diverging results with and without random effects
Dear Thierry and all,
Thanks for your continued help here. I am not versed with Bayesian
analyses.
Below is the code I currently use. The priors are basically due to
trial and error until I got expected/reasonable results.
Therefor I would be grateful for some comments on the
(in-)appropriateness of my (quite extreme) parameters.
As cov.prior I used
invwishart(df = 50, scale = diag(0.5, 1))
Thanks in advance!
Regards,
Andreas
PS: The code/results
library("blme")
dat %>%
bglmer(group ~ riskfactor + fu + riskfactor:fu + (1|patient),
family = "binomial",
data = .,
cov.prior = invwishart(df = 50, scale = diag(0.5, 1)),
fixef.prior = normal(cov = diag(9,4))) %>%
summary
## ,----
## | Cov prior : patient ~ invwishart(df = 50, scale = 0.5,
## | posterior.scale = cov, common.scale = TRUE)
## | Fixef prior: normal(sd = c(3, 3, ...), corr = c(0 ...),
## | common.scale = FALSE)
## | Prior dev : 6.2087
## |
## | Generalized linear mixed model fit by maximum likelihood (Laplace
## | Approximation) [bglmerMod]
## | Family: binomial ( logit )
## | Formula: group ~ riskfactor + fu + riskfactor:fu + (1 | patient)
## | Data: .
## |
## | AIC BIC logLik deviance df.resid
## | 540.0 560.8 -265.0 530.0 470
## |
## | Scaled residuals:
## | Min 1Q Median 3Q Max
## | -2.4984 -0.8512 0.3979 0.5038 1.6228
## |
## | Random effects:
## | Groups Name Variance Std.Dev.
## | patient (Intercept) 0.009725 0.09862
## | Number of obs: 475, groups: patient, 265
## |
## | Fixed effects:
## | Estimate Std. Error z value Pr(>|z|)
## | (Intercept) 1.3679 0.2355 5.810 6.26e-09 ***
## | riskfactornorisk -1.6776 0.2868 -5.850 4.91e-09 ***
## | fuFU 0.4718 0.3738 1.262 0.2069
## | riskfactornorisk:fuFU -1.1375 0.4539 -2.506 0.0122 *
## | ---
## | Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
## |
## | Correlation of Fixed Effects:
## | (Intr) rskfct fuFU
## | rskfctrnrsk -0.816
## | fuFU -0.617 0.502
## | rskfctrn:FU 0.503 -0.618 -0.817
## `----
On 26/11/18 17:05, Thierry Onkelinx wrote:
Dear Andreas, You'll need a very informative prior for the random intercept variance in order to keep the random intercepts reasonable small. Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be <mailto:thierry.onkelinx at inbo.be> Havenlaan 88 bus 73, 1000 Brussel www.inbo.be <http://www.inbo.be> /////////////////////////////////////////////////////////////////////////////////////////// To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey /////////////////////////////////////////////////////////////////////////////////////////// <https://www.inbo.be> Op ma 26 nov. 2018 om 17:00 schreef Leha, Andreas <andreas.leha at med.uni-goettingen.de <mailto:andreas.leha at med.uni-goettingen.de>>: Dear Thierry, thanks for looking into this! So, one solution would be a baysian analysis, right? Would you have a recommendation for me? I followed [1] and used ? library("blme") ? dat %>% ? ? bglmer(group ~ riskfactor + fu + riskfactor:fu + (1|patient), ? ? ? ? ? ?family = "binomial", ? ? ? ? ? ?data = ., ? ? ? ? ? ?fixef.prior = normal(cov = diag(9,4))) %>% ? ? summary Which runs and gives the following fixed effect estimates: ? Fixed effects: ? ? ? ? ? ? ? ? ? ? ? ? Estimate Std. Error z value Pr(>|z|) ? (Intercept)? ? ? ? ? ? ?8.2598? ? ?0.7445? 11.094? ?<2e-16 *** ? riskfactornorisk? ? ? -16.0942? ? ?1.3085 -12.300? ?<2e-16 *** ? fuFU? ? ? ? ? ? ? ? ? ? 1.0019? ? ?1.0047? ?0.997? ? 0.319 ? riskfactornorisk:fuFU? -1.8675? ? ?1.2365? -1.510? ? 0.131 These still do not seem reasonable. Thanks in advance! Regards, Andreas [1] https://stats.stackexchange.com/questions/132677/binomial-glmm-with-a-categorical-variable-with-full-successes/132678#132678 On 26/11/18 16:36, Thierry Onkelinx wrote:
> Dear Andreas,
>
> This is due to quasi complete separatation. This occurs when all
> responses for a specific combination of levels are always TRUE or
FALSE.
