A Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests

Network meta-analysis made simple: a composite likelihood approach

Network meta-analysis, also known as mixed treatments comparison meta-analysis or multiple treatments meta-analysis, extends conventional pairwise meta-analysis by simultaneously synthesizing multiple interventions in a single integrated analysis. Despite the growing popularity of network metaanalysis within comparative effectiveness research, it comes with potential challenges. For example, within-study correlations among treatment comparisons are rarely reported in the published literature. Yet, these correlations are pivotal for valid statistical inference. As demonstrated in earlier studies, ignoring these correlations can inflate mean squared errors of the resulting point estimates and lead to inaccurate standard error estimates. This paper introduces a composite likelihood-based approach that ensures accurate statistical inference without requiring knowledge of the within-study correlations. The proposed method is computationally robust and efficient, with substantially reduced computational time compared to the state-of-the-science methods implemented in R packages. The proposed method was evaluated through extensive simulations and applied to two important applications including a network meta-analysis comparing interventions for primary open-angle glaucoma, and another comparing treatments for chronic prostatitis and chronic pelvic pain syndrome.

Diagnostic performance of angiography-derived fractional flow reserve and CT-derived fractional flow reserve: A systematic review and Bayesian network meta-analysis

OBJECTIVE

Accumulating evidence has demonstrated that fractional flow reserves (FFRs) derived from invasive coronary angiograms (CA-FFRs) and coronary computed tomography angiography-derived FFRs (CT-FFRs) are promising alternatives to wire-based FFRs. However, it remains unclear which method has better diagnostic performance. This systematic review and meta-analysis aimed to compare the diagnostic performances of the two approaches.

METHODS

The Cochrane Library, PubMed, Embase, Medline (Ovid), the Chinese China National Knowledge Infrastructure Database (CNKI), VIP, and WanFang Data databases were searched for relevant studies that included comparisons between CA-FFR and CT-FFR, from their respective database inceptions until January 1, 2023. Studies where both noninvasive FFR (including CA-FFR and CT-FFR) and invasive FFR (as a reference standard) were performed for the diagnosis of ischemic coronary artery disease and were designed as prospective, paired diagnostic studies, were pulled. The diagnostic test accuracy method and Bayesian hierarchical summary receiver operating characteristic (ROC) model for network meta-analysis (NMA) of diagnostic tests (HSROC-NMADT) were both used to perform a meta-analysis on the data.

RESULTS

Twenty-six studies were included in this NMA. The results from both the diagnostic test accuracy and HSROC-NMADT methods revealed that the diagnostic accuracy of CA-FFR was higher than that of CT-FFR, in terms of sensitivity (Se; 0.86 vs. 0.84), specificity (Sp; 0.90 vs. 0.78), positive predictive value (PPV; 0.83 vs. 0.70), and negative predictive value (NPV; 0.91 vs. 0.89) for the detection of myocardial ischemia. A cumulative ranking curve analysis indicated that CA-FFR had a higher diagnostic accuracy than CT-FFR in the context of this study, with a higher area under the ROC curve (AUC; 0.94 vs. 0.87).

CONCLUSIONS

Although both of these two commonly used virtual FFR methods showed high levels of diagnostic accuracy, we demonstrated that CA-FFR had a better Se, Sp, PPV, NPV, and AUC than CT-FFR. However, this study provided only indirect comparisions; therefore, larger studies are warranted to directly compare the diagnostic performances of these two approaches.

An overview of systematic reviews on imaging tests for diagnosis of pulmonary embolism applying different network meta-analytic methods

PURPOSE

This study aimed to apply different methods of diagnostic test accuracy network meta-analysis (DTA-NMA) for studies reporting results of five imaging tests for the diagnosis of suspected pulmonary embolism (PE): pulmonary angiography (PA), computed tomography angiography (CTPA), magnetic resonance angiography (MRA), planar ventilation/perfusion (V/Q) scintigraphy and single-photon emission computed tomography ventilation/perfusion (SPECT V/Q).

