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Perturbation family: least-squares-mean weighting

Source: R/families.R
stress_weights.Rd

Refits the pre-specified model and forms the treatment contrast with the other weighting of the class covariates: equal weights over levels (SAS LSMEANS default) versus the observed covariate distribution of the analysis rows (SAS OBSMARGINS, g-computation). The point estimate differs only when a class covariate interacts with treatment or visit; the two rows otherwise coincide.

Usage

stress_weights(spec, cores = 1L)

Arguments

spec

A stress_spec().

cores

Number of worker processes; 1 fits sequentially.

Value

A stress_rows data frame with two rows, one flagged is_base.

Examples

spec <- stress_spec(mmrm::fev_data, "FEV1", "USUBJID", "AVISIT", "ARMCD", "PBO",
                    covariates = ~ RACE + SEX + FEV1_BL)
stress_weights(spec)
#>              family                    label        kind        role is_base
#> 1 LS-mean weighting    equal LS-mean weights alternative sensitivity    TRUE
#> 2 LS-mean weighting observed LS-mean weights alternative sensitivity   FALSE
#>   converged note estimate   stderr       df      lcl      ucl           p
#> 1      TRUE      4.404001 1.673014 132.8789 1.094816 7.713186 0.009483085
#> 2      TRUE      4.404001 1.673014 132.8789 1.094816 7.713186 0.009483085
#>   n_subj n_obs     aicc param covariance        method                 vcov
#> 1    197   537 3381.807    NA         us Kenward-Roger Kenward-Roger-Linear
#> 2    197   537 3381.807    NA         us Kenward-Roger Kenward-Roger-Linear
#>             covariates alpha
#> 1 RACE + SEX + FEV1_BL  0.05
#> 2 RACE + SEX + FEV1_BL  0.05