survwrapper: Function for fitting and selecting hazard functions to...

In Survomatic: Analysis of longevity data.

Description

Takes one or two vectors of event times (numeric format) and optionally corresponding vectors of indicator variables to designate right-censored events. Fits several mortality models, selects the best fitting one/s, and if two vectors were given, tests hypotheses about the model parameters.

Usage

survwrapper(x, y = NULL, models = c("g", "gm", "l", "lm"), cx = rep(1, length(x)), cy = rep(1, length(y)), ext = F, n = length(c(x, y)), AIC = F, BIC = F, breakties = "AIC", compare.matrix = NULL, constraint.matrix = NULL, thresh = 0.05, smooth = 7)

Arguments

x	A numeric vector of event times. For example, number of days an individual has survived.
y	An optional second numeric vector of event times, in the same units as x. Specifying this vector enables fitting joint models and hypothesis testing.
models	A character vector of model names: g = Gompertz, gm = Gompertz-Makeham, l = logistic, lm = logistic-Makeham, and w = Weibull. The default is to fit all the models except Weibull.
cx	A vector of 0 and 1 the same length as x. Ordinary events (e.g. death) are indicated by 1 and censored events (e.g. withdrawal from the study) are indicated by 0.
cy	A vector of 0 and 1 the same length as y. Ordinary events (e.g. death) are indicated by 1 and censored events (e.g. withdrawal from the study) are indicated by 0.
ext	Not implemented.
n	Total sample size. Should normally be left for the script to automatically calculate, but can be specified when survwrapper is called from another script repeatedly in order to speed up runtimes.
AIC	Whether to calculate the AIC (Akaike Information Criterion) for each candidate model.
BIC	Whether to calculate the BIC (Bayes Information Criterion) for each candidate model.
breakties	What criterion to use for choosing a model if more than one is justified by the comparisons.
compare.matrix	A matrix for specifying a customized comparison algorithm.
constraint.matrix	A matrix of 1's and 0's for specifying a customized set of parameter constraints to test.
thresh	Significance cutoff.
smooth	Not yet supported.

Details

In progress.

Value

x.m
y.m
xy.sm
par.differences
x
y
cx
cy
x.d
y.d
suggested.models
nx
ny

Note

Uses Nelder-Mead algorithm to find maximum likelihood estimates of model parameters.

Author(s)

Alex F. Bokov ([e-mail]), Jon A. Gelfond

References

Pletcher,S.D., Khazaeli,A.A., and Curtsinger,J.W. (2000). Why do life spans differ? Partitioning mean longevity differences in terms of age-specific mortality parameters. Journals of Gerontology Series A-Biological Sciences and Medical Sciences 55, B381-B389

See Also

optim

Examples

## Generate two sets of survival times.
population1 <- simsurv(629,type='g',p=c(7.33e-4,0.1227,0,0));
population2 <-
  simsurv(574,type='lm',p=c(5.4818e-5,0.1543,0.0023,0.6018));

## Fit models to the populations and compare the parameters.
models1vs2 <- survwrapper(population1,population2);

Survomatic documentation built on May 2, 2019, 4:09 p.m.