The impact of incomplete data on quantile regression for longitudinal data
Keywords:Dropout; Inverse probability weighting; Missing data; Multiple imputation; Quantile regression
We investigate the performance of methods for estimating the conditional quantile of a response based on longitudinal data, when outcomes are incomplete and when the correlation between repeated responses is ignored. In a simulation study, we compare the performance of the quantile regression estimator based on the complete cases, the available cases, quantile-based multiple imputation, and quantile-based inverse probability weighting. In the data setting considered, quantile-based multiple imputation is the most promising method with the best bias-efficiency trade-off. A potential drawback, however, is its computation time.
Journal of Statistical Research 2021, Vol. 55, No. 1, pp. 43-58
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