Impact of covariate model building methods on their clinical relevance evaluation in population pharmacokinetic analyses: comparison of the full model, stepwise covariate model (SCM) and SCM+ approaches

Covariate analysis in population pharmacokinetics is key for adjusting doses for patients. The main objective of this work was to compare the adequacy of various modeling approaches on covariate clinical relevance decision-making. The full model, stepwise covariate model (SCM) and SCM+ PsN algorithms were compared in a clinical trial simulation of a 383-patient population pharmacokinetic study mixing rich and sparse designs. A one-compartment model with first-order absorption was used. A base model including a body weight effect on CL/F and V/F and a covariate model including 4 additional covariates-parameters relationships were simulated. As for forest plots, ratios between covariates at a specific value and that of a typical individual were calculated with their 90% confidence interval (CI90) using standard errors. Covariates on CL, V and KA were considered relevant if their CI90 fell completely outside the reference area [0.8-1.2]. All approaches provided unbiased covariate ratio estimates. For covariates with a simulated effect, the 3 approaches correctly identify their clinical relevance. However, significant covariates were missed in up to 15% of cases with SCM/SCM+. For covariate with no simulated effects, the full model mainly identified them as non-relevant or with insufficient information while SCM/SCM+ mainly did not select them. SCM/SCM+ assume that non-selected covariates are non-relevant when it could be due to insufficient information, whereas the full model does not make this assumption and is faster. This study must be extended to other methods and completed by a more complex high-dimensional simulation framework.

Ideal cardiovascular health metrics have better identification of arthritis

BACKGROUND

This study aimed to explore the association between ideal cardiovascular health metrics (ICVHM) and arthritis (AR), as well as the interactions of various indicators in ICVHM on AR in US adults.

METHODS

We involved 17,041 participants who were interviewed by NHANES from 2011 to 2018. AR included osteoarthritis or degenerative arthritis (OA), rheumatoid arthritis (RA), and psoriatic arthritis and other arthritis (Other AR). Logistic regression was applied to analyze the association between AR and ICVHM. Mixed graphical model (MGM) was used to explore the interaction between variables in ICVHM.

RESULTS

Higher ICVHM scores had a protective effect on AR. Compared to "≤1" score, the ORs of AR in participants with 2, 3, 4, and ≥5 were 0.586, 0.472, 0.259, and 0.130, respectively. Similar results were also found in different types of AR. ICVHM has a maximum area under the curve value of 0.765 and the interaction between blood pressure and total cholesterol was 0.43.

CONCLUSIONS

ICVHM correlates significantly with AR and is better at identifying AR than individual indicators. ICVHM can be better improved by controlling the indicators with stronger interactions. Our findings provide guidance for promoting health factors, which have important implications for identification and prevention of AR.

The use of medicinal plants in common ophthalmic disorders: A systematic review with meta-analysis

Purpose

This study aimed to assess and compile the available research articles about medicinal plants used for ocular diseases.

Principal results

A total of 2949 articles were retrieved, 35 full-text articles were assessed for eligibility, and seven studies (4 observational and three experimental) with low to moderate quality were eligible and involved in the systematic review, with a total of 600 plants from 4 countries. Among the 600 plants, only 24 (4%) were used to assess the status. Both the fixed and random models of the studies showed that the included studies tended to predict the results for the observational studies (OR = 0.062, CI = 0.043-0.090 OR = 0.039, CI = 0.012-0.122) for different plants used for ocular diseases. High heterogeneity (estimated as I² = 87.078, Tau² = 1.161 and Q-value = 23.217 with a p-value of 0.000), while for experimental studies (I² = 94.928, Tau² = 23.211 and Q-value = 39.434 with a p-value of 0.000) and publication bias were reported.

Conclusion

Few articles representing approximately 600 plants of low to moderate quality reported using medicinal plants for ocular diseases. The meta-analysis confirmed the systematic review findings regarding the plants' traditional use with high heterogeneity and publication bias. A considerable gap was proven in the use of medicinal plants in ocular diseases requiring intensive research.

