Blog Archives

Zero-Inflated Poisson and Negative Binomial Models with GLMMadaptive

August 27, 2018
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Clustered/Grouped Count DataOften cluster/grouped count data exhibit extra zeros and over-dispersion. To account for these features, Poisson and negative binomial mixed effects models with an extra zero-inflation part are used. These models entail a logistic regression model for the extra zeros, and a Poisson or negative binomial model for the remaining zeros and the positive counts. In both models,...

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Mixed Models with Adaptive Gaussian Quadrature

June 14, 2018
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Mixed Models with Adaptive Gaussian Quadrature

OverviewIn this post, I would like to introduce my new R package GLMMadaptive for fitting mixed-effects models for non-Gaussian grouped/clustered outcomes using marginal maximum likelihood.Admittedly, there is a number of packages available for fitting...

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Multivariate Joint Models for Multiple Longitudinal Outcomes and a Time-to-Event

October 24, 2016
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A new, development version, of package JMbayes has been rolled-out on the dedicated GitHub repo. The major addition in this version is a set of new functions that can fit multivariate joint models for multiple longitudinal outcomes and a time-to-event. As with other GitHub packages, it can be easily installed using the install_github() function from package devtools. The new...

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An Integrated Shiny App for a Course on Repeated Measurements Analysis (completed)

March 18, 2016
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An Integrated Shiny App for a Course on Repeated Measurements Analysis (completed)

Repeated Measurements AnalysisRepeated measurements analysis, and in particular longitudinal data analysis, is one of the two most frequently used types of analysis in my field (Biostatistics) - the other being survival analysis. Starting from this yea...

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An Integrated Shiny App for a Course on Repeated Measurements Analysis (completed)

March 18, 2016
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An Integrated Shiny App for a Course on Repeated Measurements Analysis (completed)

Repeated Measurements AnalysisRepeated measurements analysis, and in particular longitudinal data analysis, is one of the two most frequently used types of analysis in my field (Biostatistics) - the other being survival analysis. Starting from this yea...

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Dynamic Predictions using Joint Models

March 4, 2016
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Dynamic Predictions using Joint Models

What are Dynamic PredictionsIn this post we will explain the concept of dynamic predictions and illustrate how these can be computed using the framework of joint models for longitudinal and survival data, and the R package JMbayes. The type of dynamic ...

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Dynamic Predictions using Joint Models

March 4, 2016
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Dynamic Predictions using Joint Models

What are Dynamic PredictionsIn this post we will explain the concept of dynamic predictions and illustrate how these can be computed using the framework of joint models for longitudinal and survival data, and the R package JMbayes. The type of dynamic predictions we will discuss here are calculated in follow-up studies in which some sample units (e.g., patients) who...

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An Integrated Shiny App for a Course on Repeated Measurements Analysis

October 16, 2015
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I will be teaching a course on statistical regression models for repeated measurements data, and I thought of creating a shiny app to let students run the code used in the course and examine the output in real time.The app is still in development mode ...

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An Integrated Shiny App for a Course on Repeated Measurements Analysis

October 16, 2015
By

I will be teaching a course on statistical regression models for repeated measurements data, and I thought of creating a shiny app to let students run the code used in the course and examine the output in real time.The app is still in development mode ...

Read more »

Two Presentations about Joint Models

August 9, 2015
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Packages JM and JMbayes @ JSM2015This year JSM features an interesting invited session about fitting joint models in different software packages -- if you're interested drop by... Here are my slides in which I give a short intro in packages JM and JMba...

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