Comparison of outcomes on hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) in anaemia associated with chronic kidney disease: network meta-analyses in dialysis and non-dialysis dependent populations.

Background: Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are oral alternatives to current standard-of-care treatments for anaemia in chronic kidney disease (CKD). We conducted network meta-analyses to indirectly compare clinical outcomes for three HIF-PHIs in dialysis and non-dialysis populations with anaemia in CKD. Methods: The evidence base comprised phase III, randomised, controlled trials evaluating daprodustat, roxadustat, or vadadustat. Three outcomes were evaluated: efficacy [change from baseline in haemoglobin (Hgb)], cardiovascular safety [time to first major adverse cardiovascular event (MACE)] and quality of life [change from baseline in 36-Item Short Form Health Survey (SF-36) Vitality score]. Analyses were performed separately for all patients and for erythropoiesis-stimulating agent (ESA) non-users at baseline (non-dialysis population) or prevalent dialysis patients (dialysis population). Bayesian Markov Chain Monte Carlo methods with non-informative priors were used to estimate the posterior probability distribution and generate pairwise treatment comparisons. Point estimates (medians of posterior distributions) and 95% credible intervals (CrI) were calculated. Results: Seventeen trials were included. In non-dialysis patients, there were no clinically meaningful differences between the three HIF-PHIs with respect to Hgb change from baseline [all patients analysis (total n = 7907): daprodustat vs. roxadustat, 0.09 g/dL (95% CrI -0.14, 0.31); daprodustat vs. vadadustat, 0.09 g/dL (-0.04, 0.21); roxadustat vs. vadadustat, 0.00 g/dL (-0.22, 0.22)] or risk of MACE [all patients analysis (total n = 7959): daprodustat vs. roxadustat, hazard ratio (HR) 1.16 (95% CrI 0.76, 1.77); daprodustat vs. vadadustat, 0.88 (0.71, 1.09); roxadustat vs. vadadustat, 0.76 (0.50, 1.16)]. Daprodustat showed a greater increase in SF-36 Vitality compared with roxadustat [total n = 4880; treatment difference 4.70 points (95% CrI 0.08, 9.31)]. In dialysis patients, Hgb change from baseline was higher with daprodustat and roxadustat compared with vadadustat [all patients analysis (total n = 11 124): daprodustat, 0.34 g/dL (0.22, 0.45); roxadustat, 0.38 g/dL (0.27, 0.49)], while there were no clinically meaningful differences in the risk of MACE between the HIF-PHIs [all patients analysis (total n = 12 320): daprodustat vs. roxadustat, HR 0.89 (0.73, 1.08); daprodustat vs. vadadustat, HR 0.99 (0.82, 1.21); roxadustat vs. vadadustat, HR 1.12 (0.92, 1.37)]. Results were similar in analyses of ESA non-users and prevalent dialysis patients. Conclusions: In the setting of anaemia in CKD, indirect treatment comparisons suggest that daprodustat, roxadustat, and vadadustat are broadly clinically comparable in terms of efficacy and cardiovascular safety (precision was low for the latter), while daprodustat may be associated with reduction in fatigue to a greater extent than roxadustat.

Novel presentational approaches were developed for reporting network meta-analysis.

OBJECTIVES: To present graphical tools for reporting network meta-analysis (NMA) results aiming to increase the accessibility, transparency, interpretability, and acceptability of NMA analyses. STUDY DESIGN AND SETTINGS: The key components of NMA results were identified based on recommendations by agencies such as the National Institute for Health and Care Excellence (United Kingdom). Three novel graphs were designed to amalgamate the identified components using familiar graphical tools such as the bar, line, or pie charts and adhering to good graphical design principles. RESULTS: Three key components for presentation of NMA results were identified, namely relative effects and their uncertainty, probability of an intervention being best, and between-study heterogeneity. Two of the three graphs developed present results (for each pairwise comparison of interventions in the network) obtained from both NMA and standard pairwise meta-analysis for easy comparison. They also include options to display the probability best, ranking statistics, heterogeneity, and prediction intervals. The third graph presents rankings of interventions in terms of their effectiveness to enable clinicians to easily identify "top-ranking" interventions. CONCLUSIONS: The graphical tools presented can display results tailored to the research question of interest, and targeted at a whole spectrum of users from the technical analyst to the nontechnical clinician.

