# summary.aareg: Summarize an aareg fit In survival: Survival Analysis

## Description

Creates the overall test statistics for an Aalen additive regression model

## Usage

 ```1 2``` ```## S3 method for class 'aareg' summary(object, maxtime, test=c("aalen", "nrisk"), scale=1,...) ```

## Arguments

 `object` the result of a call to the `aareg` function `maxtime` truncate the input to the model at time "maxtime" `test` the relative time weights that will be used to compute the test `scale` scales the coefficients. For some data sets, the coefficients of the Aalen model will be very small (10-4); this simply multiplies the printed values by a constant, say 1e6, to make the printout easier to read. `...` for future methods

## Details

It is not uncommon for the very right-hand tail of the plot to have large outlying values, particularly for the standard error. The `maxtime` parameter can then be used to truncate the range so as to avoid these. This gives an updated value for the test statistics, without refitting the model.

The slope is based on a weighted linear regression to the cumulative coefficient plot, and may be a useful measure of the overall size of the effect. For instance when two models include a common variable, "age" for instance, this may help to assess how much the fit changed due to the other variables, in leiu of overlaying the two plots. (Of course the plots are often highly non-linear, so it is only a rough substitute). The slope is not directly related to the test statistic, as the latter is invariant to any monotone transformation of time.

## Value

a list is returned with the following components

 ` table ` a matrix with rows for the intercept and each covariate, and columns giving a slope estimate, the test statistic, it's standard error, the z-score and a p-value ` test ` the time weighting used for computing the test statistics ` test.statistic ` the vector of test statistics ` test.var ` the model based variance matrix for the test statistic ` test.var2 ` optionally, a robust variance matrix for the test statistic ` chisq ` the overall test (ignoring the intercept term) for significance of any variable ` n ` a vector containing the number of observations, the number of unique death times used in the computation, and the total number of unique death times

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```afit <- aareg(Surv(time, status) ~ age + sex + ph.ecog, data=lung, dfbeta=TRUE) summary(afit) ## Not run: slope test se(test) robust se z p Intercept 5.05e-03 1.9 1.54 1.55 1.23 0.219000 age 4.01e-05 108.0 109.00 106.00 1.02 0.307000 sex -3.16e-03 -19.5 5.90 5.95 -3.28 0.001030 ph.ecog 3.01e-03 33.2 9.18 9.17 3.62 0.000299 Chisq=22.84 on 3 df, p=4.4e-05; test weights=aalen ## End(Not run) summary(afit, maxtime=600) ## Not run: slope test se(test) robust se z p Intercept 4.16e-03 2.13 1.48 1.47 1.450 0.146000 age 2.82e-05 85.80 106.00 100.00 0.857 0.392000 sex -2.54e-03 -20.60 5.61 5.63 -3.660 0.000256 ph.ecog 2.47e-03 31.60 8.91 8.67 3.640 0.000271 Chisq=27.08 on 3 df, p=5.7e-06; test weights=aalen ## End(Not run) ```