A review of methods for addressing components of interventions in meta-analysis

Effectiveness of interventions to prevent or reverse pre-frailty and frailty in middle-aged community dwelling adults: A systematic review

INTRODUCTION

Frailty, marked by diminished physiological capacity and higher health risks, is less understood in middle-aged individuals (40-65 years) than older adults. This review synthesises intervention studies for pre-frailty and frailty in this demographic, assessing effectiveness, feasibility, and implementation factors including participant experience and cost-effectiveness.

METHOD

Registered on the Open Science Framework and adhering to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) and the template for intervention description and replication (TIDieR) guidelines, this review searched six databases for interventions targeting middle-aged adults. Dual screening, data extraction, risk assessment, and Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) certainty evaluation were conducted. Findings were narratively synthesized due to heterogeneity.

RESULTS

Eight studies (2018-2023) with 2838 participants were included. Resistance training and multicomponent exercise reduced frailty; though, not always significantly. Low-intensity exercises and education-based interventions yielded mixed results, suggesting a need for further research. Positive participant experiences and cost-effectiveness of interventions such as resistance training and educational interventions supports their feasibility. Varying quality, methodologies and levels of bias indicated a need for more rigorous future research.

DISCUSSION

This review reveals an evidence gap in middle-aged frailty interventions. Multicomponent interventions and resistance training showed promise, but their comparative effectiveness remains uncertain. Educational and low-intensity interventions need further research to establish their effectiveness. The findings diverge from those in older adults, emphasising the need for age-specific approaches. Future studies should employ higher-quality methods and explore emerging technologies to enhance intervention effectiveness for pre-frailty and frailty in middle-aged adults.

Components and Delivery Formats of Cognitive Behavioral Therapy for Chronic Insomnia in Adults: A Systematic Review and Component Network Meta-Analysis

Importance

Chronic insomnia disorder is highly prevalent, disabling, and costly. Cognitive behavioral therapy for insomnia (CBT-I), comprising various educational, cognitive, and behavioral strategies delivered in various formats, is the recommended first-line treatment, but the effect of each component and delivery method remains unclear.

Objective

To examine the association of each component and delivery format of CBT-I with outcomes.

Data Sources

PubMed, Cochrane Central Register of Controlled Trials, PsycInfo, and International Clinical Trials Registry Platform from database inception to July 21, 2023.

Study Selection

Published randomized clinical trials comparing any form of CBT-I against another or a control condition for chronic insomnia disorder in adults aged 18 years and older. Insomnia both with and without comorbidities was included. Concomitant treatments were allowed if equally distributed among arms.

Data Extraction and Synthesis

Two independent reviewers identified components, extracted data, and assessed trial quality. Random-effects component network meta-analyses were performed.

Main Outcomes and Measures

The primary outcome was treatment efficacy (remission defined as reaching a satisfactory state) posttreatment. Secondary outcomes included all-cause dropout, self-reported sleep continuity, and long-term remission.

Results

A total of 241 trials were identified including 31 452 participants (mean [SD] age, 45.4 [16.6] years; 21 048 of 31 452 [67%] women). Results suggested that critical components of CBT-I are cognitive restructuring (remission incremental odds ratio [iOR], 1.68; 95% CI, 1.28-2.20) third-wave components (iOR, 1.49; 95% CI, 1.10-2.03), sleep restriction (iOR, 1.49; 95% CI, 1.04-2.13), and stimulus control (iOR, 1.43; 95% CI, 1.00-2.05). Sleep hygiene education was not essential (iOR, 1.01; 95% CI, 0.77-1.32), and relaxation procedures were found to be potentially counterproductive(iOR, 0.81; 95% CI, 0.64-1.02). In-person therapist-led programs were most beneficial (iOR, 1.83; 95% CI, 1.19-2.81). Cognitive restructuring, third-wave components, and in-person delivery were mainly associated with improved subjective sleep quality. Sleep restriction was associated with improved subjective sleep quality, sleep efficiency, and wake after sleep onset, and stimulus control with improved subjective sleep quality, sleep efficiency, and sleep latency. The most efficacious combination-consisting of cognitive restructuring, third wave, sleep restriction, and stimulus control in the in-person format-compared with in-person psychoeducation, was associated with an increase in the remission rate by a risk difference of 0.33 (95% CI, 0.23-0.43) and a number needed to treat of 3.0 (95% CI, 2.3-4.3), given the median observed control event rate of 0.14.

