Questions tagged [time-varying-covariate]

A variable that records something about a study unit in a longitudinal study that changes over the course of the study.

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6 views

In a repeated measures model where assessments are performed over time, should baseline data be excluded if baseline covariate is used?

In a model where subjects are evaluated over time and a baseline (time=0) covariate is used (eg, score ~ baseline + group + time + group*time), it seems that baseline data are often suggested to be ...
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9 views

Model (regression) stepwise function (time-varying variable) for several observations

For each observation, we have a plot like this: where the x-axis is the time and the y-axis is the value of interest. For each observation, the time of the jumps can be different, as well as the ...
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26 views

Recurrent Survival Analysis with Time Dependent Covariates

I am analyzing the time (since 2000) of a policy adoption using R. These policies can be updated and so I think a recurrent Prentice Williams Peterson (PWP) model is appropriate. The challenge is that ...
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13 views

Interpreting a hazard ratio for time-dependent coefficients

I have a cox model with two main effects (x,y) and two interaction terms (xt, yt) where t is time. I am having some trouble interpreting the hazard ratios. Does the hazard ration become more of an ...
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2answers
51 views

Age as a time-dependent covariate

I have some confusion around time-dependent covariates / coefficients. I'm trying to run a cox regression with age as a time-dependent covariate. Lets say the variable is age-at-first-drivers-license (...
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How to conceptualise Hierarchial m-state time-event model with multiple absortion states;non exclusive states

I am designing a clinical study to investigate factors modifying the relationship between toxin exposure and its consequences. Since the original work is sensitive, I share an identical scenario. ...
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35 views

Logistic regression with repeated mesures and unique outcome

I have one independent continuous and time-dependent variable X, repeatedly measured (from 1 to 4 times) in different patients during some period of time. My dependent variable Y is binary and is ...
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1answer
25 views

Simple elaboration on Joint Models' linear equations/trajectory functions

I have been struggling with grasping the intuition behind joint models, and I hope someone can elucidate a particular aspect of theirs. Joint models, first of all, are essentially combinations of ...
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78 views

Survival analysis: how to account for immortal time with time-dependent exposure

I am working on a survival analysis to look at time to preterm birth (birth before 37 weeks). I have a time-dependent exposure that can occur anytime at or after 28 weeks, defined using a heaviside ...
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25 views

Checking PH assumption for a time dependent (T_COV) variable in a Cox PH-regression: with or without the original covariate in the regression?

I am performing a cox proportional hazard regression on survival, in a sample in which almost everyone dies in the follow up period. I have little knowledge on statistics in general but i am reading ...
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15 views

Predict value based on independent values and time [closed]

I want to use Python to predict a value of a chemical reaction. As an input I have time units (0,2,4..) and the concentrations of 2 solutions. As an output I have a chemical measurement. As an ...
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1answer
80 views

Survival analysis with time dependent covariates and non-proportional hazards in R

I am attempting to do a survival analysis which will examine the effects of both rainfall (a time-dependant variable) and altitude on nest survival in a species of wasp found in NW Ecuador. I have ...
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Survival Analysis: Time-Varying Covariates, Predictable Process in Real Life

In classical survival analysis, in Cox regression, we assume that the hazard takes the form \begin{align} \lambda(t)&=\lambda_0(t)\exp(\beta^T z(t)) \end{align} Where $\lambda_0(t)$ is the ...
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55 views

How to add the effect of structual change points (level shift, local time trends, changes in seasonal pulses ) in ARIMA IN PYTHON?

I am working on a time series forecasting problem.As I came to know that I was not considering structural changes and seasonal dummies and was building a simple ARIMA that was causing a very poor fit. ...
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56 views

Including Time Invariant Covariates in a Random Intercept Model

Let us say we have a random intercept model for $n$ individuals $$y_{i,t} = x_{i,t}'\beta + \alpha_i + \epsilon_{i,t} \hspace{35pt} i = 1,...,n$$ where $x_{i,t}'\beta$ is a set of time variant ...
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how to model with non-binary time-varying covariates

I've been looking online for resources but to no avail, wondering if anyone could help. Our study is a 4 year longitudinal study looking at teachers' Lesson study experience's impact on students' math ...
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35 views

Interpretation of Cox time-varying covariate

I have fitted the Cox model with time-varying covariates and the results are here where gdp_time is time-varying covariate. and here are the values for gdp_time time1 = 0, time2 = 1, time3 = 0. ...
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1answer
366 views

What is the difference between covariate and confounding variables?

