Nicky J Welton

Effects of glucosamine, chondroitin, or placebo in patients with osteoarthritis of hip or knee: network meta-analysis

Objective To determine the effect of glucosamine, chondroitin, or the two in combination on joint pain and on radiological progression of disease in osteoarthritis of the hip or knee. Design Network meta-analysis. Direct comparisons within trials were combined with indirect evidence from other trials by using a Bayesian model that allowed the synthesis of multiple time points. Main outcome measure Pain intensity. Secondary outcome was change in minimal width of joint space. The minimal clinically important difference between preparations and placebo was prespecified at −0.9 cm on a 10 cm visual analogue scale. Data sources Electronic databases and conference proceedings from inception to June 2009, expert contact, relevant websites. Eligibility criteria for selecting studies Large scale randomised controlled trials in more than 200 patients with osteoarthritis of the knee or hip that compared glucosamine, chondroitin, or their combination with placebo or head to head. Results 10 trials in 3803 patients were included. On a 10 cm visual analogue scale the overall difference in pain intensity compared with placebo was −0.4 cm (95% credible interval −0.7 to −0.1 cm) for glucosamine, −0.3 cm (−0.7 to 0.0 cm) for chondroitin, and −0.5 cm (−0.9 to 0.0 cm) for the combination. For none of the estimates did the 95% credible intervals cross the boundary of the minimal clinically important difference. Industry independent trials showed smaller effects than commercially funded trials (P=0.02 for interaction). The differences in changes in minimal width of joint space were all minute, with 95% credible intervals overlapping zero. Conclusions Compared with placebo, glucosamine, chondroitin, and their combination do not reduce joint pain or have an impact on narrowing of joint space. Health authorities and health insurers should not cover the costs of these preparations, and new prescriptions to patients who have not received treatment should be discouraged.

Evidence Synthesis for Decision Making 2: A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials

We set out a generalized linear model framework for the synthesis of data from randomized controlled trials. A common model is described, taking the form of a linear regression for both fixed and random effects synthesis, which can be implemented with normal, binomial, Poisson, and multinomial data. The familiar logistic model for meta-analysis with binomial data is a generalized linear model with a logit link function, which is appropriate for probability outcomes. The same linear regression framework can be applied to continuous outcomes, rate models, competing risks, or ordered category outcomes by using other link functions, such as identity, log, complementary log-log, and probit link functions. The common core model for the linear predictor can be applied to pairwise meta-analysis, indirect comparisons, synthesis of multiarm trials, and mixed treatment comparisons, also known as network meta-analysis, without distinction. We take a Bayesian approach to estimation and provide WinBUGS program code for a Bayesian analysis using Markov chain Monte Carlo simulation. An advantage of this approach is that it is straightforward to extend to shared parameter models where different randomized controlled trials report outcomes in different formats but from a common underlying model. Use of the generalized linear model framework allows us to present a unified account of how models can be compared using the deviance information criterion and how goodness of fit can be assessed using the residual deviance. The approach is illustrated through a range of worked examples for commonly encountered evidence formats.

Bayesian methods for evidence synthesis in cost-effectiveness analysis

Recently, health systems internationally have begun to use cost-effectiveness research as formal inputs into decisions about which interventions and programmes should be funded from collective resources. This process has raised some important methodological questions for this area of research. This paper considers one set of issues related to the synthesis of effectiveness evidence for use in decision-analytic cost-effectiveness (CE) models, namely the need for the synthesis of all sources of available evidence, although these may not ‘fit neatly’ into a CE model.Commonly encountered problems include the absence of head-to-head trial evidence comparing all options under comparison, the presence of multiple endpoints from trials and different follow-up periods. Full evidence synthesis for CE analysis also needs to consider treatment effects between patient subpopulations and the use of nonrandomised evidence.Bayesian statistical methods represent a valuable set of analytical tools to utilise indirect evidence and can make a powerful contribution to the decision-analytic approach to CE analysis. This paper provides a worked example and a general overview of these methods with particular emphasis on their use in economic evaluation.

Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials

Inconsistency can be thought of as a conflict between “direct” evidence on a comparison between treatments B and C and “indirect” evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed.

