LPJSM_binary: LPJSM for snSMART with binary outcomes (3 active treatments...

In sidiwang/snSMART: Small N Sequential Multiple Assignment Randomized Trial Methods

LPJSM for snSMART with binary outcomes (3 active treatments or placebo and two dose level)

Description

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.

Usage

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, ...)

Arguments

data	dataset with columns named as treatment_stageI, response_stageI, treatment_stageII and response_stageII
six	if TRUE, will run the six beta model, if FALSE will run the two beta model. Default is six = TRUE
DTR	if TRUE, will also return the expected response rate and its standard error of dynamic treatment regimens
...	optional arguments that are passed to geepack::geeglm() function.
object	object to print
x	object to summarize.

Value

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

References

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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.8813")}

See Also

BJSM_binary
sample_size

Examples

data <- data_binary

LPJSM_result <- LPJSM_binary(data = data, six = TRUE, DTR = TRUE)

summary(LPJSM_result)

sidiwang/snSMART documentation built on Oct. 8, 2024, 9:31 p.m.