Network meta-analysis with integrated nested Laplace approximations

Further analysis of tuberculosis in eight high-burden countries based on the Global Burden of Disease Study 2021 data

BACKGROUNDS

Most significant findings from the Global Tuberculosis (TB) Report 2023 indicate that India, Indonesia, China, the Philippines, Pakistan, Nigeria, Bangladesh, and the Democratic Republic of the Congo (DRC) collectively contribute to approximately two-thirds of global TB cases. This study aims to provide crucial data-driven insights and references to improve TB control measures through a comprehensive analysis of these eight high-burden countries.

METHODS

The eight high-burden TB countries analyzed in this study include India, Indonesia, China, the Philippines, Pakistan, Nigeria, Bangladesh, and the DRC. Age-standardized incidence rates (ASIR) of TB were derived from the Global Burden of Diseases Study 2021 data. Temporal trends were analyzed using Joinpoint regression. An age-period-cohort model was applied to examine the risk ratios (RR) of TB across diverse age groups, periods, and birth cohorts. A Bayesian age-period-cohort framework was employed to predict the ASIR of TB by 2030.

RESULTS

The study found that the Philippines (average annual percentage change = 3.1%, P < 0.001) exhibited an upward trend from 1990 to 2021. In India, the Philippines, Pakistan, and Bangladesh, the RR of TB incidence exceeded 1 after individuals reached 25 years old. Notably, the RR has shown a consistent upward trend since 2001, peaking during the period of 2017-2021 with an estimated RR of 1.5 (P < 0.001) in the Philippines. Similarly, the highest RR was observed during the period of 2017-2021 reaching 1.1 (P < 0.001) in the DRC. In the Philippines, the markedly increasing RR values for TB have been observed among individuals born after 1997-2001. Projections suggest that the ASIR of TB is expected to follow a continued upward trajectory, with an estimated rate of 392.9 per 100,000 by 2030 in the Philippines; India and Indonesia are projected to achieve less than 20.0% of the target set by the World Health Organization (WHO).

CONCLUSIONS

Among the eight high-burden countries, the Philippines, India and Indonesia are diverging from the goals set by the WHO, and the risk of TB in the Philippines and the DRC shows a trend toward affecting younger populations, which suggests that the management strategies for TB patients need to be further strengthened.

Bayesian model selection for generalized linear mixed models

We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo-likelihood approach. Our Bayesian approach assumes a flat prior for the fixed effects and includes both approximate reference prior and half-Cauchy prior choices for the variances of random effects. Since the flat prior on the fixed effects is improper, we develop a fractional Bayes factor approach to obtain posterior probabilities of the several competing models. Simulation studies with Poisson GLMMs with spatial random effects and overdispersion random effects show that our approach performs favorably when compared to widely used competing Bayesian methods including deviance information criterion and Watanabe-Akaike information criterion. We illustrate the usefulness and flexibility of our approach with three case studies including a Poisson longitudinal model, a Poisson spatial model, and a logistic mixed model. Our proposed approach is implemented in the R package GLMMselect that is available on CRAN.

Efficacy and safety of chemotherapy combined with different doses of IL-2 maintenance therapies for acute myeloid leukemia: A protocol for a Bayesian network meta-analysis

BACKGROUND

Acute myeloid leukemia (AML) is the most common malignant tumor of the hematopoietic system, which seriously threatens the lives of patients. Most AML patients have acute onset, severe condition, and poor prognosis. The present study aimed to comprehensively evaluate the effectiveness and safety of chemotherapy combined with different doses of interleukin-2 (IL-2) maintenance treatments in AML by Bayesian network meta-analysis (NMA).

METHODS

From its inception until October 2021, we will search PubMed, Cochrane Library, CNKI, Embase, and other databases to comprehensively collect randomized controlled trials (RCTs) of chemotherapy combined with different doses of IL-2 maintenance therapies for AML. Two independent researchers will complete the literature screening and data extraction according to the inclusion and exclusion criteria, and then independently conduct a bias risk assessment of all the evidence. Bayesian NMA was used to evaluate all the evidence comprehensively. Use STATA16.0 and WinBUGS1.4.3 software to process and analyze all data, and classify the quality of evidence in NMA according to grading of recommendations assessment, development, and evaluation .

RESULTS

The study will evaluate the efficacy and safety of chemotherapy combined with different doses of IL-2 maintenance therapies for AML.

CONCLUSION

The study will provide a basis for the efficacy and safety of chemotherapy combined with different doses of IL-2 maintenance therapies for AML. We hope that this study can provide meaningful support for clinicians and patients.

PROTOCOL REGISTRATION NUMBER

INPLASY202140106.

ETHICAL APPROVAL

Since the study is based on published or registered RCTs, ethical approval and patient informed consent are abandoned.

