[R-sig-ME] Mixed model and repeated measures in R
Robert Long
|ongrob604 @end|ng |rom gm@||@com
Mon Jun 17 12:10:31 CEST 2019
Hi Despina
According to your output, you have 270 observations in total, and 270
unique combinations of side, scan date and id. So there is a problem
straight away.
Side appears to have 2 levels : left and right, so I do not see much
justification for treating is as random, so as a first step I would reduce
the random structure to (1 |
ID/SCAN_DATE) and include side as a fixed effect.
Regards
Rob
On Sun, Jun 16, 2019 at 8:13 PM DESPINA MICHAILIDOU <
de.michailidou using gmail.com> wrote:
> Hi All,
>
> I am trying to run regression analysis adjusted for repeated measures in R.
> The imaging pathology finding defined as Vert_effect, CA_effect,
> Vert_Intens etc is the outcome variable whereas the clinical symptom
> defined as Comb_PH_tod, Comb_PNP_tod etc, is the predictor variable. Other
> predictor variables that I am using are the daily prednisone use (Pred) and
> the use of immunosuppresive therapy (Immune_Categorical) or not. As the
> prednisone variable is being read as character in R i converted it to
> numeric because it is a number. For example some patients are getting 2 mg
> of prednisone but some others 40 mg. I have two subset of diagnoses, the
> one is TAK and the second one is GCA. As some patients have either right
> side posterior headache or left side posterior headache or both or none and
> either right side vertebral intensity (imaging study pathology) or left
> side vertebral intensity, or both or none vertebral intensity, for each
> patient I created two rows per subject. The first row represents the right
> sided symptoms and imaging pathology findings and the second row represents
> the left sided symptoms and imaging findings. My repeated measures are the
> side of the symptoms and imaging findings (had to create a separate
> variable for the right and left side symptoms and right and left imaging
> findings that I called it Side and put in there R, L, R, L etc), the ID of
> the patients and the Scan_date visit. Some patients had one scan visit but
> some other patients had multiple scan visits, and that is why I am
> considering scan visit date as a repeated measure. *So this is the code
> that i am using and for the subset of TAK i get this output*
>
> > TAK_data <- subset(Despina, Diagnosis=="TAK")
> > glmm_Vert_Intes <- glmer (Vert_Intes ~ Comb_PH_tod + (1 |
> ID/SCAN_DATE/Side) + Pred + Immune_Catagorical , data=TAK_data,
> family=binomial(link = "logit"))
> Error in length(value <- as.numeric(value)) == 1L :
> (maxstephalfit) PIRLS step-halvings failed to reduce deviance in
> pwrssUpdate
> > summary(glmm_Vert_Intes)
>
> *whereas for the subset of GCA patients there are no issues.*
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: Vert_Intes ~ Comb_PH_tod + (1 | ID/SCAN_DATE/Side) + Pred +
> Immune_Catagorical
> Data: GCA_data
>
> AIC BIC logLik deviance df.resid
> 87.8 113.0 -36.9 73.8 263
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -0.98336 -0.00342 -0.00241 -0.00214 1.02148
>
> Random effects:
> Groups Name Variance Std.Dev.
> Side:(SCAN_DATE:ID) (Intercept) 0.00 0.00
> SCAN_DATE:ID (Intercept) 528.06 22.98
> ID (Intercept) 18.84 4.34
> Number of obs: 270, groups: Side:(SCAN_DATE:ID), 270; SCAN_DATE:ID, 135;
> ID, 54
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -10.63649 2.36017 -4.507 6.59e-06 ***
> Comb_PH_tod -8.53678 4.56601 -1.870 0.0615 .
> Pred -0.02353 0.09323 -0.252 0.8008
> Immune_Catagorical -1.41376 2.69420 -0.525 0.5998
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
> (Intr) Cm_PH_ Pred
> Comb_PH_tod 0.299
> Pred -0.289 -0.001
> Immn_Ctgrcl -0.520 -0.068 0.041
> convergence code: 0
> boundary (singular) fit: see ?isSingular
>
> *And this is the detailed code that I am using in R*
> install.packages("lme4")
> install.packages("readr")
>
> library(readr)
> library("lme4")
>
> setwd("~/Desktop/Despina")
> Despina <- read_csv("Despina.csv")
> as.factor(Despina$ID)
> as.factor(Despina$Diagnosis)
> as.factor(Despina$SCAN_DATE)
> as.factor(Despina$Side)
> as.factor(Despina$Immune_Catagorical)
> as.factor(Despina$LH_today)
> as.factor(Despina$PLH_today)
> as.factor(Despina$Dizz_today)
> as.factor(Despina$P_Diz_today)
> as.factor(Despina$CD_tod)
> as.factor(Despina$Head_today)
> as.factor(Despina$Vertig_today)
> as.factor(Despina$FTH_tod)
> as.factor(Despina$Comb_PH_tod)
> as.factor(Despina$Comb_NP_tod)
> as.factor(Despina$Comb_ANP_tod)
> as.factor(Despina$Comb_PNP_tod)
> as.factor(Despina$CNS_ever)
> as.factor(Despina$ULC_today)
> as.factor(Despina$Vert_effect)
> as.factor(Despina$CA_effect)
> as.factor(Despina$Sub_invol)
> as.factor(Despina$Ax_involv)
> as.factor(Despina$CA_intens)
> as.factor(Despina$Sub_intens)
> as.factor(Despina$Vert_Intes)
> as.factor(Despina$Ax_intens)
> as.factor(Despina$Comb_Vis_L_today)
> Despina$Pred<-as.numeric(as.character(Despina$Pred))
>
>
> TAK_data <- subset(Despina, Diagnosis=="TAK")
>
> glmm_Vert_Intes <- glmer (Vert_Intes ~ Comb_PH_tod + (1 |
> ID/SCAN_DATE/Side) + Pred + Immune_Catagorical , data=TAK_data,
> family=binomial(link = "logit"))
> summary(glmm_Vert_Intes)
>
> GCA_data <- subset(Despina, Diagnosis=="GCA")
>
> glmm_Vert_Intes <- glmer (Vert_Intes ~ Comb_PH_tod + (1 |
> ID/SCAN_DATE/Side) + Pred + Immune_Catagorical , data=GCA_data,
> family=binomial(link = "logit"))
> summary(glmm_Vert_Intes)
>
> *So my question is why i am getting this error in TAK patients and not in
> GCA patients?*
> Error in length(value <- as.numeric(value)) == 1L :
> (maxstephalfit) PIRLS step-halvings failed to reduce deviance in
> pwrssUpdate
>
> Thank you all for your time and consideration in advance.
>
> Sincerely,
> Despina
>
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