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1.
Stat Med ; 2021 May 27.
Artigo em Inglês | MEDLINE | ID: mdl-34048066

RESUMO

Treatment effects vary across different patients, and estimation of this variability is essential for clinical decision-making. We aimed to develop a model estimating the benefit of alternative treatment options for individual patients, extending a risk modeling approach in a network meta-analysis framework. We propose a two-stage prediction model for heterogeneous treatment effects by combining prognosis research and network meta-analysis methods where individual patient data are available. In the first stage, a prognostic model to predict the baseline risk of the outcome. In the second stage, we use the baseline risk score from the first stage as a single prognostic factor and effect modifier in a network meta-regression model. We apply the approach to a network meta-analysis of three randomized clinical trials comparing the relapses in Natalizumab, Glatiramer Acetate, and Dimethyl Fumarate, including 3590 patients diagnosed with relapsing-remitting multiple sclerosis. We find that the baseline risk score modifies the relative and absolute treatment effects. Several patient characteristics, such as age and disability status, impact the baseline risk of relapse, which in turn moderates the benefit expected for each of the treatments. For high-risk patients, the treatment that minimizes the risk of relapse in 2 years is Natalizumab, whereas Dimethyl Fumarate might be a better option for low-risk patients. Our approach can be easily extended to all outcomes of interest and has the potential to inform a personalized treatment approach.

4.
Stat Methods Med Res ; 30(5): 1358-1372, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33504274

RESUMO

Dose-response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose-response evidence is synthesized using either one-stage or two-stage models in a frequentist setting. We propose a hierarchical dose-response model implemented in a Bayesian framework. We develop our model assuming normal or binomial likelihood and accounting for exposures grouped in clusters. To allow maximum flexibility, the dose-response association is modelled using restricted cubic splines. We implement these models in R using JAGS and we compare our approach to the one-stage dose-response meta-analysis model in a simulation study. We found that the Bayesian dose-response model with binomial likelihood has lower bias than the Bayesian model with normal likelihood and the frequentist one-stage model when studies have small sample size. When the true underlying shape is log-log or half-sigmoid, the performance of all models depends on choosing an appropriate location for the knots. In all other examined situations, all models perform very well and give practically identical results. We also re-analyze the data from 60 randomized controlled trials (15,984 participants) examining the efficacy (response) of various doses of serotonin-specific reuptake inhibitor (SSRI) antidepressant drugs. All models suggest that the dose-response curve increases between zero dose and 30-40 mg of fluoxetine-equivalent dose, and thereafter shows small decline. We draw the same conclusion when we take into account the fact that five different antidepressants have been studied in the included trials. We show that implementation of the hierarchical model in Bayesian framework has similar performance to, but overcomes some of the limitations of the frequentist approach and offers maximum flexibility to accommodate features of the data.

5.
J Clin Epidemiol ; 133: 14-23, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-33359320

RESUMO

OBJECTIVES: This study compares three major elements of evidence-based medicine (EBM) practices, namely evidence synthesis, clinical practice guidelines (CPGs), and real-world prescriptions in the United States, regarding antidepressant treatments of major depression over the past 3 decades. STUDY DESIGN AND SETTING: We conducted network meta-analyses (NMAs) of antidepressants every 5 years up to 2016 based on a comprehensive data set of double-blind randomized controlled trials. We identified CPGs and extracted their recommendations. We surveyed the prescriptions in the United States at 5-year intervals up to 2015. RESULTS: Most drugs recommended by CPGs presented favorable performance in efficacy and acceptability in NMAs. However, CPG recommendations were often in terms of drug classes rather than individual drugs, whereas NMAs suggested distinctive difference between drugs within the same class. The update intervals of all CPGs were longer than 5 years. All the antidepressants prescribed frequently in the United States were recommended by CPGs. However, changes in prescriptions did not correspond to alterations in CPGs or to apparent changes in the effects indicated by NMAs. Many factors including marketing efforts, regulations, or patient values may have played a role. CONCLUSION: Enhancements including accelerating CPG updates and monitoring the impact of marketing on prescriptions should be considered in future EBM implementation.

