Consider a set/mixture of continuous, correlated, and censored components/chemicals that are reasonable to combine in an index and share a common outcome. These components are also interval-censored between zero and upper thresholds, or detection limits, that may be different among the components. The `miWQS` package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes. In summary, MI consists of three stages: (1) imputation, (2) analysis, and (3) pooling. First, the missing values are imputed by bootstrapping (Lubin et.al (2004) <doi:10.1289/ehp.7199>), Bayesian imputation, or placing the below the detection limits in the first quantile (BDLQ1) (Ward et.al. (2014) <doi:10.1289/ehp.1307602>). Second, the estimate.wqs() function implements WQS regression if the components are complete, imputed, or missing (Carrico et.al. (2014) <doi:10.1007/s13253-014-0180-3>) . If the data is missing, BDLQ1 is automatically implemented. Lastly, the pool.mi() function calculates the pooled statistics according to Rubin's rules (Rubin 1987).
|Author||Paul M. Hargarten [aut, cre], David C. Wheeler [aut, rev, ths]|
|Maintainer||Paul M. Hargarten <[email protected]>|
|Package repository||View on CRAN|
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