Conclusions and Relevance

The findings suggest that beneficial CBT-I packages may include cognitive restructuring, third-wave components, sleep restriction, stimulus control, and in-person delivery but not relaxation. However, potential undetected interactions could undermine the conclusions. Further large-scale, well-designed trials are warranted to confirm the contribution of different treatment components in CBT-I.

Twenty years of network meta-analysis: Continuing controversies and recent developments

Network meta-analysis (NMA) is an extension of pairwise meta-analysis (PMA) which combines evidence from trials on multiple treatments in connected networks. NMA delivers internally consistent estimates of relative treatment efficacy, needed for rational decision making. Over its first 20 years NMA's use has grown exponentially, with applications in both health technology assessment (HTA), primarily re-imbursement decisions and clinical guideline development, and clinical research publications. This has been a period of transition in meta-analysis, first from its roots in educational and social psychology, where large heterogeneous datasets could be explored to find effect modifiers, to smaller pairwise meta-analyses in clinical medicine on average with less than six studies. This has been followed by narrowly-focused estimation of the effects of specific treatments at specific doses in specific populations in sparse networks, where direct comparisons are unavailable or informed by only one or two studies. NMA is a powerful and well-established technique but, in spite of the exponential increase in applications, doubts about the reliability and validity of NMA persist. Here we outline the continuing controversies, and review some recent developments. We suggest that heterogeneity should be minimized, as it poses a threat to the reliability of NMA which has not been fully appreciated, perhaps because it has not been seen as a problem in PMA. More research is needed on the extent of heterogeneity and inconsistency in datasets used for decision making, on formal methods for making recommendations based on NMA, and on the further development of multi-level network meta-regression.

Effectiveness of sound and darkness interventions for critically ill patients' sleep quality: A systematic review and component network meta-analysis

BACKGROUND

Noise and lighting are prime factors of poor sleep quality in critically ill patients, which impair recovery and increase the risk of delirium or complications.

AIM

To identify and rank the effectiveness of sound and darkness interventions on the sleep quality of critically ill patients.

STUDY DESIGN

This systematic review and component network meta-analysis was based on the Preferred Reporting Items for Systematic Reviews incorporating the Network Meta-Analyses (PRISMA-NMA) Statement. The Embase, MEDLINE, Cochrane CENTRAL, CINAHL, Airiti Library, and Google Scholar databases were searched from inception to August 10, 2021, for randomized controlled trials (RCTs) on sound and darkness interventions targeting critically ill patients' sleep quality. We applied standard and component NMA to determine the effects of interventions. The certainty of evidence was evaluated using the Cochrane risk-of-bias tool (V.2.0) and the online Confidence in Network Meta-Analysis (CINeMA) application.

RESULTS

Twenty-four RCTs with 1507 participants who used combined interventions constituting seven competing interventions were included in the standard NMA. The combination of earplugs, eye masks, and music; eye masks alone; earplugs combined with eye masks; and music alone had beneficial intervention effects. The combination of earplugs, eye masks, and music was the best intervention, and these components had no interaction effect. An eye mask had the best relative effect, followed by music, quiet time, and earplugs.

CONCLUSIONS

This study provides clinical evidence of the effectiveness of using eye masks, music, and earplugs to improve sleep quality in critically ill patients. We also recommend future research using bedtime music, nocturnal eye masks, and quiet time, which had the best relative effects on sleep quality.

RELEVANCE TO CLINICAL PRACTICE

This study provides recommendations for interventions that nurses can use to improve critically ill patients' sleep quality.

Exploring the Effectiveness of Self-Management Interventions in Type 2 Diabetes: A Systematic Review and Network Meta-Analysis

BACKGROUND

Chronic diseases are a leading cause of global morbidity and mortality. In response to this challenge, self-management interventions (SMIs) have emerged as an essential tool in improving patient outcomes. However, the diverse and complex nature of SMIs pose significant challenges in measuring their effectiveness. This work aims to investigate the comparative effectiveness of SMIs on Type 2 diabetes mellitus (T2DM) outcomes.

