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In linear models, omitting a covariate that is orthogonal to covariates inthe model does not result in biased coefficient estimation. This in generaldoes not hold for longitudinal data, where additional assumptions are needed toget unbiased coefficient estimation in addition to the orthogonality betweenomitted longitudinal covariates and longitudinal covariates in the model. Wepropose methods to mitigate the omitted variable bias under weaker assumptions.A two-step estimation procedure is proposed for inference about theasynchronous longitudinal covariates, when such covariates are observed. Formixed synchronous and asynchronous longitudinal covariates, we get parametricrate of convergence for the coefficient estimation of the synchronouslongitudinal covariates by the two-step method. Extensive simulation studiesprovide numerical support for the theoretical findings. We illustrate theperformance of our method on dataset from the Alzheimers Disease NeuroimagingInitiative study.

Regression analysis of longitudinal data with mixed synchronous and asynchronous longitudinal covariates