6.
Res Synth Methods ; 2020 Dec 02.
Artigo em Inglês | MEDLINE | ID: mdl-33264498

RESUMO

Meta-analysis results are usually presented in forest plots, which show the individual study results and the summary effect along with their confidence intervals. In this paper, we propose a system of linear springs as a mechanical analogue of metaanalysis that enables visualisation and enhances intuition. The length of a spring corresponds to a study treatment effect and the stiffness of the spring corresponds to its inverse variance. To synthesise study springs we use two main operations: connection in parallel and connection in series. We show the equivalence between meta-analysis and linear springs for fixed effect and random effects pairwise metaanalysis and we also derive indirect treatment effects. We use examples to illustrate the different meta-analytical schemes using the corresponding system of springs. The proposed visualization tool can serve as an educational plot, especially useful for researchers with no statistical background. The analogy between meta-analysis and springs facilitates intuition for notions such as heterogeneity and the differences between fixed and random effects meta-analysis. This article is protected by copyright. All rights reserved.

7.
Res Synth Methods ; 2020 Oct 17.
Artigo em Inglês | MEDLINE | ID: mdl-33070439

RESUMO

BACKGROUND: Network meta-analysis (NMA) produces complex outputs as many comparisons between interventions are of interest. The estimated relative treatment effects are usually displayed in a forest plot or in a league table and several ranking metrics are calculated and presented. METHODS: In this article, we estimate relative treatment effects of each competing treatment against a fictional treatment of average performance using the "deviation from the means" coding that has been used to parametrize categorical covariates in regression models. We then use this alternative parametrization of the NMA model to present a ranking metric (PreTA: Preferable Than Average) interpreted as the probability that a treatment is better than a fictional treatment of average performance. RESULTS: We illustrate the alternative parametrization of the NMA model using two networks of interventions, a network of 18 antidepressants for acute depression and a network of four interventions for heavy menstrual bleeding. We also use these two networks to highlight differences among PreTA and existing ranking metrics. We further examine the agreement between PreTA and existing ranking metrics in 232 networks of interventions and conclude that their agreement depends on the precision with which relative effects are estimated. CONCLUSIONS: A forest plot with NMA relative treatment effects using "deviation from means" coding could complement presentation of NMA results in large networks and in absence of an obvious reference treatment. PreTA is a viable alternative to existing probabilistic ranking metrics that naturally incorporates uncertainty.

9.
PLoS Med ; 17(9): e1003346, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32960881

RESUMO

BACKGROUND: There is disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a living systematic review and meta-analysis to address three questions: (1) Amongst people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? (2) Amongst people with SARS-CoV-2 infection who are asymptomatic when diagnosed, what proportion will develop symptoms later? (3) What proportion of SARS-CoV-2 transmission is accounted for by people who are either asymptomatic throughout infection or presymptomatic? METHODS AND FINDINGS: We searched PubMed, Embase, bioRxiv, and medRxiv using a database of SARS-CoV-2 literature that is updated daily, on 25 March 2020, 20 April 2020, and 10 June 2020. Studies of people with SARS-CoV-2 diagnosed by reverse transcriptase PCR (RT-PCR) that documented follow-up and symptom status at the beginning and end of follow-up or modelling studies were included. One reviewer extracted data and a second verified the extraction, with disagreement resolved by discussion or a third reviewer. Risk of bias in empirical studies was assessed with an adapted checklist for case series, and the relevance and credibility of modelling studies were assessed using a published checklist. We included a total of 94 studies. The overall estimate of the proportion of people who become infected with SARS-CoV-2 and remain asymptomatic throughout infection was 20% (95% confidence interval [CI] 17-25) with a prediction interval of 3%-67% in 79 studies that addressed this review question. There was some evidence that biases in the selection of participants influence the estimate. In seven studies of defined populations screened for SARS-CoV-2 and then followed, 31% (95% CI 26%-37%, prediction interval 24%-38%) remained asymptomatic. The proportion of people that is presymptomatic could not be summarised, owing to heterogeneity. The secondary attack rate was lower in contacts of people with asymptomatic infection than those with symptomatic infection (relative risk 0.35, 95% CI 0.10-1.27). Modelling studies fit to data found a higher proportion of all SARS-CoV-2 infections resulting from transmission from presymptomatic individuals than from asymptomatic individuals. Limitations of the review include that most included studies were not designed to estimate the proportion of asymptomatic SARS-CoV-2 infections and were at risk of selection biases; we did not consider the possible impact of false negative RT-PCR results, which would underestimate the proportion of asymptomatic infections; and the database does not include all sources. CONCLUSIONS: The findings of this living systematic review suggest that most people who become infected with SARS-CoV-2 will not remain asymptomatic throughout the course of the infection. The contribution of presymptomatic and asymptomatic infections to overall SARS-CoV-2 transmission means that combination prevention measures, with enhanced hand hygiene, masks, testing tracing, and isolation strategies and social distancing, will continue to be needed.