METHODS

A rigorous analytical framework was employed to assess the relative effectiveness of different SMIs, encompassing both pairwise and network meta-analysis (NMA), as well as component network meta-analysis (CNMA). Various outcomes were considered, including glycated hemoglobin (HbA1c) control, body mass index (BMI) reduction and low-density lipoprotein (LDL) cholesterol. Visualization tools were also utilized to enhance the interpretation of results.

RESULTS

SMIs were found promising in improving clinical outcomes and patient-reported measures. However, considerable heterogeneity and inconsistency across studies challenged the validity of NMA results. CNMA along with various visualization tools offered insights into the contributions of individual SMI components, highlighting the complexity of these interventions.

DISCUSSION/CONCLUSIONS

SMIs represent a valuable approach to managing chronic conditions, but their effectiveness is context-dependent. Further research is needed to elucidate the contextual factors influencing SMI outcomes. This work contributes to a comprehensive understanding of SMIs' role in T2DM management, aiming to aid decision-makers, clinicians, and patients in selecting tailored interventions.

Dismantling and personalising task-sharing psychosocial interventions for common mental disorders: a study protocol for an individual participant data component network meta-analysis

INTRODUCTION

Common mental disorders, including depression, anxiety and related somatic health symptoms, are leading causes of disability worldwide. Especially in low-resource settings, psychosocial interventions delivered by non-specialist providers through task-sharing modalities proved to be valid options to expand access to mental healthcare. However, such interventions are usually eclectic multicomponent interventions consisting of different combinations of evidence-based therapeutic strategies. Which of these various components (or combinations thereof) are more efficacious (and for whom) to reduce common mental disorder symptomatology is yet to be substantiated by evidence.

METHODS AND ANALYSIS

Comprehensive search was performed in electronic databases MEDLINE, Embase, PsycINFO and the Cochrane Register of Controlled Trials-CENTRAL from database inception to 15 March 2023 to systematically identify all randomised controlled trials that compared any single component or multicomponent psychosocial intervention delivered through the task-sharing modality against any active or inactive control condition in the treatment of adults suffering from common mental disorders. From these trials, individual participant data (IPD) of all measured outcomes and covariates will be collected. We will dismantle psychosocial interventions creating a taxonomy of components and then apply the IPD component network meta-analysis (IPD-cNMA) methodology to assess the efficacy of individual components (or combinations thereof) according to participant-level prognostic factors and effect modifiers.

ETHICS AND DISSEMINATION

Ethics approval is not applicable for this study since no original data will be collected. Results from this study will be published in peer-reviewed journals and presented at relevant conferences.

Data visualisation approaches for component network meta-analysis: visualising the data structure

BACKGROUND

Health and social care interventions are often complex and can be decomposed into multiple components. Multicomponent interventions are often evaluated in randomised controlled trials. Across trials, interventions often have components in common which are given alongside other components which differ across trials. Multicomponent interventions can be synthesised using component NMA (CNMA). CNMA is limited by the structure of the available evidence, but it is not always straightforward to visualise such complex evidence networks. The aim of this paper is to develop tools to visualise the structure of complex evidence networks to support CNMA.

METHODS

We performed a citation review of two key CNMA methods papers to identify existing published CNMA analyses and reviewed how they graphically represent intervention complexity and comparisons across trials. Building on identified shortcomings of existing visualisation approaches, we propose three approaches to standardise visualising the data structure and/or availability of data: CNMA-UpSet plot, CNMA heat map, CNMA-circle plot. We use a motivating example to illustrate these plots.

RESULTS

We identified 34 articles reporting CNMAs. A network diagram was the most common plot type used to visualise the data structure for CNMA (26/34 papers), but was unable to express the complex data structures and large number of components and potential combinations of components associated with CNMA. Therefore, we focused visualisation development around representing the data structure of a CNMA more completely. The CNMA-UpSet plot presents arm-level data and is suitable for networks with large numbers of components or combinations of components. Heat maps can be utilised to inform decisions about which pairwise interactions to consider for inclusion in a CNMA model. The CNMA-circle plot visualises the combinations of components which differ between trial arms and offers flexibility in presenting additional information such as the number of patients experiencing the outcome of interest in each arm.

