Browsing by Subject "Network meta-analysis"

Now showing 1 - 6 of 6
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    Application of Network Meta-Analysis in The Field of Physical Activity and Health Promotion: A Case Study
    (2020-08) Su, Xiwen
    Continued advancement in the field of kinesiology and health promotion relies heavily on the synthesis of rigorous quantitative scientific evidence. As such, meta-analyses of randomized controlled trials have led to a better understanding of what 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 which 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 condition 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 the physical activity and health promotion research to identify the best intervention strategies. Given this analysis technique is novel and largely unexplored in the field of kinesiology and health promotion, care must be taken in its application to ensure reliable estimates and discernment of the effect sizes between interventions. Therefore, the purpose of this study is to first comment on the potential application and importance of NMA in the field of kinesiology and health promotion, then describe how to properly and effectively apply this technique using a specific case study evaluating the effects of different lifestyle interventions on children’s body composition, and lastly 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 of NMA, opportunities and challenges in NMA, and a case study example of the development and conduct of an NMA, as well as the interpretation of the analysis results. The case study collect original data from published randomized controlled studies investigating on some type of intervention on variables including body mass index (BMI), BMI z-score (BMIz), and body fat percentage, divided the used intervention into ten categories in total, from simple single intervention to multiple components mixed intervention (more than three), and used pre-processed data to carry out network meta-analysis. Results of analysis using mean difference (SD) between baseline and immediate post-intervention data showed that PA intervention ranked top two of the most effective approaches among other types of lifestyle interventions in all three variables, suggesting that promoting PA participation is crucial in children’s health status and childhood obesity control. While based on the analysis using combined original pre-and-post data (SE), multiple component interventions were predicted to be the best ranked intervention approach among all ten types of intervention, indicating that taking care of more aspects in children’s lifestyle may also result in an important impact for children to keep healthy and fit.
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    Bayesian Hierarchical Methods for Network Meta-Analysis
    (2014-07) Zhang, Jing
    In clinical practice, and at a wider societal level, treatment decisions in medicine need to consider all relevant evidence. Network meta-analysis (NMA) collectively analyzes many randomized controlled trials (RCTs) evaluating multiple interventions relevant to a treatment decision, expanding the scope of a conventional pairwise meta-analysis to simultaneously handle multiple treatment comparisons. NMA synthesizes both direct information, gained from direct comparison for example between treatments A and C, and indirect information obtained from A versus B and C versus B trials, and thus strengthens inference. Under current contrast-based (CB) methods for NMA of binary outcomes, which do not model the "baseline" risks and focus on modeling the relative treatment effects, the patient-centered measures including the overall treatment-specific event rates and risk differences are not provided, creating some unnecessary obstacles for patients to comprehensively understand and trade-off efficacy and safety measures. Many NMAs only report odds ratios (ORs) which are commonly misinterpreted as risk ratios (RRs) by many physicians, patients and their care givers. In order to overcome these obstacles of the CB methods, a novel Bayesian hierarchical arm-based (AB) model developed from a missing data perspective is proposed to illustrate how treatment-specific event proportions, risk differences (RD) and relative risks (RR) can be computed in NMAs. Since most of the trials in NMA only compare two of the treatments of interest, the typical data in a NMA managed as a trial-by-treatment matrix is extremely sparse, like an incomplete block structure with serious missing data problems. The previously proposed AB method assumes a missing at random (MAR) mechanism. However, in RCTs, nonignorable missingness or missingness not at random (MNAR) may occur due to deliberate choices at the design stage. In addition, those undertaking an NMA will often selectively choose treatments to include in the analysis, which will also lead to nonignorable missingness. We then extend the AB method to incorporate nonignorable missingness using \textit{selection models} method. Meta-analysts undertaking an NMA often selectively choose trials to include in the analysis. Thus inevitably, certain trials are more likely to be included in an NMA. In addition, it is difficult to include all existing trials that meet the inclusion criteria due to language barriers (i.e., some trials may be published using other languages) and other technical issues. If the omitted trials are quite different from the ones we include, then the estimates will be biased. We obtain empirical evidence on whether these selective inclusions of trials can make a difference in the results, such as treatment effect estimates in an NMA setting, using both the AB and CB methods. In the opposite direction of the fact that some trials which should have been included but are omitted, some trials may appear to deviate markedly from the others, and thus be inappropriate to be synthesized. we call these trials \textit{outlying trials} or \textit{trial-level outliers}. To the best of our knowledge, while the key NMA assumptions of inconsistency and heterogeneity have been well-studied, few previous authors have considered the issue of trial-level outliers, their detection, and guidance on whether or not to discard them from an NMA. We propose and evaluate Bayesian approaches to detect trial-level outliers in the NMA evidence structures.
