Feature - Datasets

Owned by john Putzke
Last updated: May 09, 2017
2 min read

Transforming Data Into Information!

A critical factor in the advancement of outcomes research is the ease with which data can be correctly entered, organized in meaningful ways, analyzed, and disseminated to research and healthcare staff, subjects, and the academic community. Studytrax is specifically designed to facilitate this process. The center piece of the entire analytical process is building datasets.

This website is designed as a big-picture guide to functionality supporting building datasets in Studytrax. Please send any questions or feedback to support ( support@studytrax.com).

Overview

Use Studytrax to centralize the analytical process, easily create and transfer datasets, and leverage integrated analytical tools.

  • Key Features
    1. Organize analytical activities across one or more studies
    2. Build datasets using a point and click interface
    3. Datasets may contain,
      • Any variable within or across forms
        • Raw variable value
        • Recoded variables (e.g., dummy coding, collapsing 8 groups to 3 groups)
        • Calculated variables (e.g., rate of change)
      • Automatically generated system variables
        • Medication Use (automatically matched with visit date)
          • Single Medication
            • Dose
            • Ever used (0 = No, 1 = Yes)
          • Across Multiple Medications
            • Dose equivalency scale
            • Ever used any in list (0 = No, 1 = Yes)
        • Time to study events (e.g., number of days from enrollment to 'Month 3 visit')
        • Event Status (e.g., a variable coded indicate whether event due, such as, Is 'Year 3 Follow-up' due || No = 0, Yes = 1||)
        • Coded site variable
        • Randomization ID and group status
      • Filters (e.g., query of all those with a 50% or more change in the main study endpoint over time)
      • Integrated data consolidate routines (e.g., the first and last blood pressure reading for all subjects, independent of start date)
    4. Integrated data cleaning and scrubbing routines
      • Descriptive statistics
      • Outlier identification
      • More...
    5. Export to statistical packages (e.g., SPSS, SAS, Excel) or generic CSV file
  • Examples
    1. Single study dataset of all variables, forms and visits.
    2. Dataset with automatically calculated survival length and censored event variables for generating survival curves.
  • What To Consider
    1. What dataset layout (e.g., one row per subject vs. one row per subject per visit) and data transformations (e.g., recoded variables, calculated variables, etc.) would best facilitate the analytical plan?
    2. What are the planned data queries?