> In your case, you have only two observations per patient. Hence adding
> the patient as random effect, guarantees quasi complete separation
issues.??
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
> AND FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx at inbo.be <mailto:thierry.onkelinx at inbo.be>
<mailto:thierry.onkelinx at inbo.be <mailto:thierry.onkelinx at inbo.be>>
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be <http://www.inbo.be> <http://www.inbo.be>
>
>
///////////////////////////////////////////////////////////////////////////////////////////
> To call in the statistician after the experiment is done may be no
more
> than asking him to perform a post-mortem examination: he may be
able to
> say what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer
does not
> ensure that a reasonable answer can be extracted from a given body of
> data. ~ John Tukey
>
///////////////////////////////////////////////////////////////////////////////////////////
>
> <https://www.inbo.be>
>
>
> Op ma 26 nov. 2018 om 13:48 schreef Leha, Andreas
> <andreas.leha at med.uni-goettingen.de
<mailto:andreas.leha at med.uni-goettingen.de>
> <mailto:andreas.leha at med.uni-goettingen.de
<mailto:andreas.leha at med.uni-goettingen.de>>>:
>
>? ? ?Hi all,
>
>? ? ?sent the wrong code (w/o filtering for BL).? If you want to
look at the
>? ? ?data, please use this code:
>
>? ? ?---------- cut here --------------------------------------------
>? ? ?library("dplyr")
>? ? ?library("lme4")
>? ? ?library("lmerTest")
>? ? ?## install_github("hrbrmstr/pastebin", upgrade_dependencies =
FALSE)
>? ? ?library("pastebin")
>
>? ? ?## ---------------------------------- ##
>? ? ?## load the data? ? ? ? ? ? ? ? ? ? ? ##
>? ? ?## ---------------------------------- ##
>? ? ?dat <- pastebin::get_paste("Xgwgtb7j") %>% as.character %>%
gsub("\r\n",
>? ? ?"", .) %>% parse(text = .) %>% eval
>
>
>
>? ? ?## ---------------------------------- ##
>? ? ?## have a look? ? ? ? ? ? ? ? ? ? ? ? ##
>? ? ?## ---------------------------------- ##
>? ? ?dat
>? ? ?## ,----
>? ? ?## | # A tibble: 475 x 4
>? ? ?## |? ? patient group fu? ? riskfactor
>? ? ?## |? ? <fct>? ?<fct> <fct> <fct>
>? ? ?## |? 1 p001? ? wt? ? BL? ? norisk
>? ? ?## |? 2 p002? ? wt? ? BL? ? norisk
>? ? ?## |? 3 p003? ? wt? ? BL? ? norisk
>? ? ?## |? 4 p004? ? wt? ? BL? ? norisk
>? ? ?## |? 5 p005? ? wt? ? BL? ? norisk
>? ? ?## |? 6 p006? ? wt? ? BL? ? norisk
>? ? ?## |? 7 p007? ? wt? ? BL? ? norisk
>? ? ?## |? 8 p008? ? wt? ? BL? ? norisk
>? ? ?## |? 9 p009? ? wt? ? BL? ? risk
>? ? ?## | 10 p010? ? wt? ? BL? ? norisk
>? ? ?## | # ... with 465 more rows
>? ? ?## `----
>? ? ?dat %>% str
>? ? ?## ,----
>? ? ?## | Classes ?tbl_df?, ?tbl? and 'data.frame':? 475 obs. of? 4
>? ? ?variables:
>? ? ?## |? $ patient? ?: Factor w/ 265 levels "p001","p002",..: 1 2
3 4 5 6 7
>? ? ?8 9 10 ...
