Statistical Inference Of Recurrent Event Data Based On Propensity Scores

Recurrence events typically refer to an individual experiencing a specific event within a certain time frame,and then potentially experiencing the same event again after the first event has ended.Previous research has primarily focused on statistical analysis of recurrence event duration and recurrence rate.However,theis methods do not take into account covariates,unobserved heterogeneity,and dependent observation processes that can affect recurrence event processes.Therefore,these methods may lead to biased estimates and inaccurate results.In order to overcome the problem of confounding bias,we need to use more advanced methods to establish recurrence event models.These models can help us better understand the characteristics and mechanisms of recurrence events,allowing for better prediction and treatment of these events.Recent studies have shown that panel count data is a type of recurrent event data with interval censoring.Panel count data can be modeled using interval time models,taking into account covariate influences such as age,gender,and treatment.In addition,panel count data can also consider unobserved heterogeneity in individuals,such as genetic and environmental factors.Another important issue is addressing the problems caused by dependent observation processes.In recurrence event studies,there may be correlation between adjacent observations,which can lead to biased estimates.In order to overcome the problem of confounding bias,we need to establish panel count data semi-parametric additive models and semi-parametric transformation models that include propensity score weighting.The establishment of these models allows us to more accurately and reasonably consider the special properties of panel count data when explaining the recurrence event process.This article mainly studies the impact of confounding bias on the recurrent event process,and improves the accuracy of parameter estimation by introducing propensity score adjusting the semi-parametric model.The main innovation of this article is to propose a semi-parametric transformation model and a semi-parametric additive model based on dependent observations process to analyze the confounding bias phenomenon in recurrent event data.Compared with traditional analysis methods,this article considers the recurrent event rate under dependent observation processes,overcomes the restriction of independent observation processes in interval censoring data,and avoids the confounding bias caused by directly introducing the filter function into the model.In order to obtain more accurate parameter estimates,this article also proposes inverse probability weighting estimation method and proves its asymptotic properties and rationality.The effectiveness and accuracy of the method are verified through simulation studies,and it is applied to the analysis of bladder cancer and skin cancer data.