Addressing between-study heterogeneity and inconsistency in mixed treatment comparisons: Application to stroke prevention treatments in individuals with non-rheumatic atrial fibrillation

Mixed treatment comparison models extend meta-analysis methods to enable comparisons to be made between all relevant comparators in the clinical area of interest. In such modelling it is imperative that potential sources of variability are explored to explain both heterogeneity (variation in treatment effects between trials within pairwise contrasts) and inconsistency (variation in treatment effects between pairwise contrasts) to ensure the validity of the analysis.The objective of this paper is to extend the mixed treatment comparison framework to allow for the incorporation of study-level covariates in an attempt to explain between-study heterogeneity and reduce inconsistency. Three possible model specifications assuming different assumptions are described and applied to a 17-treatment network for stroke prevention treatments in individuals with non-rheumatic atrial fibrillation.The paper demonstrates the feasibility of incorporating covariates within a mixed treatment comparison framework and using model fit statistics to choose between alternative model specifications. Although such an approach may adjust for inconsistencies in networks, as for standard meta-regression, the analysis will suffer from low power if the number of trials is small compared with the number of treatment comparators.

Meta-Analysis of Impact of Different Types and Doses of Statins on New-Onset Diabetes Mellitus

Recent reports indicate that statins are associated with an increased risk for new-onset diabetes mellitus (DM) compared with placebo and that this relation is dose dependent. The aim of this study was to perform a comprehensive network meta-analysis of randomized controlled trials (RCTs) investigating the impact of different types and doses of statins on new-onset DM. RCTs comparing different types and doses of statins with placebo were searched for using the MEDLINE, Embase, and Cochrane databases. A search of RCTs pertinent to this meta-analysis covering the period from November 1994 to October 2012 was conducted by 2 independent investigators using the MEDLINE, Cochrane, Google Scholar, and Embase databases as well as abstracts and presentations from major cardiovascular meetings. Seventeen RCTs reporting the incidence of new-onset DM during statin treatment and including a total of 113,394 patients were identified. The RCTs compared either a statin versus placebo or high-dose versus moderate-dose statin therapy. Among different statins, pravastatin 40 mg/day was associated with the lowest risk for new-onset DM compared with placebo (odds ratio 1.07, 95% credible interval 0.86 to 1.30). Conversely, rosuvastatin 20 mg/day was numerically associated with 25% increased risk for DM compared with placebo (odds ratio 1.25, 95% credible interval 0.82 to 1.90). The impact on DM appeared to be intermediate with atorvastatin 80 mg/day compared with placebo (odds ratio 1.15, 95% credible interval 0.90 to 1.50). These findings were replicated at moderate doses. In conclusion, different types and doses of statins show different potential to increase the incidence of DM.

Evidence Synthesis for Decision Making 3: Heterogeneity Subgroups, Meta-Regression, Bias, and Bias-Adjustment

In meta-analysis, between-study heterogeneity indicates the presence of effect-modifiers and has implications for the interpretation of results in cost-effectiveness analysis and decision making. A distinction is usually made between true variability in treatment effects due to variation in patient populations or settings and biases related to the way in which trials were conducted. Variability in relative treatment effects threatens the external validity of trial evidence and limits the ability to generalize from the results; imperfections in trial conduct represent threats to internal validity. We provide guidance on methods for meta-regression and bias-adjustment, in pairwise and network meta-analysis (including indirect comparisons), using illustrative examples. We argue that the predictive distribution of a treatment effect in a “new” trial may, in many cases, be more relevant to decision making than the distribution of the mean effect. Investigators should consider the relative contribution of true variability and random variation due to biases when considering their response to heterogeneity. In network meta-analyses, various types of meta-regression models are possible when trial-level effect-modifying covariates are present or suspected. We argue that a model with a single interaction term is the one most likely to be useful in a decision-making context. Illustrative examples of Bayesian meta-regression against a continuous covariate and meta-regression against “baseline” risk are provided. Annotated WinBUGS code is set out in an appendix.

