Most Common Statistical Methodologies in Recent Clinical Studies of Community-Acquired Pneumonia

Background: Training new individuals in pneumonia research is imperative to produce a new generation of clinical investigators with the expertise necessary to fill gaps in knowledge. Clinical investigators are often intimidated by their unfamiliarity with statistics. The objective of this study is to define the most common statistical methodologies in recent clinical studies of CAP to inform teaching approaches in the field. Methods: Articles met inclusion criteria if they were clinical research with an emphasis on incidence, epidemiology, or patient outcomes, searchable via PubMed or Google Scholar, published within the timeframe of January 1st 2012 to August 1st 2017, and contained Medical Subject Headings (MeSH) keywords of “pneumonia” and one of the following: “epidemiologic studies”, “health services research”, or “comparative effectiveness research” or search keywords of community-acquired pneumonia” and one of the following: “cohort study”, “observational study”, “prospective study”, “retrospective study”, “clinical trial”, “controlled trial”, or “clinical study”. Descriptive statistics for the most common statistical methods were reported. Results: Thirty articles were included in the analysis. Descriptive statistics most commonly contained within articles were frequency (n=30 [100%]) and percent (n=30 [100%]), along with medians (n=22 [73%]) and interquartile ranges (n=19 [63%]). Most commonly performed analytical statistics were the Chi-squared test (n=20 [67%]), logistic regression (n=18 [60%]), Fisher’s exact test (n=17 [57%]), Wilcoxon rank sum test (n=16 [53%]), T-test (n=13 [43%]), and Cox proportional hazards regression (n=10 [33%]). Conclusions: We identified the most common clinical research tests performed in studies of hospitalized patients with CAP. Junior investigators should become very familiar with these tests early in their research careers

Distributing Data and Analysis Software Containers For Better Data Sharing in Clinical Research

Introduction: Data sharing in clinical research is critical for increasing knowledge discovery. Data and software tools should be FAIR: Findable, Accessible, Inter-operable and Re-usable. Many bottlenecks exist in the process of a clinical investigator using shared data including data acquisition and statistical analysis. The objective of this project is to develop a structure for sharing data and providing rapid automated statistical analysis through creation of a pre-packaged, open-source software container. Methods: We use the open source software container technologies VirtualBox and Vagrant to create a template for sharing clinical data and analysis scripts as a single container. We use a timer to record the time necessary to setup and initialize the software container and view the results. Results: We have created a template for sharing data and analysis scripts together using open source software container technologies VirtualBox and Vagrant. We found the time needed to initialize the container to be 5 minutes and 36 seconds for a macOS-based machine and 7 minutes and 2 seconds for a Windows-based machine. Containers can be downloaded and executed from any Mac or Windows computer allowing both the reuse of and interaction with the data. This greatly reduces the time and effort needed to obtain and analyze clinical data. Conclusion: Reducing the time and effort needed to obtain and analyze clinical data increases the time available for data exploration and the discovery of new knowledge. This can be effectively achieved using software containers and virtualization