Assuntos
Infecções Assintomáticas/epidemiologia , Infecções por Coronavirus/epidemiologia , Pneumonia Viral/epidemiologia , Doenças Assintomáticas/epidemiologia , Betacoronavirus , Infecções por Coronavirus/fisiopatologia , Infecções por Coronavirus/transmissão , Progressão da Doença , Humanos , Programas de Rastreamento , Pandemias , Pneumonia Viral/fisiopatologia , Pneumonia Viral/transmissão , Reação em Cadeia da Polimerase Via Transcriptase Reversa
10.
BMJ Open ; 10(8): e037744, 2020 08 20.
Artigo em Inglês | MEDLINE | ID: mdl-32819946

RESUMO

OBJECTIVE: To empirically explore the level of agreement of the treatment hierarchies from different ranking metrics in network meta-analysis (NMA) and to investigate how network characteristics influence the agreement. DESIGN: Empirical evaluation from re-analysis of NMA. DATA: 232 networks of four or more interventions from randomised controlled trials, published between 1999 and 2015. METHODS: We calculated treatment hierarchies from several ranking metrics: relative treatment effects, probability of producing the best value [Formula: see text] and the surface under the cumulative ranking curve (SUCRA). We estimated the level of agreement between the treatment hierarchies using different measures: Kendall's τ and Spearman's ρ correlation; and the Yilmaz [Formula: see text] and Average Overlap, to give more weight to the top of the rankings. Finally, we assessed how the amount of the information present in a network affects the agreement between treatment hierarchies, using the average variance, the relative range of variance and the total sample size over the number of interventions of a network. RESULTS: Overall, the pairwise agreement was high for all treatment hierarchies obtained by the different ranking metrics. The highest agreement was observed between SUCRA and the relative treatment effect for both correlation and top-weighted measures whose medians were all equal to 1. The agreement between rankings decreased for networks with less precise estimates and the hierarchies obtained from [Formula: see text] appeared to be the most sensitive to large differences in the variance estimates. However, such large differences were rare. CONCLUSIONS: Different ranking metrics address different treatment hierarchy problems, however they produced similar rankings in the published networks. Researchers reporting NMA results can use the ranking metric they prefer, unless there are imprecise estimates or large imbalances in the variance estimates. In this case treatment hierarchies based on both probabilistic and non-probabilistic ranking metrics should be presented.

11.
Biometrics ; 76(4): 1240-1250, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-32720712

RESUMO

Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analyses. The most well-known reasons for small study effects include publication bias, outcome reporting bias, and clinical heterogeneity. Methods to account for small study effects in univariate meta-analysis have been extensively studied. However, detecting small study effects in a multivariate meta-analysis setting remains an untouched research area. One of the complications is that different types of selection processes can be involved in the reporting of multivariate outcomes. For example, some studies may be completely unpublished while others may selectively report multiple outcomes. In this paper, we propose a score test as an overall test of small study effects in multivariate meta-analysis. Two detailed case studies are given to demonstrate the advantage of the proposed test over various naive applications of univariate tests in practice. Through simulation studies, the proposed test is found to retain nominal Type I error rates with considerable power in moderate sample size settings. Finally, we also evaluate the concordance between the proposed tests with the naive application of univariate tests by evaluating 44 systematic reviews with multiple outcomes from the Cochrane Database.

12.
BMC Med Res Methodol ; 20(1): 190, 2020 Jul 14.
Artigo em Inglês | MEDLINE | ID: mdl-32664867

RESUMO

BACKGROUND: In pairwise meta-analysis, the contribution of each study to the pooled estimate is given by its weight, which is based on the inverse variance of the estimate from that study. For network meta-analysis (NMA), the contribution of direct (and indirect) evidence is easily obtained from the diagonal elements of a hat matrix. It is, however, not fully clear how to generalize this to the percentage contribution of each study to a NMA estimate. METHODS: We define the importance of each study for a NMA estimate by the reduction of the estimate's variance when adding the given study to the others. An equivalent interpretation is the relative loss in precision when the study is left out. Importances are values between 0 and 1. An importance of 1 means that the study is an essential link of the pathway in the network connecting one of the treatments with another. RESULTS: Importances can be defined for two-stage and one-stage NMA. These numbers in general do not add to one and thus cannot be interpreted as 'percentage contributions'. After briefly discussing other available approaches, we question whether it is possible to obtain unique percentage contributions for NMA. CONCLUSIONS: Importances generalize the concept of weights in pairwise meta-analysis in a natural way. Moreover, they are uniquely defined, easily calculated, and have an intuitive interpretation. We give some real examples for illustration.