Evaluating the cost-effectiveness of diagnostic tests in combination: is it important to allow for performance dependency?

OBJECTIVES: To investigate the importance of accounting for potential performance dependency when evaluating the cost-effectiveness of two diagnostic tests used in combination. METHODS: Two meta-analysis models were fitted to estimate the diagnostic accuracy of Wells score and Ddimer in combination. The first model assumes that the two tests perform independently of one another; thus, two separate meta-analyses were fitted to the Ddimer and Wells score data and then combined. The second model allows for any performance dependency of the two tests by incorporating published data on the accuracy of Ddimer stratified by Wells score, as well as studies of Ddimer alone and Wells score alone. The results from the two meta-analysis models were input into a decision model to assess the impact that assumptions regarding performance dependency have on the overall cost-effectiveness of the tests. RESULTS: The results highlight the importance of accounting for potential performance dependency when evaluating the cost-effectiveness of diagnostic tests used in combination. In our example, assuming the diagnostic performance of the two tests to be independent resulted in the strategy "Wells score moderate/high risk treated for DVT and Wells score low risk tested further with Ddimer" being identified as the most cost-effective at the £20,000 willingness-to-pay threshold (probability cost-effective 0.8). However, when performance dependency is modeled, the most cost-effective strategies were "Ddimer alone" and "Wells score low/moderate risk discharged and Wells score high risk further tested with Ddimer" (probability cost-effective 0.4). CONCLUSIONS: When evaluating the effectiveness and cost-effectiveness of diagnostic tests used in combination, failure to account for diagnostic performance dependency may lead to erroneous results and nonoptimal decision making.

Meta-analysis of the accuracy of two diagnostic tests used in combination: application to the ddimer test and the wells score for the diagnosis of deep vein thrombosis.

OBJECTIVES: It is standard practice for diagnostic tests to be evaluated against gold standards in isolation. In routine clinical practice, however, it is commonplace for multiple tests to be used before making definitive diagnoses. This article describes a meta-analytic modeling framework developed to estimate the accuracy of the combination of two diagnostic tests, accounting for the likely nonindependence of the tests. METHODS: A novel multicomponent framework was developed to synthesize information available on different parameters in the model. This allows data to be included from studies evaluating single tests or both tests. Different likelihoods were specified for the different sources of data and linked by means of common parameters. The framework was applied to evaluate the diagnostic accuracy of the Ddimer test and the Wells score for deep vein thrombosis, and the results were compared with those of a model in which independence of tests was assumed. All models were evaluated by using Bayesian Markov chain Monte Carlo simulation methods. RESULTS: The results showed the importance of allowing for the (likely) nonindependence of tests in the meta-analysis model when evaluating a combination of diagnostic tests. The analysis also highlighted the relatively limited impact of those studies that evaluated only one of the two tests of interest. CONCLUSIONS: The models developed allowed the assumption of independence between diagnostic tests to be relaxed while combining a broad array of relevant information from disparate studies. The framework also raises questions regarding the utility of studies limited to the evaluation of single diagnostic tests.

Graphical augmentations to the funnel plot assess the impact of additional evidence on a meta-analysis.

OBJECTIVE: We aim to illustrate the potential impact of a new study on a meta-analysis, which gives an indication of the robustness of the meta-analysis. STUDY DESIGN AND SETTING: A number of augmentations are proposed to one of the most widely used of graphical displays, the funnel plot. Namely, 1) statistical significance contours, which define regions of the funnel plot in which a new study would have to be located to change the statistical significance of the meta-analysis; and 2) heterogeneity contours, which show how a new study would affect the extent of heterogeneity in a given meta-analysis. Several other features are also described, and the use of multiple features simultaneously is considered. RESULTS: The statistical significance contours suggest that one additional study, no matter how large, may have a very limited impact on the statistical significance of a meta-analysis. The heterogeneity contours illustrate that one outlying study can increase the level of heterogeneity dramatically. CONCLUSION: The additional features of the funnel plot have applications including 1) informing sample size calculations for the design of future studies eligible for inclusion in the meta-analysis; and 2) informing the updating prioritization of a portfolio of meta-analyses such as those prepared by the Cochrane Collaboration.

Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey.