METHODS

We searched four databases (MEDLINE [via PubMed], Cochrane CENTRAL, Scopus, and Epistemonikos) from inception until June 2, 2022 to identify systematic reviews (SRs) describing diagnostic accuracy of PA, CTPA, MRA, V/Q scan and SPECT V/Q for suspected PE. Study-level data were extracted and pooled using a hierarchical summary receiver operating characteristic (HSROC) meta-regression approach and two DTA-NMA models to compare accuracy estimates of different imaging tests. Risk of bias was assessed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool and certainty of evidence using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework.

RESULTS

We identified 13 SRs, synthesizing data from 33 primary studies and for four imaging tests (PA, CTPA, MRA and V/Q scan). The HSROC meta-regression model using PA as the reference standard showed that MRA had the best overall diagnostic performance with sensitivity of 0.93 (95% confidence interval [CI]: 0.76, 1.00) and specificity of 0.94 (95% CI: 0.84, 0.99). However, DTA-NMA models indicated that V/Q scan had the highest sensitivity, while CTPA was most specific.

CONCLUSION

Selecting a different DTA-NMA method to assess multiple diagnostic tests can affect estimates of diagnostic accuracy. There is no established method, but the choice depends on the data and familiarity with Bayesian statistics.

Double-Negative Results Matter: A Reevaluation of Sensitivities for Detecting SARS-CoV-2 Infection Using Saliva Versus Nasopharyngeal Swabs

In a recent systematic review, Bastos et al. (Ann Intern Med. 2021;174(4):501-510) compared the sensitivities of saliva sampling and nasopharyngeal swabs in the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by assuming a composite reference standard defined as positive if either test is positive and negative if both tests are negative (double negative). Even under a perfect specificity assumption, this approach ignores the double-negative results and risks overestimating the sensitivities due to residual misclassification. In this article, we first illustrate the impact of double-negative results in the estimation of the sensitivities in a single study, and then propose a 2-step latent class meta-analysis method for reevaluating both sensitivities using the same published data set as that used in Bastos et al. by properly including the observed double-negative results. We also conduct extensive simulation studies to compare the performance of the proposed method with Bastos et al.'s method for varied levels of prevalence and between-study heterogeneity. The results demonstrate that the sensitivities are overestimated noticeably using Bastos et al.'s method, and the proposed method provides a more accurate evaluation with nearly no bias and close-to-nominal coverage probability. In conclusion, double-negative results can significantly impact the estimated sensitivities when a gold standard is absent, and thus they should be properly incorporated.

Intraoperative parathyroid hormone monitoring in parathyroidectomy for hyperparathyroidism: a protocol for a network meta-analysis of diagnostic test accuracy

Intraoperative parathyroid hormone (iPTH) monitoring is standard-of-care in the surgical management of hyperparathyroidism. It involves real-time determination of circulating PTH levels to guide parathyroid gland excision. There exists several iPTH monitoring criteria, such as the Miami criteria, and a lack of standardization in the timing of post-parathyroid gland excision samples. We present a protocol of a systematic review and network meta-analysis of diagnostic test accuracy to identify the iPTH criteria and post-gland excision timepoint that best predicts surgical cure in hyperparathyroidism. The database search strategy will be developed in conjunction with a librarian specialist. We will perform a search of Medline (Ovid), EMBASE (Ovid), CINAHL, Cochrane Collaboration, and Web of Science from 1990-present. Studies will be eligible if they include adult patients diagnosed with hyperparathyroidism who undergo parathyroidectomy with iPTH monitoring. We will only include studies that report diagnostic test properties for iPTH criteria and/or post-excision sampling timepoints. All screening, full-text review, data extraction, and critical appraisal will be performed in duplicate. Critical appraisal will be performed using QUADAS-2 instrument. A descriptive analysis will present study and critical appraisal characteristics. We will perform evaluation of between-study heterogeneity using I2 and Cochrane Q and where applicable, we will perform sensitivity analysis. Our network meta-analysis will include Bayesian hierarchical framework with random effects using multiple models. Ethics approval is not required. This proposed systematic review will utilize a novel Bayesian network meta-analysis model to help standardize iPTH monitoring in hyperparathyroidism, thereby optimizing patient outcomes and healthcare expenditures.

