The package fnets for the R language implements the suite of methodologies proposed by Barigozzi et al. (2022) for the network estimation and forecasting of high-dimensional time series under a factor-adjusted vector autoregressive model, which permits strong spatial and temporal correlations in the data.
We evaluate how inverse probability of treatment weighting (IPTW) and standardization-based approaches compare for obtaining marginal estimates of the odds-ratio and the hazards ratio.
Code that is written needs to be readable to ensure reliable software.
The multistate model approach allows us to consider the joint distribution of the two endpoints and to derive quantities of interest as the correlation between overall survival and progression-free survival.
Although the classical graphical procedure can be extended to the online setting, previous work has shown that it leads to low power, and other approaches, such as Adaptive-Discard (ADDIS) procedures, are preferred instead.