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Semiparametric model and inference for spontaneous abortion data with a cured proportion and biased sampling
Jin Piao1, Jing Ning2, Christina D Chambers3
1Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX 77030, USA.
Biostatistics (Oxford, England)
|May 20, 2017
Summary
Understanding medication safety during pregnancy, especially for autoimmune diseases, is crucial. This study addresses challenges in using spontaneous abortion (SAB) data to accurately assess risks for pregnant women.
Area of Science:
- Reproductive Health
- Pharmacovigilance
- Biostatistics
Background:
- Assessing medication safety for autoimmune diseases during pregnancy is vital for informed clinical decisions.
- Observational studies on spontaneous abortion (SAB) data face challenges in accurate risk inference due to sampling bias and data heterogeneity.
- A significant proportion of observed data may represent a 'cured' subgroup, complicating analysis.
Purpose of the Study:
- To develop statistical methods for accurately estimating the risk and safety of medications used by pregnant women with autoimmune diseases.
- To address the challenges of sampling bias and data heterogeneity in observational pregnancy studies.
- To simultaneously estimate the probability of being cured and the time to spontaneous abortion (SAB) for the uncured subgroup.
Main Methods:
- Utilized semiparametric models to analyze spontaneous abortion (SAB) data.
- Adjusted sampling bias in the likelihood function for maximum likelihood estimators.
- Developed an expectation-maximization algorithm to handle computational challenges.
- Applied empirical process theory to establish estimator consistency and asymptotic normality.
Main Results:
- Developed and validated novel semiparametric models for analyzing biased and heterogeneous pregnancy data.
- Demonstrated the consistency and asymptotic normality of the proposed estimators.
- Evaluated the finite sample performance of the estimators through simulation studies.
Conclusions:
- The proposed statistical methods offer a robust approach to analyzing spontaneous abortion (SAB) data in observational pregnancy studies.
- Accurate estimation of medication risks during pregnancy is improved by accounting for sampling bias and data heterogeneity.
- The methodology provides valuable tools for clinicians and pregnant women in making informed treatment decisions regarding autoimmune disease management during pregnancy.
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