Package index • mets

All functions

ACTG175: ACTG175, block randmized study from speff2trial package

BinAugmentCifstrata(): Augmentation for Binomial regression based on stratified NPMLE Cif (Aalen-Johansen)

Bootphreg(): Wild bootstrap for Cox PH regression

CPH_HPN_CRBSI: Rates for HPN program for patients of Copenhagen Cohort

ClaytonOakes(): Clayton-Oakes model with piece-wise constant hazards

Dbvn(): Derivatives of the bivariate normal cumulative distribution function

EVaddGam(): Relative risk for additive gamma model

Effbinreg(): Efficient IPCW for binary data

Event(): Event history object

EventSplit2(): Event split with two time-scales, time and gaptime

FG_AugmentCifstrata(): Augmentation for Fine-Gray model based on stratified NPMLE Cif (Aalen-Johansen)

Grandom.cif(): Additive Random effects model for competing risks data for polygenetic modelling

LinSpline(): Simple linear spline

TRACE sTRACE tTRACE: The TRACE study group of myocardial infarction

WA_recurrent(): While-Alive estimands for recurrent events

aalenMets(): Fast additive hazards model with robust standard errors

aalenfrailty(): Aalen frailty model

back2timereg(): Convert to timereg object

basehazplot.phreg(): Plotting the baselines of stratified Cox

bicomprisk(): Estimation of concordance in bivariate competing risks data

binomial.twostage(): Fits Clayton-Oakes or bivariate Plackett (OR) models for binary data using marginals that are on logistic form. If clusters contain more than two times, the algoritm uses a compososite likelihood based on all pairwise bivariate models.

binreg(): Binomial Regression for censored competing risks data

binregATE(): Average Treatment effect for censored competing risks data using Binomial Regression

binregCasewise(): Estimates the casewise concordance based on Concordance and marginal estimate using binreg

binregG(): G-estimator for binomial regression model (Standardized estimates)

binregRatio(): Percentage of years lost due to cause regression

binregTSR(): 2 Stage Randomization for Survival Data or competing Risks Data

biprobit(): Bivariate Probit model

blocksample(): Block sampling

bmt: The Bone Marrow Transplant Data

bptwin(): Liability model for twin data

calgb8923: CALGB 8923, twostage randomization SMART design

casewise(): Estimates the casewise concordance based on Concordance and marginal estimate using prodlim but no testing

casewise.test(): Estimates the casewise concordance based on Concordance and marginal estimate using timereg and performs test for independence

cif(): Cumulative incidence with robust standard errors

cifreg(): CIF regression

cluster.index(): Finds subjects related to same cluster

concordanceCor(): Concordance Computes concordance and casewise concordance

cor.cif(): Cross-odds-ratio, OR or RR risk regression for competing risks

count.history(): Counts the number of previous events of two types for recurrent events processes

daggregate(): aggregating for for data frames

dby(): Calculate summary statistics grouped by

dcor(): summary, tables, and correlations for data frames

dcut(): Cutting, sorting, rm (removing), rename for data frames

dermalridges: Dermal ridges data (families)

dermalridgesMZ: Dermal ridges data (monozygotic twins)

diabetes: The Diabetic Retinopathy Data

divide.conquer(): Split a data set and run function

divide.conquer.timereg(): Split a data set and run function from timereg and aggregate

dlag(): Lag operator

doubleFGR(): Double CIF Fine-Gray model with two causes

dprint(): list, head, print, tail

dreg(): Regression for data frames with dutility call

drelevel(): relev levels for data frames

dsort(): Sort data frame

dspline(): Simple linear spline

dtable(): tables for data frames

dtransform(): Transform that allows condition

easy.binomial.twostage(): Fits two-stage binomial for describing depdendence in binomial data using marginals that are on logistic form using the binomial.twostage funcion, but call is different and easier and the data manipulation is build into the function. Useful in particular for family design data.

evalTerminal(): Evaluates piece constant covariates at min(D,t) where D is a terminal event

event.split(): event.split (SurvSplit).

eventpois(): Extract survival estimates from lifetable analysis

familycluster.index(): Finds all pairs within a cluster (family)

familyclusterWithProbands.index(): Finds all pairs within a cluster (famly) with the proband (case/control)

fast.approx(): Fast approximation

fast.pattern(): Fast pattern

fast.reshape(): Fast reshape

glm_IPTW(): IPTW GLM, Inverse Probaibilty of Treatment Weighted GLM

gof(<phreg>): GOF for Cox PH regression

gofG.phreg(): Stratified baseline graphical GOF test for Cox covariates in PH regression

gofM.phreg(): GOF for Cox covariates in PH regression

gofZ.phreg(): GOF for Cox covariates in PH regression

haplo: haplo fun data

haplo.surv.discrete(): Discrete time to event haplo type analysis

hfactioncpx12: hfaction, subset of block randmized study HF-ACtion from WA package

iidBaseline(): Influence functions or IID decomposition of baseline for recrec/phreg/cifregFG

