Simulated data for CML patients

Description

A dataset containing information of CML patients who have received transplant. Both states before and after transplant are included in this data set. This data set is used for illustration, so the magnitude of event times are out of clinical consideration.

Format

A data frame of 1777 rows (patients) on the following 14 variables:

cp1

Time in months to chronic phase

cp1.s

Indicator of the occurrence of chronic phase; 1=occur, 0=censored

ap

Time in months to accelerated phase of CML

ap.s

Indicator of the occurrence of accelerated phase; 1=occur, 0=censored

cp2

Time in months to chronic phase after progression to advanced stage

cp2.s

Indicator whether subject come back to chronic phase after progression to an advanced stage; 1=occur, 0=censored

bc

Time in months to blast crisis phase of CML

bc.s

Indicator of the occurrence of blast crisis phase; 1=occur, 0=censored

sct

Time in months to receive transplant

sct.s

Indicator whether patient receive transplant; 1=transplant, 0=no transplant

death

Time in months to death

death.s

Death indicator; 1=death, 0=censored

age

0=age less than 50; 1=age larger than 50

sex

1=female; 2=male

Examples

data(SimCml)

Confidence interval of mean total lifetime

Description

This function is used to calculate confidence intervals of mean total lifetime using jackknife resampling.

Usage

conf.MTL(obj, state = NULL, nsim = 1000, L = 120)

Arguments

Details

This function systematically leaves out each subject from the original datset and simulates mean total lifetimes for each n-1-sized subsample. The jackknife mean and variance are calculated by aggregating n simulated mean total lifetimes. For each jackknife dataset, mean total lifetime is simulated using the algorithm described in sim.MTL.

Value

If the input object comes from optim.fit with treatment=NULL, a list object with elements:

If the input object comes from optim.fit with treatment is not NULL, a list object with elements:

See Also

optim.fit

Examples

## Not run: 
library(OptimalTiming)

##################################
## Example 1: This example shows how to calculate confidence
## intervals for different treatment strategies

## read data
data(SimCml)

## fit multistate model with treatment not equals NULL
fit=optim.fit(data=SimCml,
       transM=matrix(c(0,1,0,0,0,1,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,
       0,0,0,0,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0),7,byrow=TRUE),
       nstate=7,state_label=c("diagnose","cp1","ap","cp2","bc","sct","death"),
       event_label=c("cp1.s","ap.s","cp2.s","bc.s","sct.s","death.s"),
       treatment=c("sct","sct.s"),absorb=c("death","death.s"),
       cov=c("age"),cov_value=c(0))

## compare different treatment strategies
conf.MTL(obj=fit,nsim=1000,L=120)

##################################
## Example 2: This example shows how to calculate confidence
## intervals for a given state

## read data
data(SimCml)

## delete the information of transplant time
data=SimCml[SimCml$sct.s==0,]
del=which(names(SimCml)%in%c("sct","sct.s"))
data=data[,-del]

## fit multistate model with treatment equals NULL
fit=optim.fit(data=data,
        transM=matrix(c(0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,
        1,1,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0),6,byrow=TRUE),
        nstate=6,state_label=c("diagnose","cp1","ap","cp2","bc","death"),
        absorb=c("death","death.s"),event_label=c("cp1.s","ap.s","cp2.s","bc.s","death.s"),
        cov=c("age"),cov_value=c(0))

## calculate mean total lifetime and confidence intervals
## for state 1,2,3,4
conf.MTL(obj=fit,state=c(1,2,3,4),nsim=1000,L=120)
## End(Not run)

Fit multi-state model for optimization

Description

This function produces transition probabilities for given covariates values in multi-state models.

Usage

optim.fit(data, transM, nstate, state_label, event_label, treatment = NULL,
  absorb, cov, cov_value)

Arguments

Details

For optim.fit, transition probabilities are estimated under Markov assumption, which implies that the probability of transition to a future state depends only on the present state, not on the history. Taking covariates at baseline into consideration, transition probabilities can be subject-specific. Cox proportional hazards model is used to fit transition hazards among multiple states by assuming each transition has its own baseline hazard, and covariates have different effects on different transitions.

Let \mathbf{S}={1,2,\cdots,S} denote the states in the multi-state model and X(t) be a random process taking values from \mathbf{S}. Denote \alpha_{gh}(t) as hazard ratio or transition intensity and Z as baseline covariates. The instantaneous risk of a transition from state g into state h at time t can be fitted by semi-parametric Cox model:

\alpha_{gh}(t|Z)=\alpha_{gh,0}exp(\beta^{T}Z_{gh}).

The cumulative hazard ratio is defined as A_{gh}(t)=\int_0^t \alpha_{gh}(u)du. Primary interest in this function is to estimate transition probability P_{gh}(s,t)=P(X(t)=h|X(s)=g), indicating the chance of transition from state g at time s to state h at time t. Written in matrix form, transition probability matrix \mathbf{P}(t) can be calculated by means of a product integral: \mathbf{P}(s,t)=\prod_{(s,t]}(\mathbf{I}+d\mathbf{A}(u)), where \mathbf{A}(t) is a transition intensity matrix. Both \mathbf{P} and \mathbf{A} are S \times S matrix.

The data format required by this function is wide format, which can be regarded as the augmented data used in single event survival analysis. For example, if there is a "recurrence" state in a multi-state model, two variable are needed to describe this event, namely, "rec" and "rec.s". The former is a time variable, indicating the time from initiation of the study to the occurrence of this state, while the latter is an indicator variable with 1 for occurrence and 0 for censoring. If the event is censored for some patients, use the maximum follow-up instead of the event time. Other states are prepared in the same way. Thus, each row in the augmented data summarize all possible events for a single subject. For covariates, they are located at the end of each row.

