| Type: | Package |
| Title: | Estimate ED50 and Its Confidence Interval |
| Version: | 0.1.1 |
| Author: | Yongbo Gan, Zhijian Yang, Wei Mei |
| Maintainer: | Yongbo Gan <yongbogan@whu.edu.cn> |
| Description: | Functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi's original turning point method (1990) <doi:10.2307/2531453> and it's modified version given by us, as well as logistic regression and isotonic regression. Besides, the package also supports comparison between two estimation results. |
| Imports: | stats, boot, utils |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2019-04-23 06:29:34 UTC; Yongbo Gan |
| Repository: | CRAN |
| Date/Publication: | 2019-04-23 07:50:06 UTC |
Estimate Confidence Interval of ED50 Using Isotonic Regression
Description
Estimate confidence interval of ED50 using isotonic regression based on bootstrap method.
Usage
bootBC.ci(tObserved, tBoot, conf = 0.95)
Arguments
tObserved |
the vector of observed statistics. |
tBoot |
The matrix with R rows each of which is a bootstrap replicate of the statistics. |
conf |
Confidence level. |
Examples
library(ed50)
library(boot)
pavaData <- preparePava(groupS)
bootResult <- boot(data = groupS,
statistic = bootIsotonicRegression,
R = 10,
sim = 'parametric',
ran.gen = bootIsotonicResample,
mle = list(baselinePava = pavaData,
firstDose = 2.5,
PROBABILITY.GAMMA = 0.5),
baselinePava = pavaData,
PROBABILITY.GAMMA = 0.5)
bootBC.ci(tObserved = bootResult$t0[3],
tBoot = bootResult$t[, 3],
conf = 0.95)
Isotonic Regression Function
Description
Function of isotonic regression.
Usage
bootIsotonicRegression(data, PROBABILITY.GAMMA = 0.5, baselinePava)
Arguments
data |
the same dataframe called by the boot function. |
PROBABILITY.GAMMA |
the target effect probability in the BCD experiment; default = 0.5 and need not be specified. |
baselinePava |
the dataframe prepared by the function preparePava. |
Examples
library(ed50)
pavaData <- preparePava(groupS)
bootIsotonicRegression(data = groupS, PROBABILITY.GAMMA = 0.5, baselinePava = pavaData)
The resample function of isotonic regression
Description
The function is designed as an argument for the boot function of the Canty Bootstrap package.
Usage
bootIsotonicResample(data, mle)
Arguments
data |
Original experiment data. |
mle |
A list of additional arguments to be used by bootIsotonicResample. |
Examples
library(ed50)
pavaData <- preparePava(groupS)
bootIsotonicResample(data = groupS,
mle = list(baselinePava = pavaData,
firstDose = 2.5,
PROBABILITY.GAMMA = 0.5))
Compare ED50 Estimation of Independent Two-sample Case
Description
Test the statistical difference of two independent estimation results of ED50.
Usage
compare(group1, group2, alpha = 0.05)
Arguments
group1 |
A list object of ED50 estimation. |
group2 |
Another list object of ED50 estimation to be compared with. |
alpha |
The significant level of test. 0.05 is the defaut value. |
Value
The difference between two groups of ED50 estimation in terms of statistical significance.
References
Noguchi, K., & Marmolejo-Ramos, F. (2016). Assessing equality of means using the overlap of range-preserving confidence intervals. American Statistician, 70(4), 325-334.
Examples
library(ed50)
ans1 <- estimate(groupS$doseSequence, groupS$responseSequence, method = 'ModTurPoint')
ans2 <- estimate(groupSN$doseSequence, groupSN$responseSequence, method = 'Dixon-Mood')
compare(ans1, ans2)
Estimate ED50
Description
Estimate 50 percent effective dose using different methods.
Usage
estimate(doseSequence, doseResponse, confidence = 0.95,
method = c("Dixon-Mood", "Choi", "ModTurPoint", "Logistic",
"Isotonic"), tpCiScale = 2.4/qnorm(0.975), boot.n = 10000)
Arguments
doseSequence |
A sequence of doses given in order |
doseResponse |
A sequence of response results shown in order |
confidence |
The confidence level of interval estimate |
method |
The method used to estimate ED50, there are five methods here, respectively Dixon-Mood, Choi (Choi's Original Turning Point), ModTurPoint (Modified Turning Point), Logistic (Logistic Regression) and Isotonic (Isotonic Regression). The defaut is Dixon-Mood. |
tpCiScale |
The scale level to enlarge the confidence interval estimated by Modified
Turning Point Method. The default value is |
boot.n |
The number of boot process if Logistic method is chosen to estimate ED50. |
Value
A list of estimation result consisting of method of estimation, ED50 estimate, standard error of ED50 estimate, confidence level and estimate of confidence interval.