CONCLUSIONS

As CNMA becomes more widely used for the evaluation of multicomponent interventions, the novel CNMA-specific visualisations presented in this paper, which improve on the limitations of existing visualisations, will be important to aid understanding of the complex data structure and facilitate interpretation of the CNMA results.

Self-Compassion during COVID-19 in Non-WEIRD Countries: A Narrative Review

The coronavirus disease 2019 (COVID-19) pandemic impacted people's mental health negatively worldwide, including in non-WEIRD (Western, Educated, Industrialised, Rich and Democratic) countries. Self-compassion, kindness and understanding towards oneself in difficult times have received increasing attention in the field of mental health. Self-compassion is strongly associated with good mental health in various populations. This narrative review aimed to synthesise the evidence on self-compassion and mental health in non-WEIRD countries during the COVID-19 pandemic. MEDLINE and PsycINFO were searched for empirical studies. Self-compassion was consistently associated with positive mental health in non-WEIRD countries too. However, how, and to what degree, each component of self-compassion impacts mental health remains to be evaluated across different cultures. Future research such as multi-national intervention studies, or component network meta-analysis, is needed to advance our understanding of how self-compassion improves mental health in different populations.

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study

BACKGROUND

Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to "reconnect" a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA.

METHODS

We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities.

RESULTS

CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist.

CONCLUSIONS

CNMA methods are feasible for connected networks but questionable for disconnected networks.

Lifestyle medicine for anxiety symptoms: A meta-analysis of randomized controlled trials

BACKGROUND

Lifestyle medicine (LM) is gaining increasing attention as a treatment option for anxiety, but the current state of evidence has not yet been systematically examined.

METHODS

Six electronic databases were systematically searched from inception to February 2022. Randomized controlled trials (RCTs) comparing the effects of multicomponent LM interventions on anxiety symptoms with either care-as-usual, waitlist, no intervention, or attention control group on anxiety symptoms were identified.

RESULTS

A total of 53 RCTs with 18,894 participants were included for qualitative synthesis, in which 45 RCTs with data available were included for meta-analysis. Multicomponent LM intervention was significantly more effective than the control groups in reducing anxiety symptoms at immediate posttreatment (d = 0.19, p < .001) and at short-term follow-up (d = 0.29, p < .001). However, no significant difference at medium-term was found (p = .14), whereas more studies are needed to study the long-term effects. The subgroup analyses suggested that baseline anxiety symptoms was a significant moderator, suggesting that those with moderate level of baseline anxiety symptoms appeared to have greater improvements (d = 0.66, p < .05).

LIMITATIONS

Minimal anxiety symptoms at baseline contributed to the floor effect and influenced the degree of improvement. The included RCTs had a high risk of bias in general with potential publication bias detected.

CONCLUSION

The findings of this meta-analysis provided support for the positive effects of multicomponent LM interventions for anxiety symptoms. Future research is needed to determine the long-term effects of multicimponent LM and the optimal baseline anxiety severity.

Bayesian unanchored additive models for component network meta-analysis

Component network meta-analysis (CNMA) models are an extension of standard network meta-analysis (NMA) models which account for the use of multicomponent treatments in the network. This article contributes innovatively to several statistical aspects of CNMA. First, by introducing a unified notation, we establish that currently available methods differ in the way they assume additivity, an important distinction that has been overlooked so far in the literature. In particular, one model uses a more restrictive form of additivity than the other which we term an anchored and unanchored model, respectively. We show that an anchored model can provide a poor fit to the data if it is misspecified. Second, given that Bayesian models are often preferred by practitioners, we develop two novel unanchored Bayesian CNMA models presented under the unified notation. An extensive simulation study examining bias, coverage probabilities, and treatment rankings confirms the favorable performance of the novel models. This is the first simulation study to compare the statistical properties of CNMA models in the literature. Finally, the use of our novel models is demonstrated on a real dataset, and the results of CNMA models on the dataset are compared.

