Multi-state models are most commonly used class of methods to analyze sequence of events occurring over time and generating repeated measures with censored observations. The effect of risk factors on the transition hazard from one state to another is measured using the Cox model. In recent years, there is a growing interest to predict the disease status at different stages and endpoints. A key challenge is the simplification and generalization of the existing method for prediction for the large number of events or stages. In this research, a simple alternative method is proposed for risk prediction of the sequence of events using multistage modelling approach. The proposed multi-state model for prediction of a future event for continuous time data may not be appropriate for the discrete time. We also developed the multistage model for discrete time based on the regressive modelling approach. The proposed method of prediction is a new development using a series of events in conditional setting arising from the beginning to the endpoint. The proposed method is based on a marginal-conditional approach to link the events occurring in a trajectory. The probability of a trajectory can be calculated easily. The main improvement of proposed method for risk prediction is that it is a simple approach, compared to the existing ones, and this approach can be generalized to any number of events in the process to the endpoints.