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

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, but could be
## valid for a different set of covariates
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 analysis model and/or strategy, e.g.
#> multiple imputation.
#> 
#> For example, the analysis model outcome and complete record indicator
#> are independent if, in addition to the specified covariates, the
#> following sets of variables are included as covariates in the analysis
#> model (note that this list is not necessarily exhaustive, particularly
#> if your DAG is complex):
#> 
#> mated
#> 
#> c("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 DAG for which complete records is not valid, but could be valid
## for a different estimand
checkCRA(y="bmi7", covs="matage mated", r_cra="r",
         mdag="matage -> bmi7 mated -> matage mated -> bmi7
               sep_unmeas -> mated sep_unmeas -> r matage -> bmi3
               mated -> bmi3 bmi3 -> bmi7 bmi3 -> 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.
#> 
#> There are no other variables which could be added to the model to make
#> the analysis model outcome and complete record indicator conditionally
#> independent, without changing the estimand of interest. Consider using
#> a different strategy e.g. multiple imputation.
#> 
#> Alternatively, consider whether a different estimand could be of
#> interest. For example, the analysis model outcome and complete record
#> indicator are independent given each of the following sets of
#> variables:
#> 
#> c("bmi3", "mated")
#> 
#> c("bmi3", "matage", "mated")
#> 
#> c("bmi3", "sep_unmeas")
#> 
#> c("bmi3", "matage", "sep_unmeas")
#> 
#> c("bmi3", "mated", "sep_unmeas")
#> 
#> c("bmi3", "matage", "mated", "sep_unmeas")

# Example DAG for which 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.
#> 
#> There are no other variables which could be added to the model to make
#> the analysis model outcome and complete record indicator conditionally
#> independent. Consider using a different strategy e.g. multiple
#> imputation.