Description Usage Arguments Details Value Author(s) See Also Examples
This function can be used to construct a risk-adjusted Bernoulli CUSUM chart on survival data. Specify one of the following combinations for the parameters:
glmmod + theta
p0 + theta
p0 + p1
1 |
data |
|
followup |
The followup time for every individual. At what time after entry do we consider the outcome? |
glmmod |
Generalized linear regression model used for risk-adjustment as produced by
the function
|
theta |
e^θ is the odds ratio under the alternative hypothesis. Note that: p1 = (p0 * e^θ)/((1-p0) * (1+p0 e^θ)) |
p0 |
The baseline failure probability at entrytime + followup for individuals. |
p1 |
The alternative hypothesis failure probability at entrytime + followup for individuals. |
h |
(optional) Control limit to be used for the procedure |
stoptime |
(optional) Time after which the value of the chart should no longer be determined |
The Bernoulli CUSUM chart is given by:
S_n = max(0, S_{n-1} + W_n)
with
W_n = X_n ln((p_1 * (1-p_0))/(p_0 * (1-p_1))) + ln((1-p_1)/(1-p_0))
where X_n is the outcome of the n-th (chronological) subject in the data. Instead of displaying patient numbering on the x-axis, the time of outcome is displayed.
An object of class bercusum
containing:
CUSUM
: A data.frame
containing:
$time (times at which chart is constructed),
$value (value of the chart at corresponding times),
$numobs (number of observations at corresponding times)
call
: the call used to obtain output
glmmod
: glm coefficients used for risk-adjustment, if specified
stopind
: indicator for whether the chart was stopped by the control limit
There are plot
and
runlength
methods for "bercusum" objects.
Daniel Gomon
plot.bercusum
, runlength.bercusum
Other qcchart:
bkcusum()
,
cgrcusum()
,
funnelplot()
1 2 3 4 5 6 7 8 | varsanalysis <- c("age", "sex", "BMI")
exprfitber <- as.formula(paste("(entrytime <= 365) & (censorid == 1)~",
paste(varsanalysis, collapse='+')))
surgerydat$instance <- surgerydat$Hosp_num
glmmodber <- glm(exprfitber, data = surgerydat, family = binomial(link = "logit"))
bercus <- bercusum(data = subset(surgerydat, Hosp_num == 14), glmmod = glmmodber,
followup = 100, theta = log(2))
plot(bercus)
|
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