>? ? ?## |? $ group? ? ?: Factor w/ 2 levels "wt","mut": 1 1 1 1 1 1
1 1 1
>? ? ?1 ...
>? ? ?## |? $ fu? ? ? ? : Factor w/ 2 levels "BL","FU": 1 1 1 1 1 1
1 1 1
>? ? ?1 ...
>? ? ?## |? $ riskfactor: Factor w/ 2 levels "risk","norisk": 2 2 2
2 2 2 2 2
>? ? ?1 2 ...
>? ? ?## `----
>
>? ? ?## there are 265 patients
>? ? ?## in 2 groups: "wt" and "mut"
>? ? ?## with a dichotomous risk factor ("risk" and "norisk")
>? ? ?## measured at two time points ("BL" and "FU")
>
>? ? ?dat %>% summary
>? ? ?## ,----
>? ? ?## |? ? ?patient? ? group? ? ? fu? ? ? ?riskfactor
>? ? ?## |? p001? ?:? 2? ?wt :209? ?BL:258? ?risk? :205
>? ? ?## |? p002? ?:? 2? ?mut:266? ?FU:217? ?norisk:270
>? ? ?## |? p003? ?:? 2
>? ? ?## |? p004? ?:? 2
>? ? ?## |? p005? ?:? 2
>? ? ?## |? p006? ?:? 2
>? ? ?## |? (Other):463
>? ? ?## `----
>
>? ? ?## group sizes seem fine
>
>
>
>? ? ?## ---------------------------------------------- ##
>? ? ?## first, we look at the first time point, the BL ##
>? ? ?## ---------------------------------------------- ##
>
>? ? ?## we build a cross table
>? ? ?tab_bl <-
>? ? ?? dat %>%
>? ? ?? dplyr::filter(fu == "BL") %>%
>? ? ?? dplyr::select(group, riskfactor) %>%
>? ? ?? table
>? ? ?tab_bl
>? ? ?## ,----
>? ? ?## |? ? ? riskfactor
>? ? ?## | group risk norisk
>? ? ?## |? ?wt? ? 22? ? ?86
>? ? ?## |? ?mut? ?87? ? ?63
>? ? ?## `----
>
>? ? ?## and we test using fisher:
>? ? ?tab_bl %>% fisher.test
>? ? ?## ,----
>? ? ?## |? ? Fisher's Exact Test for Count Data
>? ? ?## |
>? ? ?## | data:? .
>? ? ?## | p-value = 1.18e-09
>? ? ?## | alternative hypothesis: true odds ratio is not equal to 1
>? ? ?## | 95 percent confidence interval:
>? ? ?## |? 0.09986548 0.33817966
>? ? ?## | sample estimates:
>? ? ?## | odds ratio
>? ? ?## |? 0.1865377
>? ? ?## `----
>? ? ?log(0.187)
>? ? ?## ,----
>? ? ?## | [1] -1.676647
>? ? ?## `----
>
>? ? ?## so, we get a highly significant association of the riskfactor
>? ? ?## and the group with an log(odds ratio) of -1.7
>
>? ? ?## we get the same result using logistic regression:
>? ? ?dat %>%
>? ? ?? filter(fu == "BL") %>%
>? ? ?? glm(group ~ riskfactor, family = "binomial", data = .) %>%
>? ? ?? summary
>? ? ?## ,----
>? ? ?## | Call:
>? ? ?## | glm(formula = group ~ riskfactor, family = "binomial",
data = .)
