NQF1626 Final Report
E.T. Moseley
09/07/2018
Background
This is a report focusing on end of life care preferences and their implementation by the clinical care team. In particular, we will focus on the National Quality Forum Measure #1626:
Percentage of vulnerable adults admitted to ICU who survive at least 48 hours who have their care preferences documented within 48 hours OR documentation as to why this was not done.
For our purposeses:
Care preference documentation within 48hrs of admission, age
>74.
Care provider/Patient interactions from the MIMIC-III Intensive Care Unit Database are analyzed within the framework of a hospital admission utilizing a series of Mixed Effects Models. The patient is the random effect, so we can explain the difference in variance explained by patients compared to that explained by other effects (when the patient is introduced to other demographic and clinical variables) in the model.
The use of fixed and random effects is deliberate. A fixed effect is used when a categorical variable contains all levels of interest. A random effect is used when a categorical variable contains only a sample of all levels of interest, the samples which do not exist are unobserved variables and, since their distributions do not exist, they will be estimated. The levels of the random effect are assumed to be independent of each other; this assumption requires a priori knowledge of the phenomenon being modeled.
A mixed model is
\[\beta \sim \mathcal{N}(\mu, \sigma)\]
\[\mathbf{y} = \boldsymbol{X\beta} + \boldsymbol{Zu} + \boldsymbol{\varepsilon}\]
Statistics of Interest
- Intra Class Correlation
Calculated according to:
Wu S, Crespi CM, Wong WK. 2012. Comparison of methods for estimating the intraclass correlation coefficient for binary responses in cancer prevention cluster randomized trials. Contempory Clinical Trials 33: 869-880
- Standard Deviation of Random Effects
Calculated using:
Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48
- Marginal (variance explained by fixed effects) R^2
\[R_{GLMM(m)}² = \frac{(σ_f²)}{(σ_f² + σ_α² + σ_ε²)}\]
- Conditional (variance explained by the entire model) R^2
\[R_{GLMM(c)}² = \frac{(σ_f² + σ_α²)}{(σ_f² + σ_α² + σ_ε²)}\]
Calculated according to:
Nakagawa, S., Johnson, P.C.D., Schielzeth, H. (2017) The coefficient of determination R² and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14: 20170213.
Model 0 (No Covariates)
- Care Provider (Random)
Model 1a (Patient Demographics)
- Gender
- Age
- Ethnicity
- Language
- Marrital Status
- Care Provider (Random)
Model 1b (Clinical Variables)
- Sequential Organ Failure Assessment (SOFA)
- Care Provider (Random)
Model 2a (Demographics + ICU)
- Demographics (Model 1a)
- First Intensive Care Unit
- Care Provider (Random)
Model 2b (Clinical Variables + ICU)
- SOFA (Model 1b)
- First Intensive Care Unit
- Care Provider (Random)
Model 3 (Demographics + Clinical Variables + ICU)
We will use the Sequential Organ Failure Assessment (SOFA) from Illness Severity Scores (github) to determine the severity of illness at admission.
- Demographics (as in Model 1)
- First Intensive Care Unit
- SOFA Score (As Model 2)
- Care Provider (Random)
Attending check
attending_check <- function(dat){
## Temporary data frame
tmp_frame <- data.frame()
## Results frame
res <- data.frame()
## For each hospital admission
for (name in unique(dat$HADM_ID)){
## Subset admission
tmp_frame <- dat[dat$HADM_ID == name, ]
## If any care providers are "Attending"
if (any("Attending" %in% tmp_frame$CG_DESCRIPTION)){
## add hospital admission to results
res <- rbind(res, tmp_frame)
