Historical trends in reporting effect sizes in clinical neuropsychology journals: A call to venture beyond the results section

OBJECTIVE

For decades, quantitative psychologists have recommended that authors report effect sizes to convey the magnitude and potential clinical relevance of statistical associations. However, fewer than one-third of neuropsychology articles published in the early 2000s reported effect sizes. This study re-examines the frequency and extent of effect size reporting in neuropsychology journal articles by manuscript section and over time.

METHODS

A sample of 326 empirical articles were drawn from 36 randomly selected issues of six neuropsychology journals at 5-year intervals between 1995 and 2020. Four raters used a novel, reliable coding system to quantify the extent to which effect sizes were included in the major sections of all 326 articles.

RESULTS

Findings showed medium-to-large increases in effect size reporting in the Methods and Results sections of neuropsychology journal articles that plateaued in recent years; however, there were only very small and nonsignificant changes in effect size reporting in the Abstract, Introduction, and Discussion sections.

CONCLUSIONS

Authors in neuropsychology journals have markedly improved their effect size reporting in the core Methods and Results sections, but are still unlikely to consider these valuable metrics when motivating their study hypotheses and interpreting the conceptual and clinical implications of their findings. Recommendations are provided to encourage more widespread integration of effect sizes in neuropsychological research.

Acceptance and understanding of artificial intelligence in medical research among orthopaedic surgeons

Aims

The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons.

Methods

Semi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes.

Results

The four intersecting themes identified were: 1) validity in traditional research, 2) confusion around the definition of AI, 3) an inability to validate AI research, and 4) cautious optimism about AI research. Underpinning these themes is the notion of a validity heuristic that is strongly rooted in traditional research teaching and embedded in medical and surgical training.

Conclusion

Research involving AI sometimes challenges the accepted traditional evidence-based framework. This can give rise to confusion among orthopaedic surgeons, who may be unable to confidently validate findings. In our study, the impact of this was mediated by cautious optimism based on an ingrained validity heuristic that orthopaedic surgeons develop through their medical training. Adding to this, the integration of AI into everyday life works to reduce suspicion and aid acceptance.

Synthesis of Natural-Inspired Materials by Irradiation: Data Mining from the Perspective of Their Functional Properties in Wastewater Treatment

The present study is focused on assessing the interrelation of variables involved in the synthesis of natural-inspired copolymers by electron beam grafting while taking the functionality of the resulting materials into account. In this respect, copolymers of starch-graft-polyacrylamide (St-g-PAM) were synthesized by irradiation, and their flocculation efficiency regarding the total suspended solids (TSS), chemical oxygen demand (COD), and fatty matters (FM) was tested in coagulation-flocculation experiments at laboratory scale on wastewater from the oil industry. Data mining involved approaches related to the association (correlation and dimensionality reduction with principal component analysis (PCA)), clustering by agglomerative hierarchical clustering (AHC), classifying by classification and regression tree (CART), and prediction (decision tree prediction, multiple linear regression (MLR), and principal component regression (PCR)) of treatments applied with the variation of the monomer concentration, irradiation dose, and dose rate. The relationship mining proved that the level of COD was significantly affected by the irradiation dose and monomer concentration, and FM was mainly affected by the dose rate (significance level = 0.05). TSS showed the highest negative correlation with the tested variables. Moreover, the consequences of MLR demonstrated an acceptable accuracy (mean absolute percentage error < 5%) for COD and FM; meanwhile, linear modeling together with the consequences of PCA in the structure of PCR could help to simplify and improve the prediction accuracy of equations.

The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review

In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.

Statistical techniques for predicting rupture risk in abdominal aortic aneurysms: A contribution based on bootstrap

The morphometry of abdominal aortic aneurysms (AAA) has been recognized as one of the main factors that may predispose them to rupture. The need to quantify the morphometry of AAA on a patient-specific basis constitutes a valuable tool for assisting in rupture risk prediction. Previous results of this research group have determined the correlations between hemodynamic stresses and aneurysm morphometry by means of the Pearson coefficient. The present work aims to find how the AAA morphology correlates with the hemodynamic stresses acting on the arterial wall. To do so, the potential of the bootstrap technique has been explored. Bootstrap works appropriately in applications where few data are available (13 patient-specific AAA models were simulated). The methodology developed can be considered a contribution to predicting the hemodynamic stresses from the size and shape indices. The present work explores the use of a specific statistical technique (the bootstrap technique) to predict, based on morphological correlations, the patient-specific aneurysm rupture risk, provide greater understanding of this complex phenomenon that can bring about improvements in the clinical management of aneurysmatic patients. The results obtained using the bootstrap technique have greater reliability and robustness than those obtained by regression analysis using the Pearson coefficient, thus allowing to obtain more reliable results from the characteristics of the samples used, such as their small size and high variability. Additionally, it could be an indicator that other indices, such as AAA length, deformation rate, saccular index, and asymmetry, are important.