Estimation of Markov Chain Transition Probabilities and Rates from Fully and Partially Observed Data: Uncertainty Propagation, Evidence Synthesis, and Model Calibration

Markov transition models are frequently used to model disease progression. The authors show how the solution to Kolmogorov’s forward equations can be exploited to map between transition rates and probabilities from probability data in multistate models. They provide a uniform, Bayesian treatment of estimation and propagation of uncertainty of transition rates and probabilities when 1) observations are available on all transitions and exact time at risk in each state (fully observed data) and 2) observations are on initial state and final state after a fixed interval of time but not on the sequence of transitions (partially observed data). The authors show how underlying transition rates can be recovered from partially observed data using Markov chain Monte Carlo methods in WinBUGS, and they suggest diagnostics to investigate inconsistencies between evidence from different starting states. An illustrative example for a 3-state model is given, which shows how the methods extend to more complex Markov models using the software WBDiff to compute solutions. Finally, the authors illustrate how to statistically combine data from multiple sources, including partially observed data at several follow-up times and also how to calibrate a Markov model to be consistent with data from one specific study.

Mixed treatment comparison analysis provides internally coherent treatment effect estimates based on overviews of reviews and can reveal inconsistency

OBJECTIVES To propose methods for mixed treatment comparisons (MTC) based on pooled summaries of the type produced in overviews of reviews. STUDY DESIGN AND SETTING Overviews of reviews (umbrella reviews) summarize the results of multiple systematic reviews into a single document. They report the summary estimates from the original pairwise meta-analyses and discuss them in narrative form, with the intention of identifying the most effective treatment. We present methods for MTC synthesis, tailored for use with overviews of reviews. These generate a single internally consistent summary of all the relative treatment effects and assessments of whether the summary is consistent with the data. These methods are applied to a published overview of treatments for childhood nocturnal enuresis. We apply the methods to both fixed-effect (FE) and random-effects (RE) meta-analyses of the original trials. RESULTS The summary relative risks based on FE meta-analyses, as originally published, were highly inconsistent. Those based on RE meta-analyses were consistent and could, given standard assumptions on comparability of treatment effects in meta-analysis, form a basis for coherent decision making. CONCLUSION Along with the summaries from systematic reviews, MTC methods should be used in overviews to provide a single coherent analysis of all treatment comparisons and to check for evidence consistency.

Equity in access to total joint replacement of the hip and knee in England: cross sectional study.

Objective To explore geographical and sociodemographic factors associated with variation in equity in access to total hip and knee replacement surgery. Design Combining small area estimates of need and provision to explore equity in access to care. Setting English census wards. Subjects Patients throughout England who needed total hip or knee replacement and numbers who received surgery. Main outcome measures Predicted rates of need (derived from the Somerset and Avon Survey of Health and English Longitudinal Study of Ageing) and provision (derived from the hospital episode statistics database). Equity rate ratios comparing rates of provision relative to need by sociodemographic, hospital, and distance variables. Results For both operations there was an “n” shaped curve by age. Compared with people aged 50-59, those aged 60-84 got more provision relative to need, while those aged ≥85 received less total hip replacement (adjusted rate ratio 0.68, 95% confidence interval 0.65 to 0.72) and less total knee replacement (0.87, 0.82 to 0.93). Compared with women, men received more provision relative to need for total hip replacement (1.08, 1.05 to 1.10) and total knee replacement (1.31, 1.28 to 1.34). Compared with the least deprived, residents in the most deprived areas got less provision relative to need for total hip replacement (0.31, 0.30 to 0.33) and total knee replacement (0.33, 0.31 to 0.34). For total knee replacement, those in urban areas got higher provision relative to need, but for total hip replacement it was highest in villages/isolated areas. For total knee replacement, patients living in non-white areas received more provision relative to need (1.04, 1.00 to 1.07) than those in predominantly white areas, but for total hip replacement there was no effect. Adjustment for hospital characteristics did not attenuate the effects. Conclusions There is evidence of inequity in access to total hip and total knee replacement surgery by age, sex, deprivation, rurality, and ethnicity. Adjustment for hospital and distance did not attenuate these effects. Policy makers should examine factors at the level of patients or primary care to understand the determinants of inequitable provision.