Bayesian hierarchical models incorporating study-level covariates for multivariate meta-analysis of diagnostic tests without a gold standard with application to COVID-19

When evaluating a diagnostic test, it is common that a gold standard may not be available. One example is the diagnosis of SARS-CoV-2 infection using saliva sampling or nasopharyngeal swabs. Without a gold standard, a pragmatic approach is to postulate a "reference standard," defined as positive if either test is positive, or negative if both are negative. However, this pragmatic approach may overestimate sensitivities because subjects infected with SARS-CoV-2 may still have double-negative test results even when both tests exhibit perfect specificity. To address this limitation, we propose a Bayesian hierarchical model for simultaneously estimating sensitivity, specificity, and disease prevalence in the absence of a gold standard. The proposed model allows adjusting for study-level covariates. We evaluate the model performance using an example based on a recently published meta-analysis on the diagnosis of SARS-CoV-2 infection and extensive simulations. Compared with the pragmatic reference standard approach, we demonstrate that the proposed Bayesian method provides a more accurate evaluation of prevalence, specificity, and sensitivity in a meta-analytic framework.

Missing values and inconclusive results in diagnostic studies - A scoping review of methods

Most diagnostic studies exclude missing values and inconclusive results from the analysis or apply simple methods resulting in biased accuracy estimates. This may be due to the lack of availability or awareness of appropriate methods. This scoping review aimed to provide an overview of strategies to handle missing values and inconclusive results in the reference standard or index test in diagnostic accuracy studies. Conducting a systematic literature search in MEDLINE, Cochrane Library, and Web of Science, we could identify many articles proposing methods for addressing missing values in the reference standard. There are also several articles describing methods regarding missing values or inconclusive results in the index test. The latter encompass imputation, frequentist and Bayesian likelihood, model-based, and latent class methods. While methods for missing values in the reference standard are regularly applied in practice, this is not true for methods addressing missing values and inconclusive results in the index test. Our comprehensive overview and description of available methods may raise further awareness of these methods and will enhance their application. Future research is needed to compare the performance of these methods under different conditions to give valid and robust recommendations for their usage in various diagnostic accuracy research scenarios.

TOMAS-R: A template to identify and plan analysis for clinically important variation and multiplicity in diagnostic test accuracy systematic reviews

The Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (DTA) provides guidance on important aspects of conducting a test accuracy systematic review. In this paper we present TOMAS-R (Template of Multiplicity and Analysis in Systematic Reviews), a structured template to use in conjunction with current Cochrane DTA guidance, to help identify complexities in the review question and to assist planning of data extraction and analysis when clinically important variation and multiplicity is present. Examples of clinically important variation and multiplicity could include differences in participants, index tests and test methods, target conditions and reference standards used to define them, study design and methodological quality. Our TOMAS-R template goes beyond the broad topic headings in current guidance that are sources of potential variation and multiplicity, by providing prompts for common sources of heterogeneity encountered from our experience of authoring over 100 reviews. We provide examples from two reviews to assist users. The TOMAS-R template adds value by supplementing available guidance for DTA reviews by providing a tool to facilitate discussions between methodologists, clinicians, statisticians and patient/public team members to identify the full breadth of review question complexities early in the process. The use of a structured set of prompting questions at the important stage of writing the protocol ensures clinical relevance as a main focus of the review, while allowing identification of key clinical components for data extraction and later analysis thereby facilitating a more efficient review process.

