Package {BJM}

Plot conditional mean trajectories (CMT)

Description

This function generates the Conditional Mean Trajectories (CMT) plot. All patients in this plot experience events at the same time point, specified by condi_time2event. Several evenly spaced time points between the baseline and condi_time2event are selected for plotting. Each point is calculated using the mean value of all patients' biomarker values at that time point. The interval between two time points is defined by interval_time

Usage

cmtPlot(
  data.plot.all,
  condi_time2event,
  event_type_variable,
  event_type,
  bio_variable,
  time_variable,
  survival_variable,
  interval_time = 1/12,
  id_variable = "id"
)

Arguments

Value

Conditional mean trajectories plot.

Examples

# example without competing risks

data(pbc3)

pbc.cmt <- cmtPlot(data.plot.all = pbc3, condi_time2event = 5, 
   event_type_variable = NULL, event_type = NULL,
   bio_variable = "serBilir", time_variable = "year", 
   survival_variable = "years", 
   interval_time = 1/12
)

pbc.cmt


# example with competing risks

data(pbc3)

data.plot.all = pbc3[!is.na(pbc3$status4),]

pbc.cmt.cr <- cmtPlot(data.plot.all, condi_time2event = 5, 
   event_type_variable = 'status4', event_type = c("0", "1"),
   bio_variable = "albumin", time_variable = "year", 
   survival_variable = "years", 
   interval_time = 1/4
)

pbc.cmt.cr

conditional distribution of D|T

Description

This function computes the conditional probability density function of competing risk event type D, given the survival time T.

Usage

conditionalDT(data.predict.all, long_fit_all, survival_fit_all, l_i)

Arguments

Value

Probability matrices of competing risk event type D conditional on survival outcome T.

conditional distribution of Y|D, T, if with competing risk

Description

This function computes the conditional probability density function of longitudinal variable Y, given the survival time T with competing risk D.

Usage

conditionalYDT(
  data.predict.all,
  long_fit_all,
  survival_fit_all,
  l_i,
  survival_variable,
  time_variable,
  survivalVariableAll,
  survivalTransFunction
)

Arguments

Value

The output is a list containing probability matrices. In the presence of competing risks, this list includes two elements; otherwise, it contains only one element. Each element within the list is a probability matrix, with the number of rows (l_i) corresponding to specific time points and columns representing different patients. Every matrix element represents the conditional probability derived from the conditional distribution of longitudinal variable Y given the survival time T with competing risk D for a particular patient at a specific time point.

conditional distribution of Y|D, T, if with competing risk

Description

This function computes the conditional probability density function of longitudinal variable Y, given the survival time T with competing risk D.

Usage

conditionalYDTBio(
  Y_all,
  time_new,
  bio_i,
  data.predict.all,
  long_fit_all,
  survival_fit_all,
  l_i,
  survival_variable,
  time_variable,
  survivalVariableAll,
  survivalTransFunction
)

Arguments

Value

The output is a list containing probability matrices. In the presence of competing risks, this list includes two elements; otherwise, it contains only one element. Each element within the list is a probability matrix, with the number of rows (l_i) corresponding to specific time points and columns representing different patients. Every matrix element represents the conditional probability derived from the conditional distribution of longitudinal variable Y given the survival time T with competing risk D for a particular patient at a specific time point.

conditional distribution of Y|T, if no competing risk;

Description

This function computes the conditional probability density function of longitudinal variable Y, given the survival time T without competing risk D.

Usage

conditionalYT(
  data.predict.all,
  long_fit_all,
  l_i,
  survival_variable,
  time_variable,
  survivalVariableAll,
  survivalTransFunction
)

Arguments

Value

The output is a list containing probability matrices. In the presence of competing risks, this list includes two elements; otherwise, it contains only one element. Each element within the list is a probability matrix, with the number of rows (l_i) corresponding to specific time points and columns representing different patients. Every matrix element represents the conditional probability derived from the conditional distribution of longitudinal variable Y given the survival time T without competing risk D for a particular patient at a specific time point.

conditional distribution of Y|T, if no competing risk;

Description

This function computes the conditional probability density function of longitudinal variable Y, given the survival time T without competing risk D.

