| Title: | Estimate ED50 and Its Confidence Interval |
|---|---|
| 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. |
| Authors: | Yongbo Gan, Zhijian Yang, Wei Mei |
| Maintainer: | Yongbo Gan <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.1 |
| Built: | 2024-11-12 02:54:11 UTC |
| Source: | https://github.com/cran/ed50 |
Estimate confidence interval of ED50 using isotonic regression based on bootstrap method.
bootBC.ci(tObserved, tBoot, conf = 0.95)bootBC.ci(tObserved, tBoot, conf = 0.95)
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. |
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)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)
Function of isotonic regression.
bootIsotonicRegression(data, PROBABILITY.GAMMA = 0.5, baselinePava)bootIsotonicRegression(data, PROBABILITY.GAMMA = 0.5, baselinePava)
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. |
library(ed50) pavaData <- preparePava(groupS) bootIsotonicRegression(data = groupS, PROBABILITY.GAMMA = 0.5, baselinePava = pavaData)library(ed50) pavaData <- preparePava(groupS) bootIsotonicRegression(data = groupS, PROBABILITY.GAMMA = 0.5, baselinePava = pavaData)
The function is designed as an argument for the boot function of the Canty Bootstrap package.
bootIsotonicResample(data, mle)bootIsotonicResample(data, mle)
data |
Original experiment data. |
mle |
A list of additional arguments to be used by bootIsotonicResample. |
library(ed50) pavaData <- preparePava(groupS) bootIsotonicResample(data = groupS, mle = list(baselinePava = pavaData, firstDose = 2.5, PROBABILITY.GAMMA = 0.5))library(ed50) pavaData <- preparePava(groupS) bootIsotonicResample(data = groupS, mle = list(baselinePava = pavaData, firstDose = 2.5, PROBABILITY.GAMMA = 0.5))
Test the statistical difference of two independent estimation results of ED50.
compare(group1, group2, alpha = 0.05)compare(group1, group2, alpha = 0.05)
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. |
The difference between two groups of ED50 estimation in terms of statistical significance.
Noguchi, K., & Marmolejo-Ramos, F. (2016). Assessing equality of means using the overlap of range-preserving confidence intervals. American Statistician, 70(4), 325-334.
library(ed50) ans1 <- estimate(groupS$doseSequence, groupS$responseSequence, method = 'ModTurPoint') ans2 <- estimate(groupSN$doseSequence, groupSN$responseSequence, method = 'Dixon-Mood') compare(ans1, ans2)library(ed50) ans1 <- estimate(groupS$doseSequence, groupS$responseSequence, method = 'ModTurPoint') ans2 <- estimate(groupSN$doseSequence, groupSN$responseSequence, method = 'Dixon-Mood') compare(ans1, ans2)
Estimate 50 percent effective dose using different methods.
estimate(doseSequence, doseResponse, confidence = 0.95, method = c("Dixon-Mood", "Choi", "ModTurPoint", "Logistic", "Isotonic"), tpCiScale = 2.4/qnorm(0.975), boot.n = 10000)estimate(doseSequence, doseResponse, confidence = 0.95, method = c("Dixon-Mood", "Choi", "ModTurPoint", "Logistic", "Isotonic"), tpCiScale = 2.4/qnorm(0.975), boot.n = 10000)
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. |
A list of estimation result consisting of method of estimation, ED50 estimate, standard error of ED50 estimate, confidence level and estimate of confidence interval.
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.
library(ed50) estimate(groupS$doseSequence, groupS$responseSequence, method = 'Dixon-Mood') estimate(groupS$doseSequence, groupS$responseSequence, method = 'Logistic', boot.n = 1000)library(ed50) estimate(groupS$doseSequence, groupS$responseSequence, method = 'Dixon-Mood') estimate(groupS$doseSequence, groupS$responseSequence, method = 'Logistic', boot.n = 1000)
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.
generateData(number, useTurPoint = FALSE, start, doseStep = 1, distribution = c("Normal", "Triangle", "Chi-square"), normalMean = 0, normalStd = 1, triMean = 0, triWidth = 2, chiDegree = 1)generateData(number, useTurPoint = FALSE, start, doseStep = 1, distribution = c("Normal", "Triangle", "Chi-square"), normalMean = 0, normalStd = 1, triMean = 0, triWidth = 2, chiDegree = 1)
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. |
A data frame.
library(ed50) generateData(number = 20, start = 2, doseStep = 0.2, distribution = 'Normal') generateData(number = 40, start = 2, doseStep = 0.2, distribution = 'Chi-square')library(ed50) generateData(number = 20, start = 2, doseStep = 0.2, distribution = 'Normal') generateData(number = 40, start = 2, doseStep = 0.2, distribution = 'Chi-square')
A group of real experiment data based on up-and-down method.
groupSgroupS
A data of 36 samples and 2 variables:
A value of 0 or 1 indicating the experiment outcome. 0 refers to a failure outcome while 1 refers to a success.
The dose given in each experiment.
The data is from the article in the references below.
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 group of real experiment data based on up-and-down method.
groupSNgroupSN
A data of 38 samples and 2 variables:
A value of 0 or 1 indicating the experiment outcome. 0 refers to a failure outcome while 1 refers to a success.
The dose given in each experiment.
The data is from the article in the references below.
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 table containing parameter G used in Dixon-Mood method.
gTableOrigingTableOrigin
A data table containing 3 columns:
The ratio of dose step and estimate standard error
The value of parameter G when the estimate of ED50 falls on a dose level
The value of parameter G when the estimate of ED50 falls between two dose levels
The table is obtained from Figure 2 in the reference below
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.
Covert data using PAVA algorithm, the result is uesd for isotonic regression estimation.
preparePava(data)preparePava(data)
data |
A data frame of dose experiments. |
library(ed50) preparePava(groupS) preparePava(groupSN)library(ed50) preparePava(groupS) preparePava(groupSN)