Meta-analysis of dichotomous and ordinal tests with an imperfect gold standard

Standard methods for the meta-analysis of medical tests, without assuming a gold standard, are limited to dichotomous data. Multivariate probit models are used to analyse correlated dichotomous data, and can be extended to model ordinal data. Within the context of an imperfect gold standard, they have previously been used for the analysis of dichotomous and ordinal test data from a single study, and for the meta-analysis of dichotomous tests. However, they have not previously been used for the meta-analysis of ordinal tests. In this article, we developed a Bayesian multivariate probit latent class model for the simultaneous meta-analysis of ordinal and dichotomous tests without assuming a gold standard, which also allows one to obtain summary estimates of joint test accuracy. We fitted the models using the software Stan, which uses a state-of-the-art Hamiltonian Monte Carlo algorithm, and we applied the models to a dataset in which studies evaluated the accuracy of tests, and test combinations, for deep vein thrombosis. We demonstrate the issues with dichotomising ordinal test accuracy data in the presence of an imperfect gold standard, before applying and comparing several variations of our proposed model which do not require the data to be dichotomised. The models proposed will allow researchers to more appropriately meta-analyse ordinal and dichotomous tests without a gold standard, potentially leading to less biased estimates of test accuracy. This may lead to a better understanding of which tests, and test combinations, should be used for any given medical condition.

Network Meta-Analysis: Noninvasive Imaging Modalities for Identifying Clinically Significant Portal Hypertension

BACKGROUND

Although measurement of the hepatic venous pressure gradient (HVPG) is the current reference standard for obtaining portal venous pressures, several noninvasive imaging-based modalities have been proposed as alternatives.

AIMS

We performed a systematic review and meta-analysis to compare the diagnostic accuracy of noninvasive imaging approaches for identifying clinically significant portal hypertension (CSPH).

METHODS

Two independent reviewers conducted a literature search of PubMed, SCOPUS, and the Cochrane Library from inception until January 5, 2021. The following imaging modalities were compared to HVPG: computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance elastography, ultrasound, transient elastography (TE), shear wave elastography (SWE), acoustic radiation force impulse (ARFI) imaging, contrast-enhanced ultrasound (CEUS), and subharmonic-aided pressure estimation (SHAPE). Sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) for summary receiver operating characteristic were calculated using both frequentist random effects and Bayesian network meta-analytic approaches.

RESULTS

We analyzed 45 studies of 5678 patients. A broad overlapping confidence interval (CI) of DOR was observed among different imaging modalities: ARFI (30.5; 95% CI 12.7-73.3), CEUS and SHAPE (21.1; 95% CI 6.4-69.8), TE of liver stiffness (21.1; 95% CI 13.3-33.5), CT and MRI (13.7; 95% CI 7.40-25.4), SWE of liver stiffness (10.5; 95% CI 5.2-21.1), and ultrasound (9.5; 95% CI 4.9-18.4). The AUC of all imaging methods exceeded 0.8, indicating very good performance. At a cutoff of 80% specificity, TE, CEUS, and SHAPE exceeded 80% sensitivity.

CONCLUSION

Overall, noninvasive imaging modalities perform well for identifying CSPH. Clinicians should consider these noninvasive and cost-efficient tests when diagnosing CSPH.

Empirical comparisons of meta-analysis methods for diagnostic studies: a meta-epidemiological study

OBJECTIVES

Several methods are commonly used for meta-analyses of diagnostic studies, such as the bivariate linear mixed model (LMM). It estimates the overall sensitivity, specificity, their correlation, diagnostic OR (DOR) and the area under the curve (AUC) of the summary receiver operating characteristic (ROC) estimates. Nevertheless, the bivariate LMM makes potentially unrealistic assumptions (ie, normality of within-study estimates), which could be avoided by the bivariate generalised linear mixed model (GLMM). This article aims at investigating the real-world performance of the bivariate LMM and GLMM using meta-analyses of diagnostic studies from the Cochrane Library.