}
}
## Return control to outer level
return(res)
}Provider Caremeasure Check
provider_caremeasure_check <- function(dat){
## Create Caremeasure Variable
dat$NQF <- rep(0, each = nrow(dat))
## Temporary hospital admission data frame
tmp_frame <- data.frame()
## Temporary careprovider data frame
cg_frame <- data.frame()
## Results frame
res <- data.frame()
## For each hospital admission
for (name in unique(dat$HADM_ID)){
## Subset admission
tmp_frame <- dat[dat$HADM_ID == name, ]
## For each care provider
for (id in unique(tmp_frame$CGID)){
## Subset provider
cg_frame <- tmp_frame[tmp_frame$CGID == id, ]
## if care measure were implemented during the admission by the provider
if (any(cg_frame$CIM.machine == 1)){
## Apply credit for entire admission
cg_frame$NQF <- rep(1, each = nrow(cg_frame))
}
## Bind
res <- rbind(res, cg_frame)
}
}
## Return control to outer level
return(res)
}Team Caremeasure Check
team_caremeasure_check <- function(dat){
## Create Caremeasure Variable
dat$NQF <- rep(0, each = nrow(dat))
## Temporary hospital admission data frame
tmp_frame <- data.frame()
## Results frame
res <- data.frame()
## For each hospital admission
for (name in unique(dat$HADM_ID)){
## Subset admission
tmp_frame <- dat[dat$HADM_ID == name, ]
## If ANY care provider documented care preferences
if (any(tmp_frame$CIM.machine == 1)){
## Apply credit for entire admission
tmp_frame$NQF <- rep(1, each = nrow(tmp_frame))
}
## Bind
res <- rbind(res, tmp_frame)
}
## Return control to outer level
return(res)
}Expire Check
expire_check <- function(dat){
## Create Caremeasure Variable
dat$HOSP_DEATH <- rep(0, each = nrow(dat))
## Temporary data frame
tmp_frame <- data.frame()
## Results frame
res <- data.frame()
## For each hospital admission
for (name in unique(dat$HADM_ID)){
## Subset admission
tmp_frame <- dat[dat$HADM_ID == name, ]
## If any admission has a death
## Check if pt expired in hospital
if (any(tmp_frame$HOSPITAL_EXPIRE_FLAG == 1)){
tmp_frame$HOSP_DEATH <- rep(1, each = nrow(tmp_frame))
}
## add hospital admission to results
res <- rbind(res, tmp_frame)
}
## Return control to outer level
return(res)
}rocplot
rocplot <- function(pred, truth, ...) {
predob = prediction(pred, truth)
perf = performance(predob, "tpr", "fpr")
plot(perf, ...)
area <- auc(truth, pred)
area <- format(round(area, 4), nsmall = 4)
text(x=0.7, y=0.1, labels = paste("AUC =", area))
# the reference x=y line
segments(x0=0, y0=0, x1=1, y1=1, col="gray", lty=2)
}model_info
model_info <- function(fit){
#Summary info
model_sum <- summary(fit)
#Odds ratio, confidence interval
odds_ratio <- cbind(OR = exp(fit$coef), exp(confint(fit)))
#Create list for return
my_list <- list(model_sum, odds_ratio)
#names
names(my_list) <- c("Model Summary","OR Summary")
return(my_list)
}Data Loading & Cleansing
## Latest Dataset of NeuroNER Predictions
#dat <- read.csv("~/nqf_dat.csv", header = T, stringsAsFactors = F)
dat <- read.csv("~/nqf_caregivers/data/20180607_EOL_data_ICU.csv", header = T, stringsAsFactors = F)
dim(dat)## [1] 10250 57## Note: Notes had been logged by multiple Care providers, we will reintroduce those annotations
## Load Labeled Note Data for NQF Caremeasure Cohort (From NOTEEVENTS table)
tmp <- read.csv("~/nqf_caregivers/data/note_labels_over75.csv", header = T, stringsAsFactors = F)
dim(tmp)## [1] 11575 25## Keep only TEXT and ROW_ID from tmp
tmp <- tmp[ ,c("ROW_ID", "TEXT")]
## Inner join
dat <- merge(tmp, dat, by = "TEXT")
## Clean tmp
rm(tmp)
## Check column names
colnames(dat)## [1] "TEXT" "ROW_ID.x" "SUBJECT_ID"
