A novel method as a diagnostic tool for the detection of influential observations in the Cox proportional hazards model

It is important that the process of studying and modelling the prognosis of disability should be conducted using time-to-event data, as the dynamic nature of disability could cause intervention on the modifiable (prognostic) factors, thus changing the course to a more favourable outcome. In disability research, the Cox PH model is frequently used to identify prognostic factors for the life expectancy of people with disabilities and to evaluate the treatment effects on the time to event. Accurate detection of influential observations is an important factor when fitting the Cox PH model, as influential observations in the Cox PH model can cause model misspecification, inaccurately determined factors, missed valuable biological information and/or violation of the proportional hazard assumption. In this paper, a novel multiple case detection method for influential observations is recommended in the Cox model. The aim of the paper is to inform clinicians and researchers who use the Cox PH model for describing the survival time as a function of multiple prognostic factors, regarding the importance of the detection of influential observations that can lead to misleading conclusions if they are present in the data set. The efficiency of the proposed method is presented through the real dataset. Additionally, in the specific case of North Cyprus, the aim is emphasize the importance of survival modelling studies that determine the prognostic factors affecting the lives of people with disabilities, to improve life quality and to develop a plan for healthier and higher quality life styles programmes for people with disabilities. As a first step, it is recommended that a system of database records of disabilities should be established and maintained by the government to raise public awareness.