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Realignment and multiple imputation of longitudinal data: an application to menstrual cycle data
Author(s) -
Mumford Sunni L.,
Schisterman Enrique F.,
Gaskins Audrey J.,
Pollack Anna Z.,
Perkins Neil J.,
Whitcomb Brian W.,
Ye Aijun,
WactawskiWende Jean
Publication year - 2011
Publication title -
paediatric and perinatal epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.667
H-Index - 88
eISSN - 1365-3016
pISSN - 0269-5022
DOI - 10.1111/j.1365-3016.2011.01204.x
Subject(s) - medicine , imputation (statistics) , longitudinal data , menstrual cycle , missing data , statistics , demography , mathematics , sociology , hormone
Summary Mumford SL, Schisterman EF, Gaskins AJ, Pollack AZ, Perkins NJ, Whitcomb BW, Ye A, Wactawski‐Wende J. Realignment and multiple imputation of longitudinal data: an application to menstrual cycle data. Paediatric and Perinatal Epidemiology 2011; 25: 448–459. Reproductive hormone levels are highly variable among premenopausal women during the menstrual cycle. Accurate timing of hormone measurement is essential, especially when investigating day‐ or phase‐specific effects. The BioCycle Study used daily urine home fertility monitors to help detect the luteinising hormone (LH) surge in order to schedule visits with biologically relevant windows of hormonal variability. However, as the LH surge is brief and cycles vary in length, relevant hormonal changes may not align with scheduled visits even when fertility monitors are used. Using monitor data, measurements were reclassified according to biological phase of the menstrual cycle to more accurate cycle phase categories. Longitudinal multiple imputation methods were applied after reclassification if no visit occurred during a given menstrual cycle phase. Reclassified cycles had more clearly defined hormonal profiles, with higher mean peak hormones (up to 141%) and reduced variability (up to 71%). We demonstrate the importance of realigning visits to biologically relevant windows when assessing phase‐ or day‐specific effects and the feasibility of applying longitudinal multiple imputation methods. Our method has applications in settings where missing data may occur over time, where daily blood sampling for hormonal measurements is not feasible, and in other areas where timing is essential.