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Time-varying Causal Quartet Data

Source: R/causal_quartet_time.R
causal_collider_time.Rd

These datasets contains 100 observations, each generated under a different data generating mechanism:

  • (1) A collider

  • (2) A confounder

  • (3) A mediator

  • (4) M-bias

Usage

causal_collider_time

causal_confounding_time

causal_mediator_time

causal_m_bias_time

causal_quartet_time

Format

causal_collider_time: A dataframe with 100 rows and 7 variables:

  • covariate_baseline: known factor measured at baseline

  • exposure_baseline: exposure measured at baseline

  • outcome_baseline: outcome measured at baseline

  • exposure_followup: exposure measured at the followup visit (final time)

  • outcome_followup: outcome measured at the followup visit (final time)

  • covariate_followup: known factor measured at the followup visit (final time)

causal_confounding_time: A dataframe with 100 rows and 7 variables:

  • covariate_baseline: known factor measured at baseline

  • exposure_baseline: exposure measured at baseline

  • outcome_baseline: outcome measured at baseline

  • exposure_followup: exposure measured at the followup visit (final time)

  • outcome_followup: outcome measured at the followup visit (final time)

  • covariate_followup: known factor measured at the followup visit (final time)

causal_mediator_time: A dataframe with 100 rows and 7 variables:

  • covariate_baseline: known factor measured at baseline

  • exposure_baseline: exposure measured at baseline

  • outcome_baseline: outcome measured at baseline

  • covariate_mid: known factor measured at some mid-point

  • exposure_mid: exposure measured at some mid-point

  • outcome_mid: outcome measured at some mid-point

  • exposure_followup: exposure measured at the followup visit (final time)

  • outcome_followup: outcome measured at the followup visit (final time)

  • covariate_followup: known factor measured at the followup visit (final time)

causal_m_bias_time: A dataframe with 100 rows and 9 variables:

  • u1: unmeasured factor

  • u2: unmeasured factor

  • covariate_baseline: known factor measured at baseline

  • exposure_baseline: exposure measured at baseline

  • outcome_baseline: outcome measured at baseline

  • exposure_followup: exposure measured at the followup visit (final time)

  • outcome_followup: outcome measured at the followup visit (final time)

  • covariate_followup: known factor measured at the followup visit (final time)

An object of class tbl_df (inherits from tbl, data.frame) with 400 rows and 12 columns.

Details

There are two time points:

  • baseline

  • follow up

These datasets help demonstrate that a model that includes only pre-exposure covariates (that is, only adjusting for covariates measured at baseline), will be less prone to potential biases. Adjusting for only pre-exposure covariates "solves" the bias in datasets 1-3. It does not solve the data generated under the "M-bias" scenario, however this is more of a toy example, it has been shown many times that the assumptions needed for this M-bias to hold are often not ones we practically see in data analysis.

References

Lucy D’Agostino McGowan, Travis Gerke & Malcolm Barrett (2023) Causal inference is not just a statistics problem, Journal of Statistics and Data Science Education, DOI: 10.1080/26939169.2023.2276446

Examples


## incorrect model because covariate is post-treatment
lm(outcome_followup ~ exposure_baseline + covariate_followup,
   data = causal_collider_time)
#> 
#> Call:
#> lm(formula = outcome_followup ~ exposure_baseline + covariate_followup, 
#>     data = causal_collider_time)
#> 
#> Coefficients:
#>        (Intercept)   exposure_baseline  covariate_followup  
#>           -0.05656             0.41288             0.46683  
#> 

## correct model because covariate is pre-treatment
## even though the true mechanism dictates that the covariate is a collider,
## because the pre-exposure variable is used, the collider bias does not
## occur.
lm(outcome_followup ~ exposure_baseline + covariate_baseline,
   data = causal_collider_time)
#> 
#> Call:
#> lm(formula = outcome_followup ~ exposure_baseline + covariate_baseline, 
#>     data = causal_collider_time)
#> 
#> Coefficients:
#>        (Intercept)   exposure_baseline  covariate_baseline  
#>           -0.10534             1.00312            -0.01173  
#>