Effectiveness of relaxation techniques 'as an active ingredient of psychological interventions' to reduce distress, anxiety and depression in adolescents: a systematic review and meta-analysis

BACKGROUND

Adolescent depression and anxiety are among the leading contributors to health burden worldwide. 'Relaxation Techniques (RTs)' are a "set of strategies to improve physiological response to stress" and are frequently cited as an active ingredient of trans-diagnostic, psychosocial interventions for scaling-up care for preventing and treating these conditions in adolescents. However, there is a little evidence on the effectiveness of 'relaxation techniques' for this age group.

AIM

As a part of the Wellcome Trust's Active Ingredients commission, we did a systematic review and meta-analysis to evaluate the effectiveness of RTs to reduce the symptoms of distress, anxiety and depression in young people, aged 14 to 24 years old, globally.

METHODS

We searched 10 academic databases to include 65 Randomized Controlled Trials (RCTs) of relaxation-based interventions for young people with the symptoms of anxiety and depression. Primary outcomes were reduction in symptoms of distress, anxiety and/or depression. We employed the Cochrane risk of bias tool and GRADE (Grading of Recommendations, Assessment, Development and Evaluations) guidelines to assess certainty of outcomes pertaining to anxiety, depression and distress. Standardized mean difference was estimated using effect size.

RESULTS

The analysis of 65 RCTs with 8009 young people showed that RTs were highly effective in treating anxiety (pooled effect size of (Standardized Mean Difference-SMD) - 0.54 (95% CI - 0.69 to - 0.40); moderately effective in reducing distress (SMD = - 0.48, 95% CI - 0.71 to - 0.24) and had only a weak effect on improving depression in young people (SMD = - 0.28 (95% CI - 0.40% to - 0.15). Face-to-face delivered relaxation techniques yielded higher effect size (SMD = - 0.47, 95% CI - 0.64 to - 0.30) compared to online delivery (SMD = - 0.22, 95% CI - 0.48 to 0.04) for anxiety.

CONCLUSION

Most of the included studies were from High Income Countries (HICs) and had a high risk of bias. Further high-quality studies with low risk of bias, especially from low resource settings are needed to evaluate the evidence for effectiveness of RTs as an active ingredient of psychological interventions to reduce the symptoms of distress, anxiety and depression in young people.

Content, Structure and Delivery Characteristics of Yoga Interventions for Managing Rheumatoid Arthritis: A Systematic Review Protocol

The global burden of rheumatoid arthritis among adults is rising. Yoga might be a potential solution for managing rheumatoid arthritis. This systematic review aims to synthesise the content, structure and delivery characteristics of effective yoga interventions for managing rheumatoid arthritis. The JBI methodology for systematic reviews of effectiveness and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines will be followed. PRISMA for systematic review protocols (PRISMA-P) was used to write the protocol. Randomised controlled trials assessing the effectiveness of yoga interventions for managing rheumatoid arthritis in adults will be included in this review. We aim to search the following databases to find published and unpublished studies: ABIM, AMED, AYUSH Research Portal, CAM-QUEST, CINAHL, CENTRAL, EMBASE, MEDLINE, PeDro, PsycInfo, SPORTDiscus, TRIP, Web of Science, DART-Europe-e-theses portal, EthOS, OpenGrey and ProQuest Dissertations and Theses. No date or language restrictions will be applied. A narrative synthesis will be conducted. Meta-regression will be conducted to explore the statistical evidence for which components (content, structure and delivery characteristics) of yoga interventions are effective.

Content, Structure and Delivery Characteristics of Yoga Interventions for the Management of Osteoarthritis: A Systematic Review Protocol

The global burden of osteoarthritis among adults is rising. Yoga might be a potential solution for the management of osteoarthritis. This systematic review aims to synthesise the content, structure and delivery characteristics of effective yoga interventions for the management of osteoarthritis. The JBI methodology for systematic reviews of effectiveness and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines will be followed. Randomised controlled trials (RCTs) assessing the effectiveness of yoga interventions for the management of osteoarthritis in adults will be included in this review. We aim to search the following databases to find published and unpublished studies: MEDLINE, EMBASE, CINAHL, PsycInfo, SPORTDiscus, AMED, Web of Science, CENTRAL, TRIP, AYUSH Research Portal, ABIM, CAM-QUEST, PeDro, OpenGrey, EthOS, ProQuest Dissertations and Theses and DART-Europe-e-theses portal. No date or language restrictions will be applied. A narrative synthesis will be conducted with the help of tables. A meta-regression will be conducted to explore the statistical evidence for which the components (content, structure and delivery characteristics) of yoga interventions are effective.