Value of HMGB1 expression for assessing gastric cancer severity: a systematic meta-analysis

OBJECTIVE

To evaluate the clinical value of high mobility group box-1 (HMGB1) expression levels in patients with gastric cancer.

METHODS

Articles published from January 2000 to August 2022 were searched using PubMed, Google Scholar and Science Direct, Springer, Wiley and NIH to evaluate the clinicopathological significance of HMGB1 expression in gastric cancer.

RESULTS

A total of 156 publications were selected, of which six studies, comprising 846 patients, met the criteria for inclusion in this study. Forest plots of clinicopathological characteristics indicated that HMGB1 expression was not associated with age (odds ratio (OR) = 1.07, 95% confidence interval (CI): 0.89-1.28), sex (OR = 0.90, 95% CI: 0.81-1.00), TNM (OR = 1.39, 95% CI: 0.82-2.37), N stage (OR = 1.42, 95% CI: 0.97-2.07), or tumor differentiation (OR = 0.96, 95% CI: 0.71-1.29), but was highly correlated with pT stage (OR = 1.56, 95% CI: 1.17-2.07). Funnel plots showed no significant publication bias in the included studies in terms of age, sex, TNM, pT stage, N stage, or tumor differentiation.

CONCLUSION

HMGB1 expression was significantly correlated with tumor pT stage, but not with age, sex, TNM stage, tumor N stage, tumor differentiation, or lymphatic metastasis in patients with GC.

Evidence-Based Decision-Making 2: Systematic Reviews and Meta-Analysis

The number of studies published in the biomedical literature has dramatically increased over the last few decades. This massive proliferation of literature makes clinical medicine increasingly complex, and information from multiple studies is often needed to inform a particular clinical decision. However, available studies often vary in their design, methodological quality, and population studied, and may define the research question of interest quite differently. This can make it challenging to synthesize the conclusions of multiple studies. In addition, since even highly cited trials may be challenged over time, clinical decision-making requires ongoing reconciliation of studies which provide different answers to the same question. Because it is often impractical for readers to track down and review all the primary studies, systematic reviews and meta-analyses are an important source of evidence on the diagnosis, prognosis and treatment of any given disease. This chapter summarizes methods for conducting and reading systematic reviews and meta-analyses, as well as describes potential advantages and disadvantages of these publications.

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis

BACKGROUND

Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable.

METHODS

We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes.

RESULTS

We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features.

CONCLUSIONS

The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

Multiple uses of forest plots in presenting analysis results in health research: A Tutorial

BACKGROUND AND OBJECTIVES

Forest plots are an important graphical method in meta-analyses used to show results from individual studies and pooled analyses. Forest plots are easy and straightforward to understand because they provide tabular and graphical information about estimates of comparisons or associations, corresponding precision, and statistical significance. This visual representation also makes it easier to see variations between individual study results. Forest plots are widely used in not only systematic reviews and meta-analyses but also observational studies and clinical trials. In this study, we aimed to show readers the various uses of forest plots in displaying analysis results.

STUDY DESIGN AND SETTING

We used a tutorial type to show multiple uses of forest plots in meta-analyses, clinical trials, and observational studies according to the PICO (Population or Subgroup, Intervention or Exposure, Control, and Outcome) framework.

RESULTS

We introduced forest plots' structure, application, current practice, and research advances in health research. We provided some examples from the literature to show the various uses of forest plot-type graphics in health research including meta-analyses, clinical trials, and observational studies.

CONCLUSION

It is expected that our discussion of the current multiple uses of forest plots in meta-analyses, clinical trials, and observational studies provides a glimpse about their potential in displaying results in a way that makes comparisons between items easier.

Intervention meta-analysis: application and practice using R software

The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were “metacont”, “metabin”, and “metagen” for the overall effect size, “forest” for forest plot, “metareg” for meta-regression analysis, and “funnel” and “metabias” for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research.

