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Using multiple imputation to address the inconsistent distribution of a controlling variable when modeling an

Yujia Zhang1, Sara Crawford1, Sheree L Boulet1

  • 1Division of Reproductive Health, Centers for Disease Control and Prevention, Atlanta, GA.

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Multiple imputation for labor induction data improved Apgar score estimates in assisted reproductive technology (ART) infants. This method reduced bias and inflated standard errors compared to using un-imputed data, ensuring more reliable research findings.

Keywords:
assisted reproductive technologyinconsistent data distributionmultiple imputationweighted sequential hot deck

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Area of Science:

  • Reproductive Health
  • Biostatistics
  • Public Health

Background:

  • Longitudinal data collection methods can change over time, leading to inconsistent variable distributions.
  • The impact of these temporal data collection changes on parameter estimates is not well understood.
  • Assisted reproductive technology (ART) involves complex data collection, making it susceptible to temporal data inconsistencies.

Purpose of the Study:

  • To examine differences in infant Apgar scores based on maternal risk factors (ovulatory dysfunction vs. tubal obstruction) in ART.
  • To assess the effect of temporal changes in birth certificate data collection on parameter estimates.
  • To evaluate the utility of multiple imputation for addressing data inconsistencies in longitudinal ART studies.

Main Methods:

  • Utilized linked birth certificate data (Florida, Massachusetts, Michigan) and the National ART Surveillance System.
  • Addressed inconsistent labor induction data in Florida (post-2004) using the Cox-Iannacchione weighted sequential hot deck method for multiple imputation.
  • Compared parameter estimates for low Apgar scores using both imputed and non-imputed labor induction data.

Main Results:

  • Adjusted odds ratios for low Apgar score were 1.94 (imputed) vs. 1.83 (non-imputed).
  • Non-imputed data showed bias towards the null and inflated standard errors compared to multiple imputation.
  • The magnitude of differences between imputed and non-imputed estimates was small, suggesting robustness but highlighting the value of imputation.

Conclusions:

  • Multiple imputation effectively addressed temporal data inconsistencies in labor induction, reducing bias in Apgar score estimates.
  • The findings underscore the importance of accounting for data collection changes in longitudinal health studies.
  • Accurate parameter estimation in ART research can be enhanced through appropriate statistical methods like multiple imputation.