Statistical issues in survival analysis (Anticancer research article 16835)
May 22, 2024 The authors have presented a review of current survival methods and contemporary methods that could help handle some of the issues that arise with current methods. The authors then went through description of current methods. They discussed censoring and while they bring some issues like non-informative censoring and truncation and even missing data, they don’t offer any solutions. The authors then brought up an overview of survival functions, hazard functions, Kaplan-Meier estimator for survival and also comparing these curves. In their discussion of the log-rank test, they failed to mention it also requires an assumption of proportionality of hazard curves and even went so far as to say that there are no specific criteria to be satisfied for the correct application of the LR test but then later on do define an instance breakdowns with proportionality so the test cannot be used. The authors then go into an extended description of the Cox proportional hazard regression and the issues around proportionality. They described a number of potential causes of violation of this assumption. They then extended to a non-proportional hazard (NPH) regression model that incorporates a time-varying covariate and/or a stratified Cox regression model. However, these are typical proposed ways to deal with non-proportionality which works with one offender. They also then mentioned an extended Cox regression model that incorporates several timevarying predictors. The Yang-Prentice model was shown and they said it allows for Cox modeling and NPH. The model has worked well for NPH issues like crossing curves but does not well handle