Pharmaceutical Software User Group (PharmaSUG) 2024
Ryan Paul Lafler is an Invited Speaker in the Real World Evidence and Big Data section and a Panel Member in the Solution Development section.
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Ryan is giving a Post-Conference Training Seminar on conducting statistical analysis and modeling with the R programming language. The Training Seminar is Mastering Statistical Hypothesis Testing Using R with Comparisons to SAS.
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All attendees can register for Ryan's Post-Conference Training Seminar using the button below!
Post-Conference Training
(May 22nd, 2024)
Mastering Statistical Hypothesis Testing Using R with Comparisons to SAS
Ryan Paul Lafler; Daniela Nuñez
2024
This half-day course is open to all aspiring and experienced data scientists, statisticians, bioinformatics scientists, and clinical programmers interested in understanding, designing, and developing parametric and non-parametric statistical hypothesis tests for clinical experiments. This course leverages the R and SAS programming languages to conduct statistical hypothesis testing using real-world examples geared towards the pharmaceutical industry, clinical trials, and the biological and life sciences. Attendees are given a rigorous introduction to frequentist hypothesis testing including discussions about parametric statistical distributions, significance levels, error rates, effect sizes, statistical power, standard errors, confidence intervals, and p-values. Attendees also learn about strategies for successful experimental design, controlling for confounding and lurking covariates, handling missing values, and assessing causation against correlation. Several parametric hypothesis tests including t-tests, Chi-Squared tests, One-Way ANOVA (Analysis of Variance), Factorial ANOVA, and One-Way MANOVA (Multivariate Analysis of Variance) are covered in R with comparisons to SAS, including a thorough discussion of each test’s assumptions, use-cases, output, and limitations. Frequently used non-parametric equivalents including the Mann-Whitney U test, the Wilcoxon Signed-Rank test, and the Kruskal-Wallis test are similarly investigated and developed in R. By enrolling in this course, each attendee receives the documented R and SAS code files, their personal copy of the PDF version of the slides, and the confidence to successfully perform statistical hypothesis testing in their organization.
Presentations & Panel Discussions
Designing Artificial & Convolutional Neural Networks for Classification & Regression Tasks using TensorFlow in Python
Ryan Paul Lafler; Anna Wade
2023
Capable of accepting and mapping complex relationships hidden within structured and unstructured data, Neural Networks are composed from layers of neurons with functions that interact, preserve, and exchange information between each other to develop highly flexible and robust predictive models. Neural Networks are versatile in their applications to real-world problems; capable of regression, classification, and generating entirely new data from existing data sources, Neural Networks are accelerating the breakthroughs in deep learning methodologies. Given the recent advancements in graphical processing unit (GPU) cards, cloud computing, and the availability of interpretable APIs like the Keras interface for TensorFlow, Neural Networks are quickly moving from development to deployment in industries ranging from finance, healthcare, climatology, movies, video streaming, business analytics, and marketing given their versatility in modeling complex problems using structured, semi-structured, and unstructured data. This Paper offers users an intuitive, example-oriented guide to designing basic Artificial Neural Network and Convolutional Neural Network architectures in Python for non-parametric regression and image classification tasks.
Benefits, Challenges, & Opportunities with Open-Source Software (OSS) Integration
Kirk Paul Lafler; Ryan Paul Lafler
2023
The open-source world is alive, well and growing in popularity. This paper highlights the many benefits found with open source software (OSS) including its flexibility, agility, talent attraction, and the collaborative power of community; the trends show that open-source is ubiquitous penetrating many critical technologies we depend on, where more technology companies recognize the importance of the open-source community leading to more initiatives and sponsorships that support open-source creators; the challenges of open source including compatibility vulnerability issues, security limitations, intellectual property issues, warranty issues, and inconsistent developer practices; and the opportunities coming out of the open source community including cloud architecture, open standards, and the collaborative nature of community.