AIMC Topic: Data Interpretation, Statistical

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Using the counterfactual framework to estimate non-intention-to-treat estimands in randomised controlled trials: A methodological scoping review.

Contemporary clinical trials
BACKGROUND: Randomised controlled trials (RCTs) commonly estimate intention-to-treat (ITT) estimands. However, when nonadherence to assigned treatment occurs, ITT estimands reflect the effect of being offered treatment, rather than adhering to it and...

Area under the ROC Curve has the most consistent evaluation for binary classification.

PloS one
The proper use of model evaluation metrics is important for model evaluation and model selection in binary classification tasks. This study investigates how consistent different metrics are at evaluating models across data of different prevalence whi...

Detecting irregularities in randomized controlled trials using machine learning.

Clinical trials (London, England)
BACKGROUND: Over the course of a clinical trial, irregularities may arise in the data. Trialists implement human-intensive, expensive central statistical monitoring procedures to identify and correct these irregularities before the results of the tri...

Multi-metric comparison of machine learning imputation methods with application to breast cancer survival.

BMC medical research methodology
Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across variou...

Missing data in amortized simulation-based neural posterior estimation.

PLoS computational biology
Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet,...

Improved clinical data imputation via classical and quantum determinantal point processes.

eLife
Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canon...

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation.

Small methods
Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent...

Using multivariate pattern analysis to increase effect sizes for event-related potential analyses.

Psychophysiology
Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches...

Artificial intelligence and the scientific method: How to cope with a complete oxymoron.

Clinics in dermatology
Artificial intelligence (AI) can be a powerful tool for data analysis, but it can also mislead investigators, due in part to a fundamental difference between classic data analysis and data analysis using AI. A more or less limited data set is analyze...

Simulation-based design optimization for statistical power: Utilizing machine learning.

Psychological methods
The planning of adequately powered research designs increasingly goes beyond determining a suitable sample size. More challenging scenarios demand simultaneous tuning of multiple design parameter dimensions and can only be addressed using Monte Carlo...