-
[show abstract]
[hide abstract]
ABSTRACT: SUMMARY Information on the incidence of Chlamydia trachomatis (CT) is essential for models of the effectiveness and cost-effectiveness of screening programmes. We developed two independent estimates of CT incidence in women in England: one based on an incidence study, with estimates 'recalibrated' to the general population using data on setting-specific relative risks, and allowing for clearance and re-infection during follow-up; the second based on UK prevalence data, and information on the duration of CT infection. The consistency of independent sources of data on incidence, prevalence and duration, validates estimates of these parameters. Pooled estimates of the annual incidence rate in women aged 16-24 and 16-44 years for 2001-2005 using all these data were 0·05 [95% credible interval (CrI) 0·035-0·071] and 0·021 (95% CrI 0·015-0·028), respectively. Although, the estimates apply to England, similar methods could be used in other countries. The methods could be extended to dynamic models to synthesize, and assess the consistency of data on contact and transmission rates.
Epidemiology and Infection 06/2013; · 2.84 Impact Factor
-
Addiction 04/2013; · 4.31 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: To develop a coherent method for estimating mappings between treatment effects on disease-specific measurement (DSM) instruments and generic health-related quality-of-life (QOL) measures, when both are subject to measurement errors.
We identified three properties that must be satisfied for mappings to be logically coherent: invertability, transitivity, and invariance to linear transformation. Of the common regressions, ordinary least squares (OLS), geometric mean (GM), and orthogonal regression, only GM has all these properties, and then only in special cases. We developed a common factor model of how DSM and generic QOL scales are related, and derived expressions for coherent mapping coefficients. We showed that these are equivalent to adjusted forms of OLS or GM regressions. Where cohort data are available on just one DSM and one QOL measure, external data on the reproducibility of the DSM are required. In some circumstances, the mappings can be estimated without external data. We illustrated the estimation of mapping coefficients by using data on EuroQol five-dimensional (EQ-5D) questionnaire, 12-item short form health survey (SF-12) Mental Component Summary, and the Beck Depression Inventory (BDI), from a trial of treatments for depression.
OLS underestimates and GM overestimates mappings from DSMs to generic QOL measures. Mappings estimated by using external data on reliability were similar to those estimated by using internal data, suggesting approximate adequacy of the common factor model.
Neither OLS nor GM regression, unless corrected, is suitable for estimating mappings between disease-specific and generic QOL scales. OLS systematically underestimates mappings, but it can be adjusted by using external information on test-retest reliability.
Value in Health 01/2013; 16(1):177-84. · 2.19 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: The primary outcomes in trials are usually disease-specific measures (DSMs) designed to be responsive to changes in the condition caused by treatment. For purposes of cost-effectiveness analysis, treatment effects on the DSM are often "mapped" into treatment effects on a generic health-related quality-of-life (QOL) scale, such as EuroQol five-dimensional questionnaire. Trialists have the option of including generic QOL measures as trial outcomes. We consider the relative efficiency (estimate divided by its standard error) of treatment effects derived from the DSM, the generic QOL, the generic QOL indirectly estimated from the mapped DSM, and a pooled estimate combining the direct and indirect information on the generic QOL. By using a "common factor" theory of the relationship between the DSM and the generic QOL, we define the circumstances under which indirectly estimated generic QOL is more efficient than the direct one and when a pooled QOL estimate is more efficient than the DSM estimate. As long as the DSM is more responsive, there is always a threshold sample size above which the indirect estimate has better precision than the direct estimate. This threshold, however, increases as the (1) relative responsiveness ratio of the DSM to the generic QOL increases, (2) precision of the estimated mapping coefficient increases, and (3) true effect becomes smaller. The pooled estimate on the generic QOL may be more efficient than the DSM itself unless the reliability of the DSM is particularly high. Trials powered on DSMs are likely to have sufficient power to detect treatment effect on the generic QOL if a pooled estimate is used. We conclude that generic QOL instruments should be routinely included in randomized controlled trials. Information on mapping coefficients and on relative responsiveness should be collected more systematically to facilitate both evidence synthesis and trial design.
