TraMineRextras (version 0.6.8)

seqcta: Competing Trajectory Analysis (CTA)

Description

Competing Trajectory Analysis (CTA) aims to simultaneously study the occurrence of an event and the trajectory following it over a pre-defined period of time. The seqcta function convert the data to run the analysis.

Usage

seqcta(seqdata, subseq = 5, time = NULL, event = NULL, initial.state = NULL, covar = NULL)

Value

A data frame with the following variables

id

Numeric. The ID of the observation as the row number in the original seqdata.

time

Numeric. The time unit from the beginning of the original sequence until the occurence of the event.

event

Logical. Whether the event occured within this time unit.

lastobs

Logical. Whether this is the last observation for an individual observation, censored or not. This is useful when one want only one row per individual, for instance to plot survival curves (see example).

T1 until T...

The state sequence following the event starting from 1 (time unit after the event) until subseq time units after the event. Only available for the rows where event=TRUE.

Optional covariate list

The covariates provided with the covar argument.

Arguments

seqdata

State sequence object created with the seqdef function. The whole trajectory followed by individuals.

subseq

Numeric. The length of the trajectory following the event to be considered.

time

Numeric. The time of occurrence of the event, can be NA for censored observations. If NULL (default), initial.state should be provided.

event

Logical. Whether the event occur for each trajectory. If NULL (default) and time is provided, NA time values are used to detect censored observations.

initial.state

Character. Only used if time is not provided. If provided, the end of the first spell of the sequence, but only in initial.state state, is used as the event of interest to compute the time argument.

covar

Optional data.frame storing covariates of interest. These covariates are added to the final data set.

Author

Matthias Studer

Details

Competing Trajectory Analysis (CTA) works as follows. First, the sequence following the studied event are clustered. Second, the type of trajectory followed is linked with covariates using a competing risks model.

The seqcta function reorganizes the data to run CTA. More precisely, it provides a person-period data frame until the occurrence of the event. When the event occurs, the trajectory following it is also stored. Covariates specified using the covar arguments are also stored.

The example section below provides a step by step example of the whole procedure.

References

M. Studer, A. C. Liefbroer and J. E. Mooyaart, 2018. Understanding trends in family formation trajectories: An application of Competing Trajectories Analysis (CTA), Advances in Life Course Research 36, pp 1-12. tools:::Rd_expr_doi("10.1016/j.alcr.2018.02.003")

See Also

seqsamm, seqsha

Examples

## Create seq object for biofam data.
data(biofam)
bf.shortlab <- c("P","L","M","LM","C","LC", "LMC", "D")
bf.seq <- seqdef(biofam[,10:25], states=bf.shortlab)

## We focus on the occurrence of ending the first "P" spell and the trajectory that follows
## For the next subseq=5 years
## We also store the covariate sex and birthyr
## seqcta will transform the data to person-period until the end of the first "P" spell
## and store the following trajectory

cta <- seqcta(bf.seq, subseq=5, initial.state="P", covar=biofam[, c("sex", "birthyr")])
summary(cta)

## If the studied event is not a first state of the trajectory
## One can also provide the event using the time and event arguments
## Here we compute the time spent in "P" ourselves before providing it to seqcta

dur <- seqdur(bf.seq)
## If "P" is the first state, we use the time in this state (dur[, 1])
## Otherwise we use 0 (started immediatly at the beginning)
timeP <- ifelse(bf.seq[, 1]=="P", dur[, 1], 0)

## The event occured if timeP is inferior to the length of the sequence
## Otherwise they never left their parents.
eventP <- timeP < 16

cta2 <- seqcta(bf.seq, subseq=5, time=timeP, event=eventP, covar=biofam[, c("sex", "birthyr")])
##Identical results
summary(cta2)


## Not run to save computation time
if (FALSE) {
library(survival)

## To plot a survival curve, we only need the last observation for each individual.
## Kaplan Meier curve for the occurrence of the event
ss <- survfit(Surv(time, event)~sex, data=cta, subset=lastobs)
plot(ss, col=1:2)

## Now we cluster the trajectories following the event
## Therefore we only keep lines where the event occured.
clusterTraj <- seqdef(cta[cta$event, 5:9])
##Compute distances
diss <- seqdist(clusterTraj, method="HAM")
##Clustering with pam
library(cluster)
pclust <- pam(diss, diss=TRUE, k=5, cluster.only=TRUE)
#Naming the clusters
pclustname <- paste("Type", pclust)
##Plotting the clusters to make senses of them.
seqdplot(clusterTraj, pclustname)

##Now we store back the clustering in the original person-period data
## We start by adding a variable storing "no event" for all lines
cta$traj.event <- "No event"
## Then we store the type of following trajectory
## only for those having experienced the event
cta$traj.event[cta$event] <- pclustname


## Checking the results
summary(cta)

## Now we can estimate a competing risk model
## Several strategies are available.
## Here we use multinomial model on the person period.

library(mlogit)
summary(mlogit(traj.event~1|time+sex, data=cta, shape="wide", reflevel="No event"))
library(nnet)
summary(multinom(traj.event~time+sex+scale(birthyr), data=cta))

## The model can also be estimated with cox regression
## However, we need to estimate one model for each competing risk
## ie. the type of following trajectory in our case.

## Compute the event variable for "Type 1"

cta$eventType1 <- cta$traj.event=="Type 1"
summary(coxph(Surv(time, eventType1)~sex+scale(birthyr), data=cta, subset=lastobs))


}