The normality assumption on between-study random effects was questionable in a considerable number of Cochrane meta-analyses

Enhancing insight into regional differences: hierarchical linear models in multiregional clinical trials

BACKGROUND

The planning and analysis of multi-regional clinical trials (MRCTs) has increased in the pharmaceutical industry to facilitate global research and development. The ICH E17 guideline emphasizes the importance of considering the potential for regional differences, which may arise from shared intrinsic and extrinsic factors among patients within the same region in MRCTs. These differences remain as challenges in the design and analysis of MRCTs.

METHODS

We introduce and investigate hierarchical linear models (HLMs) that account for regional differences by incorporating known factors as covariates and unknown factors as random effects. Extending previous studies, our HLMs incorporate random effects in both the intercept and slope, enhancing the model's flexibility. The proposed figures that depict the observed distribution of the primary endpoint and covariates facilitate understanding the proposed models. Moreover, we investigate the test statistics for the overall treatment effect and derive the required sample size under the HLM, considering both a fixed number of regions and real-world budgetary constraints.

RESULTS

Our simulation studies show that when the number of regions is sufficient, HLM with random effects in the intercept and slope provides empirical type I error rates and power close to the nominal level. However, the estimate for the regional variabilities remains challenging for the small number of the regions. Budgetary constraints impact the required number of regions, while the required number of patients per region is influenced by the variability of treatment effects across regions.

CONCLUSIONS

We offer a comprehensive framework for understanding and addressing regional differences in the primary endpoint for MRCTs. Through the proposed strategies with figures and required sample size considering the budget constraints, designs for MRCT could be more efficient.

The Role of Double-Zero-Event Studies in Evidence Synthesis: Evaluating Robustness Using the Fragility Index

RATIONALE

Zero-event counts are common in clinical studies, particularly when assessing rare adverse events. These occurrences can result from low event rates, short follow-up periods, and small sample sizes. When both intervention and control groups report zero events in a clinical trial, the study is referred to as a double-zero-event study, which presents methodological challenges for evidence synthesis. There has been ongoing debate about whether these studies should be excluded from evidence synthesis, as traditional two-stage meta-analysis methods may not estimate an effect size for them. Recent research suggests that these studies may still contain valuable clinical and statistical information.

AIMS AND OBJECTIVES

This study examines the role of double-zero-event studies from the perspective of the fragility index (FI), a popular metric for assessing the robustness of clinical results. We aim to determine how including or excluding double-zero-event studies affects FI derivations in meta-analyses.

METHODS

We conducted an illustrative case study to demonstrate how double-zero-event studies can impact FI derivations. Additionally, we performed a large-scale analysis of 12,184 Cochrane meta-analyses involving zero-event studies to assess the prevalence and effect of double-zero-event studies on FI calculations.

RESULTS

Our analysis revealed that FI derivations in 6608 (54.2%) of these meta-analyses involved double-zero-event studies. Excluding double-zero-event studies could lead to artificially inflated FI values, potentially misrepresenting the results as more robust than they are.

CONCLUSIONS

We advocate for retaining double-zero-event studies in meta-analyses and emphasise the importance of carefully considering their role in FI assessments. Including these studies ensures a more accurate evaluation of the robustness of clinical results in evidence synthesis.

Prevalence of rare anatomic variants - publication bias due to selective reporting in meta-analyses studies

INTRODUCTION

Meta-analyses of prevalence studies reporting rare anatomic variants are prone to selective reporting of non-null, confirmatory results, thus resulting in publication bias.

Longitudinal Changes in Human Milk Minerals and Vitamins in the Chinese Population: A Scoping Review

This scoping review aims to investigate longitudinal changes in minerals and vitamins concentrations in human milk among the Chinese population. Following the PRISMA-ScR guidelines, a comprehensive and systematic literature search was conducted using both English and Chinese databases. Data were extracted and categorized into six defined lactation stages. We found that the concentration of most minerals decreased throughout the lactation period, although calcium (Ca) and magnesium (Mg) fluctuated slightly across lactation periods. Fat-soluble vitamins also showed a decline throughout the lactation period, while water-soluble vitamins exhibited an increasing trend. However, folic acid, biotin, and pantothenic acid demonstrated a downward trend. Overall, this review has identified the longitudinal changes in minerals and vitamins concentrations in human milk among the Chinese population. In order to conduct a more in-depth examination of maternal characteristics and nutritional factors of the composition of human milk, it is recommended to utilize standardized protocols for the collection and analysis of human milk samples.

