funmediation {funmediation}

funmediation: Fit funmediation model

Description

Calculate indirect effect of a binary treatment on a scalar response as mediated by a longitudinal functional trajectory (see Baron & Kenny, 1986; Lindquist, 2012; Coffman et al., 2021).

Usage

funmediation(
  data,
  treatment,
  mediator,
  outcome,
  id,
  time,
  tve_covariates_on_mediator = NULL,
  tie_covariates_on_mediator = NULL,
  covariates_on_outcome = NULL,
  interpolate = TRUE,
  tvem_penalize = TRUE,
  tvem_penalty_order = 1,
  tvem_spline_order = 3,
  tvem_num_knots = 3,
  tvem_do_loop = FALSE,
  tvem_use_bic = FALSE,
  binary_mediator = FALSE,
  binary_outcome = FALSE,
  nboot = 200,
  boot_level = 0.05,
  show_progress = FALSE
)

Arguments

data

The dataset containing the data to be analyzed, in long format (one row per observation, multiple per individual).

treatment

The name of the variable containing the treatment assignment, assumed to be unidimensional (either binary or else numeric). We recommend a binary (dichotomous) treatment with 0 for control and 1 for experimental). The values of this variable should be the same for each row for a given subject. If there are more than one treatment variables, such as a dummy-coded exposure with more than two levels, specify them as a formula such as ~x1+x2.

mediator

The name of the mediator variable. The values of this variable can (and should) vary within each subject.

outcome

The name of the outcome variable. The values of this variable should be the same for each row for a given subject.

id

The name of the variable identifying each subject.

time

The name of the time variable.

tve_covariates_on_mediator

The covariates with time-varying-effects, if any, to be included in the model predicting the mediator from the treatment.

tie_covariates_on_mediator

The covariates with time-invariant effects, if any, to be included in the model predicting the mediator from the treatment.

covariates_on_outcome

The covariates, if any, to be included in the model predicting the outcome from the treatment. They are assumed to be subject-level (time-invariant both in value and in effect).

interpolate

What kind of presmoothing to use in the penalized functional regression – specifically, whether to interpolate each subject's trajectory on the mediator (TRUE) or fit a spline to each subject's trajectory on the mediator (FALSE). This will be counted as TRUE if binary_mediator is TRUE because it does not make as much sense to interpolate a binary outcome.

tvem_penalize

Input to be passed on to the tvem function

tvem_penalty_order

Input to be passed on to the tvem function

tvem_spline_order

Input to be passed on to the tvem function

tvem_num_knots

If tvem_do_loop is FALSE, then tvem_num_knots is passed on to the tvem function as num_knots, an integer representing the number of interior knots per B-spline. If tvem_do_loop is TRUE then tvem_num_knots is reinterpreted as the highest number of interior knots to try.

tvem_do_loop

Whether to use a loop to select the number of knots with a pseudo-AIC or pseudo-BIC, passed on to the tvem function

tvem_use_bic

This parameter only matters if tvem_do_loop is TRUE. If tvem_do_loop is TRUE and tvem_use_bic is TRUE, then the information criterion used will be a pseudolikelihood version of BIC. If tvem_do_loop is TRUE and tvem_use_bic is FALSE, then the information criterion used will be a pseudolikelihood version of AIC instead. If tvem_do_loop is FALSE then tvem_use_bic is ignored.

binary_mediator

Whether the mediator should be modeled as dichotomous with a logistic model (TRUE), or numerical with a normal model (FALSE).

binary_outcome

Whether the outcome should be modeled as dichotomous with a logistic model (TRUE), or numerical with a normal model (FALSE).

nboot

Number of bootstrap samples for bootstrap significance test of the overall effect. This test is done using the boot function from the boot package by Angelo Canty and Brian Ripley. It differs somewhat from the bootstrap approach used in a similar context by Lindquist (2012). We recommend using at least 200 bootstrap samples and preferably 500 or more.

boot_level

One minus the nominal coverage for the bootstrap confidence interval estimates.

show_progress

Whether to display intermediate updates on the progress of the bootstrap simulations. If show_progress==FALSE then the funmediation function runs silently but results can be viewed via the print and plot methods. If show_progress==TRUE then progress messages will be printed.

Value

An object of type funmediation. The components of an object of type funmediation are as follows:

original_results

The estimates from the fitted models for predicting the mediator from the treatment, predicting the outcome from the mediator and treatment, and predicting the outcome from the treatment alone.

bootstrap_results

The estimate and confidence interval of the indirect effect using a bootstrap approach.