Comparison of efficacy and safety of complementary and alternative therapies for primary trigeminal neuralgia: A protocol for a Bayesian network meta analysis

BACKGROUND

Primary trigeminal neuralgia (PTN) is a type of peripheral neuralgia that seriously affects people's lives. In recent years, complementary therapies and alternative therapies have played a significant role in treating PTN. However, there is a lack of comparison among all the complementary and alternative therapies at present. Thus, the aim of this study is to discuss the efficacy and safety of diverse complementary and alternative therapies by Bayesian network meta-analysis (NMA).

METHODS

We will retrieve the Chinese and English databases to gather related randomized controlled trials (RCTs) of complementary and alternative therapies for treating PTN. The deadline is November 2020. Two independent researchers will be in charge of screening qualified literature, extracting data, and independently evaluating bias risks involved in the research. Pairwise meta-analysis and Bayesian network meta-analysis will be performed to assess all evidence. Then, we will use STATA16.0 as well as WinBUGS1.4.3 software for data analysis. Besides, the quality of NMA evidence will be classified by grading of recommendations assessment development and evaluation (GRADE).

RESULTS

This study will compare and rank the efficacy and safety of different complementary and alternative therapies in treating primary trigeminal neuralgia.

CONCLUSION

Complementary and alternative therapies play an essential role in treating primary trigeminal neuralgia. We expect our study will furnish meaningful evidence support for clinicians and patients.

PROTOCOL REGISTRATION NUMBER

INPLASY2020120026.

ETHICAL APPROVAL

Since the study is based on published or registered RCTs, ethical approval and patient informed consent are abandoned.

Large-scale external validation and comparison of prognostic models: an application to chronic obstructive pulmonary disease

BACKGROUND

External validations and comparisons of prognostic models or scores are a prerequisite for their use in routine clinical care but are lacking in most medical fields including chronic obstructive pulmonary disease (COPD). Our aim was to externally validate and concurrently compare prognostic scores for 3-year all-cause mortality in mostly multimorbid patients with COPD.

METHODS

We relied on 24 cohort studies of the COPD Cohorts Collaborative International Assessment consortium, corresponding to primary, secondary, and tertiary care in Europe, the Americas, and Japan. These studies include globally 15,762 patients with COPD (1871 deaths and 42,203 person years of follow-up). We used network meta-analysis adapted to multiple score comparison (MSC), following a frequentist two-stage approach; thus, we were able to compare all scores in a single analytical framework accounting for correlations among scores within cohorts. We assessed transitivity, heterogeneity, and inconsistency and provided a performance ranking of the prognostic scores.

RESULTS

Depending on data availability, between two and nine prognostic scores could be calculated for each cohort. The BODE score (body mass index, airflow obstruction, dyspnea, and exercise capacity) had a median area under the curve (AUC) of 0.679 [1st quartile-3rd quartile = 0.655-0.733] across cohorts. The ADO score (age, dyspnea, and airflow obstruction) showed the best performance for predicting mortality (difference AUCADO - AUCBODE = 0.015 [95% confidence interval (CI) = -0.002 to 0.032]; p = 0.08) followed by the updated BODE (AUCBODE updated - AUCBODE = 0.008 [95% CI = -0.005 to +0.022]; p = 0.23). The assumption of transitivity was not violated. Heterogeneity across direct comparisons was small, and we did not identify any local or global inconsistency.

CONCLUSIONS

Our analyses showed best discriminatory performance for the ADO and updated BODE scores in patients with COPD. A limitation to be addressed in future studies is the extension of MSC network meta-analysis to measures of calibration. MSC network meta-analysis can be applied to prognostic scores in any medical field to identify the best scores, possibly paving the way for stratified medicine, public health, and research.

A design-by-treatment interaction model for network meta-analysis and meta-regression with integrated nested Laplace approximations

Network meta-analysis (NMA) is gaining popularity for comparing multiple treatments in a single analysis. Generalized linear mixed models provide a unifying framework for NMA, allow us to analyze datasets with dichotomous, continuous or count endpoints, and take into account multiarm trials, potential heterogeneity between trials and network inconsistency. To perform inference within such NMA models, the use of Bayesian methods is often advocated. The standard inference tool is Markov chain Monte Carlo (MCMC), which is computationally expensive and requires convergence diagnostics. A deterministic approach to do fully Bayesian inference for latent Gaussian models can be achieved by integrated nested Laplace approximations (INLA), which is a fast and accurate alternative to MCMC. We show how NMA models fit in the class of latent Gaussian models and how NMA models are implemented using INLA and demonstrate that the estimates obtained by INLA are in close agreement with the ones obtained by MCMC. Specifically, we emphasize the design-by-treatment interaction model with random inconsistency parameters (also known as the Jackson model). Also, we have proposed a network meta-regression model, which is constructed by incorporating trial-level covariates to the Jackson model to explain possible sources of heterogeneity and/or inconsistency in the network. A publicly available R package, nmaINLA, is developed to automate the INLA implementation of NMA models, which are considered in this paper. Three applications illustrate the use of INLA for a NMA.