Causal Inference and Prediction in Cohort-Based Analyses
Summary Survival Curve And Comparison Between Strata.
Area Under ROC Curve From Sensitivities And Specificities.
Cut-Off Estimation Of A Prognostic Marker (Only One Observed Group).
Cut-Off Estimation Of A Prognostic Marker (Two Groups Are observed).
Marginal Effect for Binary Outcome by G-computation.
Marginal Effect for Binary Outcome by Super Learned G-computation.
Marginal Effect for Censored Outcome by G-computation with a Cox Regre...
Log-Rank Test for Adjusted Survival Curves.
Adjusted Survival Curves by Using IPW.
Add Lines to a ROC Plot
Likelihood Ratio Statistic to Compare Embedded Multistate Models
3-State Time-Inhomogeneous Markov Model
3-state Relative Survival Markov Model with Additive Risks
4-State Time-Inhomogeneous Markov Model
4-state Relative Survival Markov Model with Additive Risks
Horizontal Mixture Model for Two Competing Events
Plot Method for 'rocrisca' Objects
Plot Method for 'survrisca' Objects
POsitivity-Regression Tree (PoRT) Algorithm to Identify Positivity Vio...
Cumulative Incidence Function Form Horizontal Mixture Model With Two C...
Restricted Mean Survival Times.
ROC Curves For Binary Outcomes.
Net Time-Dependent ROC Curves With Right Censored Data.
Prognostic ROC Curve Based on Survival Probabilities
Prognostic ROC Curve based on Individual Data
Summary ROC Curve For Aggregated Data.
Time-Dependent ROC Curves With Right Censored Data.
3-State Semi-Markov Model With Interval-Censored Data
3-State Semi-Markov Model
3-State Relative Survival Semi-Markov Model With Additive Risks
4-State Semi-Markov Model
4-State Relative Survival Semi-Markov Model With Additive Risks
Multiplicative-Regression Model to Compare the Risk Factors Between Tw...
Summary Survival Curve From Aggregated Data
Numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events, competing events, and multi-state data. For multistate data, semi-Markov model with interval censoring may be considered, and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables. For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders. Finally, several functions are available to assess time-dependent ROC curves or survival curves from aggregated data.