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Methodological issues in analyzing time trends in biologic fertility: protection bias
Jane Key1, Nicky Best, Michael Joffe
1Department of Epidemiology and Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, UK.
American Journal of Epidemiology
|January 8, 2009
Summary
Estimating fertility trends using time to pregnancy (TTP) can be biased by accidental pregnancies. This study found that while accidental pregnancies indicate higher fertility, they did not significantly skew European fertility trends over 50 years.
Area of Science:
- Reproductive Epidemiology
- Biostatistics
- Demography
Background:
- Time to pregnancy (TTP) is a key measure for biologic fertility.
- Accidental pregnancies introduce nonrandom missing data, potentially biasing fertility trend estimates.
- Changes in factors like contraception and abortion access can cause protection bias in TTP studies.
Purpose of the Study:
- To investigate protection bias in European fertility trend studies using TTP data over the past 50 years.
- To assess the impact of accidental pregnancies on fertility trend estimations.
- To evaluate statistical methods for correcting protection bias.
Main Methods:
- Analysis of six European datasets spanning 50 years.
- Investigation of TTP data, considering accidental pregnancies.
- Simulation studies using two multiple imputation methods to address missing TTP data.
- Standard sensitivity analyses to determine bias upper bounds.
Main Results:
- Couples with accidental pregnancies generally exhibited higher fertility.
- Accidental pregnancy rates varied inconsistently across European countries.
- Simulated data showed insufficient bias from accidental pregnancy trends to significantly impact fertility trends.
- Multiple imputation methods effectively reduced or eliminated protection bias in simulations.
Conclusions:
- Protection bias in TTP studies of European fertility trends over the past 50 years appears minimal.
- Multiple imputation offers a viable statistical approach to mitigate protection bias when suspected.
- Standard sensitivity analyses can provide a conservative estimate of potential bias.
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