OBJECTIVE: To examine the potential for publication bias, data availability bias, and reviewer selection bias in recently published meta-analyses that use individual participant data and to investigate whether authors of such meta-analyses seemed aware of these issues. DESIGN: In a database of 383 meta-analyses of individual participant data that were published between 1991 and March 2009, we surveyed the 31 most recent meta-analyses of randomised trials that examined whether an intervention was effective. Identification of relevant articles and data extraction was undertaken by one author and checked by another. RESULTS: Only nine (29%) of the 31 meta-analyses included individual participant data from "grey literature" (such as unpublished studies) in their primary meta-analysis, and the potential for publication bias was discussed or investigated in just 10 (32%). Sixteen (52%) of the 31 meta-analyses did not obtain all the individual participant data requested, yet five of these (31%) did not mention this as a potential limitation, and only six (38%) examined how trials without individual participant data might affect the conclusions. In nine (29%) of the meta-analyses reviewer selection bias was a potential issue, as the identification of relevant trials was either not stated or based on a more selective, non-systematic approach. Investigation of four meta-analyses containing data from ≥10 trials revealed one with an asymmetric funnel plot consistent with publication bias, and the inclusion of studies without individual participant data revealed additional heterogeneity between trials. CONCLUSIONS: Publication, availability, and selection biases are a potential concern for meta-analyses of individual participant data, but many reviewers neglect to examine or discuss them. These issues warn against uncritically viewing any meta-analysis that uses individual participant data as the most reliable. Reviewers should seek individual participant data from all studies identified by a systematic review; include, where possible, aggregate data from any studies lacking individual participant data to consider their potential impact; and investigate funnel plot asymmetry in line with recent guidelines.

Empirical assessment suggests that existing evidence could be used more fully in designing randomized controlled trials.

BACKGROUND: Meta-analyses of randomized controlled trials (RCTs) provide the highest level of evidence regarding the effectiveness of interventions. Less is known about how they are used to inform the design and reporting of RCTs. METHODS: A sample of RCTs published in leading medical journals in 2007 was assessed to establish whether authors considered previous trials in the design of their trial. An approach to calculate the sample size required for a significant pooled effect in an updated meta-analysis was applied to a subsample of the RCTs to illustrate the ways in which the results of an existing meta-analysis can be incorporated into the planning and reporting of new RCTs. RESULTS: Six of the 27 trials assessed (22%) reported the use of previous trial(s) for sample size calculations. Meta-analyses relating the results of the trial to previous research were cited in 37% (10 out of 27) of the report discussion sections. Previous evidence is formally incorporated into retrospective sample size calculations for three of the trials. DISCUSSION/CONCLUSION: Consulting previous research before embarking on a new trial and basing decisions about future research on the impact on an updated meta-analysis will make the reporting of research more coherent and the design of new RCTs more efficient.

Bayesian model selection for meta-analysis of diagnostic test accuracy data: Application to Ddimer for deep vein thrombosis.

A number of statistical models have been developed for meta-analysis (MA) of diagnostic test (DT) accuracy data. Here we consider these alternative MA models, explore the relationships between them, and consider the use of the deviance information criteria (DIC) to decide which is the most appropriate model for a given dataset. A Bayesian statistical approach is adopted throughout. The alternative MA models are applied to a dataset of 198 assays of Ddimer to diagnose deep vein thrombosis. In this example, based on the DIC, a bivariate random effects model for sensitivity and specificity fitted the data best. When considering the inclusion of study level covariates, allowing sensitivity to vary by study setting further improved the fit of the model. The model fitting approach is then repeated for a subset of the data, which highlights the less decisive results obtained when using the DIC with a more limited dataset. Formal approaches to model selection are often overlooked in an MA context; however, they offer sensible rationale to the analysis, particularly for complex models such as those proposed for DT accuracy. Specifically, the use of the DIC statistic appears to be well suited for deciding between potentially complex mixed-effect MA models, possibly including covariates. Copyright © 2010 John Wiley & Sons, Ltd.

Evidence synthesis as the key to more coherent and efficient research.

