[R-sig-ME] lme4 (V.1.1-35.5) Convergence Warnings & Model Reliability

[Vazquez Sanchez, Manuel]

Dear R-SIG-Mixed-Models Maling List Recipients,


I hope this email finds you well. My name is Manny, and I am a biostatistics analyst at NYU Langone Health in New York, NY. I am reaching out with a question regarding the functionality of the lme4 (Version 1.1-35.5) package, specifically its handling of model convergence in longitudinal data analyses.

One of the projects I am collaborating on involves analyzing longitudinal data from the Women's Interagency HIV Study (WIHS). Our goal is to explore the "syndemic" effects of individual-level exposures�such as depression, physical and sexual abuse, and drug use�on health outcomes like sleep quality and hypertension. The study cohort included 2,345 participants with each participant having 1-36  visits. We are using longitudinal, mixed-effects models to determine how the risk of hypertension status (binary outcome) varies with different combinations of these exposures while accounting for the repeated measurement over time as random effect.



For this analysis, we are using the glmer() function from the lme4 package. Example model:


glmer(HTN ~ AGEATBL + RACE + EDUCATION + MARRIED + INCOME + YearsFromBaseline + DRUGUSE + (1|ID), family = binomial(link = �logit�))



AGEATBL, RACE, EDUCATION, MARRIED, INCOME, and YearsFromBaseline  are confounders, and DRUGUSE is one of the syndemic factors. The random effects specified (1|ID) are to account for differences between individual participants.

In our analyses, we encounter a consistent issue: almost all of the models return convergence warnings of the form:


Warning message:



In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :



  Model failed to converge with max|grad| = 0.794027 (tol = 0.002, component 1)

To address this, we have tried the following strategies:

  *   Changing the optimizer and increasing the number of iterations, including using the built-in lme4 functions to identify the best optimizer.
  *   Reducing the dataset by limiting the number of participants to decrease the levels of random effects.
  *   Excluding participants with either too few or too many visits to regularize the time in study for the included participants.
  *
Employing confounder variable selection procedures for dimensionality reduction.


Despite these efforts, the warnings persist. While the models do not produce errors, the warnings raise concerns about the reliability of our results.  We also checked the documentation on lme4 regarding performance tips. Setting  `control=glmerControl(calc.derivs=FALSE)` eliminates the warnings, but we need to report CI's and standard deviations of fixed effects parameters for our paper.


I have two primary questions:

  1.  How reliable are the results when convergence warnings are present?
     *   Are the parameter estimates and standard errors likely to be biased or misleading?
  2.  If the results are not reliable, are there additional strategies you recommend to improve model convergence or performance?
     *
For instance, would penalized likelihood approaches or modifications to the random effects structure be viable options?



I appreciate the robust functionality of the lme4 package and its critical role in facilitating complex modeling. Any guidance you can provide on addressing these convergence issues would be immensely helpful for our work. Thank you for your time and invaluable contribution to the research community. I look forward to any and all insights.



Best regards,

Manny


Manuel R. Vazquez-Sanchez, MS
Asst Research Scientist
Department of Population Health
Division of Biostatistics
NYU Grossman School of Medicine
Manuel.VazquezSanchez using nyulangone.org
(917) 581 2034

NYU Langone Health
180 Madison Avenue, 2nd Floor #2-32A
New York, NY 10016

	[[alternative HTML version deleted]]