Value in Health 01/2013; 16(1):185-94. · 2.19 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Medical Decision Making 10/2012; · 2.33 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Published studies of the duration of asymptomatic Chlamydia trachomatis infection in women have produced diverse estimates, and most reviewers have not attempted an evidence synthesis. We review the designs of duration studies, distinguishing between the incident cases presenting soon after infection in clinic-based studies and prevalent cases ascertained in population screening studies. We combine evidence from all studies under fixed-effect (single clearance rate), random-effect (study-specific clearance rate), and mixture-of-exponentials models, in which there are either two or three classes of infection that clear at different rates. We can identify classes as 'passive' infection and fast-clearing and slow-clearing infections. We estimate models by Bayesian MCMC and compared them using posterior mean residual deviance and the deviance information criterion. The single fixed-effect clearance rate model fitted very poorly. The random-effect model was adequate but inferior to the two-class and three-class mixture of exponentials. According to the two-class model, the proportion in the first class was 23% (95% CI: 16-31%), and the mean duration of C. trachomatis infection is 1.36 years (95% CI: 1.13-1.63 years). With the three-rate model, duration was similar, but identification of the proportions in each class (19%, 31%, and 49%) was poor. Although the random-effect model was descriptively adequate, the extreme degree of between-study variation in the clearance rate it predicted lacked biological plausibility. Differences in study recruitment and sampling mechanisms, acting through a mixture-of-exponentials model, better explains the apparent heterogeneity in duration. Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine 09/2012; · 1.88 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Heterogeneity and small-study effects are major concerns for the validity of meta-analysis. Although random effects meta-analysis provides a partial solution to heterogeneity, neither takes into account the presence of small-study effects, although they can rarely be ruled out with certainty. In this paper, we facilitate a better understanding of the properties of a recently described regression-based approach to deriving a meta-analysis estimator robust to small-study effects and unexplainable heterogeneity. The weightings of studies in the meta-analysis are derived algebraically for the regression model and compared with the weightings allocated to studies by fixed and random effects models. These weightings are compared in case studies with and without small-study effects. The presence of small-study effects causes pooled estimates from fixed and random effects meta-analyses to differ, potentially markedly, as a result of the different weights allocated to individual studies. Because random effects meta-analysis gives more weight to smaller studies, it becomes more vulnerable to the small-study effects. The regression approach gives heavier weight to the larger studies than either the fixed or random effects models, leading to its dominance in the estimated pooled effect. The weighting properties of the proposed regression-derived meta-analysis estimator are presented and compared with those of the standard meta-analytic estimators. We propose that there is much to recommend the routine use of this model as a reliable way to derive a pooled meta-analysis estimate that is robust to potential small-study effects, while still accommodating heterogeneity, even though uncertainty will often be considerably larger than for standard estimators.
Statistics in Medicine 02/2012; 31(14):1407-17. · 1.88 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Willan and Eckermann describe a method for dealing with heterogeneity in value of information (VOI) calculations for prioritising and designing new research. Their article raises three fundamental (inter-related) issues for VOI methods: (1) how to make sense of the concept of uncertainty in a cost-effectiveness analysis (CEA) model, (2) the interpretation of heterogeneity in CEA, and (3) the relationship between data from a new study and the CEA model when there is heterogeneity. We discuss these three issues using an illustrative example meta-analysis of magnesium for myocardial infarction. Careful consideration of the relationship between existing (and future) evidence and the CEA model is required to provide practical VOI methods that can help research funders prioritise new research in the face of heterogeneity.
Health Economics 11/2011; 21(10):1196-200. · 2.12 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Mixed treatment comparison (MTC) meta-analyses estimate relative treatment effects from networks of evidence while preserving randomisation. We extend the MTC framework to allow for repeated measurements of a continuous endpoint that varies over time. We used, as a case study, a systematic review and meta-analysis of intraocular pressure (IOP) measurements from randomised controlled trials evaluating topical ocular hypotensives in primary open-angle glaucoma or ocular hypertension because IOP varies over the day and over the treatment course, and repeated measurements are frequently reported. We adopted models for conducting MTC in WinBUGS (The BUGS Project, Cambridge, UK) to allow for repeated IOP measurements and to impute missing standard deviations of the raw data using the predictive distribution from observations with standard deviations. A flexible model with an unconstrained baseline for IOP variations over time and time-invariant random treatment effects fitted the data well. We also adopted repeated measures models to allow for class effects; assuming treatment effects to be exchangeable within classes slightly improved model fit but could bias estimated treatment effects if exchangeability assumptions were not valid. We enabled all timepoints to be included in the analysis, allowing for repeated measures to increase precision around treatment effects and avoid bias associated with selecting timepoints for meta-analysis.The methods we developed for modelling repeated measures and allowing for missing data may be adapted for use in other MTC meta-analyses. Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine 07/2011; · 1.88 Impact Factor
-
A E Ades
Value in Health 06/2011; 14(4):414-6. · 2.19 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Understanding infectious disease dynamics and the effect on prevalence and incidence is crucial for public health policies. Disease incidence and prevalence are typically not observed directly and increasingly are estimated through the synthesis of indirect information from multiple data sources. We demonstrate how an evidence synthesis approach to the estimation of human immunodeficiency virus (HIV) prevalence in England and Wales can be extended to infer the underlying HIV incidence. Diverse time series of data can be used to obtain yearly "snapshots" (with associated uncertainty) of the proportion of the population in 4 compartments: not at risk, susceptible, HIV positive but undiagnosed, and diagnosed HIV positive. A multistate model for the infection and diagnosis processes is then formulated by expressing the changes in these proportions by a system of differential equations. By parameterizing incidence in terms of prevalence and contact rates, HIV transmission is further modeled. Use of additional data or prior information on demographics, risk behavior change and contact parameters allows simultaneous estimation of the transition rates, compartment prevalences, contact rates, and transmission probabilities.
