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Multiple Imputation For Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys
Filetype[PDF - 212.92 KB]


Details:
  • Pubmed ID:
    24223447
  • Pubmed Central ID:
    PMC3820019
  • Funding:
    R01 HD061967/HD/NICHD NIH HHS/United States
    R21 OH009320/OH/NIOSH CDC HHS/United States
    R24 HD041041/HD/NICHD NIH HHS/United States
    T32 HD007329/HD/NICHD NIH HHS/United States
  • Document Type:
  • Collection(s):
  • Description:
    Within-survey multiple imputation (MI) methods are adapted to pooled-survey regression estimation where one survey has more regressors, but typically fewer observations, than the other. This adaptation is achieved through: (1) larger numbers of imputations to compensate for the higher fraction of missing values; (2) model-fit statistics to check the assumption that the two surveys sample from a common universe; and (3) specificying the analysis model completely from variables present in the survey with the larger set of regressors, thereby excluding variables never jointly observed. In contrast to the typical within-survey MI context, cross-survey missingness is monotonic and easily satisfies the Missing At Random (MAR) assumption needed for unbiased MI. Large efficiency gains and substantial reduction in omitted variable bias are demonstrated in an application to sociodemographic differences in the risk of child obesity estimated from two nationally-representative cohort surveys.