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Data pooling enhances research by combining datasets. Variable harmonization creates comparable data from different sources, enabling efficient and novel research questions.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Data Science

Background:

  • Data pooling increases study sample size and statistical power.
  • Heterogeneous datasets present challenges for combining data due to differing variable measurements.
  • Variable harmonization offers a method to create comparable datasets from diverse sources.

Purpose of the Study:

  • To describe data harmonization strategies for generating comparable datasets from two Canadian pregnancy cohort studies.
  • To illustrate how variable harmonization facilitates data pooling for research.

Main Methods:

  • Harmonization considered multiple variable features: construct, question, response options, measurement scale, frequency, timing, and data structure.
  • Variables were classified as completely matching, partially matching, or completely unmatching.
  • Partially matching variables were harmonized into a common format.

Main Results:

  • Exact match variables were pooled directly.
  • Partially matching variables were harmonized based on measurement features and response options.
  • Completely unmatching variables could not be harmonized.

Conclusions:

  • Variable harmonization strategies enable the creation of comparable cohort datasets for data pooling.
  • These strategies are applicable to other data sources, facilitating novel research.
  • Harmonization allows for statistically efficient, timely, and cost-efficient research that is not possible with single data sources.