13.
Res Synth Methods ; 2020 Jun 10.
Artigo em Inglês | MEDLINE | ID: mdl-32524754

RESUMO

Network meta-analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes is of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every-day clinical practice, such as the odds ratios. In this article, we aim to facilitate the clinical decision-making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Our plot compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatment effects, while it illustrates absolute, rather than relative, effects of interventions. Moreover, it can be easily modified to include considerations regarding clinically important effects. To showcase our method, we use data from a network of studies in antidepressants. All analyses are performed in R and we provide the source code needed to produce the Kilim plot, as well as an interactive web application.

14.
Syst Rev ; 9(1): 140, 2020 06 12.
Artigo em Inglês | MEDLINE | ID: mdl-32532307

RESUMO

BACKGROUND: A model that can predict treatment response for a patient with specific baseline characteristics would help decision-making in personalized medicine. The aim of the study is to develop such a model in the treatment of rheumatoid arthritis (RA) patients who receive certolizumab (CTZ) plus methotrexate (MTX) therapy, using individual participant data meta-analysis (IPD-MA). METHODS: We will search Cochrane CENTRAL, PubMed, and Scopus as well as clinical trial registries, drug regulatory agency reports, and the pharmaceutical company websites from their inception onwards to obtain randomized controlled trials (RCTs) investigating CTZ plus MTX compared with MTX alone in treating RA. We will request the individual-level data of these trials from an independent platform (http://vivli.org). The primary outcome is efficacy defined as achieving either remission (based on ACR-EULAR Boolean or index-based remission definition) or low disease activity (based on either of the validated composite disease activity measures). The secondary outcomes include ACR50 (50% improvement based on ACR core set variables) and adverse events. We will use a two-stage approach to develop the prediction model. First, we will construct a risk model for the outcomes via logistic regression to estimate the baseline risk scores. We will include baseline demographic, clinical, and biochemical features as covariates for this model. Next, we will develop a meta-regression model for treatment effects, in which the stage 1 risk score will be used both as a prognostic factor and as an effect modifier. We will calculate the probability of having the outcome for a new patient based on the model, which will allow estimation of the absolute and relative treatment effect. We will use R for our analyses, except for the second stage which will be performed in a Bayesian setting using R2Jags. DISCUSSION: This is a study protocol for developing a model to predict treatment response for RA patients receiving CTZ plus MTX in comparison with MTX alone, using a two-stage approach based on IPD-MA. The study will use a new modeling approach, which aims at retaining the statistical power. The model may help clinicians individualize treatment for particular patients. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration number pending (ID#157595).

15.
Res Synth Methods ; 2020 Apr 30.
Artigo em Inglês | MEDLINE | ID: mdl-32352639

RESUMO

It is often challenging to present the available evidence in a timely and comprehensible manner. We aimed to visualize the evolution of evidence about antidepressants for depression by conducting cumulative network meta-analyses (NMAs) and to examine whether it could have helped the selection of optimal drugs. We built a Shiny web application that performs and presents cumulative NMAs based on R netmeta. We used a comprehensive dataset of double-blind randomized controlled trials of 21 antidepressants in the acute treatment of major depression. The primary outcomes were efficacy (treatment response) and acceptability (all-cause discontinuation), and treatment effects were summarized via odds ratios. We evaluated the confidence in evidence using the CINeMA (Confidence in Network Meta-Analysis) framework for a series of consecutive NMAs. Users can change several conditions for the analysis, such as the period of synthesis, among the others. We present the league tables and two-dimensional plots that combine efficacy, acceptability and level of confidence in the evidence together, for NMAs conducted in 1990, 1995, 2000, 2005, 2010, and 2016. They reveal that through the past four decades, newly approved drugs often showed initially exaggerated results, which tended to diminish and stabilize after approximately a decade. Over the years, the drugs with relative superiority changed dramatically; but as the evidence network grew larger and better connected, the overall confidence improved. The Shiny app visualizes how evidence evolved over years, emphasizing the need for a careful interpretation of relative effects between drugs, especially for the potentially amplified performance of newly approved drugs.

