Bayesian splines versus fractional polynomials in network meta-analysis

Predictive value of multiple imaging predictive models for spread through air spaces of lung adenocarcinoma: A systematic review and network meta‑analysis

Spread Through Air Spaces (STAS) is involved in lung adenocarcinoma (LUAD) recurrence, where cancer cells spread into adjacent lung tissue, impacting surgical planning and prognosis assessment. Radiomics-based models show promise in predicting STAS preoperatively, enhancing surgical precision and prognostic evaluations. The present study performed network meta-analysis to assess the predictive efficacy of imaging models for STAS in LUAD. Data were systematically sourced from PubMed, Embase, Scopus, Wiley and Web of Science, according to the Cochrane Handbook for Systematic Reviews of Interventions) and A Measurement Tool to Assess systematic Reviews 2. Using Stata software v17.0 for meta-analysis, surface under the cumulative ranking area (SUCRA) was applied to identify the most effective diagnostic method. Quality assessments were performed using Cochrane Collaboration's risk-of-bias tool and publication bias was assessed using Deeks' funnel plot. The analysis encompassed 14 articles, involving 3,734 patients, and assessed 17 predictive models for STAS in LUAD. According to comprehensive analysis of SUCRA, the machine learning (ML)_Peri_tumour model had the highest accuracy (56.5), the Features_computed tomography (CT) model had the highest sensitivity (51.9) and the positron emission tomography (pet)_CT model had the highest specificity (53.9). ML_Peri_tumour model had the highest predictive performance. The accuracy was as follows: ML_Peri_tumour vs. Features_CT [relative risk (RR)=1.14; 95% confidence interval (CI), 0.99-1.32]; ML_Peri_tumour vs. ML_Tumour (RR=1.04; 95% CI, 0.83-1.30) and ML_Peri_tumour vs. pet_CT (RR=1.04; 95% CI, 0.84-1.29). Comparative analyses revealed heightened predictive accuracy of the ML_Peri_tumour compared with other models. Nonetheless, the field of radiological feature analysis for STAS prediction remains nascent, necessitating improvements in technical reproducibility and comprehensive model evaluation.

Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network

BACKGROUND

Synthesis of clinical effectiveness from multiple trials is a well-established component of decision-making. Time-to-event outcomes are often synthesised using the Cox proportional hazards model assuming a constant hazard ratio over time. However, with an increasing proportion of trials reporting treatment effects where hazard ratios vary over time and with differing lengths of follow-up across trials, alternative synthesis methods are needed.

OBJECTIVES

To compare and contrast five modelling approaches for synthesis of time-to-event outcomes and provide guidance on key considerations for choosing between the modelling approaches.

METHODS

The Cox proportional hazards model and five other methods of estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption, were applied to a network of melanoma trials reporting overall survival: restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models.

RESULTS

All models fitted the melanoma network acceptably well. However, there were important differences in extrapolations of the survival curve and interpretability of the modelling constraints demonstrating the potential for different conclusions from different modelling approaches.

CONCLUSION

The restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can accommodate non-proportional hazards and differing lengths of trial follow-up within a network meta-analysis of time-to-event outcomes. We recommend that model choice is informed using available and relevant prior knowledge, model transparency, graphically comparing survival curves alongside observed data to aid consideration of the reliability of the survival estimates, and consideration of how the treatment effect estimates can be incorporated within a decision model.

Efficacy and safety of various primary treatment strategies for very early and early hepatocellular carcinoma: a network meta-analysis

BACKGROUND

Several treatments are available for treatment of early and very early-stage Hepatocellular Carcinoma, also known as small Hepatocellular Carcinoma (SHCC). However, there is no consensus with regards to the efficacies of these methods. We aimed at identifying the most effective initial treatment strategy for SHCC through Bayesian network meta-analyses.

METHODS

Studies published between January, 2010, and February, 2021 were searched in EMBASE, Cochrane Library, PubMed and Web of science databases, and conference proceedings for trials. The included studies reported the survival outcomes of very early and early Hepatocellular Carcinoma patients subjected to radiofrequency ablation (RFA), microwave ablation (MWA), surgical resection (SR), transarterial chemoembolization (TACE), percutaneous ethanol injection (PEI), minimally invasive liver surgery (MIS), stereotactic body radiotherapy (SBRT) and cryoablation (CA). Then, data were extracted from studies that met the inclusion criteria. Patient survival data were retrieved from the published Kaplan-Meier curves and pooled. A Bayesian random-effects model was used to combine direct and indirect evidence.

RESULTS

A total of 2058 articles were retrieved and screened, from which 45 studies assessing the efficacies of 8 different treatments in 11,364 patients were selected. The included studies had high methodological quality. Recurrence free survival* (progression/recurrence/relapse/disease/tumor-free survival were combined and redefined as RFS*) and overall survival (OS) outcomes were highest in MIS-treated patients (HR 0·57, 95% confidence intervals [CI] 0·38-0·85; HR 0.48,95% CI 0.36-0.64, respectively), followed by SR-treated patients (HR 0.60, 95% CI 0.50-0.74; HR 0.62, 95% CI 0.55-0.72, respectively). TACE was highly efficacious (58.9%) at decreasing the rates of major complications. Similar findings were obtained through sensitivity analysis, and in most of the prognostic subgroups.

CONCLUSIONS

MIS and SR exhibited the highest clinical efficacies, however, they were associated with higher rates of complications. Ablation is effective in small tumors, whereas SBRT is a relatively promising treatment option for SHCC. More well-designed, large-scale randomized controlled trials should be performed to validate our findings.

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.

Gradient Profile Estimation Using Exponential Cubic Spline Smoothing in a Bayesian Framework

Attaining reliable gradient profiles is of utmost relevance for many physical systems. In many situations, the estimation of the gradient is inaccurate due to noise. It is common practice to first estimate the underlying system and then compute the gradient profile by taking the subsequent analytic derivative of the estimated system. The underlying system is often estimated by fitting or smoothing the data using other techniques. Taking the subsequent analytic derivative of an estimated function can be ill-posed. This becomes worse as the noise in the system increases. As a result, the uncertainty generated in the gradient estimate increases. In this paper, a theoretical framework for a method to estimate the gradient profile of discrete noisy data is presented. The method was developed within a Bayesian framework. Comprehensive numerical experiments were conducted on synthetic data at different levels of noise. The accuracy of the proposed method was quantified. Our findings suggest that the proposed gradient profile estimation method outperforms the state-of-the-art methods.