# aareg

0th

Percentile

##### Aalen's additive regression model for censored data

Returns an object of class "aareg" that represents an Aalen model.

Keywords
survival
##### Usage
aareg(formula, data, weights, subset, na.action,
qrtol=1e-07, nmin, dfbeta=FALSE, taper=1,
test = c('aalen', 'variance', 'nrisk'),
model=FALSE, x=FALSE, y=FALSE)
##### Arguments
formula

a formula object, with the response on the left of a ~' operator and the terms, separated by + operators, on the right. The response must be a Surv object. Due to a particular computational approach that is used, the model MUST include an intercept term. If "-1" is used in the model formula the program will ignore it.

data

data frame in which to interpret the variables named in the formula, subset, and weights arguments. This may also be a single number to handle some speci al cases -- see below for details. If data is missing, the variables in the model formula should be in the search path.

weights

vector of observation weights. If supplied, the fitting algorithm minimizes the sum of the weights multiplied by the squared residuals (see below for additional technical details). The length of weights must be the same as the number of observations. The weights must be nonnegative and it i s recommended that they be strictly positive, since zero weights are ambiguous. To exclude particular observations from the model, use the subset argument instead of zero weights.

subset

expression specifying which subset of observations should be used in the fit. Th is can be a logical vector (which is replicated to have length equal to the numb er of observations), a numeric vector indicating the observation numbers to be included, or a character vector of the observation names that should be included. All observations are included by default.

na.action

a function to filter missing data. This is applied to the model.fr ame after any subset argument has be en applied. The default is na.fail, which returns a n error if any missing values are found. An alternative is na.excl ude, which deletes observations that contain one or more missing values.

qrtol

tolerance for detection of singularity in the QR decomposition

nmin

minimum number of observations for an estimate; defaults to 3 times the number of covariates. This essentially truncates the computations near the tail of the data set, when n is small and the calculations can become numerically unstable.

dfbeta

should the array of dfbeta residuals be computed. This implies computation of the sandwich variance estimate. The residuals will always be computed if there is a cluster term in the model formula.

taper

allows for a smoothed variance estimate. Var(x), where x is the set of covariates, is an important component of the calculations for the Aalen regression model. At any given time point t, it is computed over all subjects who are still at risk at time t. The tape argument allows smoothing these estimates, for example taper=(1:4)/4 would cause the variance estimate used at any event time to be a weighted average of the estimated variance matrices at the last 4 death times, with a weight of 1 for the current death time and decreasing to 1/4 for prior event times. The default value gives the standard Aalen model.

test

selects the weighting to be used, for computing an overall average'' coefficient vector over time and the subsequent test for equality to zero.

model, x, y

should copies of the model frame, the x matrix of predictors, or the response vector y be included in the saved result.

##### Details

The Aalen model assumes that the cumulative hazard H(t) for a subject can be expressed as a(t) + X B(t), where a(t) is a time-dependent intercept term, X is the vector of covariates for the subject (possibly time-dependent), and B(t) is a time-dependent matrix of coefficients. The estimates are inherently non-parametric; a fit of the model will normally be followed by one or more plots of the estimates.

The estimates may become unstable near the tail of a data set, since the increment to B at time t is based on the subjects still at risk at time t. The tolerance and/or nmin parameters may act to truncate the estimate before the last death. The taper argument can also be used to smooth out the tail of the curve. In practice, the addition of a taper such as 1:10 appears to have little effect on death times when n is still reasonably large, but can considerably dampen wild occilations in the tail of the plot.

##### Value

an object of class "aareg" representing the fit, with the following components:

n

vector containing the number of observations in the data set, the number of event times, and the number of event times used in the computation

times

vector of sorted event times, which may contain duplicates

nrisk

vector containing the number of subjects at risk, of the same length as times

coefficient

matrix of coefficients, with one row per event and one column per covariate

test.statistic

the value of the test statistic, a vector with one element per covariate

test.var

variance-covariance matrix for the test

test

the type of test; a copy of the test argument above

tweight

matrix of weights used in the computation, one row per event

call

a copy of the call that produced this result

##### References

Aalen, O.O. (1989). A linear regression model for the analysis of life times. Statistics in Medicine, 8:907-925.

Aalen, O.O (1993). Further results on the non-parametric linear model in survival analysis. Statistics in Medicine. 12:1569-1588.

print.aareg, summary.aareg, plot.aareg

• aareg
##### Examples
# NOT RUN {
# Fit a model to the lung cancer data set
lfit <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung,
nmin=1)
# }
# NOT RUN {
lfit
Call:
aareg(formula = Surv(time, status) ~ age + sex + ph.ecog, data = lung, nmin = 1
)

n=227 (1 observations deleted due to missing values)
138 out of 138 unique event times used

slope      coef se(coef)     z        p
Intercept  5.26e-03  5.99e-03 4.74e-03  1.26 0.207000
age  4.26e-05  7.02e-05 7.23e-05  0.97 0.332000
sex -3.29e-03 -4.02e-03 1.22e-03 -3.30 0.000976
ph.ecog  3.14e-03  3.80e-03 1.03e-03  3.70 0.000214

Chisq=26.73 on 3 df, p=6.7e-06; test weights=aalen

plot(lfit[4], ylim=c(-4,4))  # Draw a plot of the function for ph.ecog
# }
# NOT RUN {
lfit2 <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung,
nmin=1, taper=1:10)
# }
# NOT RUN {
lines(lfit2[4], col=2)  # Nearly the same, until the last point
# }
# NOT RUN {
# A fit to the mulitple-infection data set of children with
# Chronic Granuomatous Disease.  See section 8.5 of Therneau and Grambsch.
fita2 <- aareg(Surv(tstart, tstop, status) ~ treat + age + inherit +
steroids + cluster(id), data=cgd)
# }
# NOT RUN {
n= 203
69 out of 70 unique event times used

slope      coef se(coef) robust se     z        p
Intercept         0.004670  0.017800 0.002780  0.003910  4.55 5.30e-06
treatrIFN-g      -0.002520 -0.010100 0.002290  0.003020 -3.36 7.87e-04
age              -0.000101 -0.000317 0.000115  0.000117 -2.70 6.84e-03
inheritautosomal  0.001330  0.003830 0.002800  0.002420  1.58 1.14e-01
steroids          0.004620  0.013200 0.010600  0.009700  1.36 1.73e-01

Chisq=16.74 on 4 df, p=0.0022; test weights=aalen
# }
`
Documentation reproduced from package survival, version 3.1-8, License: LGPL (>= 2)

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