>? ? ?## |
>? ? ?## | Deviance Residuals:
>? ? ?## |? ? ?Min? ? ? ?1Q? ?Median? ? ? ?3Q? ? ? Max
>? ? ?## | -1.7890? -1.0484? ?0.6715? ?0.6715? ?1.3121
>? ? ?## |
>? ? ?## | Coefficients:
>? ? ?## |? ? ? ? ? ? ? ? ? Estimate Std. Error z value Pr(>|z|)
>? ? ?## | (Intercept)? ? ? ? 1.3749? ? ?0.2386? ?5.761 8.35e-09 ***
>? ? ?## | riskfactornorisk? -1.6861? ? ?0.2906? -5.802 6.55e-09 ***
>? ? ?## | ---
>? ? ?## | Signif. codes:? 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1
? ? 1
>? ? ?## |
>? ? ?## | (Dispersion parameter for binomial family taken to be 1)
>? ? ?## |
>? ? ?## |? ? ?Null deviance: 350.80? on 257? degrees of freedom
>? ? ?## | Residual deviance: 312.63? on 256? degrees of freedom
>? ? ?## | AIC: 316.63
>? ? ?## |
>? ? ?## | Number of Fisher Scoring iterations: 4
>? ? ?## `----
>
>
>
>? ? ?## ------------------------------------------------- ##
>? ? ?## Now, we analyse both time points with interaction ##
>? ? ?## ------------------------------------------------- ##
>
>? ? ?dat %>%
>? ? ?? glmer(group ~ riskfactor + fu + riskfactor:fu + (1|patient),
family =
>? ? ?"binomial", data = .) %>%
>? ? ?? summary
>? ? ?## ,----
>? ? ?## | Generalized linear mixed model fit by maximum likelihood
(Laplace
>? ? ?## |? ?Approximation) [glmerMod]
>? ? ?## |? Family: binomial? ( logit )
>? ? ?## | Formula: group ~ riskfactor + fu + riskfactor:fu + (1 |
patient)
>? ? ?## |? ? Data: .
>? ? ?## |
>? ? ?## |? ? ? AIC? ? ? BIC? ?logLik deviance df.resid
>? ? ?## |? ? 345.2? ? 366.0? ?-167.6? ? 335.2? ? ? 470
>? ? ?## |
>? ? ?## | Scaled residuals:
>? ? ?## |? ? ? ?Min? ? ? ? 1Q? ? Median? ? ? ? 3Q? ? ? ?Max
>? ? ?## | -0.095863 -0.058669? 0.002278? 0.002866? 0.007324
>? ? ?## |
>? ? ?## | Random effects:
>? ? ?## |? Groups? Name? ? ? ? Variance Std.Dev.
>? ? ?## |? patient (Intercept) 1849? ? ?43
>? ? ?## | Number of obs: 475, groups:? patient, 265
>? ? ?## |
>? ? ?## | Fixed effects:
>? ? ?## |? ? ? ? ? ? ? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
>? ? ?## | (Intercept)? ? ? ? ? ? 11.6846? ? ?1.3736? ?8.507?
