This function evaluates, the performance metrics for fitting dose-response models (using asymptotic approximations or simulations). Note that some metrics are available via the print method and others only via the summary method applied to planMod objects. The implemented metrics are
Root of the mean-squared error to estimate the placebo-adjusted
dose-response averaged over the used dose-levels, i.e. a rather
discrete set (dRMSE). Available via the print method of planMod objects.
Root of the mean-squared error to estimate the
placebo-adjusted dose-response (cRMSE)
averaged over fine (almost continuous) grid at 101 equally spaced
values between placebo and the maximum dose. NOTE: Available via the
summary method applied to planMod objects.
Ratio of the placebo-adjusted mean-squared error (at the
observed doses) of model-based vs ANOVA approach (Eff-vs-ANOVA). This
can be interpreted on the sample size scale. NOTE: Available via the
summary method applied to planMod objects.
Power that the (unadjusted) one-sided 1-alpha confidence interval
comparing the dose with maximum effect vs placebo is
larger than tau. By default alpha = 0.025 and
tau = 0 (Pow(maxDose)). Available via the print method of planMod objects.
Probability that the EDp estimate is within the true [EDpLB,
EDpUB] (by default p=0.5, pLB=0.25 and
pUB=0.75). This metric gives an idea on the ability to
characterize the increasing part of the dose-response curve
(P(EDp)). Available via the print method of planMod objects.
Length of the quantile range for a target dose (TD or
EDp). This is calculated by taking the difference of the dUB and dLB
quantile of the empirical distribution of the dose estimates.
(lengthTDCI and lengthEDpCI). It is NOT calculated by
calculating confidence interval lengths in each simulated data-set
and taking the mean. NOTE: Available via the
summary method of planMod objects.
A plot method exists to summarize dose-response and dose estimations graphically.
planMod(model, altModels, n, sigma, S, doses, asyApprox = TRUE,
simulation = FALSE, alpha = 0.025, tau = 0, p = 0.5,
pLB = 0.25, pUB = 0.75, nSim = 100, cores = 1,
showSimProgress = TRUE, bnds, addArgs = NULL)# S3 method for planMod
plot(x, type = c("dose-response", "ED", "TD"),
p, Delta, placAdj = FALSE, xlab, ylab, ...)
# S3 method for planMod
summary(object, digits = 3, len = 101,
Delta, p, dLB = 0.05, dUB = 0.95, ...)
Character vector determining the dose-response model(s) to be used for fitting the data. When more than one dose-response model is provided the best fitting model is chosen using the AIC. Built-in models are "linlog", "linear", "quadratic", "emax", "exponential", "sigEmax", "betaMod" and "logistic" (see drmodels).
An object of class Mods, defining the true mean vectors under which operating characteristics should be calculated.
Either a vector n and sigma or S need to be
specified. When n and sigma are specified it is
assumed computations are made for a normal homoscedastic ANOVA model
with group sample sizes given by n and residual standard
deviation sigma, i.e. the covariance matrix used for the
estimates is thus sigma^2*diag(1/n) and the degrees of
freedom are calculated as sum(n)-nrow(contMat). When a single
number is specified for n it is assumed this is the sample
size per group and balanced allocations are used.
When S is specified this will be used as covariance matrix for the estimates.
Doses to use
Logicals determining, whether asymptotic approximations or simulations should be calculated. If multiple models are specified in model asymptotic approximations are not available.
Significance level for the one-sided confidence interval for model-based contrast of best dose vs placebo. Tau is the threshold to compare the confidence interval limit to. CI(MaxDCont) gives the percentage that the bound of the confidence interval was larger than tau.
p determines the type of EDp to estimate. pLB and pUB define the bounds for the EDp estimate. The performance metric Pr(Id-ED) gives the percentage that the estimated EDp was within the true EDpLB and EDpUB.
Number of simulations
Number of cores to use for simulations. By default 1 cores is used, note that cores > 1 will have no effect Windows, as the mclapply function is used internally.
In case of simulations show the progress using a progress-bar.
Bounds for non-linear parameters. This needs to be a list with list
entries corresponding to the selected bounds. The names of the list
entries need to correspond to the model names. The
defBnds function provides the default selection.
See the corresponding argument in function
fitMod. This argument is directly passed to
fitMod.
An object of class planMod
Type of plot to produce
Additional arguments determining what dose estimate to plot, when type = "ED" or type = "TD"
When type = "dose-response", this determines whether dose-response estimates are shown on placebo-adjusted or original scale
Labels for the plot (ylab only applies for type = "dose-response")
Number of equally spaced points to determine the mean-squared error on a grid (cRMSE).
Which quantiles to use for calculation of lengthTDCI
and lengthEDpCI. By default dLB = 0.05 and dUB = 0.95,
so that this corresponds to a 90% interval.
object: A planMod object. digits: Digits in summary output
Additional arguments (currently ignored)
Bjoern Bornkamp
TBD
fitMod
if (FALSE) {
doses <- c(0,10,25,50,100,150)
fmodels <- Mods(linear = NULL, emax = 25,
logistic = c(50, 10.88111), exponential= 85,
betaMod=rbind(c(0.33,2.31),c(1.39,1.39)),
doses = doses, addArgs=list(scal = 200),
placEff = 0, maxEff = 0.4)
sigma <- 1
n <- rep(62, 6)*2
model <- "quadratic"
pObj <- planMod(model, fmodels, n, sigma, doses=doses,
simulation = TRUE,
alpha = 0.025, nSim = 200,
p = 0.5, pLB = 0.25, pUB = 0.75)
print(pObj)
## to get additional metrics (e.g. Eff-vs-ANOVA, cRMSE, lengthTDCI, ...)
summary(pObj, p = 0.5, Delta = 0.3)
plot(pObj)
plot(pObj, type = "TD", Delta=0.3)
plot(pObj, type = "ED", p = 0.5)
}
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