## [4] "HADM_ID" "ROW_ID.y" "CHARTDATE"
## [7] "CHARTTIME" "STORETIME" "CATEGORY"
## [10] "DESCRIPTION" "CGID" "ISERROR"
## [13] "ADMITTIME" "DISCHTIME" "DEATHTIME"
## [16] "ADMISSION_TYPE" "ADMISSION_LOCATION" "DISCHARGE_LOCATION"
## [19] "INSURANCE" "LANGUAGE" "RELIGION"
## [22] "MARITAL_STATUS" "ETHNICITY" "EDREGTIME"
## [25] "EDOUTTIME" "DIAGNOSIS" "HOSPITAL_EXPIRE_FLAG"
## [28] "HAS_CHARTEVENTS_DATA" "GENDER" "DOB"
## [31] "DOD" "DOD_HOSP" "DOD_SSN"
## [34] "EXPIRE_FLAG" "ICUSTAY_ID" "DBSOURCE"
## [37] "FIRST_CAREUNIT" "LAST_CAREUNIT" "FIRST_WARDID"
## [40] "LAST_WARDID" "INTIME" "OUTTIME"
## [43] "LOS" "AGE" "ADMISSION_NUMBER"
## [46] "DAYS_UNTIL_DEATH" "TIME_SINCE_ADMIT" "CGID.1"
## [49] "HADM_ID.1" "FAM.machine" "CIM.machine"
## [52] "LIM.machine" "CAR.machine" "COD.machine"
## [55] "check.CGID" "check.dadm_id" "CIM.or.FAM"
## [58] "Died.in.Hospital"## What is HADM_ID.1?
head(table(dat$HADM_ID.1))##
## #N/A 100102 100153 100347 100391 100525
## 32 8 3 12 15 2## What is HADM_ID?
head(table(dat$HADM_ID))##
## 100102 100153 100347 100391 100525 100575
## 8 3 12 15 2 11## #N/A? Clean HADM_ID.1
dat$HADM_ID.1 <- NULL
## What is CGID.1?
head(table(dat$CGID.1))##
## #N/A 14010 14022 14037 14045 14056
## 32 22 1 93 37 14## What is CGID
head(table(dat$CGID))##
## 14010 14022 14037 14045 14056 14080
## 22 1 93 37 14 6## #N/A? Clean CGID.1
dat$CGID.1 <- NULL
## What is check.CGID
head(table(dat$check.CGID))## 0
## 11575## Clean it
dat$check.CGID <- NULL
## What is check.dadm_id?
head(table(dat$check.dadm_id))## 0
## 11575## Clean it
dat$check.dadm_id <- NULL
## Clean column names
dat$ROW_ID.y <- NULL
colnames(dat)[which(colnames(dat) == "ROW_ID.x")] <- "ROW_ID"
## Load CAREGIVERS Table for join on CGID
cg <- read.csv("~/nqf_caregivers/data/mimic/CAREGIVERS.csv",
header = T, stringsAsFactors = F)
## Change column name of "NOTEEVENTS.DESCRIPTION" to explicitly mention that it describes the note
colnames(dat)[which(colnames(dat) == "DESCRIPTION")] <- "NOTE_DESCRIPTION"
## Change column name of "CAREGIVERS. DESCRIPTION" to explicitly mention that it describes the careprovider
colnames(cg)[which(colnames(cg) == "DESCRIPTION")] <- "CG_DESCRIPTION"
## Remove ROW_ID from CG
cg$ROW_ID <- NULL
## Remove TEXT
dat$TEXT <- NULL
## Merge to caregivers
dat <- merge(dat, cg, by = "CGID")
dim(dat)## [1] 11575 54## Clean CG
rm(cg)
sofa <- read.csv("~/nqf_caregivers/data/sofa.csv", header = T, stringsAsFactors = F)
#oasis <- read.csv("~/nqf_caregivers/data/oasis.csv", header = T, stringsAsFactors = F)
#saps <- read.csv("~/nqf_caregivers/data/saps.csv", header = T, stringsAsFactors = F)
colnames(sofa) <- toupper(colnames(sofa))
dat <- merge(dat, sofa, by = c("SUBJECT_ID", "HADM_ID", "ICUSTAY_ID"))
dim(dat)## [1] 11575 61## Clean environment
rm(sofa)
## Clean ethnicity to Black/White/Other
dat[(grepl("WHITE|PORTUGUESE", dat$ETHNICITY)),]$ETHNICITY <- "WHITE"
dat[(grepl("ASIAN", dat$ETHNICITY)),]$ETHNICITY <- "OTHER"
dat[(grepl("BLACK", dat$ETHNICITY)),]$ETHNICITY <- "BLACK"
dat[(grepl("HISPANIC", dat$ETHNICITY)),]$ETHNICITY <- "OTHER"
dat[(grepl("MIDDLE|NATIVE|MULTI|DECLINED|UNABLE|OTHER|NOT", dat$ETHNICITY)),]$ETHNICITY <- "OTHER"
## Clean Marital Status to Married, Single, Widowed, Unknown
dat$MARITAL_STATUS[dat$MARITAL_STATUS == ""] <- "UNKNOWN (DEFAULT)"
dat$MARITAL_STATUS[dat$MARITAL_STATUS == "UNKNOWN (DEFAULT)"] <- "UNKNOWN"
dat$MARITAL_STATUS[dat$MARITAL_STATUS == "SEPARATED"] <- "SINGLE"
dat$MARITAL_STATUS[dat$MARITAL_STATUS == "DIVORCED"] <- "SINGLE"
## Clean admission data
dat$ADMISSION_TYPE[dat$ADMISSION_TYPE == "URGENT"] <- "EMERGENCY"
## Clean Languages
dat$LANGUAGE <- ifelse(dat$LANGUAGE == "ENGL", "ENGL", "OTHER")
#write.csv(dat, file = "~/nqf_caregivers/data/nqf_dat.csv", row.names = F)NQF Care Measure Check
prov <- provider_caremeasure_check(attending_check(dat))
prov <- expire_check(prov)
team <- team_caremeasure_check(attending_check(dat))
team <- expire_check(team)Provider-Level Analysis
At this level, we will see if providers documented patient care preferences at least once in the first 48hrs of the hospital admission. Here, the clinician/patient interaction is the unit of analysis.