Usage

conditionalYTBio(
  Y_all,
  time_new,
  bio_i,
  data.predict.all,
  long_fit_all,
  l_i,
  survival_variable,
  time_variable,
  survivalVariableAll,
  survivalTransFunction
)

Arguments

Value

The output is a list containing probability matrices. In the presence of competing risks, this list includes two elements; otherwise, it contains only one element. Each element within the list is a probability matrix, with the number of rows (l_i) corresponding to specific time points and columns representing different patients. Every matrix element represents the conditional probability derived from the conditional distribution of longitudinal variable Y given the survival time T without competing risk D for a particular patient at a specific time point.

Dynamic prediction function

Description

The time values in the prediction data subset must be less than the specified prediction.time which is the prediction time. The time points for longitudinal repeated measurements must not surpass the prediction time.

Usage

dynamicPrediction(
  data.predict.all,
  long_fit_all,
  survival_fit_all,
  prediction.time,
  horizon,
  time_variable,
  survivalVariableAll,
  survivalTransFunction,
  bandcount1 = 10,
  bandcount2 = 40
)

Arguments

Value

A probability matrix, where the rows (l_i) correspond to specific time points and the columns to individual patients. Each element within the matrix signifies the probability of a future event occurring, as dynamically predicted for each patient at each time point.

Examples

data(pbc3)

data.survival.fitting =  pbc3[!duplicated(pbc3$id), ]

formMarginalSurv = Surv(years, status3) ~ age + sex
formConditionalCR = NULL

survival_fit_all = survivalSub(data.survival.fitting, formMarginalSurv, 
                               formConditionalCR)

LongSubFixed = list(
  "long1" = serBilir ~ year + age + sex +  (years) + (years) * year,  
  "long2" = prothrombin ~ year + age + sex + (years) + (years) * year,  
  "long3" = albumin ~ year + age + age * year + sex + (years) + (years) * year,  
  "long4" = alkaline ~ year + age + sex + (years) + (years) * year, 
  "long5" = SGOT ~ year + age + sex + (years) + (years) * year, 
  "long6" = platelets ~ year + age + sex + (years)  + (years) * year)

LongSubRandom =list(
  "long1" =  ~ year| id,   
  "long2" =  ~ year| id,    
  "long3" =  ~ year| id,    
  "long4" =  ~ year| id,    
  "long5" =  ~ year| id,    
  "long6" =  ~ year| id)

survivalVariableAll = list(
  "Tyears1",  "Tyears2", "Tyears3", "Tyears4"
)

survivalTransFunction = list(
  fun1 = function(x){abs(x - 1)}, 
  fun2 = function(x){abs(x - 3)}, 
  fun3 = function(x){abs(x - 5)}, 
  fun4 = function(x){abs(x - 7)}
)

# Complete case analysis
data.fit.all = list()
for(i in 1:length(LongSubFixed)){
  data.fit.all[[i]] = pbc3[pbc3$status3 == 1, ]
}

# fitting longitudinal submodel
long_fit_all = longitudinalSub(data.fit.all, LongSubFixed, LongSubRandom)

i_PID = 2
data.raw.predict.1 = pbc3[pbc3$id == i_PID, ]

data.predict.all = list()
for(i in 1:length(LongSubFixed)){
  data.predict.all[[i]] = data.raw.predict.1[data.raw.predict.1$year <= 3,]
}

# predict risk probability
risk.prob = dynamicPrediction(data.predict.all, long_fit_all, survival_fit_all, 
                              prediction.time = 3, 
                              horizon = 3, time_variable = "year",
                              survivalVariableAll, survivalTransFunction,
                              bandcount1 = 10, bandcount2 = 10)

Dynamic prediction function for future biomarker

Description

The time values in the prediction data subset must be less than the specified prediction.time which is the prediction time. The time points for longitudinal repeated measurements must not surpass the prediction time.