BACKGROUND: Systematic review and meta-analysis currently underpin much of evidence-based medicine. Such methodologies bring order to previous research, but future research planning remains relatively incoherent and inefficient. METHODS: To outline a framework for evaluation of health interventions, aimed at increasing coherence and efficiency through i) making better use of information contained within the existing evidence-base when designing future studies; and ii) maximising the information available and thus potentially reducing the need for future studies. RESULTS: The framework presented insists that an up-to-date meta-analysis of existing randomised controlled trials (RCTs) should always be considered before future trials are conducted. Such a meta-analysis should inform critical design issues such as sample size determination. The contexts in which the use of individual patient data meta-analysis and mixed treatment comparisons modelling may be beneficial before further RCTs are conducted are considered. Consideration should also be given to how any newly planned RCTs would contribute to the totality of evidence through its incorporation into an updated meta-analysis. We illustrate how new RCTs can have very low power to change inferences of an existing meta-analysis, particularly when between study heterogeneity is taken into consideration. CONCLUSION: While the collation of existing evidence as the basis for clinical practice is now routine, a more coherent and efficient approach to planning future RCTs to strengthen the evidence base needs to be developed. The framework presented is a proposal for how this situation can be improved.

Integration of meta-analysis and economic decision modeling for evaluating diagnostic tests.

Meta-analysis of diagnostic test accuracy data is more difficult than of effectiveness data because of 1) statistical challenges of dealing with multiple measures of accuracy (e.g., sensitivity and specificity) simultaneously and 2) incorporating threshold effects. A number of meta-analysis models are in use, ranging from naïve synthesis of independent sensitivity and specificity to optimization of a hierarchical summary receiver operating characteristic (SROC) curve. Little work has been done on how such analyses should inform decision models. This article aims to present a unified framework for the synthesis of primary data and economic evaluation of alternative diagnostic testing strategies using Bayesian Markov Chain Monte Carlo simulation methods. The authors extend this previous work by using systematic review to derive model parameters, fully allowing for uncertainty in their estimation, and formally incorporating variability between study results into the decision analysis. Using a simple decision model comparing alternative testing strategies for suspected deep vein thrombosis as an example, the authors consider how to use outputs of different alternative meta-analysis models in decision models. They also explore the limitations of diagnostic test studies, particularly when there is no obvious threshold value. To correct some of the limitations of diagnostic test studies, they propose that tests with implicit and explicit thresholds should be studied using distinctly different frameworks. Specifically, when a threshold exists, quantitative threshold information should be included in meta-analysis models to aid interpretation of SROCs. Setting policy to relate to a specific point may be much more difficult for studies with implicit thresholds.

Cost-effectiveness and value of information analyses of neuraminidase inhibitors for the treatment of influenza.

OBJECTIVES: To assess the cost-effectiveness of alternative strategies for the treatment of suspected influenza in otherwise healthy adults and to identify future research priorities using value of information analysis. METHODS: A decision model was used to estimate the costs and effects, in terms of quality-adjusted life-years (QALYs) of amantadine, zanamivir, and oseltamivir for the treatment of influenza in otherwise healthy adults using data predominantly from meta-analysis of randomized controlled trials. Probabilistic sensitivity analysis using Monte Carlo simulation was conducted. The expected value of perfect information for the entire model and for individual parameters was calculated. RESULTS: Based on mean costs and effects, zanamivir is dominated by oseltamivir. The incremental cost-effectiveness ratio for amantadine (compared with no treatment) is pound 11,000 and pound 44,000 for oseltamivir (compared with amantadine). The probability that amantadine is cost-effective at a willingness to pay of pound 30,000 per QALY is 0.74, falling to 0.49 at pound 20,000 per QALY. Global expected value of perfect information (EVPI) is pound 2 m over 15 years if a willingness to pay threshold of pound 30,000 per QALY is assumed rising to pound 9.6 m at pound 45,000 per QALY. EVPI for only one parameter exceeds pound 500,0000 at pound 30,000 per QALY: the quality of life for untreated influenza. CONCLUSIONS: At traditionally accepted values of willingness to pay for health benefits, it is unlikely that additional research would be an efficient use of scarce resources. The only exception to this would be to examine the health-related quality of life impact of influenza in an untreated patient group. If a higher threshold value were acceptable, there are a small group of parameters that may warrant further investigation. These would, however, require comparative, potentially expensive, research studies.

Recent developments in meta-analysis.

The art and science of meta-analysis, the combination of results from multiple independent studies, is now more than a century old. In the last 30 years, however, as the need for medical research and clinical practice to be based on the totality of relevant and sound evidence has been increasingly recognized, the impact of meta-analysis has grown enormously. In this paper, we review highlights of recent developments in meta-analysis in medical research. We outline in particular how emphasis has been placed on (i) heterogeneity and random-effects analyses; (ii) special consideration in different areas of application; (iii) assessing bias within and across studies; and (iv) extension of ideas to complex evidence synthesis. We conclude the paper with some remarks on ongoing challenges and possible directions for the future.

