Questions tagged [survival-analysis]

Survival analysis is concerned with modelling the time before subjects change state, typically time until death or failure. One key feature of such data is that they can be censored, that is, some subjects will not have changed state before the study ends.

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Survival analysis metric on time series data

I created a model that estimates the probability of failure of an asset (based on Weibull CDF, value between 0 and 1). I have a data point every minute. I want to measure the model's success based on ...
rvdinter's user avatar
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Kaplan Meier Estimator vs Weibull distribution for water pipe failures

I have a dataset of about 2.6k, featuring all the failures of the water pipe over 20 years. I have also added the right censored data, totaling the dataset (including failures) to be approximately ...
Eugene Tang's user avatar
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Interpreting PHREG frailty results

I am struggling to understand when, using proc phreg to produce a frailty model, one would use the "unadjusted" p-value vs. the adjusted. See, e.g., https://documentation.sas.com/doc/en/...
kat's user avatar
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Predicting Year-End Outcome from Monthly (and Annual) Data

I have data on customers' usage of various product features over time. Each month, a customer can choose to use a feature or not. I want to create a live system that produces the probability of a user ...
pyassign67's user avatar
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Survival analysis on time series data for predictive maintenance

I want to train a survival analysis model for predictive maintenance on an asset (confidential, let's say it's a motor). The dataset consists of hourly readings of multiple sensors, the type of motor, ...
rvdinter's user avatar
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Video anomaly detection/ Evaluation AUC

I have trained an unsupervised anomaly detector for surveillance videos. After inference, I rescale the scores between max/min from the resulting scores array. scores = (scores - min(scores))/max(...
TecK97's user avatar
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predictions based on irregular repeated measures?

I need to make a model that predicts certain medical outcomes based on the answer to health-related questionnaires. Providers have patients fill out these questionnaires more than once, at irregular ...
Glenn Wright's user avatar
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Late entry in Survival Analysis

I would like to ask how to deal with new entries of individuals in Survival Analysis. I have a study about the time to event of several individuals who suffer from a disease. The study starts on a ...
gbarel's user avatar
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Memory issues for AalenAdditiveFitter in Lifelines packages in Python

We are working on a problem related to survival analysis. We have already implemented Cox Proportional-Hazard Model and Accelerated Failure Time algorithm. Now we want to see how the covariates change ...
Protik Nag's user avatar
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Like Time-To-Event analysis, but looking at the timing of events that do or do not happen on a binary outcome

I have a problem where every observation has a binary outcome that occurs at the end of a fixed period, and the predictor variables describe a few types of event that either happen on some day within ...
SupplyRobot's user avatar
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What is the best way to model survival when the hazard rate decreases over time?

The standard survival analysis model - for example the model which forms the basis for the proportional hazards model - assumes the hazard rate is constant. In many applications this would be the ...
JJ Levine's user avatar
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What is the typical things in Data that i have to look for, when implementing Survival Models using Machine Learning?

Problem Scenario I am working on an industry specific problem focussed on predicting the failure of a seal/gasket in the given time interval(T) in a high-pressure-compression environment. Whenever ...
AvidJoe's user avatar
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CoxPH model with Frailty and L1 regularization

This question stems from an approach proposed by Dr. Silverman, "Predicting Horse Race winners through A Regularized Conditional Logistic Regression with Frailty." In this paper, he proposes ...
Redratz's user avatar
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Survival analysis to estimate kanban tasks completion times

I am working on a problem to estimate task completion time in kanban (project management tool). While doing EDA, I looked at tasks that are either done or cancelled. In this case, I defined the ...
Sharath's user avatar
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How to do prediction on survival data, using Random Forest

I should make prediction on survival data, using the random Forest method. My question is: should I follow the same approach as in logistic regression? taking into account only the status variable or ...
Seydou GORO's user avatar
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analyze the effect of some new changes to business rules on customers retention and sales

I am trying to analyze the effect of a particular business rule on customer behavior. Background: I have two call centers operating in my company. One is an in-house call center and the other one is a ...
NewbietoPython's user avatar
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Can I use survival analysis model to predict probability of an item sold

I am building a model to calculate probability of items being sold( at least within a reasonable amount of time). I know when the item hit the market and when/if the item sold. What is the best ...
Hiro Nakagame's user avatar
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How to compute score and predict for outcome after N days

Let's say I have a medical dataset/EHR dataset that is retrospective and longitudinal in nature. Meaning one person has multiple measurements across multiple time points (in the past). I did post here ...
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Modeling count data with time-dependent rate

For processes of discrete events occurring in continuous time with time-independent rate, we can use count models like Poisson or Negative Binomial. For discrete events that can occur once per sample ...
Bridgeburners's user avatar
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state transition classification on terminal state

I have data on a unit $i$ which enters an entry state $S_0$. This unit has some covariates $x_i$ I would like to predict the probability the unit will reach the terminal state $S_{pos}$ or $S_{neg}$. ...
Hanan Shteingart's user avatar
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Measuring chance ("risk") of being in some class

I don't know if this question fits better here or in statistics, but I think here is more appropriate. I have a dataset with several companies and its features and also I have the information if they ...
Lucas's user avatar
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How to impute right-censored data

I have a dataset of vectors representing movement with various characteristics. Some vectors represents the movement that was stopped by external factor and therefore, observed value for length of ...
jakes's user avatar
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In survival analysis, which is the correct way to introduce a variable which changes the survival rate but occurs at different times?

I am making a survival analysis with a cox regression with proportional hazards, we want to analyze wheter the introduction of a phenomenon influences the time until the death of an individual. A ...
Juan Esteban de la Calle's user avatar
4 votes
2 answers
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How do I predict survival curves using xgboost?