An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots

Maps of aboveground forest biomass based on different input data and modelling approaches have been recently produced for Europe, opening up the possibility for several applications and products not obtainable by summary statistics. However, the accuracy assessment of the existing maps is limited by the lack of reference data consistent over the study region and representative of the maps cells. Here, we used harmonized forest biomass data for 26 European countries derived by National Forest Inventories using a common biomass definition and estimator to assess four biomass maps. The assessment was performed at regional, national and sub-national scales using harmonized statistics derived from almost half million ground plot measurements, and at pixel level using a subset of 22,166 plots covering most European forest types. The field plots were temporally aligned with the maps using growth rates and further screened using an innovative approach based on tree cover variability to remove the plots not representative of the map cells. The harmonized reference data showed that all maps tended to overestimate at low biomass (<100 Mg ha^-1) and underestimate at medium - high biomass (>100 Mg ha^-1), resulting in an overall negative bias (23-43 Mg ha^-1 at national level) relative to the harmonized estimates. The maps relative errors ranged from 29% to 40% at national level and increased at higher resolutions, reaching 58-67% at pixel level. We also assessed the effect of the harmonization of the national statistics and report that the harmonized biomass values present significant differences compared to the national estimates for 14 countries, and provide a slightly higher stock (+3.8%) at European scale. We show that harmonized and representative reference data are essential to properly assess the accuracy of biomass maps, and we further identify the factors affecting the maps performance and provide indications for their improvements.

A systematic meta-analysis on the efficacy of pre-clinically tested nephroprotectants at preventing aminoglycoside nephrotoxicity

Nephrotoxicity limits the use of aminoglycoside antibiotics. Kidney damage is produced mainly in the renal tubule due to an inflammatory and oxidative process. At preclinical level, many drugs and natural products have been tested as prospective protectors of aminoglycoside nephrotoxicity. The main objective of this work was to make a systematic literature review of preclinical studies about aminoglycoside nephrotoxicity protection and a statistical analysis based on the meta-analysis methodology. Studies published up to January 2016 were identified. After applying inclusion criteria, 54 studies were chosen. The size of the experimental groups, means and standard deviations of data on renal function (i.e. plasma creatinine and blood urea nitrogen [BUN] concentrations) were extracted and registered in a database. The studies were grouped according to the mechanism of nephroprotection and their route of administration. The Mean Difference (95% confidence interval) was calculated for each study and group. 40 of 54 products tested produced an amelioration of aminoglycoside nephrotoxicity based on creatinine results. Also a dose dependent protective effect was observed (both in creatinine and BUN). Products orally administered were more effective than via i.p. Products with attributed antioxidant activity were the most used and those which proved statistically significant nephroprotection as a class effect. Aminoglycoside tubular reuptake inhibitors, excretion inducers and calcium channel blockers also showed a promising and rather homogeneous class tendency towards nephroprotection, although more research is necessary to obtain solid and conclusive results, based on a larger number of studies.

The effect of green leafy and cruciferous vegetable intake on the incidence of cardiovascular disease: A meta-analysis

Does the consumption of green leafy vegetables including cruciferous vegetables significantly reduce the incidence of cardiovascular disease? This research question was answered via employing the statistical methods of meta-analysis by synthesizing relevant worldwide studies that address the association between the consumption of green leafy vegetables and risk of incidence of said diseases. All meta-analysis calculations included determination of effect sizes of relative risk, and their respective 95% confidence intervals, heterogeneity of the studies, relative weights for each study, and significance (p) for each study. Eight studies met the inclusion criteria, which investigated the relationship between the incidences of total cardiovascular disease with the intake of green leafy vegetables. The overall effect size (random effect model) was: RR = 0.842 (95% CI = 0.753 to 0.941), p = 0.002, which indicates a significant 15.8% reduced incidence of cardiovascular disease.