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Estimation of regression quantiles in complex surveys with data missing at random: An application to birthweight
1Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, University College London, UK m.geraci@ucl.ac.uk.
Statistical Methods in Medical Research
|April 25, 2013
Summary
Estimating population parameters from complex survey data is challenging due to design features and nonresponse. This study introduces a novel method for analyzing conditional quantiles, improving bias reduction for survey data analysis.
Area of Science:
- Statistics
- Survey Methodology
- Biostatistics
Background:
- Complex survey data analysis requires accounting for design features and nonresponse to minimize bias.
- Estimating population parameters is complicated by missing data and intricate survey designs.
Purpose of the Study:
- To address challenges in estimating conditional quantiles of continuous outcomes from complex survey data.
- To develop and implement robust statistical methods for handling survey design and nonresponse in quantile regression.
Main Methods:
- Utilizing survey design variables within the analysis model.
- Implementing a bootstrap variance estimation approach.
- Employing multiple imputation by chained equations for missing data, preserving distributional properties.
Main Results:
- A novel method for conditional quantile estimation in complex surveys was developed.
- The proposed imputation method effectively handles distributional relationships and bounded outcomes.
- A significant finding regarding parental conflict theory in birthweight determinants was identified.
Conclusions:
- The study provides a robust framework for conditional quantile estimation with complex survey data.
- The methods enhance the accuracy of statistical inference in the presence of design complexities and missing data.
- The findings offer new insights into birthweight determinants, particularly concerning parental conflict.
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