The xgboost package enables survival modeling using parameter arguments: objective = "survival:cox" and eval_metric = "cox-nloglik". The predict method for the resulting model only outputs risk ...
Iyar Lin's user avatar
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Can I apply survival analysis to predict if a user will revisit the website?

I have one business problem in hand which is to predict if a user will revisit the website or not within 6 months. I need to majorly understand what are the factors which make the user return and also ...
asspsss's user avatar
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Can i use survival analysis to predict if a person will "die" or not, and then get the survival time if the person does?

I want to determine, given a project, "How long will it take for this project to be successful ?" Therefore, survival analysis seems like a perfect fit in this case (as I do have some projects that ...
MBB's user avatar
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2 answers
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Python lifelines - ConvergenceWarning: Newton-Raphson failed to converge sufficiently in Cox prop hazard

When calling CoxPHFitter() on my full dataset I'm getting the following error: ...
Serendipity's user avatar
1 vote
1 answer
457 views

Time Series Classification with multiple rows per date

I have a time series data set with the lifecycle of 9000 different B2B sales leads. What I call lifecycle consists of a dataset with one registry per day for every different sales Lead identifier with ...
NuValue's user avatar
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3 votes
1 answer
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Survival regression with major event that won't happen

I would like to do some survival regression about the duration before the "death" of an individual. The final purpose is to know, given an individual, how long it should take before he'll most likely "...
MBB's user avatar
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3 votes
1 answer
6k views

What is the outcome of a Cox regression in xgboost?

I am creating a model using xgboost. Regarding its parameters, its objective is survival:cox ...
Kush Patel's user avatar
3 votes
1 answer
287 views

How to get the survival duration prediction for each individual in the data by using the Kaplan-Meier method?

I am trying to learn how to use the Kaplan-Meier survival estimator model in the lifelines package. The documentation says that the KaplanMeierFitter.fit function ...
Kristada673's user avatar
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140 views

How to do multivariate survival analysis on dataset having only categorical variables

I have a dataset where I have around 50 independent variables used to run survival analysis on the target variables. But out of these 50 variables, 46 variables are categorical variables i.e. having ...
Rajeev Motwani's user avatar
1 vote
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222 views

Variable selection methods in survival analysis

I am using two models for my research. One is the coxph regression model and other is survival trees. I have found the scikit-survival package in python which is able to identify contributing ...
Rajeev Motwani's user avatar
2 votes
1 answer
76 views

What kind of algorithm should I use to build ML model that can predict just next reoccurence of an event in the future (at irregular time interval)?

I'm quite new to machine learning and statistics. I've a dataset from some ecommerce sale's history. It's almost 2k instances, and features include personId (string), productCategory (string/...
S. Joshi's user avatar
3 votes
1 answer
890 views

How to treat missing data for survival analysis

I have a dataset consisting of questionnaires from patient survey data. There are around 10 questions which are asked during several stages of treatment like during first day of visit, after a week, ...
Rajeev Motwani's user avatar
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1 answer
110 views

How to model manufacturing shift data with irregular production times?

Problem Setting: Let's say there are three shifts a day in a manufacturing plant. The plant suffers from irregular power supply thereby affecting in how it is functioning in time and the shift's ...
bb5kb's user avatar
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3 votes
1 answer
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Survival Analysis: Pseudo Observation Vs Stratified Cox Regression. Which one is better?

I've been looking into the Cox Regression method for Survival Analysis in Churn Prediction. Cox regression will allow us to determine the probability that a subscriber will unsubscribe after a time $t$...
Ajay H's user avatar
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12 votes
2 answers
4k views

Predict task duration

I'm trying to create a regression model that predicts the duration of a task. The training data I have consists of roughly 40 thousand completed tasks with these variables: Who performed the task (~...
Jurgy's user avatar
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6 votes
1 answer
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How to use survival analysis for predictive maintenance for time series data?

So, I have a dataset with daily operating conditions for different machines and a flag saying if it failed or not. Here is a snapshot of the data. How can I use survival analysis or any other ...
Chandresh Gupta's user avatar
3 votes
2 answers
2k views

Machine Learning or Survival Analysis?

I am working on building prediction model for disk failures (time taken to occur a disk failure and what parameters could strongly affect disk failures). I am bit confused on- What data preprocessing ...
Rohan's user avatar
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567 views

Predicting purchase order?

What is the best option or rather options to predict how much order a customer will place in the future, say next 3 months on a monthly basis. Also will a customer place an order. I used ARIMA to ...
Raj's user avatar
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4 votes
1 answer
1k views

Time dependent Classification problem

I am trying to solve a decision making problem. In it, information evolves and increases with time for each event observed, and the history of the event may be useful. The problem is as follows: in ...
ARB's user avatar
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1 vote
2 answers
748 views

R plot(surv(), newdata=...) draws same lines many times - why?

I'm new to R and cannot make plotting work as desired. The problem is that R seems to draw the same four lines over and over again, redundantly. The details of the case I'm having are as follows. I ...
Laurimann's user avatar
6 votes
3 answers
758 views

Data driven approach to define a churn user

I'm trying to define a churn prediction model for an online service (betting/gambling). A lot of papers talk about churn analysis/prediction for telco companies where defining a churn user is ...
Geims Bond's user avatar
4 votes
1 answer
309 views

What are available methods for modeling startup survival rates?

I am interested in modeling startup companies failure and success rates to describe what is the representative startup. I have 40 companies in a dataset. Each company is represented as a list of all ...
blue-dino's user avatar
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