Survival analysis or time-to-event analysis in data science refers to predicting the amount of time until a specific event will occur. The prediction of this event of interest is done through statistical methods or machine learning methods. Survival analysis utilization in a wide range of industries plays a critical role in predictive modeling, risk assessment, decision-making, and personalized medicine.
Exploring the key aspects of the topic in detail, we will cover:
There are three types of event prediction: time series, purchase, and churn prediction.
It involves forecasting the future values in a specific sequence of data points. The sequence is in concern of time and varies depending on the type of analysis. The time series analysis is performed through moving averages, Long Short-Term Memory (LSTM) networks for deep learning, and Autoregressive Integrated Moving Averages (ARIMA).
This event informs about the probability of a customer purchasing in the future. It is important to tailor the marketing strategies. Machine Learning algorithms like logistic regression or classification models are employed for purchase prediction. The features of use here are browsing behavior, demographic information, and past purchase history.
The churn prediction is aimed at the identification of the customer’s choice to stop the business with the company. The importance is widely seen in subscription-based services to know customer retention rates. Machine Learning models like decision trees and logistic regression are employed to get hold of the answers. The significant features in churn prediction are customer feedback, interaction history, and usage patterns.
Understanding the time period of survival, purchase, or churn might portray a negative impression, but it is capable of having a positive impact on the business. Multiple domains leverage the concept, and here is how:
It uses information like comorbidities, medications, demographics, and procedures to improve healthcare costs. The event of interest here is disease recurrence, rehospitalization, and cancer survival. The outcome is the probability of hospitalization in the calculated time period.
The information on enrollment, finances, semester, pre-enrollment, and demographics are utilized to enhance the quality of education. The event of interest is student dropout, and the outcome is the probability of occurrence of an event of interest in the specific time period.
It is used for interpreting the probability of going bankrupt or default. Considering relevant factors, it predicts the time to stock price change and loan repayment.
The projects, Twitter, creators, and temporal are used to encourage success in business. The event of interest is project success, and outcomes are its occurrence in the estimated time.
The engineering field uses survivability analysis to predict the time to failure or the product's reliability. It is utilized for optimizing the maintenance processes and schedule.
This is done to estimate the unemployment duration using features like job details, user demographics, experience, and economics.
Aiming at digital advertising, it aims to predict the time a user will take to click the link of the ad. The user and ad information and website statistics are important here.
Marketing industries use it for understanding customer loyalty and retention. It is the direct application of churn and purchase prediction that is used to modify customer behavior.
The key concepts or fundamental terms in survival analysis are:
The function indicates the probability of the non-occurrence of the event of interest in the expected time period. In other words, it refers to the probability of survival till a specific time without experiencing the event of interest.
Also displayed as h(t), it refers to the probability of the first occurrence of an event in a specific time period. Alternatively, it is ‘the instantaneous rate of occurrence of the event of interest at a given time, conditional on the individual having survived up to that time’. The hazard function can be high or low. The measure of value is directly proportional to the risk and is graphed exponentially.
It is the total risk that the event will occur within a specific time. It is depicted as H(t) and is integral to the hazard function.
The ratio is a comparison of the hazard function of two different groups. The ratio value 1 indicates the same hazard for both groups. Further, a greater than one ratio indicates a higher hazard for the first group, and a lesser than one ratio indicates a lower hazard for the first group. It is represented as HR and depends on the hazard function predicted from the Cox PH model.
The participants in the survival analysis might not experience the event of interest by the end of the study. The phenomenon is termed censoring and has multiple probabilities of occurrence.
The statistical method for survival analysis is categorized into three methods: parametric, semi-parametric, and non-parametric.
There are Machine Learning survival analysis methods as well, which are Ensemble, survival trees, neural network, Bayesian methods, and Support Vector Machines. Let's have a brief discussion about each:
Kaplan Meier Curve: It is also a non-parametric method specific for survival function calculation from censored time to event data.
Log Rank Test: It is the model that compares the survival curves in different groups.
Cox Proportional Hazards Model: A type of semi-parametric regression model, it estimates the influence of the effect of predictor variables on hazard data.
Survival trees: It is curated using recursive splitting of tree nodes, where nodes are indicative of time span. They are of two types: bagging survival trees and random survival forests.
Survival analysis is an important concept of Data science that indicates the amount of time remaining until a certain event. Besides this significant concept, there are multiple others that are used in combination with programming languages. Regardless of industries, the wide practicability makes these topics an evergreen hot topic in the industries.
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Ans. Lifelines and statsmodels are two Python survival analysis libraries. The lifelines implement survival models like Kaplan-Meier, Cox Proportional Hazards and Nelson-Aalen, and statsmodels include functionality for survival analysis, including Kaplan-Meier estimators.
Ans. The survival analysis is also referred to as ‘time-t-event analysis’ or ‘failure time analysis’.
Ans. The life table analysis calculates the cumulative hazard at specific time points depending on observed events. The Kaplan-Meier estimates survival function according to the censored observed data.
Ans. It is the abbreviated form for Quality-Adjusted Life Years. It is the integrated measure to enhance the patient’s quality and quantity of life in the healthcare industry.
Ans. The advantages of survival analysis are its ability to handle censored data, flexibility of application, accounting for varying follow-up times, and time-to-event information.
Ans. Logistic regression finds application in binary or categorical outcomes, but survival analysis with time-to-event data emphasizes an event of interest.
Ans. The risk set is the participants of the survival analysis who are at risk of experiencing the event of interest in the particular chosen time period.