# 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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### Which machine learning method to use for geographic systems prediction?

I am trying to do experiments on geographic systems prediction. We're working on classifying the location where we sell product most. So, we need to analyze the hestorical data and predict the success ...
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### Comparing two populations over time (longitudinal population study)

I have two sets of data for men and women, measuring the turnover of the most popular points of one characteristic of the population, for each year since 1912. This is popular data from an insurance ...
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### What are differences between the terms “time series analysis” and “longitudinal data analysis”

When talking about longitudinal data, we may refer to data collected over time from the same subject / study unit repeatedly, thus there are correlations for the observations within the same subject, ...
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### Predicted values for fixed effect quantile regression

I'm currently working with the method proposed by Koenker (2004) and Lamarche(2010) on fixed effects for quantile regression, for this I'm using the RQPD code in R. I would like to get the predicted ...
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### Comparing groups using SPSS

I have a dataset with 2 measures that were tested over 30 time points. I have a two groups of people who have both measures at all 30 time points. How do I compare the groups using SPSS?
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### Linkage research using longitudial data

I have data from three companies that span 10 years. Two sets of variables (mean value of 300 answers pr. year pr. Company), that have the acceptable cronbach's alpha: a) the average employee ...
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### Repeatedly measured, longitudinal and ordinal data

Description of the problem: The effect of a certain treatment is tested as follows: For n subjects, the endpoint is measured three times before and after the treatment is administered, respectively. ...
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### Should the outcome variable be measured at least twice for a longitudinal study?

I am trying to find the association between BMI and onset age of a condition with linear regression model. I have multiple records of BMI measurement. But the outcome variable, onset age of condition, ...
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### How to assess whether IQ predicts emotional experiment more at time 1 or time 2 controlling for covariates?

I have the following data set of a two-wave longitudinal study in educational psychology. A group of 400 people did some questionnaire on IQ, personality and two different emotional states (sadness ...
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### Translog Returns to Scale Variation

I estimated the so called translog production function: $$y_{it} = \beta_0 + \beta_1 k_{it} + \beta_2 l_{it} + \beta_3 k_{it}^2 + \beta_4 l_{it} \times k_{it} + \beta_5 l_{it}^2 + \epsilon_{it},$$ ...
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### Longitudinal paired analysis

I need your advice regarding a complicated design. I am testing data regarding new eye drops that suppose to decrease some quantitative measure. For every subject, one eye is randomly assigned with ...
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### Correlation with change score and baseline

My question is similar to this question, but I was not allowed to comment on it, so I will ask again tailored to my specific problem: I computed a correlation between a measure of brain volume and ...
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### Does it make sense to impute missing covariate data when the imputed value is a function of other covariates in the regression model?

We are building a model that adjusts for standard covariates (e.g., age, gender) and for the outcome at baseline. It would be ideal to adjust for each subject's baseline value like so:  Y = ...
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### Test for measuring change when you have same primary sampling units but different respondents in two waves of longitudinal survey

Which test should I apply for measuring change in proportions and means when I have same primary sampling units but different respondents in two waves of longitudinal survey? Should I apply McNemar's ...
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### Intercept only logistic GEE model

I have repeated measures binomial data for several subjects across several experimental sessions. I am interested in testing whether or not across all experimental sessions the group proportion ...
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### Clustering on three levels: time, group, individual –- how to correctly specify the model in R?

I would like to run a lagged random effects regression. The data is from an experiment in which participants were assigned to groups of five and participated in an interactive game for 20 rounds. ...
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### How to properly analyse longitudinal data from experiments?

I am wondering how one should properly analyse longitudinal data from simple experiments (not observational studies). Imagine an experiment where subjects are first measured at baseline, randomised to ...
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### Predicting a single timepoint from longitudinal data

I'm working on a project and I haven't quite figured out the best way to analyze the data. Any help would be much appreciated. Design: Dependent variable: variable A measured at time 1 only. ...
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### Covariance pattern models versus generalized estimating equation models

Can somebody please explain the major differences between covariance pattern models (Hedeker and Gibbons, Chapter 6, 2006; Jennrich and Schluchter 1986) and generalized estimating equation models ...
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### multiple imputation for a longitudinal study

i have an experiment wherein respondents were tested in two time points. however, respondents were tested at t1 and t2 OR t1 and t3 OR t1 and t4. Hence, data is missing at t2,t3,and t4 for 3/4 of ...
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### Specifying the correlation structure of an unevely spaced time series in GEE with geepack

I have counts of plants from different sites over a number of years. In each census year, all sites were surveyed, but the gaps between census years vary (between 1 and 4 years between consecutive ...
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### How many clusters for linear mixed models and GEE?

I have a data set with repeated measurements on subjects. The total sample size is $n=118$ and the number of clusters (i.e. subjects) is $m=49$. The smallest cluster is of size 2 and the largest ...
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### Longitudinal data using functional principal components mixed effect models

I have collected speech perception performance in children during one-year followed-up (four time points). A battery of speech perception measures (i.e., five measures) were used at each test ...
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### ICC in a linear mixed model - longnitudial data - why is this the correlation between time periods?

I'm confused by the description of the intraclass correlation (ICC) for a linear mixed model with longitudinal data from this material. A screen capture is shown below. What I'm confused about is ...
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### Do autocorrelated residual patterns remain even in models with appropriate correlation structures, & how to select the best models?

Context This question uses R, but is about general statistical issues. I'm analysing the effects of mortality factors (% mortality due to disease and parasitism) on moth population growth rate over ...
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### Can repeated measures be done on non-normally distributed variables using the Kruskal-Wallis test?

What do you recommend? I planned to do repeated-measures ANOVAs on 3 sets of data collected at 3 different points in time (initial n=1,500). The first data set appears to be non-normally distributed, ...
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### Before-after measurement and ordinal longitudinal analysis

Two types of treatment were considered to relieve pain after surgery. Pain is measured as follows: first 30 min after first interpleural (IP) injection (hour zero) in recovery room, then every 4 ...
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### Standard error computation with interaction term

I aimed at studying the effects of an exposure (E) on fetal growth estimated by repeated ultrasound measurements (n = 2 measures per participant in the following example). I used interaction terms of ...
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### In a longitudinal study, should I impute the outcome Y, measured at time 2, for individuals who were lost to follow-up?

I have repeat measures at 2 times points in a sample of people. There are 18k people at time 1, and 13k at time 2 (5000 lost to follow-up). I want to regress a set of predictors X measured at time 1 ...
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### Dealing with nested factors and correlation structure in a GEE model

This question is of a previous one that has not been answered yet (see the details of our experiment and question here). We are now exploring a GEE (Generalized estimating equation) approach that ...
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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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### 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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### 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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### 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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### 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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### 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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### 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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### 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 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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### 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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### 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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### 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 ...