Usage

dynamicPredictionBio(
  bio_i,
  data.predict.all,
  long_fit_all,
  survival_fit_all,
  prediction.time,
  horizon,
  time_variable,
  survivalVariableAll,
  survivalTransFunction,
  bandcount2 = 40,
  bandcount3 = 300
)

Arguments

Value

A probability matrix, where the rows (l_i) correspond to specific time points and the columns to individual patients. Each element within the matrix signifies the probability of a future event occurring, as dynamically predicted for each patient at each time point.

Examples

data(pbc3)

data.survival.fitting =  pbc3[!duplicated(pbc3$id), ]

formMarginalSurv = Surv(years, status3) ~ age + sex
formConditionalCR = NULL

survival_fit_all = survivalSub(data.survival.fitting, formMarginalSurv, 
                               formConditionalCR)

LongSubFixed = list(
  "long1" = serBilir ~ year + age + sex +  (years) + (years) * year,  
  "long2" = prothrombin ~ year + age + sex + (years) + (years) * year,  
  "long3" = albumin ~ year + age + age * year + sex + (years) + (years) * year,  
  "long4" = alkaline ~ year + age + sex + (years) + (years) * year, 
  "long5" = SGOT ~ year + age + sex + (years) + (years) * year, 
  "long6" = platelets ~ year + age + sex + (years)  + (years) * year)

LongSubRandom =list(
  "long1" =  ~ year| id,   
  "long2" =  ~ year| id,    
  "long3" =  ~ year| id,    
  "long4" =  ~ year| id,    
  "long5" =  ~ year| id,    
  "long6" =  ~ year| id)

survivalVariableAll = list(
  "Tyears1",  "Tyears2", "Tyears3", "Tyears4"
)

survivalTransFunction = list(
  fun1 = function(x){abs(x - 1)}, 
  fun2 = function(x){abs(x - 3)}, 
  fun3 = function(x){abs(x - 5)}, 
  fun4 = function(x){abs(x - 7)}
)

# Complete case analysis
data.fit.all = list()
for(i in 1:length(LongSubFixed)){
  data.fit.all[[i]] = pbc3[pbc3$status3 == 1, ]
}

# fitting longitudinal submodel
long_fit_all = longitudinalSub(data.fit.all, LongSubFixed, LongSubRandom)

i_PID = 2
data.raw.predict.1 = pbc3[pbc3$id == i_PID, ]

data.predict.all = list()
for(i in 1:length(LongSubFixed)){
  data.predict.all[[i]] = data.raw.predict.1[data.raw.predict.1$year <= 3,]
}

Y_predict = dynamicPredictionBio(bio_i = 1, data.predict.all, long_fit_all, 
                                 survival_fit_all, prediction.time = 3, 
                                 horizon = 3, time_variable = "year",
                                 survivalVariableAll, survivalTransFunction,
                                 bandcount2 = 40, bandcount3 = 400)

The process involves estimating parameters for a multivariate linear mixed-effects model, which simultaneously analyzes multiple dependent variables that may be correlated. This approach incorporates both fixed effects, which are consistent across the population, and random effects, accounting for variations within groups or subjects. By fitting this model, one can assess the influence of predictor variables on several longitudinal outcomes while considering the inherent variability in the data due to random effects.

Description

The process involves estimating parameters for a multivariate linear mixed-effects model, which simultaneously analyzes multiple dependent variables that may be correlated. This approach incorporates both fixed effects, which are consistent across the population, and random effects, accounting for variations within groups or subjects. By fitting this model, one can assess the influence of predictor variables on several longitudinal outcomes while considering the inherent variability in the data due to random effects.

Usage

longitudinalSub(data.fit.all, LongSubFixed, LongSubRandom)

Arguments

Value

This structure comprises a list with four components. The initial element, labeled lfit, consists of a collection of outcomes from fitting multiple univariate linear mixed models, where each entry within lfit corresponds to the results obtained through the application of the lme function from the nlme package. The second element is the estimated variance-covariance matrix derived from the random effects in a multivariate linear mixed model. The third and fourth elements, LongSubFixed and LongSubRandom, respectively, mirror the inputs provided to the model.

