reestimate_pwr: Sample size re-estimation method

In Palash63/RelativeM: Calculate the sample size using the Relative Time method under NPH and NPT secnerios

Description

Sample size re-estimation using the Conditional Power and Predicitve power approach

Usage

reestimate_pwr(alpha, D, d_k, beta0, RT, scenario)

Arguments

alpha	Numeric values
D	Numeric values
d_k	Numeric values
beta0	Numeric values
RT	Numeric values
scenario	Values should be between 0 and 1

Examples

reestimate_pwr(alpha = 0.05,D = 82,d_k = 53,beta0 = 0.459,RT = 1.78,scenario = 1)
cond_result <- reestimate_pwr(alpha = 0.05,D = 82,d_k = 53,beta0 = 0.459,RT = 1.78,scenario = 1)
cond_result
event_cond <- search.best.n.fixed(nstart = 150,nmax = 300,D = 82,beta0 = 0.459,scenario = 1)
event_trend <- search.best.n.trend(nstart = 60,nmax = 300,D = 82,beta0 = 0.459,scenario = 1)
event_pred <- search.best.n.pred(nstart = 60,nmax = 300,D = 82,beta0 = 0.459,scenario = 1)
required_cond <- (event_cond*2 - cond_result$d_k)
required_trend <- (event_trend*2 - cond_result$d_k)
required_pred <- (event_pred*2 - cond_result$d_k)

Palash63/RelativeM documentation built on Dec. 18, 2021, 6:39 a.m.