| Type: | Package |
| Title: | Duration Estimation for Biomarker Enrichment Studies and Trials |
| Version: | 0.1.0 |
| Depends: | R (≥ 3.5.0) |
| Maintainer: | Hong Zhang <hzhang@wpi.edu> |
| Description: | A general framework using mixture Weibull distributions to accurately predict biomarker-guided trial duration accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identified. Efficiency gains of biomarker-guided trials can be quantitatively compared to the traditional all-comers design. For reference, see Zhang et al. (2024) <doi:10.48550/arXiv.2401.00540>. |
| License: | GPL-2 |
| Imports: | stats, survival, flexsurv |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.0 |
| NeedsCompilation: | no |
| Packaged: | 2024-01-09 15:41:40 UTC; consi |
| Author: | Hong Zhang  [aut,
cre] |
| Repository: | CRAN |
| Date/Publication: | 2024-01-10 14:23:06 UTC |
Calculate the study duration based on Weibull distributions.
Description
Calculate the study duration based on Weibull distributions.
Usage
calcDuration(
d,
n,
proportion,
SHAPEV,
SCALEV,
LAMW,
A,
BETA,
conf.level = 0.9,
nsim = 10000
)
Arguments
d |
- scalar, target number of events. |
n |
- scalar, sample size. |
proportion |
- vector of percentages of the subgroup. |
SHAPEV |
- vector of shape parameters of Weibull survival distributions of the subgroups. |
SCALEV |
- vector of scale parameters of Weibull survival distributions of the subgroups. |
LAMW |
- vector of exponential drop-out distribution parameters of the subgroups. |
A |
- vector of enrollment durations of the subgroups. |
BETA |
- vector of beta distribution parameters of the subgroups. |
conf.level |
- scalar, confidence level, default 0.9. |
nsim |
- scalar, number of repetitions, default 1e4. |
Value
study duration estimate, d_med, and the confidence interval (d_lower, d_upper), as well as all the realizations, Z_d, of the study duration from the simulation.
References
Hong Zhang, Jie Pu, Shibing Deng, Satrajit Roychoudhury, Haitao Chu and Douglas Robinson. "Study Duration Prediction for Clinical Trials with Time-to-Event Endpoints Using Mixture Distributions Accounting for Heterogeneous Population", arXiv:2401.00540.
Examples
res_weibull = getWeilbull(dat_udca)
res_beta = getBeta(as.numeric(dat_udca$entry.dt))
prop = c(table(dat_udca$group)/length(dat_udca$group))
SHAPEV = res_weibull$shape
SCALEV = res_weibull$scale
LAMW = rep(-log(1 - 0.1)/6, 4)
A = rep(res_beta$a/30.416, 4) # convert days to months
BETA = rep(res_beta$b_Mean, 4)
myres1 = calcDuration(d=50, n=169, proportion=prop, SHAPEV, SCALEV, LAMW=LAMW, A, BETA)
c(myres1$d_lower, myres1$d_med, myres1$d_upper)
# drop-out will make the target number of events not achievable
myres2 = calcDuration(d=80, n=169, proportion=prop, SHAPEV, SCALEV, LAMW=LAMW, A, BETA)
c(myres2$d_lower, myres2$d_med, myres2$d_upper)
# If there is no drop-out
myres3 = calcDuration(d=80, n=169, proportion=prop, SHAPEV, SCALEV, LAMW=rep(0, 4), A, BETA)
c(myres3$d_lower, myres3$d_med, myres3$d_upper)
A derived biomarker dataset from a trial of usrodeoxycholic acid
Description
dat_udca is derived from the udca data from the R survival package with biomarker variables defined as the baseline bilirubin level > 1.
Usage
dat_udca
Format
A data frame with 169 rows and 12 variables:
- id
subject id
- trt
treatment assignment
- stage
disease stage
- bili
bilirubin level
- riskscore
disease risk score
- futime
follow up time in days
- status
event indicator
- time
follow up time in months
- entry.dt
enrollment date
- last.dt
last visit date
- BMpos
biomarker positive indicator (bili>1)
- group
subgroup indicator. 1 : trt=0&BMpos=FALSE; 2 : trt=1&BMpos=FALSE; 3 : trt=0&BMpos=TRUE; 4 : trt=1&BMpos=TRUE.
Source
udca data from the R survival package. https://CRAN.R-project.org/package=survival
Examples
data("dat_udca")
Estimate parameters of the distribution of a*Beta(1, b) .
Description
Estimate parameters of the distribution of a*Beta(1, b) .
Usage
getBeta(x)
Arguments
x |
- vector of real positive numbers. |
Value
estimates of a and b. For a, it is the range of x. For b, two estimates are provided: the MLE, and the method of moment (matching mean).
References
Hong Zhang, Jie Pu, Shibing Deng, Satrajit Roychoudhury, Haitao Chu and Douglas Robinson. "Study Duration Prediction for Clinical Trials with Time-to-Event Endpoints Using Mixture Distributions Accounting for Heterogeneous Population", arXiv:2401.00540.
Examples
getBeta(as.numeric(dat_udca$entry.dt))
Estimate parameters of the Weibull survival distributions for each subgroup.
Description
Estimate parameters of the Weibull survival distributions for each subgroup.
Usage
getWeilbull(dat)
Arguments
dat |
- data frame of three columns: 1) time: follow up time; 2) status: indicator, 0=no event, 1=event; 3) group, integer 1,...,n, for each subgroup of patients. |
Value
shape and scale parameters of the Weibull distribution for each subgroup.
References
Hong Zhang, Jie Pu, Shibing Deng, Satrajit Roychoudhury, Haitao Chu and Douglas Robinson. "Study Duration Prediction for Clinical Trials with Time-to-Event Endpoints Using Mixture Distributions Accounting for Heterogeneous Population", arXiv:2401.00540.
Examples
# dat_udca already has time, status and group columns defined,
getWeilbull(dat_udca)