References
Dixon, W. J., & Mood, A. M. (1948). A method for obtaining and analyzing sensitivity data. Publications of the American Statistical Association, 43(241), 109-126. Choi, S. C. (1990). Interval estimation of the ld50based on an up-and-down experiment. Biometrics, 46(2), 485-492. Pace, N. L., & Stylianou, M. P. (2007). Advances in and limitations of up-and-down methodology: a precis of clinical use, study design, and dose estimation in anesthesia research. Anesthesiology, 107(1), 144-52.
Examples
library(ed50)
estimate(groupS$doseSequence, groupS$responseSequence, method = 'Dixon-Mood')
estimate(groupS$doseSequence, groupS$responseSequence, method = 'Logistic', boot.n = 1000)
G Table
Description
A table containing parameter G used in Dixon-Mood method.
Usage
gTableOrigin
Format
A data table containing 3 columns:
- Ratio
The ratio of dose step and estimate standard error
- G1
The value of parameter G when the estimate of ED50 falls on a dose level
- G2
The value of parameter G when the estimate of ED50 falls between two dose levels
Source
The table is obtained from Figure 2 in the reference below
References
Dixon, W. J., & Mood, A. M. (1948). A method for obtaining and analyzing sensitivity data. Publications of the American Statistical Association, 43(241), 109-126.
Generate Simulation Data of Up-and-Down Experiment
Description
The function is used to generate simulation data of up-and-down experiment, and provide three cases that tolerance distribution obeys normal, triangle or chi-square distribution.
Usage
generateData(number, useTurPoint = FALSE, start, doseStep = 1,
distribution = c("Normal", "Triangle", "Chi-square"), normalMean = 0,
normalStd = 1, triMean = 0, triWidth = 2, chiDegree = 1)
Arguments
number |
The number of experiments in a trail. |
useTurPoint |
A logical value indicating whether the parameter |
start |
The first dose level given in this trail. |
doseStep |
A fix value that represents the difference between two adjacent dose levels. |
distribution |
The tolerance distribution, including normal, triangle and chi-square distribution, and the default distribution is N(0, 1). |
normalMean |
Parameter mean of normal distribution, the default value is 0. |
normalStd |
Parameter std of normal distribution, the default value is 1. |
triMean |
Parameter mean of triangle distribution, the default value is 0. |
triWidth |
Parameter width of triangle distribution, the default value is 2. |
chiDegree |
Parameter degree of freedom of chi-square distribution, the default value is 1. |
Value
A data frame.
Examples
library(ed50)
generateData(number = 20, start = 2, doseStep = 0.2, distribution = 'Normal')
generateData(number = 40, start = 2, doseStep = 0.2, distribution = 'Chi-square')
A Real Experiment Dose Data
Description
A group of real experiment data based on up-and-down method.
Usage
groupS
Format
A data of 36 samples and 2 variables:
- responseSequence
A value of 0 or 1 indicating the experiment outcome. 0 refers to a failure outcome while 1 refers to a success.
- doseSequence
The dose given in each experiment.
Source
The data is from the article in the references below.
References
Niu B, Xiao JY, Fang Y, et al. Sevoflurane-induced isoelectric EEG and burst suppression: differential and antagonistic effect of added nitrous oxide. Anaesthesia 2017; 72: 570-9.
A Real Experiment Dose Data
Description
A group of real experiment data based on up-and-down method.
Usage
groupSN
Format
A data of 38 samples and 2 variables:
- responseSequence
A value of 0 or 1 indicating the experiment outcome. 0 refers to a failure outcome while 1 refers to a success.
- doseSequence
The dose given in each experiment.
Source
The data is from the article in the references below.
References
Niu B, Xiao JY, Fang Y, et al. Sevoflurane-induced isoelectric EEG and burst suppression: differential and antagonistic effect of added nitrous oxide. Anaesthesia 2017; 72: 570-9.
Covert Data Using PAVA Algorithm
Description
Covert data using PAVA algorithm, the result is uesd for isotonic regression estimation.
Usage
preparePava(data)
Arguments
data |
A data frame of dose experiments. |
Examples
library(ed50)
preparePava(groupS)
preparePava(groupSN)