Adriani Nikolakopoulou

Background: Cancers of the oesophagus, gastroesophageal junction (GEJ), and proximal stomach share epidemiological, molecular, and therapeutic characteristics. Despite their similarities, treatment guidelines vary, and the classification of GEJ tumours remains debated. While multiple meta-analyses have addressed subsets of these malignancies, a comprehensive synthesis comparing all treatment modalities across this disease spectrum is lacking. This study aims to perform a systematic review and network meta-analysis (NMA) to compare the overall survival outcomes of different treatment modalities, including surgery, chemotherapy, chemoradiotherapy, immunotherapy, and multimodal approaches, in patients with oesophageal, GEJ, and gastric cancers. Methods and analysis: We will conduct a systematic literature search in MEDLINE (PubMed) and The Cochrane Library (CENTRAL) without date restrictions. Randomised controlled trials (RCTs) comparing eligible treatments will be included. The primary outcome will be overall survival, defined as time from diagnosis to death from any cause. Secondary outcomes will include progression-free survival, disease-specific survival, dropout rates, treatment-related adverse effects, patterns of relapse, R0 resection rates, surgical morbidity, and mortality. We will look for individual patient data (IPD) from primary studies’ authors and registries. We will assess risk of bias with the Cochrane risk of bias RoB2 tool. We plan to present each pairwise comparison with risk ratios and 95% confidence intervals from random-effects meta-analysis. A random-effects NMA model will simultaneously compare all treatments in the network. We will rank interventions using P-scores. In case we manage to access IPD and we believe the transitivity assumption does not hold for a specific comparison (or we have a disconnected network), we will use population adjustment methods to estimate an indirect treatment comparison. We will apply CINeMA to assess confidence in the findings and we will report results according to PRISMA-NMA. Discussion: Neoplasms of the oesophagus, the gastroesophageal junction, and the stomach are increasingly being studied together in clinical trials, a trend driven by continuous research on their molecular characteristics and shared therapeutic patterns. This NMA aims to pool evidence employing recent advances in meta-analytic models and critically assess the confidence in the results by implementing the CINeMA approach. Systematic review registration: Registered in PROSPERO (CRD42025634169).

A key output of network meta-analysis (NMA) is the relative ranking of treatments; nevertheless, it has attracted substantial criticism. Existing ranking methods often lack clear interpretability and fail to adequately account for uncertainty, overemphasizing small differences in treatment effects. We propose a novel framework to estimate treatment hierarchies in NMA using a probabilistic model, focusing on a clinically relevant treatment-choice criterion (TCC). Initially, we define a TCC based on smallest worthwhile differences (SWD), converting NMA relative treatment effects into treatment preference format. These data are then synthesized using a probabilistic ranking model, assigning each treatment a latent “ability” parameter, representing its propensity to yield clinically important and beneficial true treatment effects relative to the rest of the treatments in the network. Parameter estimation relies on the maximum likelihood theory, with standard errors derived asymptotically from the Hessian matrix. To facilitate the use of our methods, we launched the R package mtrank. We applied our method to two clinical datasets: one comparing 18 antidepressants for major depression and another comparing 6 antihypertensives for the incidence of diabetes. Our approach provided robust, interpretable treatment hierarchies that account for a concrete TCC. We further examined the agreement between the proposed method and existing ranking metrics in 153 published networks, concluding that the degree of agreement depends on the precision of the NMA estimates. Our framework offers a valuable alternative for NMA treatment ranking, mitigating overinterpretation of minor differences. This enables more reliable and clinically meaningful treatment hierarchies.

Background: Reporting of COVID-19 prognostic models frequently falls short of established standards. The TRIPOD checklist and its 2024 AI extension (TRIPOD + AI) provide a comprehensive framework for assessing reporting quality. We therefore evaluated and compared reporting completeness in conventional versus machine-learning models. Methods: Studies reporting the development, and internal and external validation of prognostic prediction models for COVID-19 using either conventional or machine learning-based algorithms were included. Literature searches were conducted in MEDLINE, Epistemonikos.org, and Scopus (up to July 31, 2024). Studies using conventional statistical methods were evaluated under TRIPOD, while machine learning-based studies were assessed using TRIPOD + AI. Data extraction followed TRIPOD and TRIPOD + AI checklists, measuring adherence per article and per checklist item. The protocol was prospectively registered at the Open Science Framework ( https://osf.io/kg9yw ). Results: A total of 53 studies describing 71 prognostic models were identified. Overall, adherence to both guidelines was low, with significantly poorer compliance among machine learning-based studies (TRIPOD + AI) compared to conventional model studies (TRIPOD) (28.4% vs. 38.1%, 95% CI of difference: 4.1–15.4). No study fully adhered to abstract reporting requirements, and appropriate titles were included in only a minority of cases (29.0%, 95% CI: 16.1–46.6 for TRIPOD; 13.6%, 95% CI: 4.8–33.3 for TRIPOD + AI). Sample size calculations were not fully reported in any study. Reporting of methods and results sections was poor across both frameworks. Conclusion: Lower adherence among machine learning studies reflects the relatively recent publication of the TRIPOD + AI guidelines (April 2024), which postdate many of the included studies. Both conventional and machine learning-based prediction models showed insufficient reporting, with major gaps in model description and performance reporting. Greater compliance with reporting guidelines is critical to improving the clarity, reproducibility, and clinical value of prediction model research.