Biostatistics 05/2011; 12(4):666-81. · 2.14 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: To extend, apply, and evaluate a regression-based approach to adjusting meta-analysis for publication and related biases. The approach uses related meta-analyses to improve estimation by borrowing strength on the degree of bias.
The proposed adjustment approach is described. Adjustments are applied both independently and by borrowing strength across journal-extracted data on the effectiveness of 12 antidepressant drugs from placebo-controlled trials. The methods are also applied to Food and Drug Administration (FDA) data obtained on the same 12 drugs. Results are compared, viewing the FDA observed data as gold standard.
Estimates adjusted for publication biases made independently for each drug were very uncertain using both the journal and FDA data. Adjusted estimates were much more precise when borrowing strength across meta-analyses. Reassuringly, adjustments in this way made to the journal data agreed closely with the observed estimates from the FDA data, while the adjusted FDA results changed only minimally from those observed from the FDA data.
The method worked well in the case study considered and therefore further evaluation is encouraged. It is suggested that this approach may be especially useful when adjusting several meta-analyses on similar interventions and outcomes, particularly when there are small numbers of studies.
Journal of clinical epidemiology 04/2011; 64(11):1230-41. · 2.96 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Studies of clinical efficacy commonly report more than one clinical endpoint. For example, randomized controlled trials of treatments for cancer will normally report time to disease progression as well as overall survival. It is likely that disease progression will be associated with higher mortality rates. Disease progression rates will also have consequences for the societal economic burden of the disease. Economic evaluation of the cost-effectiveness of different treatment regimes therefore requires the joint estimation of both disease progression and mortality. We describe a model to combine evidence from studies reporting time to event summaries for disease progression and/or mortality, motivated by a systematic review of 1st-line treatment for advanced breast cancer to provide inputs for an economic evaluation as part of the National Institute for Health and Clinical Excellence (NICE) clinical guideline on treatment of advanced breast cancer in England and Wales. The review identified a network of treatment comparisons, which provides the basis for indirect comparison. A variety of outcomes were reported: overall survival, time to progression (overall and responders only), and the proportion of responder, stable, progressive disease, and non-assessable patients. There were only five trials, and not all trials reported all outcomes. The scarcity of the available evidence required us to make strong assumptions in order to identify model parameters. However, this evidence structure often occurs in health technology assessment (HTA) of treatments for cancer. We discuss the validity of the assumptions made, and the potential to assess their validity in other applications of HTA of cancer therapies. Copyright © 2011 John Wiley & Sons, Ltd.
Research Synthesis Methods. 03/2011; 1(3‐4):239 - 257.
-
[show abstract]
[hide abstract]
ABSTRACT: Cost-effectiveness analysis often requires information on the effectiveness of interventions on multiple outcomes, and commonly these take the form of competing risks. Nevertheless, methods for synthesis of randomized controlled trials with competing risk outcomes are limited.
The aim of this study was to develop and illustrate flexible evidence synthesis methods for trials reporting competing risk results, which allow for studies with different follow-up times, and that take account of the statistical dependencies between outcomes, regardless of the number of outcomes and treatments.
We propose a competing risk meta-analysis based on hazards, rather than probabilities, estimated in a Bayesian Markov chain Monte Carlo (MCMC) framework using WinBUGS software. Our approach builds on existing work on mixed treatment comparison (network) meta-analysis, which can be applied to any number of treatments, and any number of competing outcomes, and to data sets with varying follow-up times. We show how a fixed effect model can be estimated, and two random treatment effect models with alternative structures for between-trial variation. We suggest methods for choosing between these alternative models.
We illustrate the methods by applying them to a data set involving 17 trials comparing nine antipsychotic treatments for schizophrenia including placebo, on three competing outcomes: relapse, discontinuation because of intolerable side effects, and discontinuation for other reasons.