Examples

LongSubFixed = list(
  "long1" = serBilir ~ year + age + sex +  (years) + (years) * year,  
  "long2" = prothrombin ~ year + age + sex + (years) + (years) * year,  
  "long3" = albumin ~ year + age + age * year + sex + (years) + (years) * year,  
  "long4" = alkaline ~ year + age + sex + (years) + (years) * year, 
  "long5" = SGOT ~ year + age + sex + (years) + (years) * year, 
  "long6" = platelets ~ year + age + sex + (years)  + (years) * year)

LongSubRandom =list(
  "long1" =  ~ year| id,   
  "long2" =  ~ year| id,    
  "long3" =  ~ year| id,    
  "long4" =  ~ year| id,    
  "long5" =  ~ year| id,    
  "long6" =  ~ year| id)

survivalVariableAll = list(
  "Tyears1",  "Tyears2", "Tyears3", "Tyears4"
)

survivalTransFunction = list(
  fun1 = function(x){abs(x - 1)}, 
  fun2 = function(x){abs(x - 3)}, 
  fun3 = function(x){abs(x - 5)}, 
  fun4 = function(x){abs(x - 7)}
)

# Complete case analysis
data.fit.all = list()
for(i in 1:length(LongSubFixed)){
  data.fit.all[[i]] = pbc3[pbc3$status3 == 1, ]
}

# fitting longitudinal submodel
long_fit_all = longitudinalSub(data.fit.all, LongSubFixed, LongSubRandom)

Variance-covariance matrix Reference: package ‘lmm’ and package "joineRML" function mvlme.

Description

Variance-covariance matrix Reference: package ‘lmm’ and package "joineRML" function mvlme.

Usage

longitudinalSubVar(thetaLong, l, tol.em, verbose)

Marginal distribution of T

Description

Marginal distribution of T

Usage

marginalT(data.predict.all, long_fit_all, survival_fit_all, l_i, upper_bound)

Arguments

Value

Marginal density probability of the survival variable T is represented as a probability matrix. In this matrix, the rows (l_i) are aligned with specific time points, while the columns correspond to individual patients. Each entry in the matrix denotes the marginal density probability of survival for a given patient at a particular time point.

Mayo Clinic primary biliary cirrhosis data

Description

The dataset originates from the Mayo Clinic trial on primary biliary cirrhosis (PBC) of the liver, carried out from 1974 to 1984. It includes data from 424 PBC patients who were referred to the Mayo Clinic within this decade and met the eligibility requirements for a randomized placebo-controlled trial of D-penicillamine. However, only the initial 312 cases from the dataset were enrolled in the randomized trial. Thus, the dataset specifically pertains to these 312 patients, for whom the data is largely complete.

Usage

data(pbc2)

Format

A data frame with 1945 observations on the following 20 variables:

id

patients identifier; in total there are 312 patients.

years

number of years between registration and the earlier of death, transplantation, or study analysis time.

status

a factor with levels alive, transplanted and dead.

drug

a factor with levels placebo and D-penicil.

age

at registration in years.

sex

a factor with levels male and female.

year

number of years between enrollment and this visit date, remaining values on the line of data refer to this visit.

ascites

a factor with levels No and Yes.

hepatomegaly

a factor with levels No and Yes.

spiders

a factor with levels No and Yes.

edema

a factor with levels No edema (i.e. no edema and no diuretic therapy for edema), edema no diuretics (i.e. edema present without diuretics, or edema resolved by diuretics), and edema despite diuretics (i.e. edema despite diuretic therapy).

serBilir

serum bilirubin in mg/dl.

serChol

serum cholesterol in mg/dl.

albumin

albumin in mg/dl.

alkaline

alkaline phosphatase in U/liter.

