Create balancing formulas

Creates balancing formulas relating exposure to all relevant time-varying and time invariant confounders at each exposure time point to be used to create IPTW weights.

Usage

createFormulas(
  obj,
  type = c("full", "short", "update"),
  custom = NULL,
  keep_conf = NULL,
  bal_stats = NULL,
  verbose = FALSE,
  save.out = FALSE
)

# S3 method for class 'devMSM_formulas'
print(x, ...)

Arguments

obj

initialized MSM object from initMSM()

type

type of formula to create from 'full' (includes all lagged time-varying confounders), 'short' (includes time-varying confounders at t-1 lag only), or 'update' (adds to 'short' formulas any imbalanced time-varying confounders at lags great than t-1)

custom

(optional) custom list of formulas at each exposure time point (default is to create automatically according to type)

keep_conf

(optional) For 'short' formulas only, list of variable names reflecting confounders that should be included always.

bal_stats

list of balance statistics from assessBalance()

verbose

(optional) TRUE or FALSE indicator for printing output to console. default is FALSE.

save.out

(optional) Either logical or a character string. If TRUE, it will output the result to a default file name within home_dir set in initMSM(). You can load the data with x <- readRDS(file). To use a non-default file name, specify a character string with the file name. It will save relative to home_dir. There might be naming conflicts where two objects get saved to the same file. In these cases, users should specify a custom name. default is FALSE.

x

devMSM_formulas object from createFormulas()

...

ignored

Value

a list containing balancing formulas. It is the length of the number of exposure variables.

Examples

library(devMSMs)
data <- data.frame(
  ID = 1:50,
  A.1 = rnorm(n = 50),
  A.2 = rnorm(n = 50),
  A.3 = rnorm(n = 50),
  B.1 = rnorm(n = 50),
  B.2 = rnorm(n = 50),
  B.3 = rnorm(n = 50),
  C = rnorm(n = 50),
  D.3 = rnorm(n = 50)
)
obj <- initMSM(
  data,
  exposure = c("A.1", "A.2", "A.3"),
  ti_conf = c("C"),
  tv_conf = c("B.1", "B.2", "B.3", "D.3")
)

# Full Formulas
f <- createFormulas(obj, type = "full")
print(f)
#> USER ALERT: Please manually inspect the full balancing formula below:
#> At time point 1, the full formula for A.1 is: 
#> A.1 ~ C
#> 
#> USER ALERT: Please manually inspect the full balancing formula below:
#> At time point 2, the full formula for A.2 is: 
#> A.2 ~ C + B.1 + A.1
#> 
#> USER ALERT: Please manually inspect the full balancing formula below:
#> At time point 3, the full formula for A.3 is: 
#> A.3 ~ C + B.1 + B.2 + A.1 + A.2

# Short Formulas
f <- createFormulas(obj, type = "short")
print(f)
#> USER ALERT: Please manually inspect the short balancing formula below that includes time-varying confounders at t-1 only:
#> At time point 1, the short formula for A.1 is: 
#> A.1 ~ C
#> 
#> USER ALERT: Please manually inspect the short balancing formula below that includes time-varying confounders at t-1 only:
#> At time point 2, the short formula for A.2 is: 
#> A.2 ~ C + B.1 + A.1
#> 
#> USER ALERT: Please manually inspect the short balancing formula below that includes time-varying confounders at t-1 only:
#> At time point 3, the short formula for A.3 is: 
#> A.3 ~ C + B.2 + A.2

# Update Formulas
w <- createWeights(data = data, formulas = f)
b <- assessBalance(data = data, weights = w)
f <- createFormulas(obj, type = "update", bal_stats = b)
print(f)
#> USER ALERT: Please manually inspect the updated balancing formula below that includes time-varying confounders at t-1 and those greater at further lags that remained imbalanced:
#> At time point 1, the update formula for A.1 is: 
#> A.1 ~ C
#> 
#> USER ALERT: Please manually inspect the updated balancing formula below that includes time-varying confounders at t-1 and those greater at further lags that remained imbalanced:
#> At time point 2, the update formula for A.2 is: 
#> A.2 ~ C + B.1 + A.1
#> 
#> USER ALERT: Please manually inspect the updated balancing formula below that includes time-varying confounders at t-1 and those greater at further lags that remained imbalanced:
#> At time point 3, the update formula for A.3 is: 
#> A.3 ~ C + B.2 + A.2 + A.1