Bivariate random-effects meta-analysis and the estimation of between-study correlation.

BACKGROUND: When multiple endpoints are of interest in evidence synthesis, a multivariate meta-analysis can jointly synthesise those endpoints and utilise their correlation. A multivariate random-effects meta-analysis must incorporate and estimate the between-study correlation (rhoB). METHODS: In this paper we assess maximum likelihood estimation of a general normal model and a generalised model for bivariate random-effects meta-analysis (BRMA). We consider two applied examples, one involving a diagnostic marker and the other a surrogate outcome. These motivate a simulation study where estimation properties from BRMA are compared with those from two separate univariate random-effects meta-analyses (URMAs), the traditional approach. RESULTS: The normal BRMA model estimates rhoB as -1 in both applied examples. Analytically we show this is due to the maximum likelihood estimator sensibly truncating the between-study covariance matrix on the boundary of its parameter space. Our simulations reveal this commonly occurs when the number of studies is small or the within-study variation is relatively large; it also causes upwardly biased between-study variance estimates, which are inflated to compensate for the restriction on rhoB. Importantly, this does not induce any systematic bias in the pooled estimates and produces conservative standard errors and mean-square errors. Furthermore, the normal BRMA is preferable to two normal URMAs; the mean-square error and standard error of pooled estimates is generally smaller in the BRMA, especially given data missing at random. For meta-analysis of proportions we then show that a generalised BRMA model is better still. This correctly uses a binomial rather than normal distribution, and produces better estimates than the normal BRMA and also two generalised URMAs; however the model may sometimes not converge due to difficulties estimating rhoB. CONCLUSION: A BRMA model offers numerous advantages over separate univariate synthesises; this paper highlights some of these benefits in both a normal and generalised modelling framework, and examines the estimation of between-study correlation to aid practitioners.

Evidence-based sample size calculations based upon updated meta-analysis.

Meta-analyses of randomized controlled trials (RCTs) provide the highest level of evidence regarding the effectiveness of interventions and as such underpin much of evidence-based medicine. Despite this, meta-analyses are usually produced as observational by-products of the existing literature, with no formal consideration of future meta-analyses when individual trials are being designed. Basing the sample size of a new trial on the results of an updated meta-analysis which will include it, may sometimes make more sense than powering the trial in isolation. A framework for sample size calculation for a future RCT based on the results of a meta-analysis of the existing evidence is presented. Both fixed and random effect approaches are explored through an example. Bayesian Markov Chain Monte Carlo simulation modelling is used for the random effects model since it has computational advantages over the classical approach. Several criteria on which to base inference and hence power are considered. The prior expectation of the power is averaged over the prior distribution for the unknown true treatment effect. An extension to the framework allowing for consideration of the design for a series of new trials is also presented. Results suggest that power can be highly dependent on the statistical model used to meta-analyse the data and even very large studies may have little impact on a meta-analysis when there is considerable between study heterogeneity. This raises issues regarding the appropriateness of the use of random effect models when designing and drawing inferences across a series of studies.

Predicting costs over time using Bayesian Markov chain Monte Carlo methods: an application to early inflammatory polyarthritis.

This article focuses on the modelling and prediction of costs due to disease accrued over time, to inform the planning of future services and budgets. It is well documented that the modelling of cost data is often problematic due to the distribution of such data; for example, strongly right skewed with a significant percentage of zero-cost observations. An additional problem associated with modelling costs over time is that cost observations measured on the same individual at different time points will usually be correlated. In this study we compare the performance of four different multilevel/hierarchical models (which allow for both the within-subject and between-subject variability) for analysing healthcare costs in a cohort of individuals with early inflammatory polyarthritis (IP) who were followed-up annually over a 5-year time period from 1990/1991. The hierarchical models fitted included linear regression models and two-part models with log-transformed costs, and two-part model with gamma regression and a log link. The cohort was split into a learning sample, to fit the different models, and a test sample to assess the predictive ability of these models. To obtain predicted costs on the original cost scale (rather than the log-cost scale) two different retransformation factors were applied. All analyses were carried out using Bayesian Markov chain Monte Carlo (MCMC) simulation methods.

