurs quite often in studies concerning solid tumours or leukaemia. Then the time varying coefficients model is its most popular extension used. The function f(t) that measures the time variation of a covariate, can be assessed through several smoothing techniques, such as cubic splines. However, for practical propose, it is more convenient to assess f(t) by a step function. The main drawback of this approach is the lack of stability since there is no standard method of defining the cutpoints of the underlined step function. The variation in the effect of a predictor can be assumed to be monotonic during the observational period. In these cases, we propose a method to estimate f(t) based on the isotonic regression framework. Applying the idea of Grambsch and Therneau, where smoothing the Schoenfeld residuals plotted against time reveal the shape of the underlined f(t) function, we use the Pooled Adjacent Violators Algorithm as smoother. As a result a set of cutpoints is returned without any a priori information about their location. Subsequently, the corresponding step function is introduced in the model and the standard likelihood-based method is applied to estimate it while adjusting for other covariates. This approach presents the advantage that additional decisions that can effect the result, as the number of knots in cubic splines, do not need to be taken. The performance of the provided PH test and the stability of the method are explored in a simulation study

What guidance are researchers given on how to present network meta-analyses to end-users such as policymakers and clinicians? A systematic review

© 2014 Sullivan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Introduction: Network meta-analyses (NMAs) are complex methodological approaches that may be challenging for non-technical end-users, such as policymakers and clinicians, to understand. Consideration should be given to identifying optimal approaches to presenting NMAs that help clarify analyses. It is unclear what guidance researchers currently have on how to present and tailor NMAs to different end-users. Methods: A systematic review of NMA guidelines was conducted to identify guidance on how to present NMAs. Electronic databases and supplementary sources were searched for NMA guidelines. Presentation format details related to sample formats, target audiences, data sources, analysis methods and results were extracted and frequencies tabulated. Guideline quality was assessed following criteria developed for clinical practice guidelines. Results: Seven guidelines were included. Current guidelines focus on how to conduct NMAs but provide limited guidance to researchers on how to best present analyses to different end-users. None of the guidelines provided reporting templates. Few guidelines provided advice on tailoring presentations to different end-users, such as policymakers. Available guidance on presentation formats focused on evidence networks, characteristics of individual trials, comparisons between direct and indirect estimates and assumptions of heterogeneity and/or inconsistency. Some guidelines also provided examples of figures and tables that could be used to present information. Conclusions: Limited guidance exists for researchers on how best to present NMAs in an accessible format, especially for non-technical end-users such as policymakers and clinicians. NMA guidelines may require further integration with end-users' needs, when NMAs are used to support healthcare policy and practice decisions. Developing presentation formats that enhance understanding and accessibility of NMAs could also enhance the transparency and legitimacy of decisions informed by NMAs.The Canadian Institute of Health Research (CIHR) Drug Safety and Effectiveness Network (Funding reference number – 116573)

Immunomodulators and immunosuppressants for relapsing-remitting multiple sclerosis: A network meta-analysis

This is the protocol for a review and there is no abstract. The objectives are as follows: We aim to compare the efficacy and acceptability of immunomodulators and immunosuppressants to treat participants with RRMS and to generate a clinically useful hierarchy of available immunotherapies according to their efficacy and acceptability

Prevalence of evidence of inconsistency and its association with network structural characteristics in 201 published networks of interventions

BACKGROUND: Network meta-analysis (NMA) has attracted growing interest in evidence-based medicine. Consistency between different sources of evidence is fundamental to the reliability of the NMA results. The purpose of the present study was to estimate the prevalence of evidence of inconsistency and describe its association with different NMA characteristics. METHODS: We updated our collection of NMAs with articles published up to July 2018. We included networks with randomised clinical trials, at least four treatment nodes, at least one closed loop, a dichotomous primary outcome, and available arm-level data. We assessed consistency using the design-by-treatment interaction (DBT) model and testing all the inconsistency parameters globally through the Wald-type chi-squared test statistic. We estimated the prevalence of evidence of inconsistency and its association with different network characteristics (e.g., number of studies, interventions, intervention comparisons, loops). We evaluated the influence of the network characteristics on the DBT p-value via a multivariable regression analysis and the estimated Pearson correlation coefficients. We also evaluated heterogeneity in NMA (consistency) and DBT (inconsistency) random-effects models. RESULTS: We included 201 published NMAs. The p-value of the design-by-treatment interaction (DBT) model was lower than 0.05 in 14% of the networks and lower than 0.10 in 20% of the networks. Networks including many studies and comparing few interventions were more likely to have small DBT p-values (less than 0.10), which is probably because they yielded more precise estimates and power to detect differences between designs was higher. In the presence of inconsistency (DBT p-value lower than 0.10), the consistency model displayed higher heterogeneity than the DBT model. CONCLUSIONS: Our findings show that inconsistency was more frequent than what would be expected by chance, suggesting that researchers should devote more resources to exploring how to mitigate inconsistency. The results of this study highlight the need to develop strategies to detect inconsistency (because of the relatively high prevalence of evidence of inconsistency in published networks), and particularly in cases where the existing tests have low power

Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects.

Network meta-analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta-analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi-parametric, non-iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples.DJ, RT and IRW are employed by the UK Medical Research Council (code U105260558). JB is supported by the UK MRC grant numbers G0902100 and MR/K014811/1.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/sim.675