Network meta-analysis allows the synthesis of relative effects from several treatments. Two broad approaches are available to synthesize the data: arm-synthesis and contrast-synthesis, with several models that can be fitted within each. Limited evaluations comparing these approaches are available. We re-analyzed 118 networks of interventions with binary outcomes using three contrast-synthesis models (CSM; one fitted in a frequentist framework and two in a Bayesian framework) and two arm-synthesis models (ASM; both fitted in a Bayesian framework). We compared the estimated log odds ratios, their standard errors, ranking measures and the between-trial heterogeneity using the different models and investigated if differences in the results were modified by network characteristics. In general, we observed good agreement with respect to the odds ratios, their standard errors and the ranking metrics between the two Bayesian CSMs. However, differences were observed when comparing the frequentist CSM and the ASMs to each other and to the Bayesian CSMs. The network characteristics that we investigated, which represented the connectedness of the networks and rareness of events, were associated with the differences observed between models, but no single factor was associated with the differences across all of the metrics. In conclusion, we found that different models used to synthesize evidence in a network meta-analysis (NMA) can yield different estimates of odds ratios and standard errors that can impact the final ranking of the treatment options compared.

Background: In nutrition research, randomised controlled trials (RCTs) and cohort studies provide complementary evidence. This meta-epidemiological study aims to evaluate the agreement of effect estimates from individual nutrition RCTs and cohort studies investigating a highly similar research question and to investigate determinants of disagreement. Methods: MEDLINE, Epistemonikos, and the Cochrane Database of Systematic Reviews were searched from January 2010 to September 2021. We matched individual RCTs to cohort studies based on population, intervention/exposure, comparator, and outcome (PI/ECO) characteristics. Two reviewers independently extracted study characteristics and effect estimates and rated the risk of bias using RoB2 and ROBINS-E. Agreement of matched RCTs/cohort studies was analysed by pooling ratio of risk ratios (RRR) and difference of (standardised) mean differences (DSMD). Results: We included 64 RCT/cohort study pairs with 4,136,837 participants. Regarding PI/ECO similarity, 20.3% pairs were “more or less identical”, 71.9% “similar but not identical” and 7.8% “broadly similar”. Most RCTs were classified as “low risk of bias” (26.6%) or with “some concerns” (65.6%); cohort studies were mostly rated with “some concerns” (46.6%) or “high risk of bias” (47.9%), driven by inadequate control of important confounding factors. Effect estimates across RCTs and cohort studies were in high agreement (RRR 1.00 (95% CI 0.91–1.10, n = 54); and DSMD − 0.26 (95% CI − 0.87–0.35, n = 7)). In meta-regression analyses exploring determinants of disagreements, risk-of-bias judgements tend to have had more influence on the effect estimate than “PI/ECO similarity” degree. Conclusions: Effect estimates of nutrition RCTs and cohort studies were generally similar. Careful consideration and evaluation of PI/ECO characteristics and risk of bias is crucial for a trustworthy utilisation of evidence from RCTs and cohort studies.