?<2e-16 ***
>? ? ?## | riskfactornorisk? ? ? ?-1.5919? ? ?1.4166? -1.124? ? 0.261
>? ? ?## | fuFU? ? ? ? ? ? ? ? ? ? 0.4596? ? ?1.9165? ?0.240? ? 0.810
>? ? ?## | riskfactornorisk:fuFU? -0.8183? ? ?2.1651? -0.378? ? 0.705
>? ? ?## | ---
>? ? ?## | Signif. codes:? 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1
? ? 1
>? ? ?## |
>? ? ?## | Correlation of Fixed Effects:
>? ? ?## |? ? ? ? ? ? ?(Intr) rskfct fuFU
>? ? ?## | rskfctrnrsk -0.746
>? ? ?## | fuFU? ? ? ? -0.513? 0.510
>? ? ?## | rskfctrn:FU? 0.478 -0.576 -0.908
>? ? ?## `----
>
>? ? ?## I get huge variation in the random effects
>? ? ?##
>? ? ?## And the risk factor at BL gets an estimated log(odds ratio)
of -1.6
>? ? ?## but one which is not significant
>? ? ?---------- cut here --------------------------------------------
>
>
>? ? ?On 26/11/18 12:10, Leha, Andreas wrote:
>? ? ?> Hi all,
>? ? ?>
>? ? ?> I am interested in assessing the association of a
(potential) risk
>? ? ?> factor to a (binary) grouping.
>? ? ?>
>? ? ?> I am having trouble with diverging results from modeling one
time
>? ? ?point
>? ? ?> (without random effect) and modeling two time points (with
random
>? ? ?effect).
>? ? ?>
>? ? ?> When analysing the first time point (base line, BL) only, I
get a
>? ? ?highly
>? ? ?> significant association.
>? ? ?> Now, I want to see, whether there is an interaction between
time and
>? ? ?> risk factor (the risk factor is not constant).? But when
analysing
>? ? ?both
>? ? ?> time points, the estimated effect at BL is estimated to be not
>? ? ?significant.
>? ? ?>
>? ? ?> Now my simplified questions are:
>? ? ?> (1) Is there an association at BL or not?
>? ? ?> (2) How should I analyse both time points with this data?
>? ? ?>
>? ? ?> The aim is to look for confounding with other factors.? But I'd
>? ? ?like to
>? ? ?> understand the simple models before moving on.
>? ? ?>
>? ? ?> Below you find a reproducible example and the detailed results.
>? ? ?>
>? ? ?> Any suggestions would be highly appreciated!
>? ? ?>
>? ? ?> Regards,
>? ? ?> Andreas
>? ? ?>
>? ? ?>
>? ? ?>
>? ? ?> PS: The code / results
>? ? ?>
>? ? ?> ---------- cut here --------------------------------------------
>? ? ?> library("dplyr")
>? ? ?> library("lme4")
>? ? ?> library("lmerTest")
>? ? ?> ## install_github("hrbrmstr/pastebin", upgrade_dependencies
= FALSE)
>? ? ?> library("pastebin")
>? ? ?>
>? ? ?> ## ---------------------------------- ##
>? ? ?> ## load the data? ? ? ? ? ? ? ? ? ? ? ##
>? ? ?> ## ---------------------------------- ##
>? ? ?> dat <- pastebin::get_paste("Xgwgtb7j") %>%
>? ? ?>? ?as.character %>%
>? ? ?>? ?gsub("\r\n", "", .) %>%
>? ? ?>? ?parse(text = .) %>%
>? ? ?>? ?eval
>? ? ?>
>? ? ?>
>? ? ?>
>? ? ?> ## ---------------------------------- ##
>? ? ?> ## have a look? ? ? ? ? ? ? ? ? ? ? ? ##
>? ? ?> ## ---------------------------------- ##
>? ? ?> dat
>? ? ?> ## ,----
>? ? ?> ## | # A tibble: 475 x 4
>? ? ?> ## |? ? patient group fu? ? riskfactor
>? ? ?> ## |? ? <fct>? ?<fct> <fct> <fct>
>? ? ?> ## |? 1 p001? ? wt? ? BL? ? norisk
>? ? ?> ## |? 2 p002? ? wt? ? BL? ? norisk
>? ? ?> ## |? 3 p003? ? wt? ? BL? ? norisk
>? ? ?> ## |? 4 p004? ? wt? ? BL? ? norisk
>? ? ?> ## |? 5 p005? ? wt? ? BL? ? norisk
>? ? ?> ## |? 6 p006? ? wt? ? BL? ? norisk
>? ? ?> ## |? 7 p007? ? wt? ? BL? ? norisk
>? ? ?> ## |? 8 p008? ? wt? ? BL? ? norisk
>? ? ?> ## |? 9 p009? ? wt? ? BL? ? risk
>? ? ?> ## | 10 p010? ? wt? ? BL? ? norisk
>? ? ?> ## | # ... with 465 more rows
>? ? ?> ## `----
>? ? ?> dat %>% str
>? ? ?> ## ,----
>? ? ?> ## | Classes ?tbl_df?, ?tbl? and 'data.frame':? ? ? ? 475
obs. of?