npc plotcr nonparcuminc simnordic corsim.prostate alpha2kendall alpha2spear coefmat piecewise.twostage surv.boxarea faster.reshape piecewise.data simBinPlack simBinFam simBinFam2 simSurvFam corsim.prostate.random simnordic.random simCox sim grouptable jumptimes folds ace.family.design ascertained.pairs CCbinomial.twostage coarse.clust concordanceTwinACE concordanceTwostage fast.cluster force.same.cens ilap kendall.ClaytonOakes.twin.ace kendall.normal.twin.ace make.pairwise.design make.pairwise.design.competing matplot.mets.twostage object.defined p11.binomial.twostage.RV predictPairPlack simbinClaytonOakes.family.ace simbinClaytonOakes.pairs simbinClaytonOakes.twin.ace simClaytonOakes.family.ace simClaytonOakes.twin.ace simFrailty.simple simCompete.simple simCompete.twin.ace twin.polygen.design procform procform3 procformdata drop.specials: For internal use

interval.logitsurv.discrete(): Discrete time to event interval censored data

ipw(): Inverse Probability of Censoring Weights

ipw2(): Inverse Probability of Censoring Weights

km(): Kaplan-Meier with robust standard errors

lifecourse(): Life-course plot

lifetable(<matrix>) lifetable(<formula>): Life table

logitSurv(): Proportional odds survival model

mediatorSurv(): Mediation analysis in survival context

medweight(): Computes mediation weights

melanoma: The Melanoma Survival Data

mena: Menarche data set

mets.options(): Set global options for mets

migr: Migraine data

mlogit(): Multinomial regression based on phreg regression

multcif: Multivariate Cumulative Incidence Function example data set

np: np data set

phreg(): Fast Cox PH regression

phregR(): Fast Cox PH regression and calculations done in R to make play and adjustments easy

phreg_IPTW(): IPTW Cox, Inverse Probaibilty of Treatment Weighted Cox regression

phreg_rct(): Lu-Tsiatis More Efficient Log-Rank for Randomized studies with baseline covariates

plack.cif(): plack Computes concordance for or.cif based model, that is Plackett random effects model

plot(<phreg>): Plotting the baselines of stratified Cox

plot_twin(): Scatter plot function

pmvn(): Multivariate normal distribution function

predict(<phreg>): Predictions from proportional hazards model

predictRisk(): Risk predictions to work with riskRegression package

predictRisk(<binreg>): Risk predictions to work with riskRegression package

print(<casewise>): prints Concordance test

prob.exceed.recurrent(): Estimation of probability of more that k events for recurrent events process

prt: Prostate data set

random.cif(): Random effects model for competing risks data

rchaz(): Simulation of Piecewise constant hazard model (Cox).

rchazC(): Piecewise constant hazard distribution

rcrisk(): Simulation of Piecewise constant hazard models with two causes (Cox).

recreg(): Recurrent events regression with terminal event

recurrentMarginal(): Fast recurrent marginal mean when death is possible

resmean.phreg(): Restricted mean for stratified Kaplan-Meier or Cox model with martingale standard errors

resmeanATE(): Average Treatment effect for Restricted Mean for censored competing risks data using IPCW

resmeanIPCW(): Restricted IPCW mean for censored survival data

rpch(): Piecewise constant hazard distribution

sim.cause.cox(): Simulation of cause specific from Cox models.

sim.cif(): Simulation of output from Cumulative incidence regression model

sim.cox(): Simulation of output from Cox model.

sim.recurrent(): Simulation of two-stage recurrent events data based on Cox/Cox or Cox/Ghosh-Lin structure

simAalenFrailty(): Simulate from the Aalen Frailty model

simClaytonOakes(): Simulate from the Clayton-Oakes frailty model

simClaytonOakesWei(): Simulate from the Clayton-Oakes frailty model

simGLcox(): Simulation of two-stage recurrent events data based on Cox/Cox or Cox/Ghosh-Lin structure

simMultistate(): Simulation of illness-death model

simRecurrentII(): Simulation of recurrent events data based on cumulative hazards with two types of recurrent events

simRecurrentTS(): Simulation of recurrent events data based on cumulative hazards: Two-stage model

summary(<cor>): Summary for dependence models for competing risks

summaryGLM(): Reporting OR (exp(coef)) from glm with binomial link and glm predictions

survival.twostage(): Twostage survival model for multivariate survival data

survivalG(): G-estimator for Cox and Fine-Gray model

test.conc(): Concordance test Compares two concordance estimates

tetrachoric(): Estimate parameters from odds-ratio

ttpd: ttpd discrete survival data on interval form

twin.clustertrunc(): Estimation of twostage model with cluster truncation in bivariate situation

twinbmi: BMI data set

twinlm(): Classic twin model for quantitative traits

twinsim(): Simulate twin data

twinstut: Stutter data set

twostageMLE(): Twostage survival model fitted by pseudo MLE

twostageREC(): Fittting of Two-stage recurrent events random effects model based on Cox/Cox or Cox/Ghosh-Lin marginals