What do covariate and confounding variables have in common and how do they differ? And what are their specific effects in causal inference? (in statistics and causal inference)
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What is the best calculation method to account for individual change, volatility, observation windows and time decays in time series data? ARIMA, ETS?

I am looking at applying a theoretical best calculation method to some particular time series (ts) data. Ideally the calculation method would encompass relative change in individual ts, volatility of ...
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1answer
22 views

How to determine earliest point of change in repeated cross sectional study?

I have an experiment where animals are exposed to a temperature change and then a continuous response variable is measured at set time points after the change. I have a baseline, 8 hours after change, ...
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2answers
249 views

GEE vs mixed model for time-varying covariate

Assuming the attached dataset, I am looking to examine whether participants who are treated are more/less likely to have high addiction severity compared to non-treated. Both treatment and high ...
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1answer
96 views

How to solve a classification problem when the independent variables/covariates/feature vectors form a time series?

Say we've a time indexed sequence of feature vectors/covariates/independent variables $x_t$ at time $t$. Say also we've a corresponding time indexed sequence of variates/dependent variables $y_t$. Now ...
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1answer
23 views

Difficulty working out which statistical test to perform [closed]

I am writing a research paper looking at how reported PTSD scores vary in those who have lost a loved one to homicide. I am investigating whether there is a difference in the scores at different ...
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28 views

Cox Model with time-varying covariates in mlr

I am using the mlr package in R to perform survival analysis. mlr includes the Cox Proportional Hazards Model (function coxph from library survival) as one of its integrated learners. As I understand ...
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How to model change in independent variable for Cox Proportional Hazards Model?

I'm interested in calculating a time to event model (Cox Proportional Hazards Model) and one of my independent variables has multiple measurements. I'm planning on using a time varying covariate for ...
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1answer
65 views

Time dependent variable in survival analysis using Cox regression

Trying to determine how to analyse a time dependent variable (rainfall) in a survival analysis. Two rows of example data for animal A and animal B as below: Each value next to the animal represents ...
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103 views

How to validate(with sample-split data) and calibrate Cox model with time-dependent covaraites?

I am building 2 cox models: Without time-dependent covariates With time-dependent covariates. 1.The first model (without time-dependent variables) as specified below in R works fine and I have no ...
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31 views

Time dependent variable (daily rainfall) in a cox survival analysis

I'm trying to analyse the effect of rainfall (a time dependent variable) on animal survival. In order for an animal to be measured as 'survived' in my study it needs to be found for a minimal period ...
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19 views

Predict malfunctionings with classifiers trained on truncated data?

I have an historical data-set of machines malfunctionings. I have data from different sensors, and a response variable of malfunctioning or not (1/0). I have difficulties in creating a classifier ...
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1answer
38 views

Survival Analysis Time Dependent Variables

I am currently learning Survival Analysis for a project and I struggle a bit with the notion of Time-Dependent Covariates. Let's take the following example: I would like to know what is the ...
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36 views

How is exchangeability related to covariate shift?

I understand that exchangeability refers to the notion that the order of data in a sequence does not affect the joint distribution of that data. In a sense, the current data we possess is from the ...
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1answer
96 views

Piecewise survival analysis?

