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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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A Sample Size Extractor for RCT Reports.

Fengyang Lin1, Hao Liu1, Paul Moon2

  • 1Department of Biomedical Informatics, Columbia University, New York, NY, United States.

Studies in Health Technology and Informatics
|June 8, 2022
PubMed
Summary

This study introduces an automated tool to extract total sample sizes from randomized controlled trials (RCTs) abstracts. The new method improves accuracy, especially with transfer learning, reducing manual annotation efforts.

Keywords:
Natural Language ProcessingRandomized Controlled TrialSample Size

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Area of Science:

  • Medical Informatics
  • Clinical Trials Methodology

Background:

  • Sample size is crucial for determining the statistical power of randomized controlled trials (RCTs).
  • Accurate extraction of sample size information from clinical trial literature is essential for meta-analyses and research synthesis.
  • Existing tools may lack direct reporting of total sample sizes or require significant manual input.

Purpose of the Study:

  • To develop and evaluate an automated extractor for identifying total sample sizes within RCT abstracts.
  • To compare the performance of the developed extractor against existing state-of-the-art tools.
  • To assess the impact of transfer learning on the accuracy of sample size extraction.

Main Methods:

  • A hybrid approach combining syntactic and machine learning techniques was employed.
  • The extractor was evaluated on two distinct datasets: Covid-19 abstracts (Covid-Set) and general RCT abstracts (General-Set).
  • Transfer learning was utilized, leveraging a large public corpus of annotated abstracts to enhance performance.

Main Results:

  • The extractor achieved an average F1 score of 0.73 on the Covid-Set and 0.60 on the General-Set using exact matches.
  • Loose match F1 scores exceeded 0.74 for both datasets.
  • Without transfer learning, the extractor improved F1 scores by at least 4% compared to the state-of-the-art tool and reported total sample sizes directly.

Conclusions:

  • The developed sample size extractor demonstrates improved accuracy in identifying total sample sizes from RCT abstracts.
  • Transfer learning significantly enhances extraction performance and reduces the need for extensive manual annotation.
  • This tool offers a more efficient and accurate method for sample size data extraction from clinical trial literature.