SGOT

SGOT in U/ml.

platelets

platelets per cubic ml/1000.

prothrombin

prothrombin time in seconds.

histologic

histologic stage of disease.

status2

a numeric vector with the value 1 denoting if the patient was dead, and 0 if the patient was alive or transplanted.

Source

pbc.

References

Fleming T, Harrington D. Counting Processes and Survival Analysis. 1991; New York: Wiley.

Therneau T, Grambsch P. Modeling Survival Data: Extending the Cox Model. 2000; New York: Springer-Verlag.

Mayo Clinic primary biliary cirrhosis data used as example code

Description

The dataset originates from the Mayo Clinic trial on primary biliary cirrhosis (PBC) of the liver, carried out from 1974 to 1984. It includes data from 424 PBC patients who were referred to the Mayo Clinic within this decade and met the eligibility requirements for a randomized placebo-controlled trial of D-penicillamine. However, only the initial 312 cases from the dataset were enrolled in the randomized trial. Thus, the dataset specifically pertains to these 312 patients, for whom the data is largely complete.

Usage

data(pbc2)

Format

A data frame with 1945 observations on the following 20 variables:

id

patients identifier; in total there are 312 patients.

years

number of years between registration and the earlier of death, transplantation, or study analysis time.

status

a factor with levels alive, transplanted and dead.

drug

a factor with levels placebo and D-penicil.

age

at registration in years.

sex

a factor with levels male and female.

year

number of years between enrollment and this visit date, remaining values on the line of data refer to this visit.

ascites

a factor with levels No and Yes.

hepatomegaly

a factor with levels No and Yes.

spiders

a factor with levels No and Yes.

edema

a factor with levels No edema (i.e. no edema and no diuretic therapy for edema), edema no diuretics (i.e. edema present without diuretics, or edema resolved by diuretics), and edema despite diuretics (i.e. edema despite diuretic therapy).

serBilir

serum bilirubin in mg/dl.

serChol

serum cholesterol in mg/dl.

albumin

albumin in mg/dl.

alkaline

alkaline phosphatase in U/liter.

SGOT

SGOT in U/ml.

platelets

platelets per cubic ml/1000.

prothrombin

prothrombin time in seconds.

histologic

histologic stage of disease.

status2

a numeric vector with the value 1 denoting if the patient was dead, and 0 if the patient was alive or transplanted.

status3

a numeric vector with the value 1 denoting if the patient was dead or transplanted, and 0 if the patient was alive.

status4

a numeric vector with the value 1 denoting if the patient was transplanted, and 0 if the patient was dead. Used for competing risks.

status5

a numeric vector with the value 2 if the patient was transplanted, 1 denoting if the patient was dead, and 0 if the patient was alive. Used for competing risks with censored.

Tyears1

a numeric vector with a transformed value of time-to-event outcome.

Tyears2

a numeric vector with a transformed value of time-to-event outcome.

Tyears3

a numeric vector with a transformed value of time-to-event outcome.

Tyears4

a numeric vector with a transformed value of time-to-event outcome.

Source

pbc.

References

Fleming T, Harrington D. Counting Processes and Survival Analysis. 1991; New York: Wiley.

Therneau T, Grambsch P. Modeling Survival Data: Extending the Cox Model. 2000; New York: Springer-Verlag.

Plot of risk and future biomarker with density using dynamic prediction

Description

This function gives the risk and biomarker prediction plot.

Usage

predictPlot(
  data.predict.all.one,
  long_fit_all,
  survival_fit_all,
  prediction.time = 4,
  horizon = seq(0, 3, 0.5),
  time_variable,
  survivalVariableAll,
  survivalTransFunction,
  bandcount1 = 10,
  bandcount2 = 10,
  bandcount3 = 200,
  bio_his = 1,
  bio_pred = 1,
  density = 1
)

Arguments

Value

Plot of risk and future biomarker with density using dynamic prediction.

