LPJSM_binary function

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

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, ...)

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.

Returns

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

Examples

data <- data_binary

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

summary(LPJSM_result)

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. 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

See Also

BJSM_binary

sample_size