PS_SAM_data | R Documentation |
This dataset demonstrates the construction of a Propensity Score-Integrated (PS) SAM prior. It simulates a two-arm randomized clinical trial (RCT) with a 2:1 randomization ratio between treatment and control arms, considering both binary and continuous endpoints.
PS_SAM_data
A data frame with 600 observations.
"A" is the treatment assignment (1 = treated, 0 = control).
"G" is the study indicator (1 = current, 0 = historical).
"X_1
" is a binary covariate.
"X_2
" is a continuous covariate.
"X_3
" is a continuous covariate.
"Y_{binary}
" is binary outcome.
"Y_{continuous}
" is continuous outcome.
The dataset includes:
Sample size for treatment arm: n_t = 200
.
Sample size for control arm: n_c = 100
.
Sample size for historical control study: n_h = 300
.
Covariates for the control arm were generated from
X_1 \sim Ber(0.5), ~~ X_2 \sim N(0, 1), ~~ X_3 \sim N(0.5, 1),
where Ber(\cdot)
stands for Bernoulli distribution. Covariates for the
historical controls were generated from a mixture distribution, with half
were generated the same as for the control arm, while the other half were
drawn from
X_1 \sim Ber(0.8), ~~ X_2 \sim N(-0.4, 1), ~~ X_3 \sim N(-0.2, 1).
For the binary endpoint, y_i
were generated from the logit model:
logit(\Pr(y_i = 1 | X_{1i}, X_{2i}, X_{3i}, A_i)) = -1.4 - 0.5
X_{1i} + X_{2i} + 2 X_{3i} + \lambda A_i,
where \lambda
is the treatment effect size, and we let \lambda = 0.9
to generate a moderate treatment effect size so that they study has a reasonable
power.
For the continuous endpoint, y_i
were generated from the following
normal model:
y_i = 1.8 X_{1i} + 0.9 X_{2i} - 2 X_{3i} + \lambda A_i + \epsilon_i,
where we let \lambda = 1
, and \epsilon_i \sim N(0, 3.5^2)
.
This dataset enables evaluation of the PS-SAM prior's performance in addressing heterogeneity between the RCT control arm and historical controls.
# Load the dataset
data(PS_SAM_data)
# View the structure
str(PS_SAM_data)
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