Examples

data(pbc3)

data.survival.fitting =  pbc3[!duplicated(pbc3$id), ]

formMarginalSurv = Surv(years, status3) ~ age + sex
formConditionalCR = NULL

survival_fit_all = survivalSub(data.survival.fitting, formMarginalSurv, 
                               formConditionalCR)

LongSubFixed = list(
  "long1" = serBilir ~ year + age + sex +  (years) + (years) * year,  
  "long2" = prothrombin ~ year + age + sex + (years) + (years) * year,  
  "long3" = albumin ~ year + age + age * year + sex + (years) + (years) * year,  
  "long4" = alkaline ~ year + age + sex + (years) + (years) * year, 
  "long5" = SGOT ~ year + age + sex + (years) + (years) * year, 
  "long6" = platelets ~ year + age + sex + (years)  + (years) * year)

LongSubRandom =list(
  "long1" =  ~ year| id,   
  "long2" =  ~ year| id,    
  "long3" =  ~ year| id,    
  "long4" =  ~ year| id,    
  "long5" =  ~ year| id,    
  "long6" =  ~ year| id)

survivalVariableAll = list(
  "Tyears1",  "Tyears2", "Tyears3", "Tyears4"
)

survivalTransFunction = list(
  fun1 = function(x){abs(x - 1)}, 
  fun2 = function(x){abs(x - 3)}, 
  fun3 = function(x){abs(x - 5)}, 
  fun4 = function(x){abs(x - 7)}
)

# Complete case analysis
data.fit.all = list()
for(i in 1:length(LongSubFixed)){
  data.fit.all[[i]] = pbc3[pbc3$status3 == 1, ]
}

# fitting longitudinal submodel
long_fit_all = longitudinalSub(data.fit.all, LongSubFixed, LongSubRandom)

i_PID = 2
data.raw.predict.plot = pbc3[pbc3$id == i_PID, ]
data.predict.all.pre = list(data.raw.predict.plot, data.raw.predict.plot, data.raw.predict.plot,
                            data.raw.predict.plot, data.raw.predict.plot, data.raw.predict.plot)

# plot biomarker 1 history,  predict future biomarker

predictPlot(data.predict.all.pre, long_fit_all, survival_fit_all, 
            prediction.time = 5, bio_his = 1, bio_pred = 1,
            horizon = seq(0.5, 3.0, 0.5), time_variable = "year",
            survivalVariableAll, survivalTransFunction,
           bandcount1 = 10, bandcount2 = 10, bandcount3 = 200)
       

    

Print method for dynamicPrediction.BJM objects

Description

Automatically called when you type the result of dynamicPrediction() at the console.

Usage

## S3 method for class 'dynamicPrediction.BJM'
print(
  x,
  prediction.time = NULL,
  horizon = NULL,
  subject_ids = NULL,
  digits = 4,
  ...
)

print_dynamicPrediction(
  x,
  prediction.time = NULL,
  horizon = NULL,
  subject_ids = NULL,
  digits = 4,
  ...
)

Arguments

Value

Invisibly returns x.

Print method for dynamicPredictionBio.BJM objects

Description

Automatically called when you type the result of dynamicPredictionBio() at the console.

Usage

## S3 method for class 'dynamicPredictionBio.BJM'
print(
  x,
  bio_i = NULL,
  long_fit_all = NULL,
  prediction.time = NULL,
  horizon = NULL,
  subject_ids = NULL,
  digits = 4,
  ...
)

print_dynamicPredictionBio(
  x,
  bio_i = NULL,
  long_fit_all = NULL,
  prediction.time = NULL,
  horizon = NULL,
  subject_ids = NULL,
  digits = 4,
  ...
)

Arguments

Value

Invisibly returns x.

Print method for longitudinalSub.BJM objects

Description

Automatically called when you type long_fit_all at the console.

Usage

## S3 method for class 'longitudinalSub.BJM'
print(x, digits = 4, ...)

print_longitudinalSub(x, digits = 4, ...)

Arguments

Value

Invisibly returns x.

Print method for survivalSub.BJM objects

Description

Automatically called when you type survival_fit_all or print(survival_fit_all) at the console. Displays a JMbayes2-style formatted summary of the survival sub-model.