Comparisons of the mean differences and standardized mean differences for continuous outcome measures on the same scale

When conducting systematic reviews and meta-analyses of continuous outcomes, the mean differences (MDs) and standardized mean differences (SMDs) are 2 commonly used choices for effect measures. The SMDs are motivated by scenarios where studies collected in a systematic review do not report the continuous measures on the same scale. The standardization process transfers the MDs to be unit-free measures that can be synthesized across studies. As such, some evidence synthesis researchers tend to prefer the SMD over the MD. However, other researchers have concerns about the interpretability of the SMD. The standardization process could also yield additional heterogeneity between studies. In this paper, we use simulation studies to illustrate that, in a scenario where the continuous measures are on the same scale, the SMD could have considerably poorer performance compared with the MD in some cases. The simulations compare the MD and SMD in various settings, including cases where the normality assumption of continuous measures does not hold. We conclude that although the SMD remains useful for evidence synthesis of continuous measures on different scales, the SMD could have substantially greater biases, greater mean squared errors, and lower coverage probabilities of CIs than the MD. The MD is generally more robust to the violation of the normality assumption for continuous measures. In scenarios where continuous measures are inherently comparable or can be transformed to a common scale, the MD is the preferred choice for an effect measure.

Artificial intelligence in fracture detection with different image modalities and data types: A systematic review and meta-analysis

Artificial Intelligence (AI), encompassing Machine Learning and Deep Learning, has increasingly been applied to fracture detection using diverse imaging modalities and data types. This systematic review and meta-analysis aimed to assess the efficacy of AI in detecting fractures through various imaging modalities and data types (image, tabular, or both) and to synthesize the existing evidence related to AI-based fracture detection. Peer-reviewed studies developing and validating AI for fracture detection were identified through searches in multiple electronic databases without time limitations. A hierarchical meta-analysis model was used to calculate pooled sensitivity and specificity. A diagnostic accuracy quality assessment was performed to evaluate bias and applicability. Of the 66 eligible studies, 54 identified fractures using imaging-related data, nine using tabular data, and three using both. Vertebral fractures were the most common outcome (n = 20), followed by hip fractures (n = 18). Hip fractures exhibited the highest pooled sensitivity (92%; 95% CI: 87-96, p< 0.01) and specificity (90%; 95% CI: 85-93, p< 0.01). Pooled sensitivity and specificity using image data (92%; 95% CI: 90-94, p< 0.01; and 91%; 95% CI: 88-93, p < 0.01) were higher than those using tabular data (81%; 95% CI: 77-85, p< 0.01; and 83%; 95% CI: 76-88, p < 0.01), respectively. Radiographs demonstrated the highest pooled sensitivity (94%; 95% CI: 90-96, p < 0.01) and specificity (92%; 95% CI: 89-94, p< 0.01). Patient selection and reference standards were major concerns in assessing diagnostic accuracy for bias and applicability. AI displays high diagnostic accuracy for various fracture outcomes, indicating potential utility in healthcare systems for fracture diagnosis. However, enhanced transparency in reporting and adherence to standardized guidelines are necessary to improve the clinical applicability of AI. Review Registration: PROSPERO (CRD42021240359).

Challenges and Concerns in the Utilization of Meta-Analysis Software: Navigating the Landscape of Scientific Synthesis

Meta-analysis has emerged as a pivotal tool for synthesizing evidence in scientific research, facilitated by the advent of meta-analysis software. While these tools have significantly streamlined the synthesis process, challenges and concerns persist, impacting the reliability and validity of meta-analytic findings. This editorial addresses key issues in the use of meta-analysis software, including heterogeneity, publication bias, data quality, model dependence, and user competence. As the scientific community increasingly relies on meta-analytic approaches, collaborative efforts are needed to establish standardized reporting guidelines, enhance data quality, and improve transparency. This study highlights the importance of addressing these challenges to ensure the continued evolution of meta-analysis as a robust and informative method for evidence synthesis in scientific research.