>? ? ?4 variables:
>? ? ?> ## |? $ patient? ?: Factor w/ 265 levels "p001","p002",..: 1
2 3 4
>? ? ?5 6 7
>? ? ?> 8 9 10 ...
>? ? ?> ## |? $ group? ? ?: Factor w/ 2 levels "wt","mut": 1 1 1 1 1
1 1 1
>? ? ?1 1 ...
>? ? ?> ## |? $ fu? ? ? ? : Factor w/ 2 levels "BL","FU": 1 1 1 1 1
1 1 1
>? ? ?1 1 ...
>? ? ?> ## |? $ riskfactor: Factor w/ 2 levels "risk","norisk": 2 2
2 2 2
>? ? ?2 2 2
>? ? ?> 1 2 ...
>? ? ?> ## `----
>? ? ?>
>? ? ?> ## there are 265 patients
>? ? ?> ## in 2 groups: "wt" and "mut"
>? ? ?> ## with a dichotomous risk factor ("risk" and "norisk")
>? ? ?> ## measured at two time points ("BL" and "FU")
>? ? ?>
>? ? ?> dat %>% summary
>? ? ?> ## ,----
>? ? ?> ## |? ? ?patient? ? group? ? ? fu? ? ? ?riskfactor
>? ? ?> ## |? p001? ?:? 2? ?wt :209? ?BL:258? ?risk? :205
>? ? ?> ## |? p002? ?:? 2? ?mut:266? ?FU:217? ?norisk:270
>? ? ?> ## |? p003? ?:? 2
>? ? ?> ## |? p004? ?:? 2
>? ? ?> ## |? p005? ?:? 2
>? ? ?> ## |? p006? ?:? 2
>? ? ?> ## |? (Other):463
>? ? ?> ## `----
>? ? ?>
>? ? ?> ## group sizes seem fine
>? ? ?>
>? ? ?>
>? ? ?>
>? ? ?> ## ---------------------------------------------- ##
>? ? ?> ## first, we look at the first time point, the BL ##
>? ? ?> ## ---------------------------------------------- ##
>? ? ?>
>? ? ?> ## we build a cross table
>? ? ?> tab_bl <-
>? ? ?>? ?dat %>%
>? ? ?>? ?dplyr::select(group, riskfactor) %>%
>? ? ?>? ?table
>? ? ?> tab_bl
>? ? ?> ## ,----
>? ? ?> ## |? ? ? riskfactor
>? ? ?> ## | group risk norisk
>? ? ?> ## |? ?wt? ? 35? ? 174
>? ? ?> ## |? ?mut? 170? ? ?96
>? ? ?> ## `----
>? ? ?>
>? ? ?> ## and we test using fisher:
>? ? ?> tab_bl %>% fisher.test
>? ? ?> ## ,----
>? ? ?> ## |? ? Fisher's Exact Test for Count Data
>? ? ?> ## |
>? ? ?> ## | data:? .