I am trying to analyze time-to-event data (time to completion of a task). Looking at the KM curves, there is a distinct behavioral change around 12 months. This makes sense, because at 12 months there ...
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1answer
135 views

Repeated measures mixed model correlation between measurements

I am interested in looking at the correlation between two types of heart function measurements over time. test1 can be considered the true value. The true value (<...
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1answer
76 views

Survival Analysis on recurrent behavior time series predictor

We are trying to build a credit model to predict the default time (or finally closed the loans as censored). The predictor is a high-dimension time series of current observed previous payment behavior....
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1answer
354 views

Fitting a time-varying coefficient model in python [closed]

I have some data that varies with time and some that stays constant (e.g., location, race stay constant). Is it possible to implement a mixed time-varying coefficient model in python? What I mean is:...
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9 views

Making use of time intervals in computing conditional probabilities in sequential data with time

I have sequential data X and a binary target y (which is the last event in the sequence). Each row represents a unique sequence of events with a label, 0 or 1. Potentially, I need to estimate a ...
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1answer
251 views

significance of slope (different than zero) in triple interaction (with factors) for mixed model

I have the following mixed model (package {lme4} in R) : ...
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39 views

What plots and summary statistics are usually used for exploratory data analysis of multivariate time series? [closed]

For multivariate cross-sectional data, tools such as scatterplot matrices, the five numbers summary, faceted boxplots and so on, allow an efficient exploration of a new data set, and can suggest how ...
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63 views

How to make predictions and subsequently validate the model using Cox time varying model using the lifelines library?

So, I am using this library to model the risk of customers unsubscribing to a service (with unsubscribed meaning event happened and subscribed meaning no event). This is the link to the lifelines ...
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1answer
167 views

Using SVM and Logistic Regression for survival analysis

I am trying to use SVM and Logistic Regression for survival analysis but I am not able to properly find the implementation in R or python? I was wondering if it was possible to predict whether a ...
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How Rain fall data series adjusted with % irrigated area in the locality in turn calculate production risk

I want to calculate the production risk based on rain fall data, both the variable is of time series data. we have % irrigated area also. if rain fall less area may be a irrigation by means of canal ...
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109 views

clearing up my thinking on longitudinal methods in applied machine learning

Although my graduate training is in econometrics (ABD FTW!), I spent a few years doing more heavily IT-related work before -- now -- transitioning to a data science-y role. Questions about using ...
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2answers
131 views

Predicting recidivism for male prisoners [closed]

The following question takes ground in the this example with time varying covariates. The following code will read data from a url, parse it to the right format (allowing for time varying covariates) ...
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0answers
46 views

Has a distribution changed over time (by category)

I have information on percentage spending on 5 household categories (food, clothing, shelter, entertainment, education & sport, other) for all families in a county in 2005. I have the same ...
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0answers
226 views

Predictive model with time-dependent predictors

I am working on a project to predict whether undergraduate students will return for their 2nd semester. I would like to make initial predictions for incoming students based on predictor data available ...
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1answer
77 views

How do I write a state space model and how do you find the unknown parameters of phi, mu, and matrix A$_t,$ along with covariance matrices Q and R?

Consider a system process given by $x_t=-0.9x_{t-2}+z_t$,$t=1,2,…,n$ with observation $y_t=x_t+v_t$ where ${z_t}$ and ${v_t}$ are independent white noise with variances $σ^2$ and $σ_v^2$. Assume ...
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60 views

including the same variable as a time-varying and time-invariant covariate in MLM

I am wondering if the same variable can be used as both Time-varying and time-invariant in the same GEE model. The data set includes: DV - a proportion of young employees in an organization Another ...
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1answer
129 views

KM survival time transfer function in Cox reg

I have a binary risk factor that can change value over time. I corrected the data using tmerge(). It turns out that the risk factor loses its effect over time. ...
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0answers
76 views

How to model LCGA with time-varying covariate

We have the following data set that contains: Variable $X$ - measures a proportion of young employees in an organization Variable $Y$- measures a proportion of young managers in the same ...
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1answer
49 views

Looking for techniques to understand the impact of a discrete events (interventions) on a continuous response variable in time series

I am trying to model the effect of one or more discrete interventions (e.g., taking a pill, attending therapy) on a continuous outcome (e.g., pain level of a patient over time). The features are ...