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Realignment and multiple imputation of longitudinal data: an application to menstrual cycle data.
Sunni L Mumford1, Enrique F Schisterman, Audrey J Gaskins
1Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Rockville, MD 20852, USA.
Paediatric and Perinatal Epidemiology
|August 9, 2011
Summary
Accurate reproductive hormone measurement requires precise timing. This study reclassified menstrual cycle data to better align hormone measurements with biological phases, improving data accuracy and reducing variability.
Area of Science:
- Reproductive endocrinology
- Biostatistics
- Chronobiology
Background:
- Premenopausal reproductive hormone levels fluctuate significantly throughout the menstrual cycle.
- Precise timing of hormone measurements is crucial for studying day- or phase-specific effects.
- Existing methods using fertility monitors may not perfectly align with hormonal variability due to brief luteinizing hormone (LH) surges and variable cycle lengths.
Purpose of the Study:
- To reclassify menstrual cycle data based on biological phases to improve the accuracy of hormone measurements.
- To assess the impact of reclassification on hormonal profiles and variability.
- To demonstrate the feasibility of applying longitudinal multiple imputation for missing data in this context.
Main Methods:
- Utilized daily urine home fertility monitors to detect the luteinizing hormone (LH) surge for visit scheduling.
- Reclassified collected hormone measurements according to biological menstrual cycle phases.
- Applied longitudinal multiple imputation methods to address missing data points after reclassification.
Main Results:
- Reclassified cycles exhibited more distinct hormonal profiles.
- Mean peak hormone levels increased significantly (up to 141%) after reclassification.
- Hormonal variability was substantially reduced (up to 71%) following data reclassification.
Conclusions:
- Realigning study visits to biologically relevant windows is essential for accurate assessment of phase- or day-specific hormonal effects.
- Longitudinal multiple imputation is a feasible method for handling missing data in longitudinal hormonal studies.
- This approach enhances data reliability in settings where frequent blood sampling is impractical.
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