Content, Structure, and Delivery Characteristics of Yoga Interventions for Managing Hypertension: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Objectives

This systematic review aimed to synthesize the content, structure, and delivery characteristics of effective yoga interventions used for managing hypertension and to compare these characteristics with ineffective interventions.

Design and Method

The JBI and the PRISMA guidelines were followed in this systematic review. RCTs conducted among hypertensive adults were included. RCTs reporting at least one of the major components of yoga (i.e., asana, pranayama, and dhyana and relaxation practices) and comparing them with no intervention or any intervention were eligible. Sixteen databases were searched for published and unpublished studies without any date and language restrictions till March 15, 2021.

Results

The literature search yielded 13,130 records. 34 RCTs (evaluating 38 yoga interventions) met the inclusion criteria. Overall, included studies had low methodological quality mostly due to inadequate reporting. Yoga reduced SBP and DBP compared to a control intervention (MD -6.49 and -2.78; 95CI% -8.94- -4.04 and -4.11- -1.45, respectively). Eighteen, 14 and 20 interventions were effective in improving SBP, DBP, or either, respectively. 13 out of 20 effective interventions incorporated all the 3 major components of yoga and allocated similar durations to each component whereas ineffective interventions were more focused on the asana and duration of asana practice was longer. The most common duration and frequency of effective interventions were 45 min/session (in 5 interventions), 7 days/week (in 5 interventions), and 12 weeks (in 11 interventions) whereas the most common session frequency was 2 days a week (in 7 interventions) in ineffective interventions. Effective interventions were mostly center-based (in 15 interventions) and supervised (in 16 interventions) and this was similar with ineffective interventions.

Conclusion

Despite the low quality and heterogeneity of included studies, our findings suggest yoga interventions may effectively manage hypertension. The differences between the effective and ineffective interventions suggest that effective yoga interventions mostly incorporated asana, pranayama, and dhyana and relaxation practices and they had a balance between these three components and included regular practice. They were mostly delivered in a center and under supervision. Future studies should consider developing and evaluating an intervention for managing hypertension using the synthesized findings of the effective interventions in this review.

Systematic Review Registration

[PROSPERO], identifier [CRD42019139404].

Bayesian models for aggregate and individual patient data component network meta-analysis

Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics.

Heterogeneous Coefficients, Control Variables, and Identification of Multiple Treatment Effects

Multi-dimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the heterogeneous coefficients are mean independent from treatments given the controls, a simple identification condition is that the generalized propensity scores (Imbens, 2000) be bounded away from zero and that their sum be bounded away from one, with probability one. Our analysis extends to distributional and quantile treatment effects, as well as corresponding treatment effects on the treated. These results generalize the classical identification result of Rosenbaum and Rubin (1983) for binary treatments.

Modeling Multicomponent Interventions in Network Meta-Analysis

There is a rapid increase in trials assessing healthcare interventions consisting of a combination of drugs (polytherapies) or multiple components. In the latter type of interventions (also known as complex interventions), the aspect of complexity is of paramount importance. For example, nonpharmacological interventions, such as psychological interventions or self-management interventions, usually share common components that relate to the nature of intervention, who delivers it, or where and how. In a network of trials, there is often the need to identify the most effective (or safest) component and/or combination of components. Four key meta-analytical approaches have been presented in the literature to handle complex interventions. These include (a) the single-effect model, (b) the full interaction model, (c) the additive main effects model, and (d) the two-way interaction model. In this chapter, we present and discuss the advantages and limitations of these approaches. We illustrate these methods using a network that assesses the relative effects of self-management interventions on waist size in patients with type 2 diabetes.