[R-sig-ME] Mixed model and repeated measures in R

DESPINA MICHAILIDOU de@m|ch@|||dou @end|ng |rom gm@||@com
Mon Jun 17 14:05:52 CEST 2019 

Thank you so much for your reply. Appreciate it.
Despina

Sent from my iPhone

> On Jun 17, 2019, at 6:10 AM, Robert Long <longrob604 using gmail.com> wrote:
> 
> 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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>> 
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