16.
Lancet Infect Dis ; 20(10): 1182-1192, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32470329

RESUMO

BACKGROUND: Antibiotic prophylaxis is frequently continued for 1 day or more after surgery to prevent surgical site infection. Continuing antibiotic prophylaxis after an operation might have no advantage compared with its immediate discontinuation, and it unnecessarily exposes patients to risks associated with antibiotic use. In 2016, WHO recommended discontinuation of antibiotic prophylaxis after surgery. We aimed to update the evidence that formed the basis for that recommendation. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, CINAHL, CENTRAL, and WHO regional medical databases for randomised controlled trials (RCTs) on postoperative antibiotic prophylaxis that were published from Jan 1, 1990, to July 24, 2018. RCTs comparing the effect of postoperative continuation versus discontinuation of antibiotic prophylaxis on the incidence of surgical site infection in patients undergoing any surgical procedure with an indication for antibiotic prophylaxis were eligible. The primary outcome was the effect of postoperative surgical antibiotic prophylaxis continuation versus its immediate discontinuation on the occurrence of surgical site infection, with a prespecified subgroup analysis for studies that did and did not adhere to current best practice standards for surgical antibiotic prophylaxis. We calculated summary relative risks (RRs) with corresponding 95% CIs using a random effects model (DerSimonian and Laird). We evaluated heterogeneity with the χ2 test, I2, and τ2, and visually assesed publication bias with a contour-enhanced funnel plot. This study is registered with PROSPERO, CRD42017060829. FINDINGS: We identified 83 relevant RCTs, of which 52 RCTs with 19 273 participants were included in the primary meta-analysis. The pooled RR of surgical site infection with postoperative continuation of antibiotic prophylaxis versus its immediate discontinuation was 0·89 (95% CI 0·79-1·00), with low heterogeneity in effect size between studies (τ2=0·001, χ2 p=0·46, I2=0·7%). Our prespecified subgroup analysis showed a significant association between the effect estimate and adherence to best practice standards of surgical antibiotic prophylaxis: the RR of surgical site infection was reduced with continued antibiotic prophylaxis after surgery compared with its immediate discontinuation in trials that did not meet best practice standards (0·79 [95% CI 0·67-0·94]) but not in trials that did (1·04 [0·85-1·27]; p=0·048). Whether studies adhered to best practice standards explained all variance in the pooled estimate from the primary meta-analysis. INTERPRETATION: Overall, we identified no conclusive evidence for a benefit of postoperative continuation of antibiotic prophylaxis over its discontinuation. When best practice standards were followed, postoperative continuation of antibiotic prophylaxis did not yield any additional benefit in reducing the incidence of surgical site infection. These findings support WHO recommendations against this practice. FUNDING: None.


Assuntos
Antibacterianos/administração & dosagem , Antibacterianos/farmacologia , Cuidados Pós-Operatórios , Infecção da Ferida Cirúrgica/prevenção & controle , Esquema de Medicação , Humanos
17.
J Clin Epidemiol ; 124: 42-49, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-32302680

RESUMO

OBJECTIVES: Network meta-analysis (NMA) may produce more precise estimates of treatment effects than pairwise meta-analysis. We examined the relative contribution of network paths of different lengths to estimates of treatment effects. STUDY DESIGN AND SETTING: We analyzed 213 published NMAs. We categorized network shapes according to the presence or absence of at least one closed loop (nonstar or star network) and derived the graph density, radius, and diameter. We identified paths of different lengths and calculated their percentage contribution to each NMA effect estimate, based on their contribution matrix. RESULTS: Among the 213 NMAs included in analyses, 33% of the information came from paths of length 1 (direct evidence), 47% from paths of length 2 (indirect paths with one intermediate treatment) and 20% from paths of length 3. The contribution of paths of different lengths depended on the size of networks, presence of closed loops, and graph radius, density, and diameter. Longer paths contribute more as the number of treatments and loops and the graph radius and diameter increase. CONCLUSION: The contribution of different paths depends on the size and structure of networks, with important implications for assessing the risk of bias and confidence in NMA results.

18.
PLoS Med ; 17(4): e1003082, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-32243458

RESUMO

BACKGROUND: The evaluation of the credibility of results from a meta-analysis has become an important part of the evidence synthesis process. We present a methodological framework to evaluate confidence in the results from network meta-analyses, Confidence in Network Meta-Analysis (CINeMA), when multiple interventions are compared. METHODOLOGY: CINeMA considers 6 domains: (i) within-study bias, (ii) reporting bias, (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. Key to judgments about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The contribution matrix can easily be computed using a freely available web application. In evaluating imprecision, heterogeneity, and incoherence, we consider the impact of these components of variability in forming clinical decisions. CONCLUSIONS: Via 3 examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgments, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks.