>? ? ?> ## | p-value < 2.2e-16
>? ? ?> ## | alternative hypothesis: true odds ratio is not equal to 1
>? ? ?> ## | 95 percent confidence interval:
>? ? ?> ## |? 0.07099792 0.18002325
>? ? ?> ## | sample estimates:
>? ? ?> ## | odds ratio
>? ? ?> ## |? 0.1141677
>? ? ?> ## `----
>? ? ?> log(0.114)
>? ? ?> ## ,----
>? ? ?> ## | [1] -2.171557
>? ? ?> ## `----
>? ? ?>
>? ? ?> ## so, we get a highly significant association of the riskfactor
>? ? ?> ## and the group with an log(odds ratio) of -2.2
>? ? ?>
>? ? ?> ## we get the same result using logistic regression:
>? ? ?> dat %>%
>? ? ?>? ?glm(group ~ riskfactor, family = "binomial", data = .) %>%
>? ? ?>? ?summary
>? ? ?> ## ,----
>? ? ?> ## |
>? ? ?> ## | Call:
>? ? ?> ## | glm(formula = group ~ riskfactor, family = "binomial",
data = .)
>? ? ?> ## |
>? ? ?> ## | Deviance Residuals:
>? ? ?> ## |? ? ?Min? ? ? ?1Q? ?Median? ? ? ?3Q? ? ? Max
>? ? ?> ## | -1.8802? -0.9374? ?0.6119? ?0.6119? ?1.4381
>? ? ?> ## |
>? ? ?> ## | Coefficients:
>? ? ?> ## |? ? ? ? ? ? ? ? ? Estimate Std. Error z value Pr(>|z|)
>? ? ?> ## | (Intercept)? ? ? ? 1.5805? ? ?0.1856? ?8.515? ?<2e-16 ***
>? ? ?> ## | riskfactornorisk? -2.1752? ? ?0.2250? -9.668? ?<2e-16 ***
>? ? ?> ## | ---
>? ? ?> ## | Signif. codes:? 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.?
0.1 ? ? 1
>? ? ?> ## |
>? ? ?> ## | (Dispersion parameter for binomial family taken to be 1)
>? ? ?> ## |
>? ? ?> ## |? ? ?Null deviance: 651.63? on 474? degrees of freedom
>? ? ?> ## | Residual deviance: 538.83? on 473? degrees of freedom
>? ? ?> ## | AIC: 542.83
>? ? ?> ## |
>? ? ?> ## | Number of Fisher Scoring iterations: 4
>? ? ?> ## `----
>? ? ?>
>? ? ?>
>? ? ?>
>? ? ?> ## ------------------------------------------------- ##
>? ? ?> ## Now, we analyse both time points with interaction ##
>? ? ?> ## ------------------------------------------------- ##
>? ? ?>
>? ? ?> dat %>%
>? ? ?>? ?glmer(group ~ riskfactor + fu + riskfactor:fu + (1|patient),
>? ? ?family =
>? ? ?> "binomial", data = .) %>%
>? ? ?>? ?summary
>? ? ?> ## ,----
>? ? ?> ## | Generalized linear mixed model fit by maximum
likelihood (Laplace
>? ? ?> ## |? ?Approximation) [glmerMod]
>? ? ?> ## |? Family: binomial? ( logit )
>? ? ?> ## | Formula: group ~ riskfactor + fu + riskfactor:fu + (1 |
patient)
>? ? ?> ## |? ? Data: .
>? ? ?> ## |
>? ? ?> ## |? ? ? AIC? ? ? BIC? ?logLik deviance df.resid
>? ? ?> ## |? ? 345.2? ? 366.0? ?-167.6? ? 335.2? ? ? 470
>? ? ?> ## |
>? ? ?> ## | Scaled residuals:
>? ? ?> ## |? ? ? ?Min? ? ? ? 1Q? ? Median? ? ? ? 3Q? ? ? ?Max
>? ? ?> ## | -0.095863 -0.058669? 0.002278? 0.002866? 0.007324
>? ? ?> ## |
>? ? ?> ## | Random effects:
>? ? ?> ## |? Groups? Name? ? ? ? Variance Std.Dev.
