Analysis of Multivariate Event Times

Vignettes

- Package overview
- README.md
- Analysis of bivariate binomial data: Twin analysis
- Analysis of multivariate binomial data: family analysis
- Analysis of multivariate survival data
- A practical guide to Human Genetics with Lifetime Data
- Average treatment effect (ATE) for Competing risks and binary outcomes
- Average treatment effect (ATE) for Restricted mean survival and years lost of Competing risks
- Binomial Regression for Survival and Competing Risks Data
- Cumulative Incidence Regression
- Discrete Interval Censored Survival Models
- dUtility data-frame manipulations
- G-Computation or standardization for the Cox, Fine-Gray and binomial regression models for survival data
- GEE cluster standard errors and predictions for glm objects
- Haplotype Discrete Survival Models
- Marginal modelling of clustered survival data
- Mediation Analysis for survival data
- Recurrent events
- Twin models

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**aalenfrailty:**Aalen frailty model**aalenMets:**Fast additive hazards model with robust standard errors**back2timereg:**Convert to timereg object**base1cumhaz:**rate of CRBSI for HPN patients of Copenhagen**base44cumhaz:**rate of Occlusion/Thrombosis complication for catheter of HPN...**base4cumhaz:**rate of Mechanical (hole/defect) complication for catheter of...**basehazplot.phreg:**Plotting the baslines of stratified Cox**bicomprisk:**Estimation of concordance in bivariate competing risks data**BinAugmentCifstrata:**Augmentation for Binomial regression based on stratified...**binomial.twostage:**Fits Clayton-Oakes or bivariate Plackett (OR) models for...**binreg:**Binomial Regression for censored competing risks data**binregATE:**Average Treatment effect for censored competing risks data...**binregCasewise:**Estimates the casewise concordance based on Concordance and...**binregG:**G-estimator for binomial regression model (Standardized...**binregTSR:**2 Stage Randomization for Survival Data or competing Risks...**biprobit:**Bivariate Probit model**blocksample:**Block sampling**bmt:**The Bone Marrow Transplant Data**Bootphreg:**Wild bootstrap for Cox PH regression**bptwin:**Liability model for twin data**casewise:**Estimates the casewise concordance based on Concordance and...**casewise.test:**Estimates the casewise concordance based on Concordance and...**cif:**Cumulative incidence with robust standard errors**cifreg:**CIF regression**ClaytonOakes:**Clayton-Oakes model with piece-wise constant hazards**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...**count.history:**Counts the number of previous events of two types for...**covarianceRecurrent:**Estimation of covariance for bivariate recurrent events with...**daggregate:**aggregating for for data frames**Dbvn:**Derivatives of the bivariate normal cumulative distribution...**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**drcumhaz:**Rate for leaving HPN program for patients of Copenhagen**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**Browse all...**

diabetes | R Documentation |

The data was colleceted to test a laser treatment for delaying blindness in patients with dibetic retinopathy. The subset of 197 patiens given in Huster et al. (1989) is used.

This data frame contains the following columns:

- id
a numeric vector. Patient code.

- agedx
a numeric vector. Age of patient at diagnosis.

- time
a numeric vector. Survival time: time to blindness or censoring.

- status
a numeric vector code. Survival status. 1: blindness, 0: censored.

- trteye
a numeric vector code. Random eye selected for treatment. 1: left eye 2: right eye.

- treat
a numeric vector. 1: treatment 0: untreated.

- adult
a numeric vector code. 1: younger than 20, 2: older than 20.

Huster W.J. and Brookmeyer, R. and Self. S. (1989) MOdelling paired survival data with covariates, Biometrics 45, 145-56.

```
data(diabetes)
names(diabetes)
```

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