METHODS

We compared the bivariate LMM and GLMM using the relative differences in the overall sensitivity and specificity, their 95% CI widths, between-study variances, and the correlation between the (logit) sensitivity and specificity. We also explored their relationships with the number of studies, number of subjects, overall sensitivity and overall specificity.

RESULTS

Among the extracted 1379 meta-analyses, point estimates of overall sensitivities and specificities by the bivariate LMM and GLMM were generally similar, but their CI widths could be noticeably different. The bivariate GLMM generally produced narrower CIs than the bivariate LMM when meta-analyses contained 2-5 studies. For meta-analyses with <100 subjects or the overall sensitivities or specificities close to 0% or 100%, the bivariate LMM could produce substantially different AUCs, DORs and DOR CI widths from the bivariate GLMM.

CONCLUSIONS

The variation of estimates calls into question the appropriateness of the normality assumption within individual studies required by the bivariate LMM. In cases of notable differences presented in these methods' results, the bivariate GLMM may be preferred.

Diagnostic test accuracy network meta-analysis methods: A scoping review and empirical assessment

OBJECTIVE

To a) identify methodological and application papers reporting a model developed specifically for diagnostic test accuracy network meta-analysis (DTA-NMA) or a hierarchical meta-regression method for comparing at least three index tests; b) review and summarize the characteristics of the methods and the application papers; and c) compare DTA-NMA and hierarchical meta-regression methods empirically.

STUDY DESIGN AND SETTINGS

We performed a scoping review and searched major databases until 3^rd March 2021. We assessed the characteristics of the identified methods, conducted a descriptive analysis of characteristics of the application articles, and applied the DTA-NMA and meta-regression methods to the available data.

RESULTS

We included 49 articles, of which 9 were methodological (describing 11 DTA-NMA methods) and 40 were application papers (data available for 32 DTA-NMAs). Our results showed a steep increase in recent years in DTA-NMA publications. DTA-NMA models may lead to different results. Although sensitivity estimates were comparable between meta-regression and DTA-NMA models, specificity estimates were higher in meta-regression.

CONCLUSIONS

The choice of a DTA-NMA model will depend on the available data, including the use of different thresholds for test positivity, different study designs, and software familiarity. Selection between the methods may impact on the NMA results, especially for specificity.

Progress and challenges of network meta-analysis

In the past years, network meta-analysis (NMA) has been widely used among clinicians, guideline makers, and health technology assessment agencies and has played an important role in clinical decision-making and guideline development. To inform further development of NMAs, we conducted a bibliometric analysis to assess the current status of published NMA methodological studies, summarized the methodological progress of seven types of NMAs, and discussed the current challenges of NMAs.

Screening Methods for Diagnosing Cystic Fibrosis-Related Diabetes: A Network Meta-Analysis of Diagnostic Accuracy Studies

BACKGROUND

Cystic fibrosis-related diabetes (CFRD) has become more common due to higher life expectancy with cystic fibrosis. Early recognition and prompt treatment of CFRD leads to improved outcomes.

METHODS

We performed a network meta-analysis (NMA) in order to identify the most valuable diagnostic metrics for diagnosing CFRD out of available screening tools (index test), using the oral glucose tolerance test as a reference standard. Pooled sensitivity (Se), specificity (Sp), and superiority indices were calculated and used to rank the index tests.

RESULTS

A total of 31 articles with 25 index tests were eligible for inclusion. Two-day, continuous glucose monitoring (CGM) ranked the highest (Se: 86% Sp: 76%), followed by glucose measurement from blood capillary samples (Se: 70%, Sp: 82%) and three-day CGM (Se: 96%, Sp: 56%). When we compared the CGM of different durations, two-day CGM performed best (Se: 88%, Sp: 80%), followed by three-day (Se: 96%, Sp: 59%) and six-day CGM (Se: 66%, Sp: 79%).

