Inspect complete records analysis model

Check complete records analysis is valid under the proposed analysis model and directed acyclic graph (DAG). Validity means that the proposed approach will allow unbiased estimation of the estimand(s) of interest, including regression parameters, associations, and causal effects.

Usage

checkCRA(y, covs, r_cra, mdag)

Arguments

y

The analysis model outcome, specified as a string

covs

The analysis model covariate(s), specified as a string (space delimited)

r_cra

The complete record indicator, specified as a string

mdag

The DAG, specified as a string using dagitty syntax, or as a dagitty graph object

Value

A message indicating whether complete records analysis is valid under the proposed DAG and analysis model outcome and covariate(s)

Details

The DAG should include all observed and unobserved variables related to the analysis model variables and their missingness, as well as all required missingness indicators.

In general, complete records analysis is valid if the analysis model outcome and complete record indicator are unrelated, conditional on the specified covariates. This is determined using the proposed DAG by checking whether the analysis model and complete record indicator are 'd-separated', given the covariates.

References

Hughes R, Heron J, Sterne J, Tilling K. 2019. Accounting for missing data in statistical analyses: multiple imputation is not always the answer. Int J Epidemiol. doi:10.1093/ije/dyz032

Bartlett JW, Harel O, Carpenter JR. 2015. Asymptotically Unbiased Estimation of Exposure Odds Ratios in Complete Records Logistic Regression. Am J Epidemiol. doi:10.1093/aje/kwv114

Examples

# Example DAG for which complete records analysis is not valid
checkCRA(y="bmi7", covs="matage", r_cra="r",
         mdag="matage -> bmi7 mated -> matage mated -> bmi7
               sep_unmeas -> mated sep_unmeas -> r")
#> Based on the proposed directed acyclic graph (DAG), the analysis model
#> outcome and complete record indicator are not independent given
#> analysis model covariates. Hence, in general, complete records analysis
#> is not valid.
#> 
#> In special cases, depending on the type of analysis model and estimand
#> of interest, complete records analysis may still be valid. See, for
#> example, Bartlett et al. (2015) (https://doi.org/10.1093/aje/kwv114)
#> for further details.
#> 
#> Consider using a different strategy e.g. multiple imputation, or a
#> different analysis model, noting that a different analysis model may
#> not be aligned with your estimand.
#> 
#> For example, the analysis model outcome and complete record indicator
#> are independent given each of the following sets of variables:
#> 
#> mated
#> 
#> c("matage", "mated")
#> 
#> sep_unmeas
#> 
#> c("matage", "sep_unmeas")
#> 
#> c("mated", "sep_unmeas")
#> 
#> c("matage", "mated", "sep_unmeas")
# For the DAG in the example above, complete records analysis is valid
## if a different set of covariates is used
checkCRA(y="bmi7", covs="matage mated", r_cra="r",
         mdag="matage -> bmi7 mated -> matage mated -> bmi7
               sep_unmeas -> mated sep_unmeas -> r")
#> Based on the proposed directed acyclic graph (DAG), the analysis model
#> outcome and complete record indicator are independent given analysis
#> model covariates. Hence, complete records analysis is valid.
# Example where complete records analysis is never valid
checkCRA(y="bmi7", covs="matage mated", r_cra="r",
         mdag="matage -> bmi7 mated -> matage mated -> bmi7
               sep_unmeas -> mated sep_unmeas -> r bmi7 -> r")
#> Based on the proposed directed acyclic graph (DAG), the analysis model
#> outcome and complete record indicator are not independent given
#> analysis model covariates. Hence, in general, complete records analysis
#> is not valid.
#> 
#> In special cases, depending on the type of analysis model and estimand
#> of interest, complete records analysis may still be valid. See, for
#> example, Bartlett et al. (2015) (https://doi.org/10.1093/aje/kwv114)
#> for further details.
#> 
#> Consider using a different strategy e.g. multiple imputation.