A Meta-analysis of Surgical Outcomes of T4a and Infranotch T4b Oral Cancers

OBJECTIVE

To determine the overall surgical outcomes of infranotch T4b oral cancers and compare them with T4a oral cancers.

METHODS

PubMed, EMBASE and Cochrane databases from 2000 to 2022 were systematically searched. Clinical studies reporting at least one outcome following curative surgery and adjuvant therapy for comparison of patients with either infranotch T4b (IN-T4b) or T4a tumour. The heterogeneity of the included studies was determined using Tau-squared, Chi-squared, and the Higgins I2 test. The random effects model was used to determine the log odds ratio (logOR).

RESULTS

The systematic review comprised 11,790 patients from 16 included studies. Seven studies were included in the meta-analysis (n = 11,381). For IN-T4b patients, the pooled 2 and 5 year overall survival (OS) were 59.3% and 53.2%, 2 and 5 year disease-free survival (DFS) 57.9% and 48.4%, 2 and 5 year disease-specific survival (DSS) 72% and 68%, and 2 and 5 year local control (LC), 47% and 56%, respectively. There was no statistically significant difference in 2 year OS [logOR = 0.28 (-0.47, 1.03), p = 0.46, confidence interval (CI) = 95%], 5 year OS [logOR = 0.7 (-0.4, 1.8), p = 0.54, CI = 95%], 2 year DFS [logOR = 0.22 (-0.35, 0.79), p = 0.45, CI = 95%], 5 year DFS [logOR = 0.17 (-0.42, 0.77), p = 0.57, CI = 95%], 2 year LC [logOR = 0.47 (-0.33, 1.26), p = 0.25, CI = 95%] and 5 year LC [logOR = 0.34 (-0.31, 0.99), p = 0.31, CI = 95%] between IN-T4b and T4a oral cancers.

CONCLUSION

Results of this meta-analysis suggest that IN-T4b oral cancers have similar outcomes to T4a oral cancers, which supports down-staging IN-T4b cancers to T4a cancers.

Evidence of correlations between human partners based on systematic reviews and meta-analyses of 22 traits and UK Biobank analysis of 133 traits

Positive correlations between mates can increase trait variation and prevalence, as well as bias estimates from genetically informed study designs. While past studies of similarity between human mating partners have largely found evidence of positive correlations, to our knowledge, no formal meta-analysis has examined human partner correlations across multiple categories of traits. Thus, we conducted systematic reviews and random-effects meta-analyses of human male-female partner correlations across 22 traits commonly studied by psychologists, economists, sociologists, anthropologists, epidemiologists and geneticists. Using ScienceDirect, PubMed and Google Scholar, we incorporated 480 partner correlations from 199 peer-reviewed studies of co-parents, engaged pairs, married pairs and/or cohabitating pairs that were published on or before 16 August 2022. We also calculated 133 trait correlations using up to 79,074 male-female couples in the UK Biobank (UKB). Estimates of the 22 mean meta-analysed correlations ranged from rmeta = 0.08 (adjusted 95% CI = 0.03, 0.13) for extraversion to rmeta = 0.58 (adjusted 95% CI = 0.50, 0.64) for political values, with funnel plots showing little evidence of publication bias across traits. The 133 UKB correlations ranged from rUKB = -0.18 (adjusted 95% CI = -0.20, -0.16) for chronotype (being a 'morning' or 'evening' person) to rUKB = 0.87 (adjusted 95% CI = 0.86, 0.87) for birth year. Across analyses, political and religious attitudes, educational attainment and some substance use traits showed the highest correlations, while psychological (that is, psychiatric/personality) and anthropometric traits generally yielded lower but positive correlations. We observed high levels of between-sample heterogeneity for most meta-analysed traits, probably because of both systematic differences between samples and true differences in partner correlations across populations.