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    Bayesian hierarchical methods for network meta-analysis
    (2014-07) Zhang, Jing
    In clinical practice, and at a wider societal level, treatment decisions in medicine need to consider all relevant evidence. Network meta-analysis (NMA) collectively analyzes many randomized controlled trials (RCTs) evaluating multiple interventions relevant to a treatment decision, expanding the scope of a conventional pairwise meta-analysis to simultaneously handle multiple treatment comparisons. NMA synthesizes both direct information, gained from direct comparison for example between treatments A and C, and indirect information obtained from A versus B and C versus B trials, and thus strengthens inference. Under current contrast-based (CB) methods for NMA of binary outcomes, which do not model the "baseline" risks and focus on modeling the relative treatment effects, the patient-centered measures including the overall treatment-specific event rates and risk differences are not provided, creating some unnecessary obstacles for patients to comprehensively understand and trade-off efficacy and safety measures. Many NMAs only report odds ratios (ORs) which are commonly misinterpreted as risk ratios (RRs) by many physicians, patients and their care givers. In order to overcome these obstacles of the CB methods, a novel Bayesian hierarchical arm-based (AB) model developed from a missing data perspective is proposed to illustrate how treatment-specific event proportions, risk differences (RD) and relative risks (RR) can be computed in NMAs. Since most of the trials in NMA only compare two of the treatments of interest, the typical data in a NMA managed as a trial-by-treatment matrix is extremely sparse, like an incomplete block structure with serious missing data problems. The previously proposed AB method assumes a missing at random (MAR) mechanism. However, in RCTs, nonignorable missingness or missingness not at random (MNAR) may occur due to deliberate choices at the design stage. In addition, those undertaking an NMA will often selectively choose treatments to include in the analysis, which will also lead to nonignorable missingness. We then extend the AB method to incorporate nonignorable missingness using \textit{selection models} method. Meta-analysts undertaking an NMA often selectively choose trials to include in the analysis. Thus inevitably, certain trials are more likely to be included in an NMA. In addition, it is difficult to include all existing trials that meet the inclusion criteria due to language barriers (i.e., some trials may be published using other languages) and other technical issues. If the omitted trials are quite different from the ones we include, then the estimates will be biased. We obtain empirical evidence on whether these selective inclusions of trials can make a difference in the results, such as treatment effect estimates in an NMA setting, using both the AB and CB methods. In the opposite direction of the fact that some trials which should have been included but are omitted, some trials may appear to deviate markedly from the others, and thus be inappropriate to be synthesized. we call these trials \textit{outlying trials} or \textit{trial-level outliers}. To the best of our knowledge, while the key NMA assumptions of inconsistency and heterogeneity have been well-studied, few previous authors have considered the issue of trial-level outliers, their detection, and guidance on whether or not to discard them from an NMA. We propose and evaluate Bayesian approaches to detect trial-level outliers in the NMA evidence structures.
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    Detecting Inconsistency and Using Non-randomized Studies in Research Synthesis
    (2016-05) Zhao, Hong
    In scientific research, multiple studies on the same intervention occur for many reasons, such as using different study populations or designs. Research synthesis attempts to integrate different data on the same topic for the purpose of making generalizations, and provides us with a formal method to systematically combine all available evidence. In this thesis, we focus on the synthesis of evidence from multiple clinical trials using network meta-analysis, and the more challenging problem of combining information from randomized clinical trials and less rigorous observational studies. Network meta-analysis (NMA) is an extension of standard pairwise meta-analysis to permit combination of results on more than two treatments. This enables both direct and indirect comparisons of treatments, and addresses the comparative effectiveness or safety of the treatments based on all sources of data. Current NMA methods are usually based on a contrast-based (CB) model to estimate the relative treatment effects for each study. While popular and often effective, this model suffers from certain limitations. An alternative is the arm-based (AB) model, which estimates the mean response directly for each treatment. Compared to the CB framework, AB models are more straightforward to interpret, especially when implemented in a missing-data framework, by allowing use of a common baseline treatment across all trials. In a NMA, when direct and indirect evidence differ, the analysis is said to suffer from inconsistency, and the treatment effect estimates may be biased. Inconsistency detection methods using CB models have already been developed, but no corresponding method based on the newer AB models has yet been proposed. Here, we develop a Bayesian AB approach to detecting inconsistency. After detecting inconsistency, formal diagnostic tests should be performed to check whether this violation of assumption results in the change of treatment effects. Therefore, we next explore whether the trial-arm combinations that are sources of inconsistency are influential or outlying observations. To do this, we modify the "constraint case" method to produce diagnostics suitable for generalized linear models in NMA using either AB or CB models, where the outcome is binary. Lastly, we develop methods to combine the data from a randomized clinical trial and a propensity score-matched non-randomized study using commensurate priors. The approach determines the proper degree of borrowing from the non-randomized data by the similarity of the estimated treatment effects in the two studies. Performance of all our methods is evaluated via both example datasets and simulation studies. In summary, this dissertation work enables improved research synthesis in biomedical applications and sheds light on future research directions in the aforementioned areas.