If the time of new treatment initiation is provided in data, the argument treatment should be assigned as, eg.treatment=c("sct","sct.s"). Additionally, the argument state_lable and event_label shoud be arranged in such order: pre-treatment state, treatment state, post-treatment states and absorbing state. Assume treatment may take place at any pre-treatment states. In this case, optim.fit function automatically fit two multistate models, one for post-treatment states if a new treatment is carried out , and the other for pre-treatment states if a new treatment is not carried out. Thus, comparison among strategies of whether and when to initiate new treatment can be performed in sim.MTL function. If treatment=NULL, a single multistate model will be fitted.

Value

If treatment is NULL, a list object called "overall" is output with elements:

If treatment is not NULL, three list objects called "overall", "treat","no_treat" are output. A list "overall" contains elements:

A list "no_treat" contains elements by fitting a model for pre-treatment states:

A list "treat" contains elements by fitting a model if a new treatment is carried out:

References

de Wreede LC, Fiocco M, and Putter H (2010). The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models. Computer Methods and Programs in Biomedicine 99, 261–274.

Examples

## Not run: 
library(OptimalTiming)
## read data
data(SimCml)

## fit multistate model if the time to new treatment initiation is available in SimCml
fit=optim.fit(data=SimCml,
         transM=matrix(c(0,1,0,0,0,1,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,
         0,0,0,0,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0),7,byrow=TRUE),
         nstate=7,state_label=c("diagnose","cp1","ap","cp2","bc","sct","death"),
         event_label=c("cp1.s","ap.s","cp2.s","bc.s","sct.s","death.s"),
         treatment=c("sct","sct.s"),absorb=c("death","death.s"),
         cov=c("age"),cov_value=c(0))

## view the content of this object
objects(fit)

## output transition probabilities
fit$treat$tranProb
fit$no_treat$tranProb
## End(Not run)

Simulate mean total lifetime

Description

This function is used to simulate mean total lifetime for a given initial state according to the estimated transition probabilities.

Usage

sim.MTL(obj, state = NULL, nsim = 1000, L = 120)

Arguments

Details

This part describes the algorithm used to simulate mean total lifetime in detail. For an initial state, we first extract the transition probability data frame for this state, and cumulate probabilities in row direction. In the transformed data frame, the first column of which impels the time points where transition probabilities are measured, and the last column is accumulated to be 1, indicating the addition of the chance to depart from a state and the chance to remain at the state equals 1. Then, we generate a random value from uniform distribution Unif(0,1) to determine the next state by comparing this value to cumulative probabilities. The state, whose cumulative transition probability from initial state firstly surpasses the uniform value, is defined as the next state. The time interval, from the initiation of the study to where the transition take place, is defined as the interim residual life. These two variables (next state and interim residual life) are recorded for late use. Subsequently, we regard the next state as the initial state, and repeat this searching process until the absorbing state is reached or the interim residual lifetime surpasses the prespecified threshold (L). Finally, the mean total life is either the last interim residual lifetime from the initiation of study to the occurrence of absorbing state, or the prespecified threshold (L) if the absorbing has not reached yet.

If a state is given in this function, we set this state as initial state and perform the algorithm mentioned above for nsim times, and average the output to obtain mean total lifetime.

According to different type of input object, this function return different results. If the object comes from optim.fit with treatment=NULL, this function is used to simulate mean total lifetime for a given state. If the object comes from optim.fit with treatment not equals NULL, this function is used to compare mean total lifetimes of subjects who receive the new treatment to those who do not receive the new treatment.

Value

If the input object comes from optim.fit with treatment=NULL, a list object with elements:

If the input object comes from optim.fit with treatment not equals NULL, a list object with elements:

See Also

optim.fit

Examples

## Not run: 
library(OptimalTiming)

##################################
## Example 1: This example shows how to use this package to find
## the optimal timing of new treatment initiation

## read data
data(SimCml)

## fit multistate model with treatment not equals NULL
fit=optim.fit(data=SimCml,
         transM=matrix(c(0,1,0,0,0,1,0,0,0,1,0,1,1,1,0,0,0,1,1,1,1,0,0
         ,0,0,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0),7,byrow=TRUE),
         nstate=7,state_label=c("diagnose","cp1","ap","cp2","bc","sct","death"),
         event_label=c("cp1.s","ap.s","cp2.s","bc.s","sct.s","death.s"),
         treatment=c("sct","sct.s"),absorb=c("death","death.s"),
         cov=c("age"),cov_value=c(0))

## compare different treatment strategies
sim.MTL(obj=fit,nsim=1000,L=120)

##################################
## Example 2: This example shows how to obtain mean total lifetime
## for a given state

## read data
data(SimCml)

## delete the information of transplant time
data=SimCml[SimCml$sct.s==0,]
del=which(names(SimCml)%in%c("sct","sct.s"))
data=data[,-del]

## fit multistate model with treatment equals NULL
fit=optim.fit(data=data,
        transM=matrix(c(0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,1,
        1,1,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,0),6,byrow=TRUE),
        nstate=6,state_label=c("diagnose","cp1","ap","cp2","bc","death"),
        absorb=c("death","death.s"),
        event_label=c("cp1.s","ap.s","cp2.s","bc.s","death.s"),
        cov=c("age"),cov_value=c(0))

## calculate mean total lifetime when the initiate state is cp1 or ap
sim.MTL(obj=fit,state=c(2,3),nsim=1000,L=120)
## End(Not run)

internal

Description

internal

Usage

sim.los(tpA, startstate0 = 1, absorbstate = 5, nstate = 5, ntrans = 9,
  n = 1000, L = 120)

Details

internal