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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.
Journal of Modern Applied Statistical Methods : JMASM
|November 6, 2018
Summary
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.
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.
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