# Tagged Questions

Longitudinal data consists of data that are collected repeatedly on the same subjects. When there is a long series of data, time series analysis may be appropriate. For shorter series, mixed models (aka multilevel models) may be appropriate.

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### Data analysis for a one group pre/post test

I am measuring teachers' beliefs about education practices before and after a specific training workshop to determine if the workshop has a positive effect in changing teachers' beliefs towards ...
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### Instrumental variables and mixed/multilevel models

I want to estimate a growth model to model the growth trajectories of individuals $j$ over multiple time points $t$ by applying a standard mixed/mutilevel model (also known as random coefficient ...
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### Random effect with 0 variance in GLMM

In this study, subjects are measured continuously over the day via electrocardiography (ECG). During the day, certain trigger events occur randomly. Once all the data is collected, the trigger events ...
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### Why is using cross-sectional data to infer / predict longitudinal changes a Bad Thing?

I'm looking for a paper which I hope exists, but don't know if it does. It could be a set of case studies, and / or an argument from probability theory, about why using cross-sectional data to infer / ...
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### Longitudinal Study into attention bias and insomnia - What test to use?

I am doing a study into attentional bias (as measured by the Stroop test) and insomnia (as measured by the Insomnia Severity Index - ISI). I will be comparing the mean reaction times of the Stroop ...
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### Ordinal and longitudinal data

I have repeated measures over time from n subjects responding to a daily Likert scale question, so my data is ordinal and longitudinal over a few months. Not all subjects answer every daily survey of ...
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### Simulating longitudinal lognormal data in R

I want to design a study that will eventually allow me to estimate centile curves for a given longitudinal outcome measured in a sample. I want to simulate data that I can then use to evaluate likely ...
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### Analysis of longitudinal data with very few points

I'm trying to analyse some data I've recently gotten my hands on, but I'm not entirely sure which model to use. One suggestion has been a Mixed Model, Repeated Measurements ANOVA, but I'm not sure if ...
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### Does the sandwich estimator in GEE protect against both correlation misspecification and heteroscedasticity?

The relative merits of GEE with exchangeable correlation or GEE with independence and the sandwich estimate have been discussed, but I couldn't find a post specifically addressing my question. I have ...
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### Test if people drop out or decrease bets after repeated losses

I have data on a series of winning and losing bets over 5 rounds of betting with attrition after each round. I am using a decision tree like the following to display the data. The nodes towards ...
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### lme: random effects for replicated growth curves

I am measuring the evolution of the brain response to a visual stimulation over time. The measures are done every seconds from 1 second to 14 seconds (each measure at time t gives a value summarizing ...
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### Visualizing longitudinal data with binary outcome

For longitudinal data with a numeric outcome, I can use spaghetti plots to visualize the data. For example something like this (taken from the UCLA Stats site): ...
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### Effect of Age-Period-Cohort on prediction and interactions

I am performing a longitudinal analysis and I am curious if the predictors we are including in the model will introduce any unexpected effects. We have subjects with multiple points of follow-up ...
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### Clustering longitudinal (trajectory) data

I am hoping to implement an unsupervised technique that identifies distinct clusters of individuals based on longitudinal data: 100 continuous or categorical variables measured at different ages. A ...
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### Weightining using TraMineR

I have read some posts on weighting. However, I am still unclear on the sort of weights I need to use. I am using data from the Longitudinal Survey of Australian Youth (LSAY). This survey provides ...
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### How to specify in r spatial covariance structure similar to SAS sp(pow) in a marginal model?

I'm currently translating existing code from SAS to R. I'm working on longitudinal data (CD4 count over time). I have the following SAS code : ...
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### how to perform factor analysis for clustered longitudinal binary data?

I measure a longitudinal binary outcome (correctness of detection, 0: incorrect, 1: correct) with respect to 5 different experimental conditions (1 baseline and 4 treatments). The outcome is always ...
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### Dealing with dropping non-responders in clinical trials

I'm wondering what are some good approaches for analyzing trials where non-responders are dropped out at some set point. This is not my field, so I may be unaware of standard things! Let's say there ...
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### What if time variable is not significant in longitudinal analysis, can we remove it in the model?

In my longitudinal data, I firstly build a model with two fixed effects, session.week and sync. The former one is just the time variable. I actually have two subquestions : (1) What if time ...
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### Sample size for one sample repeated measures

I am conducting a study in which I want to follow a group of subjects over time. My outcome of interest is continuous and I will measure it at 4 time points. I am trying to calculate the sample size ...
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### What do “marginal” and “conditional” mean in “marginal models” and “conditional models”?

