scqe {scqe}

Stability controlled quasi-experiment (scqe)

Description

Main scqe function. Computes scqe estimates and corresponding confidence intervals.

Usage

scqe(
  post,
  treatment,
  outcome,
  min_outcome,
  max_outcome,
  delta,
  min_delta,
  max_delta,
  cohort,
  untr_pre,
  untr_post,
  tr_post,
  tr_pre,
  Y_tr_post,
  Y_untr_post,
  Y_tr_pre,
  Y_untr_pre,
  untr,
  tr,
  Y_tr,
  Y_untr,
  alpha = 0.05,
  ...
)

Arguments

post

Binary vector corresponding to T = 0, 1 for each observation.

treatment

Binary or continuous vector corresponding (usually) to [0,1] (no treatment or treatment) for each observation.

outcome

Continuous vector representing the outcome for each observation.

min_outcome

Minimum value for the outcome. Optional, not used if outcome is supplied.

max_outcome

Maximum value for the outcome. Optional, not used if outcome is supplied.

delta

Single value or vector of possible values for change in average non-treatment outcome between cohorts (if applicable).

min_delta

Minimum delta. Optional, not used if delta is supplied.

max_delta

Maximum delta. Optional, not used if delta is supplied.

cohort

Numeric, 1 or 2 depending on cohort membership.

untr_pre

Integer number of untreated patients in the first cohort if applicable (summary statistics input) (T=0).

untr_post

Integer number of untreated patients in the second cohort if applicable (summary statistics input) (T=1).

tr_post

Integer number of treated patients in the second cohort if applicable (summary statistics input) (T=1).

tr_pre

Integer number of treated patients in the first cohort if applicable (summary statistics input) (T=0).

Y_tr_post

Outcome for patients who received treatment at time T=1 (summary statistics input).

Y_untr_post

Outcome for patients who did not receive treatment at time T=1 (summary statistics input).

Y_tr_pre

Outcome for patients who did receive treatment at time T=0 (summary statistics input).

Y_untr_pre

Outcome for patients who did not receive treatment at time T=0 (summary statistics input).

untr

Integer number of untreated patients (summary statistics input).

tr

Integer number of treated patients (summary statistics input).

Y_tr

Outcome for treated patients (summary statistics input).

Y_untr

Outcome for untreated patients (summary statistics input).

alpha

Numeric alpha for confidence interval (default is alpha = 0.05).

...

Extra optional arguments.

Value

scqe object, results table

References

Hazlett, C. (2019), 'Estimating causal effects of new treatments despite self-selection: The case of experimental medical treatments.' Journal of Causal Inference.

Examples

set.seed(1234)
post = c(rep(0,100), rep(1,100))
tx = c(rep(0, 100), rbinom(n = 100, prob = 0.27, size = 1))
y = rbinom(n = 200, prob = 0.1 + .02 * post - 0.05 * tx, size = 1)

# Two cohorts, full data
scqe.2cohort.full = scqe(post = post, treatment = tx, outcome = y,
                        delta = seq(from = -0.1, to = 0.1, by = 0.05))
plot(scqe.2cohort.full)
summary(scqe.2cohort.full)

# One cohort, full data
scqe.1cohort.full = scqe(treatment = tx, outcome = y,
                        delta=seq(from = -0.1, to = 0.1, by = 0.05))
plot(scqe.1cohort.full)
summary(scqe.1cohort.full)

# Two cohorts, summary data only
scqe.2cohort.sum = scqe(untr_pre = 200,untr_post = 150, tr_post = 50,
                       tr_pre = 0, Y_tr_post = 20, Y_untr_post = 1,
                       Y_tr_pre = 0, Y_untr_pre = 5, min_delta = 0.1,
                       max_delta = 1)
plot(scqe.2cohort.sum)
summary(scqe.2cohort.sum)

# One cohort, summary data only
scqe.1cohort.sum = scqe(untr = 100, tr = 200, Y_untr = 5, Y_tr = 50,
                        min_delta= 0.1, max_delta = 1)
plot(scqe.1cohort.sum)
summary(scqe.1cohort.sum)