Provides measures of effect sizes for summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. Includes functions for Cohen's d, robust effect size, Cohen's q, partial eta-squared, coefficient of variation, odds ratio, likelihood ratios, sensitivity, specificity, positive and negative predictive values, Youden index, number needed to treat, number needed to diagnose, and predictive summary index.

Performs biomedical named entity recognition,
    Unified Medical Language System (UMLS) concept mapping, and negation
    detection using the Python 'spaCy', 'scispaCy', and 'medspaCy' packages, and 
    transforms extracted data into a wide format for inclusion in machine
    learning models. The development of the 'scispaCy' package is described by
    Neumann (2019) <doi:10.18653/v1/W19-5034>. The 'medspacy' package uses
    'ConText', an algorithm for determining the context of clinical statements
    described by Harkema (2009) <doi:10.1016/j.jbi.2009.05.002>. Clinspacy
    also supports entity embeddings from 'scispaCy' and UMLS 'cui2vec' concept
    embeddings developed by Beam (2018) <arXiv:1804.01486>.

Every research team have their own script for data management, statistics and 
   most importantly hemodynamic indices. The purpose is to standardize scripts 
   utilized in clinical research. The hemodynamic indices can be used in a long-format dataframe, 
   and add both periods of interest (trigger-periods), and delete artifacts with deleter-files. 
   Transfer function analysis (Claassen et al. (2016) <doi:10.1177/0271678X15626425>) and
   Mx (Czosnyka et al. (1996) <doi:10.1161/01.str.27.10.1829>) can be calculated using this package.

Are you spending too much time fetching and managing clinical trial data? Struggling with complex queries and bulk data extraction? What if you could simplify this process with just a few lines of code? Introducing 'clintrialx' - Fetch clinical trial data from sources like 'ClinicalTrials.gov' <https://clinicaltrials.gov/> and the 'Clinical Trials Transformation Initiative - Access to Aggregate Content of ClinicalTrials.gov' database <https://aact.ctti-clinicaltrials.org/>, supporting pagination and bulk downloads. Also, you can generate HTML reports based on the data obtained from the sources!