data analysis Archives - Ger Inberg

DrugExposure Diagnostics

Ger — Sat, 01 Apr 2023 09:32:00 +0000

DrugExposureDiagnostics: A Comprehensive R Package for Assessing Drug Exposure in Clinical Research

Drug exposure is an essential aspect of clinical research, as it directly affects the efficacy and safety of drugs. Measuring drug exposure accurately and understanding the factors that influence it is crucial for clinical decision-making. This is where the R package DrugExposureDiagnostics comes in handy.

As the author of this R package, I am excited to introduce you to this powerful tool for analyzing drug exposure data. Before delving into the package, let’s first understand what drug exposure is and why it is crucial in clinical research.

Drug exposure refers to the extent to which a drug enters and stays in the body, thereby producing its intended therapeutic effects. Measuring drug exposure accurately involves capturing key metrics, such as drug concentrations, AUC, Cmax, and Tmax. By doing so, researchers can evaluate drug efficacy and safety and make informed decisions regarding dosing and administration.

One way to capture drug exposure data is through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), developed by the Observational Health Data Sciences and Informatics (OHDSI) community. The OMOP CDM standardizes and integrates data from various sources, allowing for large-scale observational studies and analysis.

This is where the R package DrugExposureDiagnostics comes in. It is a comprehensive tool for analyzing drug exposure data in the OMOP CDM format. The package includes functions for calculating various exposure metrics, handling missing data, and summarizing data at different levels, such as by subject or visit. Additionally, it provides tools for identifying outliers and comparing exposure between groups.

DrugExposureDiagnostics has been extensively tested and validated, ensuring that it produces accurate results. The package has been released on the Comprehensive R Archive Network (CRAN), making it easily accessible to R users worldwide. To use the package, simply install it using the install.packages() function in R and load it using the library() function.

If you are interested in learning more about DrugExposureDiagnostics or trying it out for yourself, visit the package github

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CITO public analysis

Ger — Fri, 12 Mar 2021 07:24:00 +0000

CITO is an institute in the Netherlands that support governments and schools so that they can develop world-class testing and monitoring systems to complete their educational programs. They have a lot of data regarding testing scores and it could be interesting to combine this data with public data. For example, are testing scores of children living in deprived areas worse than average?

Exploratory Analysis

Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics. This is often done by using data visualization methods. The main purpose of EDA is to help look at data before making any assumptions. For me it’s one of the nicest parts of the data science! Since you don’t know yet what’s in the data and there will always be surprises. It’s like you are on holiday and exploring the area that you seeing for the first time:-)

For example, is a certain variable in the data normally distributed or not? Is there any missing data or duplicated values? In my experience, yes in most cases, there is missing and duplicated data. We need to fix these issues before we can do the real analysis. This phase is called data cleaning, you might have heard about this before.

Representativeness Analyses

In general, a representative sample is a group or set chosen from a larger statistical population that adequately replicates the larger group according to whatever characteristic or quality is under study. In case of CITO, we like to know if the sample data set has more or less the same characteristics regarding scores. For example, are the average and standard deviation of the sample data set close to the ones of the total data set. I have plotted the distributions of the 2 data sets in a single chart, in order to compare them. In the subtitle one can find the average, standard deviation and the median.

Below you can find some of the charts I made for both EDA and the representativeness Analyses. The code is available in a public repository on github. It can be run using a docker container, R and renv for library management.

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Interactive Gene Explorer

Ger — Wed, 06 Mar 2019 03:42:36 +0000

Since almost 2 years I am working for a global Bio pharmaceutical company. Together with researchers, I am working on applications to analyze (sometimes big) data to find medicines for patients with serious and life-threatening diseases. As you can imagine, the work is confidential and so I am not able to share data or results with other people. However, there are a couple of initiatives to open-source some of the packages that are developed within the company, which is awesome! The Shiny contest gave me a good opportunity to display some of these packages in a Shiny application.

Periscope

Periscope is a new R package which provides a predefined but flexible template for new Shiny applications with a default dashboard layout. Furthermore it provides user alerts, a nice busy indicator, application reset and logging features.

One of the most important features of the Shiny applications created with this framework is the separation by file of functionality that exists in one of the three Shiny scopes. These scopes are: global, server-global, and server-local. The framework forces application developers to consciously consider scoping in Shiny applications by making scoping distinctions very clear without interfering with normal application development. Scoping consideration is important for performance and scaling. This is critical when working with large datasets and/or across many users. In addition to providing a template application, the framework also contains a number of convenient modules.

(multi)file download button module
downloadable table module.

CanvasXpress

The plots in the application are created using CanvasXpress, which is a package for (interactive) data visualization especially for reproducible research. It supports a large number of visualizations to display scientific and non-scientific data which includes: Area, AreaLine, Bar, BarLine, Boxplot, Bubble, Candlestick, Chord, Circular, Contour, Correlation, Density, Donnut, DotLine, Dotplot, Genome, Heatmap, Histogram, Kaplan-Meier, Layout, Line, Map, Network, NonLinear-Fit, Oncoprint, ParallelCoordinates, Pie, Radar, Remote-Graphs, Sankey, Scatter2D, Scatter3D, ScatterBubble2D, Stacked, StackedLine, StackedPercent, StackedPercentLine, Sunburst, TagCloud, Tree, Treemap, Venn, Video, Violin. This blogpost gives a good overview on how to get started with CanvasXpress.

View the Interactive Gene Explorer or the source code on github.

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Transparent healthcare

Ger — Wed, 24 Aug 2016 13:03:43 +0000

A couple of weeks ago, the Dutch insurance company CZ released a list of treatments with its prices at various hospitals. This is quite unique, because till now the health sector is far from transparent. Hopefully this is part of a trend that will benefit patients in choosing the best hospital for their needs.

Based on the released pricelist, I have done some analysis. See below for the main points from this analysis or view the visualization I made.

Treatments with biggest difference in price

The top 3 treatments (in Dutch) with biggest price difference

1 of 2 dagbehandelingen of 6 of meer polikliniekbezoeken bij een huidaandoening met bultjes en schilfers (min =€183,87, max =€989,01, diff = €805,14)
Begeleiding door IC-arts of intensivist van een patient die vervoerd wordt van de IC van het ene ziekenhuis naar de IC van een ander ziekenhuis, waarbij vervoer inclusief wachttijd ambulance, overdrac tarief (min =€205,19, max =€978,84, diff = €773,65)
Medebehandeling door: een klinisch geriater (min =€166,67, max =€935,07, diff = €768,40)

Hospitals with (on average) the most/least expensive treatments

As seen in the last paragraph, the differences in price for a given treatment can be big. It is interesting to know which hospitals are the most expensive and which ones are the cheapest. However, a hospital can be expensive for a given treatment but cheap for another treatment. A reason for this could be that the hospital is specialized in certain treatments.

The top 3 hospitals (in Dutch) with on average the cheapest treatments. The price in brackets is the average amount below the average price for all treatments the hospital provides.

Stichting Nederlands Kenniscentrum Arbeid & Longaandoeningen (€ 233,55)
F.P.L. Van Loon ( € 184,09)
St. Psoriasisdagbehandelingscentrum Midden-Nederland ( € 183,77)

And the top 3 hospitals (in Dutch) with on average the most expensive treatments.

Epilepsie Centrum Kempenhaeghe (€ -247,80)
Stichting Beste Zorg (nok Brunssum) ( € -218,30)
Diabeter B.V. ( € -127,58)

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