CONCLUSIONS

Considering its overall performance ranking, as well as the high sensitivity, two-day CGM appears to be a promising screening test for CFRD.

Application of network meta-analysis in the field of physical activity and health promotion

Continued advancement in the field of physical activity and health promotion relies heavily on the synthesis of rigorous scientific evidence. As such, systematic reviews and meta-analyses of randomized controlled trials have led to a better understanding of which intervention strategies are superior (i.e., produce the greatest effects) in physical activity-based health behavior change interventions. Indeed, standard meta-analytic approaches have allowed researchers in the field to synthesize relevant experimental evidence using pairwise procedures that produce reliable estimates of the homogeneity, magnitude, and potential biases in the observed effects. However, pairwise meta-analytic procedures are only capable to discerning differences in effects between a select intervention strategy and a select comparison or control condition. In order to maximize the impact of physical activity interventions on health-related outcomes, it is necessary to establish evidence concerning the comparative efficacy of all relevant physical activity intervention strategies. The development of network meta-analysis (NMA)-most commonly used in medical-based clinical trials-has allowed for the quantification of indirect comparisons, even in the absence of direct, head-to-head trials. Thus, it stands to reason that NMA can be applied in physical activity and health promotion research to identify the best intervention strategies. Given that this analysis technique is novel and largely unexplored in the field of physical activity and health promotion, care must be taken in its application to ensure reliable estimates and discernment of the effect sizes among interventions. Therefore, the purpose of this review is to comment on the potential application and importance of NMA in the field of physical activity and health promotion, describe how to properly and effectively apply this technique, and suggest important considerations for its appropriate application in this field. In this paper, overviews of the foundations of NMA and commonly used approaches for conducting NMA are provided, followed by assumptions related to NMA, opportunities and challenges in NMA, and a step-by-step example of developing and conducting an NMA.

A Bayesian multivariate meta-analysis of prevalence data

When conducting a meta-analysis involving prevalence data for an outcome with several subtypes, each of them is typically analyzed separately using a univariate meta-analysis model. Recently, multivariate meta-analysis models have been shown to correspond to a decrease in bias and variance for multiple correlated outcomes compared with univariate meta-analysis, when some studies only report a subset of the outcomes. In this article, we propose a novel Bayesian multivariate random effects model to account for the natural constraint that the prevalence of any given subtype cannot be larger than that of the overall prevalence. Extensive simulation studies show that this new model can reduce bias and variance when estimating subtype prevalences in the presence of missing data, compared with standard univariate and multivariate random effects models. The data from a rapid review on occupation and lower urinary tract symptoms by the Prevention of Lower Urinary Tract Symptoms Research Consortium are analyzed as a case study to estimate the prevalence of urinary incontinence and several incontinence subtypes among women in suspected high risk work environments.

Methods and reporting of systematic reviews of comparative accuracy were deficient: a methodological survey and proposed guidance

OBJECTIVE

The objective of this study was to examine methodological and reporting characteristics of systematic reviews and meta-analyses which compare diagnostic test accuracy (DTA) of multiple index tests, identify good practice, and develop guidance for better reporting.

STUDY DESIGN AND SETTING

Methodological survey of 127 comparative or multiple tests reviews published in 74 different general medical and specialist journals. We summarized methods and reporting characteristics that are likely to differ between reviews of a single test and comparative reviews. We then developed guidance to enhance reporting of test comparisons in DTA reviews.

RESULTS

Of 127 reviews, 16 (13%) reviews restricted study selection and test comparisons to comparative accuracy studies while the remaining 111 (87%) reviews included any study type. Fifty-three reviews (42%) statistically compared test accuracy with only 18 (34%) of these using recommended methods. Reporting of several items-in particular the role of the index tests, test comparison strategy, and limitations of indirect comparisons (i.e., comparisons involving any study type)-was deficient in many reviews. Five reviews with exemplary methods and reporting were identified.

