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Area of Science:

  • Biostatistics
  • Clinical Data Management
  • Computational Biology

Background:

  • Clinical databases frequently yield data unsuitable for direct statistical analysis.
  • Repeatedly measured variables in clinical research necessitate data format transformations.
  • Manual data transformation between wide and long formats is often complex and time-consuming.

Purpose of the Study:

  • To present efficient methods for transforming clinical data formats.
  • To highlight the utility of R packages for data reshaping.
  • To facilitate statistical analysis of longitudinal clinical data.

Main Methods:

  • Utilizing R packages for data manipulation.
  • Employing 'melting' and 'casting' functions to transform data frames.
  • Demonstrating aggregation over unique identifier variables.

Main Results:

  • Successful transformation of clinical data between wide and long formats using R.
  • Efficient handling of repeatedly measured variables.
  • Introduction of auxiliary functions (たとえば colsplit(), funstofun()) for specific data manipulation tasks.

Conclusions:

  • R packages provide powerful tools for clinical data format transformation.
  • These methods significantly reduce the complexity of preparing data for statistical analysis.
  • The described techniques enhance the efficiency of clinical research data management.