What do "marginal" and "conditional" mean in "marginal models" and "conditional models"? Are they related to marginal distributions and conditional distributions? Thanks!
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### Finding correlations in longitudinal data analysis

We are doing research on video lecture watching. We offer a course which lasts four 5 weeks, and there are 9 or 10 videos in each week. We organize group-watching activities. A group usually is ...
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### longitudinal correlation in SPSS

My clinical study aim is looking at treatment effect on two outcomes. Each subject underwent 3 measurements at 3 different time points at baseline, 6 and 12 months after initiated treatment. We ...
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### Difference between longitudinal design and time series

What is/are the difference(s) between a longitudinal design and a time series?
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### Pre-post Logistic regression

A friend of mine approached me to help her to interpret her multinomial logistic regression model. They had measured people as 1 of 2 states at 2 time periods. So, each person can have 1 of 4 ...
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### R/Stata package for zero-truncated negative binomial GEE?

this is my first post. I'm truly grateful for this community. I am trying to analyze longitudinal count data that is zero-truncated (probability that response variable = 0 is 0), and the mean != ...
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### Panel data and analysis VS longitudinal data and analysis?

Wikipedia says In statistics and econometrics, the term panel data refers to multi-dimensional data frequently involving measurements over time. Panel data contain observations on multiple ...
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### Univariate clustering for longitudinal cohort

We have screening information on thousands of patients followed for several years. We also have their cancer outcomes, whether or not such cancers were identified by screening or were otherwise ...
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### Statistics for comparing curve fitting models

I would like to get some statistical advice. I have individuals from which longitudinal growth data are collected (their growth curves with yearly measurements). This growth data I’ve fitted by ...
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### Is there a statistical model for modelling variables that are measured in varying amounts and in different time points per individual?

I have been trying to model a dataset of variables where each individual is measured a different number of times, and at a different point in time. Most of my variables are counts, but some are not ...
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### longitudinal models for prevalence

I am trying to analyze some prevalence data and estimate the effect on prevalence of some health condition of an intervention that was introduced at some time point. The data concern a number of ...
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### Computing the probability of death in a medical longitudinal study

I have a longitudinal dataset of patients where the variables include the patients gender, age and defects the patient has during a certain time period, such as patient having diabetes, high blood, ...
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### How to test random effects in a multilevel model in R

I have been reading a good book called Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence by Judith Singer and John Willet. The book shows that by modeling in 2 levels, we can ...
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### How to predict the time gap between two conditions

Could anyone suggest the best method to predict the time gap between two events? For example, given that diabetics are at higher risk of developing hypertension, I would like to predict the time gap ...
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### Effect size comparison

I have two groups – at two different periods – some of the participants at the first period do not have data for the second period. I only have mean, SD and n for the two groups at each time point. I ...
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### Clustering Longitudinal Data with kml in R

I have data of probability values which vary over time: ...
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### Is there an R package for continuous time longitudinal binary responses?

The bild package appears to be an excellent package for serial binary responses. But it is for discrete time. I would like to specify a smooth function of time ...
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### Parametric segmented or piecewise regression with heteroscedastic errors

I am fitting longitudinal data with an increase in variance over time. The standard physiological model is a bi- or tri-linear model with variable breakpoints. The estimated parameters are used to ...
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### Repeated measures ANOVA

I have to apologise about my lack of experience but hopefully someone can clarify things for me. I am interested in looking at change in psychosocial functioning over time and compare it between ...
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### How many observations per subject are necessary to fit a random slope in a mixed model?

I am working on a project that collected data retrospectively on subjects. There are subjects with multiple points of follow up per person, anywhere from 1 to 3 measurements. The timing of such ...
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### Testing change in variance over 5 time points and regression to the mean

Good morning I searched regarding change in variance over time, but everything I saw was about relatively long time series. I have a series of 5 time points, equally spaced, but with different ...
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### Inverse probability weighted estimation of censored longitudinal data in R?

I'm looking for a package for estimating the mean of a longitudinal response under monotone dropout using a "state-of-the-art" method based on GEEs, in R (AIPW methods, doubly robust, whatever you ...
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### 50% missing data in longitudinal study for ANCOVA in SPSS

I have a longitudinal study over 4 time points with two groups, comparing an intervention and a control. I have at least 50 % missing data. the pattern of missingness is MCAR according to Littles ...
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### Cross-sectional analysis using longtitudinal data, what is the best method?

I have a small problem regarding the fact of doing a cross sectional analysis using a longitudinal data set. I have a set of countries (87 countries), with different observations measured in different ...
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### Can longitudinal and cross-sectional studies be “controlled”?

I have heard it said that longitudinal and cross-sectional studies are forms of observational studies. Why can't longitudinal and cross-sectional studies be considered "controlled studies"? By ...
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### What are the differences between “Mixed Effects Modelling” and “Latent Growth Modelling”?

I'm decently familiar with mixed effects models (MEM), but a colleague recently asked me how it compares to latent growth models (LGM). I did a bit of googling, and it seems that LGM is a variant of ...
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### Structural equation model with very small samples

I have two sets of a longitudinal data that I hypothesize to measure same latent construct.I am trying to test this hypothesis using Structural equation modelling technique. Basically, I am trying to ...
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### SVM regression with longitudinal data

I have about 500 variables per patient, each variable has one continous value and is measured at three different time points (after 2 month and after 1 year). With the regression I would like to ...