Mixed comparison of stroke prevention treatments in individuals with nonrheumatic atrial fibrillation.

BACKGROUND: We aimed to identify different stroke prevention treatments for atrial fibrillation assessed in randomized controlled trials and to compare them within a single evidence synthesis framework. METHODS: We updated the Cochrane review on anticoagulants and antiplatelet therapy for nonrheumatic atrial fibrillation to include randomized controlled trials published between January 2000 and March 2005 identified via the CENTRAL database and MEDLINE. A mixed-treatment comparison method was used to combine direct within-trial, between-treatment comparisons with indirect trial evidence while maintaining randomization. RESULTS: Data were combined from 19 clinical trials that included 17 833 patients randomized to 9 treatment strategies, including placebo. For prevention of ischemic stroke, adjusted standard-dose warfarin sodium (relative rate [RR], 0.35; 95% credible interval [CrI], 0.24 to 0.52), adjusted low-dose warfarin (RR, 0.35; 95% CrI, 0.19 to 0.60), ximelagatran (RR, 0.34; 95% CrI, 0.18 to 0.61), and aspirin (RR, 0.64; 95% CrI, 0.44 to 0.88) were all associated with a significantly lower rate of ischemic stroke compared with placebo. For major and fatal bleeding episodes, there was some evidence of an increased risk for all treatments but none were statistically significant. Assuming a baseline risk of 51 ischemic stroke events per 1000 person-years, it can be estimated that adjusted standard-dose warfarin could prevent 28 (95% CrI, -37 to -19) ischemic strokes at the expense of 11 (95% CrI, -1 to +39) major or fatal bleeding episodes. In comparison, aspirin could prevent 16 (95% CrI, -26 to -5) ischemic strokes at the expense of 6 (95% CrI, -3 to +27) major or fatal bleeding episodes. CONCLUSIONS: A lower rate of ischemic stroke and a higher rate of major bleeding episodes were found to be associated with oral anticoagulants compared with aspirin, and both anticoagulants and aspirin were found to be associated with a reduction in the rate of stroke compared with placebo.

Role of multivitamins and mineral supplements in preventing infections in elderly people: systematic review and meta-analysis of randomised controlled trials.

OBJECTIVE: To evaluate the effectiveness of multivitamins and mineral supplements in reducing infections in an elderly population. DESIGN: Systematic review and meta-analysis of randomised controlled trials. DATA SOURCES: Medline and other databases. Reference lists of identified articles were inspected for further relevant articles. SELECTION OF STUDIES: Trials were included if they evaluated the effect of multivitamins and mineral supplements on infections in an elderly population. REVIEW METHODS: Studies were assessed for the methodological quality by using the Jadad instrument. If the data required for the analyses were not available from the published articles we requested them from the original study authors. Meta-analysis was undertaken on three outcomes: the mean difference in number of days spent with infection, the odds ratio of at least one infection in the study period, and the incidence rate ratio for the difference in infection rates. Data on adverse events were also extracted. RESULTS: Eight trials met our inclusion criteria. Owing to inconsistency in the outcomes reported, only a proportion of the trials could be included in each meta-analysis. Multivitamins and mineral supplements were found to reduce the mean annual number of days spent with infection (three studies) by 17.5 (95% confidence interval 11 to 24, P < 0.001). The odds ratio for at least one infection in the study period (three studies) was 1.10 (0.81 to 1.50, P = 0.53). The infection rate ratio (four studies) was 0.89 (0.78 to 1.03, P = 0.11). Reporting of adverse events was poor. CONCLUSION: The evidence for routine use of multivitamin and mineral supplements to reduce infections in elderly people is weak and conflicting. Study results are heterogeneous, and this is partially confounded by outcome measure.

A Bayesian approach to evaluating net clinical benefit allowed for parameter uncertainty.

