LPJSM_binary {snSMART} | R Documentation |
A joint-stage regression model (LPJSM) is a frequentist modeling approach that incorporates the responses of both stages as repeated measurements for each subject. Generalized estimating equations (GEE) are used to estimate the response rates of each treatment. The marginal response rates for each DTR can also be obtained based on the GEE results.
LPJSM_binary(data, six = TRUE, DTR = TRUE, ...)
## S3 method for class 'LPJSM_binary'
summary(object, ...)
## S3 method for class 'summary.LPJSM_binary'
print(x, ...)
## S3 method for class 'LPJSM_binary'
print(x, ...)
data |
dataset with columns named as |
six |
if TRUE, will run the six beta model, if FALSE will run the two
beta model. Default is |
DTR |
if TRUE, will also return the expected response rate and its standard error of dynamic treatment regimens |
... |
optional arguments that are passed to |
object |
object to print |
x |
object to summarize. |
a list
containing
GEE_output |
- original output of the GEE (geeglm) model |
pi_hat |
- estimate of response rate/treatment effect |
sd_pi_hat |
- standard error of the response rate |
pi_DTR_hat |
- expected response rate of dynamic treatment regimens (DTRs) |
pi_DTR_se |
- standard deviation of DTR estimates |
Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M., 2018. A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs). Statistics in medicine, 37(26), pp.3723-3732. URL: doi:10.1002/sim.7900
Chao, Y.C., Trachtman, H., Gipson, D.S., Spino, C., Braun, T.M. and Kidwell, K.M., 2020. Dynamic treatment regimens in small n, sequential, multiple assignment, randomized trials: An application in focal segmental glomerulosclerosis. Contemporary clinical trials, 92, p.105989. URL: doi:10.1016/j.cct.2020.105989
Fang, F., Hochstedler, K.A., Tamura, R.N., Braun, T.M. and Kidwell, K.M., 2021. Bayesian methods to compare dose levels with placebo in a small n, sequential, multiple assignment, randomized trial. Statistics in Medicine, 40(4), pp.963-977. URL: doi:10.1002/sim.8813
data <- data_binary
LPJSM_result <- LPJSM_binary(data = data, six = TRUE, DTR = TRUE)
summary(LPJSM_result)