Development of Supervised Learning Predictive Models for Highly Non-linear Biological, Biomedical, and General Datasets

In highly non-linear datasets, attributes or features do not allow readily finding visual patterns for identifying common underlying behaviors. Therefore, it is not possible to achieve classification or regression using linear or mildly non-linear hyperspace partition functions. Hence, supervised learning models based on the application of most existing algorithms are limited, and their performance metrics are low. Linear transformations of variables, such as principal components analysis, cannot avoid the problem, and even models based on artificial neural networks and deep learning are unable to improve the metrics. Sometimes, even when features allow classification or regression in reported cases, performance metrics of supervised learning algorithms remain unsatisfyingly low. This problem is recurrent in many areas of study as, per example, the clinical, biotechnological, and protein engineering areas, where many of the attributes are correlated in an unknown and very non-linear fashion or are categorical and difficult to relate to a target response variable. In such areas, being able to create predictive models would dramatically impact the quality of their outcomes, generating an immediate added value for both the scientific and general public. In this manuscript, we present RV-Clustering, a library of unsupervised learning algorithms, and a new methodology designed to find optimum partitions within highly non-linear datasets that allow deconvoluting variables and notoriously improving performance metrics in supervised learning classification or regression models. The partitions obtained are statistically cross-validated, ensuring correct representativity and no over-fitting. We have successfully tested RV-Clustering in several highly non-linear datasets with different origins. The approach herein proposed has generated classification and regression models with high-performance metrics, which further supports its ability to generate predictive models for highly non-linear datasets. Advantageously, the method does not require significant human input, which guarantees a higher usability in the biological, biomedical, and protein engineering community with no specific knowledge in the machine learning area.

Techniques for estimating health care costs with censored data: an overview for the health services researcher

Objective

The aim of this study was to review statistical techniques for estimating the mean population cost using health care cost data that, because of the inability to achieve complete follow-up until death, are right censored. The target audience is health service researchers without an advanced statistical background.

Methods

Data were sourced from longitudinal heart failure costs from Ontario, Canada, and administrative databases were used for estimating costs. The dataset consisted of 43,888 patients, with follow-up periods ranging from 1 to 1538 days (mean 576 days). The study was designed so that mean health care costs over 1080 days of follow-up were calculated using naïve estimators such as full-sample and uncensored case estimators. Reweighted estimators – specifically, the inverse probability weighted estimator – were calculated, as was phase-based costing. Costs were adjusted to 2008 Canadian dollars using the Bank of Canada consumer price index (http://www.bankofcanada.ca/en/cpi.html).

Results

Over the restricted follow-up of 1080 days, 32% of patients were censored. The full-sample estimator was found to underestimate mean cost ($30,420) compared with the reweighted estimators ($36,490). The phase-based costing estimate of $37,237 was similar to that of the simple reweighted estimator.

Conclusion

The authors recommend against the use of full-sample or uncensored case estimators when censored data are present. In the presence of heavy censoring, phase-based costing is an attractive alternative approach.

Investigation of spatial and temporal trends in water quality in Daya Bay, South China Sea

The objective is to identify the spatial and temporal variability of the hydrochemical quality of the water column in a subtropical coastal system, Daya Bay, China. Water samples were collected in four seasons at 12 monitoring sites. The Southeast Asian monsoons, northeasterly from October to the next April and southwesterly from May to September have also an important influence on water quality in Daya Bay. In the spatial pattern, two groups have been identified, with the help of multidimensional scaling analysis and cluster analysis. Cluster I consisted of the sites S3, S8, S10 and S11 in the west and north coastal parts of Daya Bay. Cluster I is mainly related to anthropogenic activities such as fish-farming. Cluster II consisted of the rest of the stations in the center, east and south parts of Daya Bay. Cluster II is mainly related to seawater exchange from South China Sea.