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    Statistical methods for meta-analysis
    (2017-05) Lin, Lifeng
    Meta-analysis has become a widely-used tool to combine findings from independent studies in various research areas. This thesis deals with several important statistical issues in systematic reviews and meta-analyses, such as assessing heterogeneity in the presence of outliers, quantifying publication bias, and simultaneously synthesizing multiple treatments and factors. The first part of this thesis focuses on univariate meta-analysis. We propose alternative measures to robustly describe between-study heterogeneity, which are shown to be less affected by outliers compared with traditional measures. Publication bias is another issue that can seriously affect the validity and generalizability of meta-analysis conclusions. We present the first work to empirically evaluate the performance of seven commonly-used publication bias tests based on a large collection of actual meta-analyses in the Cochrane Library. Our findings may guide researchers in properly assessing publication bias and interpreting test results for future systematic reviews. Moreover, instead of just testing for publication bias, we further consider quantifying it and propose an intuitive publication bias measure, called the skewness of standardized deviates, which effectively describes the asymmetry of the collected studies’ results. The measure’s theoretical properties are studied, and we show that it can also serve as a powerful test statistic. The second part of this thesis introduces novel ideas in multivariate meta-analysis. In medical sciences, a disease condition is typically associated with multiple risk and protective factors. Although many studies report results of multiple factors, nearly all meta-analyses separately synthesize the association between each factor and the disease condition of interest. We propose a new concept, multivariate meta-analysis of multiple factors, to synthesize all available factors simultaneously using a Bayesian hierarchical model. By borrowing information across factors, the multivariate method can improve statistical efficiency and reduce biases compared with separate analyses. In addition to synthesizing multiple factors, network meta-analysis has recently attracted much attention in evidence-based medicine because it simultaneously combines both direct and indirect evidence to compare multiple treatments and thus facilitates better decision making. First, we empirically compare two network meta-analysis models, contrast- and arm-based, with respect to their sensitivity to treatment exclusions. The arm-based method is shown to be more robust to such exclusions, mostly because it can use single-arm studies while the contrast-based method cannot. Then, focusing on the currently popular contrast-based method, we theoretically explore the key factors that make network meta-analysis outperform traditional pairwise meta-analyses. We prove that evidence cycles in the treatment network play critical roles in network meta-analysis. Specifically, network meta-analysis produces posterior distributions identical to separate pairwise meta-analyses for all treatment comparisons when a treatment network does not contain cycles. This equivalence is illustrated using simulations and a case study.
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    Statistical methods for multivariate meta-analysis
    (2018-07) Lian, Qinshu
    As health problems get more complicated, the medical decisions and policies are rarely determined by evidence on a single effect. In recent years, there is a wide acknowledgment of the drawbacks of using separate univariate meta-analyses to solve a clearly multivariate problem. This has led to increased attention to multivariate meta-analysis, which is a generalization of standard univariate meta-analysis to synthesize evidence on multiple outcomes or treatments. Recently developments in multivariate meta-analysis have been driven by a wide variety of application areas. This thesis focuses on three areas in which multivariate meta-analysis is highly important but is not yet well developed: network meta-analysis of diagnostic tests, meta-analysis of observational studies accounting for exposure misclassification, and meta-regression methods adjusting for post-randomization variables. In studies evaluating the accuracy of diagnostic tests, three designs are commonly used, crossover, randomized, and non-comparative. Existing methods for meta-analysis of diagnostic tests mainly consider simple cases in which the reference test in all or none of the studies can be considered a gold standard test, and in which all studies use either a randomized or non-comparative design. To overcome the limitations of current methods, the Bayesian hierarchical summary receiver operating characteristic model is extended to network meta-analysis of diagnostic tests to simultaneously compare multiple tests within a missing data framework. The method accounts for correlations between multiple tests and for heterogeneity between studies. It also allows different studies to include different subsets of diagnostic tests and provides flexibility in the choice of summary statistics. 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. A novel Bayesian approach is proposed to fill 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. The proposed model accounts for heterogeneity between studies and can be extended to allow different studies to have different exposure measurements. Meta-regression is widely used in systematic reviews to investigate sources of heterogeneity and the association of study-level covariates with treatment effectiveness. Although existing meta-regression approaches have been successful in adjusting for baseline covariates, these methods have several limitations in adjusting for post-randomization variables. We propose a joint meta-regression method adjusting for post-randomization variables. The proposed method simultaneously estimates the treatment effect on the primary outcome and on the post-randomization variables. It takes both between- and within-study variability in post-randomization variables into consideration. Missing data is allowed in the primary outcome and the post-randomization variables, and uncertainty in the missing data is taken into consideration. All the proposed models are evaluated in simulation studies and are illustrated using real meta-analytic datasets.