Assuntos
Doença da Artéria Coronariana/diagnóstico por imagem , Eletrocardiografia/normas , Teste de Esforço/normas , Imagem Cinética por Ressonância Magnética/normas , Metanálise em Rede , Ensaios Clínicos Controlados Aleatórios como Assunto/normas , Intervalos de Confiança , Doença da Artéria Coronariana/epidemiologia , Eletrocardiografia/métodos , Teste de Esforço/métodos , Humanos , Imagem Cinética por Ressonância Magnética/métodos , Ensaios Clínicos Controlados Aleatórios como Assunto/métodos
19.
Lancet ; 395(10228): 986-997, 2020 03 21.
Artigo em Inglês | MEDLINE | ID: mdl-32199486

RESUMO

Fewer than half of new drugs have data on their comparative benefits and harms against existing treatment options at the time of regulatory approval in Europe and the USA. Even when active-comparator trials exist, they might not produce meaningful data to inform decisions in clinical practice and health policy. The uncertainty associated with the paucity of well designed active-comparator trials has been compounded by legal and regulatory changes in Europe and the USA that have created a complex mix of expedited programmes aimed at facilitating faster access to new drugs. Comparative evidence generation is even sparser for medical devices. Some have argued that the current process for regulatory approval needs to generate more evidence that is useful for patients, clinicians, and payers in health-care systems. We propose a set of five key principles relevant to the European Medicines Agency, European medical device regulatory agencies, US Food and Drug Administration, as well as payers, that we believe will provide the necessary incentives for pharmaceutical and device companies to generate comparative data on drugs and devices and assure timely availability of evidence that is useful for decision making. First, labelling should routinely inform patients and clinicians whether comparative data exist on new products. Second, regulators should be more selective in their use of programmes that facilitate drug and device approvals on the basis of incomplete benefit and harm data. Third, regulators should encourage the conduct of randomised trials with active comparators. Fourth, regulators should use prospectively designed network meta-analyses based on existing and future randomised trials. Last, payers should use their policy levers and negotiating power to incentivise the generation of comparative evidence on new and existing drugs and devices, for example, by explicitly considering proven added benefit in pricing and payment decisions.


Assuntos
Aprovação de Equipamentos/normas , Aprovação de Drogas/métodos , Segurança de Equipamentos , Segurança , Biomarcadores Farmacológicos/análise , Tolerância a Medicamentos , Medicina Baseada em Evidências , Humanos , Estados Unidos , United States Food and Drug Administration
20.
BMJ Open ; 10(1): e035073, 2020 01 19.
Artigo em Inglês | MEDLINE | ID: mdl-31959613

RESUMO

INTRODUCTION: There is evidence that different psychosocial interventions could reduce the risk of relapse in schizophrenia, but a comprehensive evidence based on their relative efficacy is lacking. We will conduct a network meta-analysis (NMA), integrating direct and indirect comparisons from randomised controlled trials (RCTs) to rank psychosocial treatments for relapse prevention in schizophrenia according to their efficacy, acceptability and tolerability. METHODS AND ANALYSIS: We will include all RCTs comparing a psychosocial treatment aimed at preventing relapse in patients with schizophrenia with another psychosocial intervention or with a no treatment condition (waiting list, treatment as usual). We will include studies on adult patients with schizophrenia, excluding specific subpopulations (eg, acutely ill patients). Primary outcome will be the number of patients experiencing a relapse. Secondary outcomes will be acceptability (dropout), change in overall, positive, negative and depressive symptoms, quality of life, adherence, functioning and adverse events. Published and unpublished studies will be sought through database searches, trial registries and websites. Study selection and data extraction will be conducted by at least two independent reviewers. We will conduct random-effects NMA to synthesise all evidence for each outcome and obtain a comprehensive ranking of all treatments. NMA will be conducted in R within a frequentist framework. The risk of bias in studies will be evaluated using the Cochrane Risk of Bias tool and the credibility of the evidence will be evaluated using Confidence in Network Meta-Analysis (CINeMA). Subgroup and sensitivity analyses will be conducted to assess the robustness of the findings. ETHICS AND DISSEMINATION: No ethical issues are foreseen. Results from this study will be published in peer-reviewed journals and presented at relevant conferences. PROSPERO REGISTRATION NUMBER: CRD42019147884.

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