CONCLUSION

Reporting quality of reviews which evaluate and compare multiple tests is poor. The guidance developed, complemented with the exemplars, can assist review authors in producing better quality comparative reviews.

A Bayesian approach for correcting exposure misclassification in meta-analysis

In observational studies, misclassification of exposure is ubiquitous and can substantially bias the estimated association between an outcome and an exposure. Although misclassification in a single observational study has been well studied, few papers have considered it in a meta-analysis. Meta-analyses of observational studies provide important evidence for health policy decisions, especially when large randomized controlled trials are unethical or unavailable. It is imperative to account properly for misclassification in a meta-analysis to obtain valid point and interval estimates. In this paper, we propose a novel Bayesian approach to filling this methodological gap. We simultaneously synthesize two (or more) meta-analyses, with one on the association between a misclassified exposure and an outcome (main studies), and the other on the association between the misclassified exposure and the true exposure (validation studies). We extend the current scope for using external validation data by relaxing the "transportability" assumption by means of random effects models. Our model accounts for heterogeneity between studies and can be extended to allow different studies to have different exposure measurements. The proposed model is evaluated through simulations and illustrated using real data from a meta-analysis of the effect of cigarette smoking on diabetic peripheral neuropathy.

An innovative and comprehensive technique to evaluate different measures of medication adherence: The network meta-analysis

BACKGROUND

Poor medication adherence is associated with adverse health outcomes and higher costs of care. However, inconsistencies in the assessment of adherence are found in the literature.

OBJECTIVE

To evaluate the effect of different measures of adherence in the comparative effectiveness of complex interventions to enhance patients' adherence to prescribed medications.

METHODS

A systematic review with network meta-analysis was performed. Electronic searches for relevant pairwise meta-analysis including trials of interventions that aimed to improve medication adherence were performed in PubMed. Data extraction was conducted with eligible trials evaluating short-period adherence follow-up (until 3 months) using any measure of adherence: self-report, pill count, or MEMS (medication event monitoring system). To standardize the results obtained with these different measures, an overall composite measure and an objective composite measure were also calculated. Network meta-analyses for each measure of adherence were built. Rank order and surface under the cumulative ranking curve analyses (SUCRA) were performed.

RESULTS

Ninety-one trials were included in the network meta-analyses. The five network meta-analyses demonstrated robustness and reliability. Results obtained for all measures of adherence were similar across them and to both composite measures. For both composite measures, interventions comprising economic + technical components were the best option (90% of probability in SUCRA analysis) with statistical superiority against almost all other interventions and against standard care (odds ratio with 95% credibility interval ranging from 0.09 to 0.25 [0.02, 0.98]).

CONCLUSION

The use of network meta-analysis was reliable to compare different measures of adherence of complex interventions in short-periods follow-up. Analyses with longer follow-up periods are needed to confirm these results. Different measures of adherence produced similar results. The use of composite measures revealed reliable alternatives to establish a broader and more detailed picture of adherence.

Systematic reviews and meta-analyses addressing comparative test accuracy questions

BACKGROUND

While most relevant clinical questions are comparative, most diagnostic test accuracy studies focus on the accuracy of only one test. If we combine these single-test evaluations in a systematic review that aims to compare the accuracy of two or more tests to indicate the most accurate one, the resulting comparative accuracy estimates may be biased.

METHODS AND RESULTS

Systematic reviews comparing the accuracy of two tests should only include studies that evaluate both tests in the same patients and against the same reference standard. However, these studies are not always available. And even if available, they may still be biased. For example because they included a specific patient group that would not have been tested with two or more tests in actual practice. Combining comparative and non-comparative studies in a comparative accuracy meta-analysis requires novel statistical approaches.

CONCLUSION

In order to improve decision-making about the use of test in practice, better designed and reported primary diagnostic studies are needed. Meta-analytic and network-type approaches available for therapeutic questions need to be extended to comparative diagnostic accuracy questions.