Simulation | Estimation and inference | Censored observations | SessionInfo | Bibliography

Estimation | Cross-validation | Specification of general functional forms | Mixture models | SessionInfo | Bibliography

Influence functions | Working with influence functions with lava::estimate | Examples | Example: population mean | Example: generalized linear model | Example: right-censored outcomes | Example: random effects model / structural equation model | Example: quantile | Combining influence functions | Estimators computed on different subsets | Based on the above Lemma we can therefore combine estimators that are estimatedon different datasets. Let the observed data be$(Z_ | \begin | Renaming and subsetting parameters | Clustered data (non-iid case) | Computational aspects | IF building blocks: transformations and the delta theorem | Example: Pearson correlation | Linear contrasts and hypothesis testing | Standardization | Average Treatment Effects | SessionInfo | Bibliography

Composite outcomes involving death and marks | SessionInfo

Survival | Concordance and Casewise | Competing risk using additive Gamma | Competing risk modeling using the Liabilty Threshold model | SessionInfo

Overview | Biprobit with random effects | Pairwise odds ratio model | Additive gamma model | The twin-stutter data | Bivariate Probit model | Polygenic modelling | Additive gamma random effects | SessionInfo

Overview | Simulated family data | Additive Gamma model | Pairwise fitting | Pairwise dependence modelling | Pairwise odds ratio model | SessionInfo

Overview | Gamma distributed frailties | Additive Gamma frailties | Univariate gamma (clayton-oakes) model twostage models | Piecewise constant Clayton-Oakes model | Multivariate gamma twostage models | Family data | Pairwise specification of random effects and variances | Pairwise dependence modelling | Univariate plackett model twostage models | SessionInfo

Average treatment effect | Average treatment for Competing risks data | Average treatment effect for binary or continuous responses | SessionInfo

RMST | Average treatment effect | SessionInfo

Binomial Regression for censored data | Details | Examples | Absolute risk differences and ratio | SessionInfo

Overview | Hazard based, Cox models | Delayed entry | Parametric hazard models | Multistate models: The Illness Death model | Cumulative incidence | Cumulative incidence I | CIF Delayed entry | Recurrent events | SessionInfo

Fine-Gray model | SE's for the baseline and predictions of FG | Comparison | Strata dependent Censoring weights | Logistic-link | Administrative Censoring | SessionInfo

Discrete Interval Censored survival times | Discrete TTP | SessionInfo

Simple data manipulation for data-frames | dUtility functions | Renaming, deleting, keeping, dropping variables | Looking at the data | Sorting the data | Making new variables for the analysis | Making factors (groupings) | Working with factors | Making a factor from existing numeric variable and vice versa | SessionInfo

G-computation for the Cox and Fine-Gray models | G-computation for the binomial regression | SessionInfo

Utility functions for GLM objects | SessionInfo

Haplotype Analysis for discrete TTP | SessionInfo

First setting up some data | Semi-parametric regression models | Non-parametric marginal mean | IPCW binomial regression | IPCW RMST,RMTL for competing risks | Multinomial regression or cumulative odds regression | SessionInfo

Overview | HF-action data | Exceed threshold | Cumulative time lost for recurrent events | SessionInfo

Overview | Goodness of fit | Computational aspects | Cluster stratified Cox models | SessionInfo

Overview | Simulated Data | Mediation Weights | Binomial Regression | Binomial regression IPCW Mediation Analysis | Mediation Analysis | Multinomial regression | SessionInfo

Two-Stage Randomization for counting process outcomes | Simple Randomization: Lu-Tsiatis marginal Cox model | Two-Stage Randomization CALGB-9823 for survival outcomes | Recurrent events: Simple Randomization | Twostage Randomization: Recurrent events | Causal assumptions for Two-Stage Randomization: Recurrent events | SessionInfo

Overview | Simulation of recurrent events | Utility functions | Marginal Mean | Generating data | Improving efficiency | Regression models for the marginal mean | Administrative censoring for the Ghosh-Lin model | Two-stage modelling | Simulations with specific structure | Other marginal properties | Multiple events | Looking at simulations with dependence | SessionInfo

Two-Stage Randomization CALGB-9823 | SessionInfo

Twin analysis, continuous traits | Polygenic model | Bibliography

Introduction | Continuous outcome | Simulated data | Parametric estimation | Cross-fitting | Flexible nuisance models | Binary outcome | Estimation | Conditional average treatment effects | Session info

Introduction | Built-in learners | Usage | Update formula | Cross-validation | Prediction filter | Defining new learners | Fit function with formula and data arguments | Fit function with x and y arguments | Fit function with single data argument | Specials in formula

Introduction | Simulation | Estimation | MLE | DRE | Interactions | Risk-difference | Influence-function | SessionInfo