Usage

## S3 method for class 'survivalSub.BJM'
print(x, digits = 4, ...)

print_survivalSub(x, digits = 4, ...)

Arguments

Value

Invisibly returns x.

Examples

data(pbc3)
data.survival.fitting <- pbc3[!duplicated(pbc3$id), ]
formMarginalSurv  <- Surv(years, status3) ~ age + sex
formConditionalCR <- status4 ~ years + age + sex
survival_fit_all  <- survivalSub(data.survival.fitting,
                                 formMarginalSurv, formConditionalCR)
survival_fit_all   # triggers print.survivalSub.BJM automatically

Combined print summary for a fitted BJM

Description

Combined print summary for a fitted BJM

Usage

print_BJM(long_fit_all, survival_fit_all, digits = 4)

Arguments

Value

Invisibly returns a named list with both fit objects.

Construct variance

Description

Construct variance

Usage

process_variance(
  num_i,
  time_new,
  bio_i,
  data.predict.all,
  long_fit_all,
  time_variable
)

Plot of risk using dynamic prediction

Description

This function gives the risk prediction plot.

Usage

riskPlot(
  data.predict.all.pre,
  long_fit_all,
  survival_fit_all,
  prediction.time = NULL,
  bio_i = NULL,
  horizon,
  time_variable,
  survivalVariableAll,
  survivalTransFunction,
  bandcount1 = 10,
  bandcount2 = 10
)

Arguments

Value

Plot of risk using dynamic prediction.

Summary method for dynamicPrediction.BJM objects

Description

Like print but also shows mean, SD, and range of predicted risks.

Usage

## S3 method for class 'dynamicPrediction.BJM'
summary(
  object,
  prediction.time = NULL,
  horizon = NULL,
  subject_ids = NULL,
  digits = 4,
  ...
)

Arguments

Value

Invisibly returns object.

Summary method for dynamicPredictionBio.BJM objects

Description

Like print but also shows distribution-level summaries across subjects.

Usage

## S3 method for class 'dynamicPredictionBio.BJM'
summary(
  object,
  bio_i = NULL,
  long_fit_all = NULL,
  prediction.time = NULL,
  horizon = NULL,
  subject_ids = NULL,
  digits = 4,
  ...
)

Arguments

Value

Invisibly returns object.

Summary method for longitudinalSub.BJM objects

Description

Like print but adds per-outcome random-effects variance components and the full correlation matrix of D.

Usage

## S3 method for class 'longitudinalSub.BJM'
summary(object, digits = 4, ...)

Arguments

Value

Invisibly returns a list of per-outcome summary.lme objects.

Summary method for survivalSub.BJM objects

Description

Called via summary(survival_fit_all). Returns (and prints) an extended summary including baseline hazard range, BIC, and McFadden R2 for the competing-risks GLM.

Usage

## S3 method for class 'survivalSub.BJM'
summary(object, digits = 4, ...)

Arguments

Value

Invisibly returns a list with components cox_summary and (if competing risks) glm_summary.

Examples

data(pbc3)
data.survival.fitting <- pbc3[!duplicated(pbc3$id), ]
formMarginalSurv  <- Surv(years, status3) ~ age + sex
formConditionalCR <- status4 ~ years + age + sex
survival_fit_all  <- survivalSub(data.survival.fitting,
                                 formMarginalSurv, formConditionalCR)
summary(survival_fit_all)

Fitting survival sub-model

Description

Fitting survival sub-model

Usage

survivalSub(data.survival.fitting, formMarginalSurv, formConditionalCR)

Arguments

Value

Model fitting results of survival sub-model with or without competing risks.

Examples

data(pbc3)

data.survival.fitting =  pbc3[!duplicated(pbc3$id), ]

formMarginalSurv = Surv(years, status3) ~ age + sex
formConditionalCR = NULL

survival_fit_all = survivalSub(data.survival.fitting, formMarginalSurv, 
                               formConditionalCR)