This meta-research study aims to evaluate the agreement of effect estimates between bodies of evidence (BoE) from RCTs and cohort studies included in the same nutrition evidence synthesis, to identify factors associated with disagreement, and to replicate the findings of a previous study. We searched Medline, Epistemonikos and the Cochrane Database of Systematic Reviews for nutrition systematic reviews that included both RCTs and cohort studies for the same patient-relevant outcome or intermediate-disease marker. We rated similarity of PI/ECO (population, intervention/exposure, comparison, outcome) between BoE from RCTs and cohort studies. Agreement of effect estimates across BoE was analysed by pooling ratio of risk ratios (RRR) for binary outcomes and difference of standardised mean differences (DSMD) for continuous outcomes. We performed subgroup and sensitivity analyses to explore determinants associated with disagreements. We included 82 BoE-pairs from 51 systematic reviews. For binary outcomes, the RRR was 1.04 (95% confidence interval (CI) 0.99 to 1.10, I² = 59%, τ² = 0.02, prediction interval (PI) 0.77 to 1.41). For continuous outcomes, the pooled DSMD was − 0.09 (95% CI − 0.26 to 0.09, PI − 0.55 to 0.38). Subgroup analyses yielded that differences in type of intake/exposure were drivers towards disagreement. We replicated the findings of a previous study, where on average RCTs and cohort studies had similar effect estimates. Disagreement and wide prediction intervals were mainly driven by PI/ECO-dissimilarities. More research is needed to explore other potentially influencing factors (e.g. risk of bias) on the disagreement between effect estimates of both BoE.Trial registration: CRD42021278908

The placebo effect is the ‘effect of the simulation of treatment that occurs due to a participant’s belief or expectation that a treatment is effective’. Although the effect might be of little importance for some conditions, it can have a great role in others, mostly when the evaluated symptoms are subjective. Several characteristics that include informed consent, number of arms in a study, the occurrence of adverse events and quality of blinding may influence response to placebo and possibly bias the results of randomised controlled trials. Such a bias is inherited in systematic reviews of evidence and their quantitative components, pairwise meta-analysis (when two treatments are compared) and network meta-analysis (when more than two treatments are compared). In this paper, we aim to provide red flags as to when a placebo effect is likely to bias pairwise and network meta-analysis treatment effects. The classic paradigm has been that placebo-controlled randomised trials are focused on estimating the treatment effect. However, the magnitude of placebo effect itself may also in some instances be of interest and has also lately received attention. We use component network meta-analysis to estimate placebo effects. We apply these methods to a published network meta-analysis, examining the relative effectiveness of four psychotherapies and four control treatments for depression in 123 studies.

Objective To evaluate the comparative effectiveness and safety of analgesic medicines for acute non-specific low back pain. Design Systematic review and network meta-analysis. Data sources Medline, PubMed, Embase, CINAHL, CENTRAL, ClinicalTrials.gov, clinicialtrialsregister.eu, and World Health Organization’s International Clinical Trials Registry Platform from database inception to 20 February 2022. Eligibility criteria for study selection Randomised controlled trials of analgesic medicines (eg, non-steroidal anti-inflammatory drugs, paracetamol, opioids, anti-convulsant drugs, skeletal muscle relaxants, or corticosteroids) compared with another analgesic medicine, placebo, or no treatment. Adults (≥18 years) who reported acute non-specific low back pain (for less than six weeks). Data extraction and synthesis Primary outcomes were low back pain intensity (0-100 scale) at end of treatment and safety (number of participants who reported any adverse event during treatment). Secondary outcomes were low back specific function, serious adverse events, and discontinuation from treatment. Two reviewers independently identified studies, extracted data, and assessed risk of bias. A random effects network meta-analysis was done and confidence was evaluated by the Confidence in Network Meta-Analysis method. Results 98 randomised controlled trials (15 134 participants, 49% women) included 69 different medicines or combinations. Low or very low confidence was noted in evidence for reduced pain intensity after treatment with tolperisone (mean difference −26.1 (95% confidence intervals −34.0 to −18.2)), aceclofenac plus tizanidine (−26.1 (−38.5 to −13.6)), pregabalin (−24.7 (−34.6 to −14.7)), and 14 other medicines compared with placebo. Low or very low confidence was noted for no difference between the effects of several of these medicines. Increased adverse events had moderate to very low confidence with tramadol (risk ratio 2.6 (95% confidence interval 1.5 to 4.5)), paracetamol plus sustained release tramadol (2.4 (1.5 to 3.8)), baclofen (2.3 (1.5 to 3.4)), and paracetamol plus tramadol (2.1 (1.3 to 3.4)) compared with placebo. These medicines could increase the risk of adverse events compared with other medicines with moderate to low confidence. Moderate to low confidence was also noted for secondary outcomes and secondary analysis of medicine classes. Conclusions The comparative effectiveness and safety of analgesic medicines for acute non-specific low back pain are uncertain. Until higher quality randomised controlled trials of head-to-head comparisons are published, clinicians and patients are recommended to take a cautious approach to manage acute non-specific low back pain with analgesic medicines. Systematic review registration PROSPERO CRD42019145257