The original_results component has these components within it:

time_grid

Grid of time points on which the functional coefficients are estimated.

alpha_int_estimate

Estimated intercept function (as a vector of estimates) from the TVEM regression of the mediator, M, on treatment, X.

alpha_int_se

Estimated pointwise standard errors associated with the above.

alpha_X_estimate

Estimated time-varying treatment effect from the TVEM regression of the mediator, M, on the treatment, X.

alpha_X_se

Estimated pointwise standard errors associated with the above.

beta_int_estimate

Estimated scalar intercept from the scalar-on- function regression of the outcome, Y, on the mediator, M, and treatment, X.

beta_int_se

Estimated standard error for the above.

beta_X_estimate

Estimated scalar coefficient for the treatment, X, from the scalar-on-function regression of the outcome, Y, on the mediator, M, and treatment, X.

beta_X_se

Estimated standard error for the above.

beta_M_estimate

Estimated functional coefficient for the mediator, M, from the scalar-on-function regression of the outcome, Y, on the mediator, M, and treatment, X.

beta_M_se

Estimated pointwise standard errors associated with the above

beta_M_pvalue

The p-value for significance of the mediator, M, in predicting outcome, Y, after adjusting for treatment, X.

tau_int_estimate

Intercept from simple model predicting outcome, Y, directly from treatment, X.

tau_int_se

Estimated standard error for the above.

tau_X_estimate

Coefficient for treatment in model predicting outcome, Y, directly from treatment, X.

tau_X_se

Estimated standard error for the above.

indirect_effect_estimate

Estimated indirect effect, calculated as the dot product of the effect of treatment on mediator and the treatment- adjusted effect of mediator on outcome. It is a scalar, even though the two component effects are functions of time.

tvem_XM_details

Detailed output from the tvem function for the time- varying-effect model predicting the mediator, M, from the treatment, X.

funreg_MY_details

Detailed output from the refund::pfr function for the scalar-on-function functional regression predicting the outcome, Y, from the treatment, X, and mediator, M.

total_effect_details

Detailed output from the linear or generalized linear model predicting the outcome from the treatment alone, ignoring the mediator (i.e., total effect)

The bootstrap_results component has these components within it:

indirect_effect_boot_estimate

Bootstrap point estimate of the indirect effect (average of bootstrap sample estimates).

indirect_effect_boot_se

Bootstrap standard error for the indirect effect (standard deviation of bootstrap sample estimates).

indirect_effect_boot_norm_lower

Lower end of the bootstrap confidence interval using the normal method in boot.ci in the boot package.

indirect_effect_boot_norm_upper

Upper end of the bootstrap confidence interval using the normal method.

indirect_effect_boot_basic_lower

Lower end of the bootstrap confidence interval using the basic method in boot.ci in the boot package.

indirect_effect_boot_basic_upper

Upper end of the bootstrap confidence interval using the basic method.

indirect_effect_boot_perc_lower

Lower end of the bootstrap confidence interval using the percentile method in boot.ci in the boot package.

indirect_effect_boot_perc_upper

Upper end of the bootstrap confidence interval using the percentile method.

boot_level

The alpha level used for the bootstrap confidence interval.

boot1

The output returned from the boot function.

time.required

The amount of time spent doing the bootstrap test, including generating and analyzing all samples.

Note

This function calls the tvem function in the tvem package. It also calls the pfr function in the refund package (see Goldsmith et al., 2011) to perform penalized functional regression. Some suggestions on interpreting the output from penalized functional regression are given by Dziak et al. (2019).

References

Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality & Social Psychology, 51: 1173-1182.

Coffman, D. L., Dziak, J. J., Litson, K., Chakraborti, Y., Piper, M. E., & Li, R. #' (2021). A causal approach to functional mediation analysis with application to a smoking cessation intervention. <arXiv:2112.03960>

Dziak, J. J., Coffman, D. L., Reimherr, M., Petrovich, J., Li, R., Shiffman, S., & Shiyko, M. P. (2019). Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: interpretability for applied scientists. Statistics Surveys, 13, 150-180. <doi:10.1214/19-SS126>

Goldsmith, J., Bobb, J., Crainiceanu, C., Caffo, B., & Reich, D. (2011). Penalized functional regression. Journal of Computational and Graphical Statistics, 20(4), 830-851. <doi:10.1198/jcgs.2010.10007>

Lindquist, M. A. (2012). Functional Causal Mediation Analysis With an Application to Brain Connectivity. Journal of the American Statistical Association, 107: 1297-1309. <doi:10.1080/01621459.2012.695640>