>? ? ?> ## |? patient (Intercept) 1849? ? ?43
>? ? ?> ## | Number of obs: 475, groups:? patient, 265
>? ? ?> ## |
>? ? ?> ## | Fixed effects:
>? ? ?> ## |? ? ? ? ? ? ? ? ? ? ? ?Estimate Std. Error z value Pr(>|z|)
>? ? ?> ## | (Intercept)? ? ? ? ? ? 11.6846? ? ?1.3736? ?8.507?
?<2e-16 ***
>? ? ?> ## | riskfactornorisk? ? ? ?-1.5919? ? ?1.4166? -1.124? ? 0.261
>? ? ?> ## | fuFU? ? ? ? ? ? ? ? ? ? 0.4596? ? ?1.9165? ?0.240? ? 0.810
>? ? ?> ## | riskfactornorisk:fuFU? -0.8183? ? ?2.1651? -0.378? ? 0.705
>? ? ?> ## | ---
>? ? ?> ## | Signif. codes:? 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.?
0.1 ? ? 1
>? ? ?> ## |
>? ? ?> ## | Correlation of Fixed Effects:
>? ? ?> ## |? ? ? ? ? ? ?(Intr) rskfct fuFU
>? ? ?> ## | rskfctrnrsk -0.746
>? ? ?> ## | fuFU? ? ? ? -0.513? 0.510
>? ? ?> ## | rskfctrn:FU? 0.478 -0.576 -0.908
>? ? ?> ## `----
>? ? ?>
>? ? ?> ## I get huge variation in the random effects
>? ? ?> ##
>? ? ?> ## And the risk factor at BL gets an estimated log(odds
ratio) of -1.6
>? ? ?> ## but one which is not significant
>? ? ?> ---------- cut here --------------------------------------------
>? ? ?> _______________________________________________
>? ? ?> R-sig-mixed-models at r-project.org
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>? ? ?> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models >? ? ?> > >? ? ?-- >? ? ?Dr. Andreas Leha >? ? ?Head of the 'Core Facility >? ? ?Medical Biometry and Statistical Bioinformatics' > >? ? ?UNIVERSITY MEDICAL CENTER G?TTINGEN >? ? ?GEORG-AUGUST-UNIVERSIT?T >? ? ?Department of Medical Statistics >? ? ?Humboldtallee 32 >? ? ?37073 G?ttingen >? ? ?Mailing Address: 37099 G?ttingen, Germany >? ? ?Fax: +49 (0) 551 39-4995 >? ? ?Tel: +49 (0) 551 39-4987 >? ? ?http://www.ams.med.uni-goettingen.de/service-de.shtml >? ? ?_______________________________________________ >? ? ?R-sig-mixed-models at r-project.org
<mailto:R-sig-mixed-models at r-project.org>
>? ? ?<mailto:R-sig-mixed-models at r-project.org
<mailto:R-sig-mixed-models at r-project.org>> mailing list
--
Dr. Andreas Leha
Head of the 'Core Facility
Medical Biometry and Statistical Bioinformatics'
UNIVERSITY MEDICAL CENTER G?TTINGEN
GEORG-AUGUST-UNIVERSIT?T
Department of Medical Statistics
Humboldtallee 32
37073 G?ttingen
Mailing Address: 37099 G?ttingen, Germany
Fax: +49 (0) 551 39-4995
Tel: +49 (0) 551 39-4987
http://www.ams.med.uni-goettingen.de/service-de.shtml
Dr. Andreas Leha Head of the 'Core Facility Medical Biometry and Statistical Bioinformatics' UNIVERSITY MEDICAL CENTER G?TTINGEN GEORG-AUGUST-UNIVERSIT?T Department of Medical Statistics Humboldtallee 32 37073 G?ttingen Mailing Address: 37099 G?ttingen, Germany Fax: +49 (0) 551 39-4995 Tel: +49 (0) 551 39-4987 http://www.ams.med.uni-goettingen.de/service-de.shtml