Bayesian MCMC provides a flexible framework for synthesis of competing risk outcomes with multiple treatments, particularly suitable for embedding within probabilistic cost-effectiveness analysis.
Value in Health 12/2010; 13(8):976-83. · 2.19 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Standard approaches to analysis of randomized controlled trials (RCTs) using Markov models make it difficult to generalize treatment effects to new patient groups and synthesize evidence across trials. This paper demonstrates how pair-wise and mixed treatment comparison meta-analysis can be applied to event history data for disease progression reported by RCTs. The data, in the form of aggregated discrete time transitions, have a multi-nomial likelihood. In order for evidence synthesis to be performed a structured approach to modelling the differences in the effects of the different treatments must be taken. A multi-state continuous-time Markov model similar to others used in published economic evaluations of asthma treatments is developed, with transition rates related to the likelihood via Kolmogorov's forward equations. The formulation in terms of rates allows a flexible characterization of summary treatment effects. These ideas are applied to an illustrative data set consisting of a set of five trials comparing eight different treatments for asthma. A range of models is developed in which the relative treatment effects act on forward, backward transitions, or both, and models are compared using the DIC. Bayesian inferential techniques are used and the parameters are estimated using MCMC simulation in WinBUGS. An intuitively appealing mechanism of action involving a single parameter acting on all backward transitions was identified for the relative effects of the treatments, which allowed the estimation of a pooled treatment effect, allowing us to rank the different treatment options within each connected evidence network to ascertain which were the most clinically effective.
Statistics in Medicine 10/2010; 30(2):140-51. · 1.88 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: To propose methods for mixed treatment comparisons (MTC) based on pooled summaries of the type produced in overviews of reviews.
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.
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.
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.
Journal of clinical epidemiology 08/2010; 63(8):875-82. · 2.96 Impact Factor
-
Journal of clinical epidemiology 06/2010; 63(6):587-8. · 2.96 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: Pooling of direct and indirect evidence from randomized trials, known as mixed treatment comparisons (MTC), is becoming increasingly common in the clinical literature. MTC allows coherent judgements on which of the several treatments is the most effective and produces estimates of the relative effects of each treatment compared with every other treatment in a network.We introduce two methods for checking consistency of direct and indirect evidence. The first method (back-calculation) infers the contribution of indirect evidence from the direct evidence and the output of an MTC analysis and is useful when the only available data consist of pooled summaries of the pairwise contrasts. The second more general, but computationally intensive, method is based on 'node-splitting' which separates evidence on a particular comparison (node) into 'direct' and 'indirect' and can be applied to networks where trial-level data are available. Methods are illustrated with examples from the literature. We take a hierarchical Bayesian approach to MTC implemented using WinBUGS and R.We show that both methods are useful in identifying potential inconsistencies in different types of network and that they illustrate how the direct and indirect evidence combine to produce the posterior MTC estimates of relative treatment effects. This allows users to understand how MTC synthesis is pooling the data, and what is 'driving' the final estimates.We end with some considerations on the modelling assumptions being made, the problems with the extension of the back-calculation method to trial-level data and discuss our methods in the context of the existing literature.
Statistics in Medicine 03/2010; 29(7-8):932-44. · 1.88 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: 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.
Statistics in Medicine 04/2009; 28(14):1861-81. · 1.88 Impact Factor
-
[show abstract]
[hide abstract]
ABSTRACT: To assess the performance of novel contour enhanced funnel plots and a regression based adjustment method to detect and adjust for publication biases.
Secondary analysis of a published systematic literature review.
Placebo controlled trials of antidepressants previously submitted to the US Food and Drug Administration (FDA) and matching journal publications.
Publication biases were identified using novel contour enhanced funnel plots, a regression based adjustment method, Egger's test, and the trim and fill method. Results were compared with a meta-analysis of the gold standard data submitted to the FDA.
Severe asymmetry was observed in the contour enhanced funnel plot that appeared to be heavily influenced by the statistical significance of results, suggesting publication biases as the cause of the asymmetry. Applying the regression based adjustment method to the journal data produced a similar pooled effect to that observed by a meta-analysis of the FDA data. Contrasting journal and FDA results suggested that, in addition to other deviations from study protocol, switching from an intention to treat analysis to a per protocol one would contribute to the observed discrepancies between the journal and FDA results.
Novel contour enhanced funnel plots and a regression based adjustment method worked convincingly and might have an important part to play in combating publication biases.
BMJ (Clinical research ed.). 02/2009; 339:b2981.