BACKGROUND AND OBJECTIVE: Although randomized controlled trials (RCTs) are conducted to establish whether novel interventions work on average in the patient population, there is a growing desire to move to a more individualized approach to evaluation. The potential benefits and harms of a treatment policy may differ between individuals. If these benefits and harms are not evaluated distinctly, and in a quantitative framework, transparency can be lost in the decision-making process. METHODS: Glasziou and Irwig have outlined the concept of net clinical treatment benefit for identifying the patients for whom the potential benefits of treatment outweigh the possible side effects. This study revisits the decision whether to use warfarin to treat atrial fibrillation. In this analysis, RCT and various sorts of observational data are synthesized. RESULTS: This reanalysis brings into question the conclusions of the original analysis on who would benefit from warfarin; however, caution is advised, due to limitations in the quality of life data available. CONCLUSION: A fully realized Bayesian implementation of the model is presented. This provides a framework for including uncertainty related to the estimation of all model parameters, and permits both direct probability statements and credible intervals for specific patient groups to be expressed.

What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data.

OBJECTIVES: To compare the performance of different meta-analysis methods for pooling odds ratios when applied to sparse event data with emphasis on the use of continuity corrections. BACKGROUND: Meta-analysis of side effects from RCTs or risk factors for rare diseases in epidemiological studies frequently requires the synthesis of data with sparse event rates. Combining such data can be problematic when zero events exist in one or both arms of a study as continuity corrections are often needed, but, these can influence results and conclusions. METHODS: A simulation study was undertaken comparing several meta-analysis methods for combining odds ratios (using various classical and Bayesian methods of estimation) on sparse event data. Where required, the routine use of a constant and two alternative continuity corrections; one based on a function of the reciprocal of the opposite group arm size; and the other an empirical estimate of the pooled effect size from the remaining studies in the meta-analysis, were also compared. A number of meta-analysis scenarios were simulated and replicated 1000 times, varying the ratio of the study arm sizes. RESULTS: Mantel-Haenszel summary estimates using the alternative continuity correction factors gave the least biased results for all group size imbalances. Logistic regression was virtually unbiased for all scenarios and gave good coverage properties. The Peto method provided unbiased results for balanced treatment groups but bias increased with the ratio of the study arm sizes. The Bayesian fixed effect model provided good coverage for all group size imbalances. The two alternative continuity corrections outperformed the constant correction factor in nearly all situations. The inverse variance method performed consistently badly, irrespective of the continuity correction used. CONCLUSIONS: Many routinely used summary methods provide widely ranging estimates when applied to sparse data with high imbalance between the size of the studies' arms. A sensitivity analysis using several methods and continuity correction factors is advocated for routine practice.

Effectiveness of neuraminidase inhibitors in treatment and prevention of influenza A and B: systematic review and meta-analyses of randomised controlled trials.

OBJECTIVE: To review the clinical effectiveness of oseltamivir and zanamivir for the treatment and prevention of influenza A and B. DESIGN: Systematic review and meta-analyses of randomised controlled trials. DATA SOURCES: Published studies were retrieved from electronic bibliographic databases; supplementary data were obtained from the manufacturers. SELECTION OF STUDIES: Randomised controlled, double blind trials that were published in English, had data available before 31 December 2001, evaluated treatment or prevention of naturally occurring influenza with zanamivir or oseltamivir (if given using the formulation and dosage licensed for clinical use), and reported at least one end point of relevance. REVIEW METHODS: The main outcome measures were the median time to the alleviation of symptoms (for treatment trials) and number of flu episodes avoided (for prevention trials). Three population groups were defined: children aged 12 years and under; otherwise healthy individuals aged 12 to 65 years; and "high risk" individuals (those with certain chronic medical conditions or aged 65 years and older). RESULTS: Seventeen treatment trials and seven prevention trials identified met the inclusion criteria. All trials included compared one of the drugs against placebo or standard care. Treatment of children, otherwise healthy individuals, and high risk populations with zanamivir reduced the median duration of symptoms in days respectively by 1.0 (95% confidence interval 0.5 to 1.5), 0.8 (0.3 to 1.3), and 0.9 (-0.1 to 1.9) for the intention to treat population. The corresponding results, in days, for oseltamivir were 0.9 (0.3 to 1.5), 0.9 (0.3 to 1.4), and 0.4 (-0.7 to 1.4). The effect of giving zanamivir and oseltamivir prophylactically resulted in a relative reduction of 70-90% in the odds of developing flu, depending on the strategy adopted and the population studied. CONCLUSIONS: Evidence from randomised controlled trials consistently supports the view that both oseltamivir and zanamivir are clinically effective for treating and preventing flu. However, evidence is limited for the treatment of certain populations and for all prevention strategies.