temp <- prov
lev <- vector()
mod <- vector()
icc <- vector()
std <- vector()
t_r2m <- vector() ## Marginal R_glmm^2
t_R2c <- vector() ## Conditional R^2
d_r2m <- vector() ## Marginal R_glmm^2
d_R2c <- vector() ## Conditional R^2Data Standardization and Factoring (for modeling)
Continuous data, Age and SOFA, will be standardized in the form:
\[z = \frac{x - \mu}{\sigma}\]
temp$AGE <- (temp$AGE - mean(temp$AGE))/sd(temp$AGE)
temp$SOFA <- (temp$SOFA - mean(temp$SOFA)/sd(temp$SOFA))Baseline Model (Provider Random Effects)
gm_initial <- glmer(NQF ~ (1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## as.numeric(attr(summary(gm_initial)$varcor$CGID, "stddev"))
## View
summary(gm_initial)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 13715.3 13730.0 -6855.7 13711.3 11157
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2922 -0.8253 0.3867 0.7561 3.6611
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 2.138 1.462
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16423 0.07617 2.156 0.0311 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(gm_initial)##
## Generalized linear mixed model
##
## Family : binomial (logit)
## Formula: NQF ~ (1 | CGID)
##
## ICC (CGID): 0.3938## Results
lev <- c(lev, "Provider")
mod <- c(mod, "Model 0")
icc <- c(icc, as.numeric(sjstats::icc(gm_initial)))
std <- c(std, as.numeric(attr(summary(gm_initial)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_initial)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_initial)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_initial)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_initial)[2,2]) ## Conditional R^2
## Check assumption of normality:
plot_model(gm_initial, type = "diag")## $CGID
Model 1a (Patient Demographics)
- Gender
- Age
- Ethnicity
- Language
- Marrital Status
- Care Provider (Random)
gm_a <- glmer(NQF ~ GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_a)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 13472.8 13546.0 -6726.4 13452.8 11149
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2322 -0.7905 0.3480 0.7302 4.1883
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 2.113 1.454
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.22601 0.11618 1.945 0.05172 .
## GENDERM -0.33574 0.04934 -6.804 1.02e-11 ***
## AGE 0.25555 0.02352 10.867 < 2e-16 ***
## ETHNICITYOTHER 0.07824 0.11215 0.698 0.48541
## ETHNICITYWHITE -0.02203 0.08118 -0.271 0.78612
## LANGUAGEOTHER -0.08732 0.07153 -1.221 0.22214
## MARITAL_STATUSSINGLE 0.39320 0.06327 6.215 5.15e-10 ***
## MARITAL_STATUSUNKNOWN -0.14743 0.12465 -1.183 0.23690
## MARITAL_STATUSWIDOWED 0.17955 0.05818 3.086 0.00203 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GENDER AGE ETHNICITYO ETHNICITYW LANGUA
## GENDERM -0.266
## AGE 0.091 -0.054
## ETHNICITYOT -0.465 -0.039 -0.033
## ETHNICITYWH -0.645 -0.064 -0.062 0.654
## LANGUAGEOTH -0.036 0.012 -0.057 -0.282 -0.022
## MARITAL_STATUSS -0.300 0.247 0.023 0.102 0.096 -0.048
## MARITAL_STATUSU -0.130 0.066 -0.078 -0.072 0.047 0.025
## MARITAL_STATUSW -0.369 0.367 -0.176 0.097 0.096 0.000
## MARITAL_STATUSS MARITAL_STATUSU
## GENDERM
## AGE
## ETHNICITYOT
## ETHNICITYWH
## LANGUAGEOTH
## MARITAL_STATUSS
## MARITAL_STATUSU 0.159
## MARITAL_STATUSW 0.431 0.205tab_model(gm_a)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.25 | 1.00 – 1.57 | 0.052 |
| GENDERM | 0.71 | 0.65 – 0.79 | <0.001 |
| AGE | 1.29 | 1.23 – 1.35 | <0.001 |
| ETHNICITYOTHER | 1.08 | 0.87 – 1.35 | 0.485 |
| ETHNICITYWHITE | 0.98 | 0.83 – 1.15 | 0.786 |
| LANGUAGEOTHER | 0.92 | 0.80 – 1.05 | 0.222 |
| MARITAL_STATUSSINGLE | 1.48 | 1.31 – 1.68 | <0.001 |
| MARITAL_STATUSUNKNOWN | 0.86 | 0.68 – 1.10 | 0.237 |
| MARITAL_STATUSWIDOWED | 1.20 | 1.07 – 1.34 | 0.002 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 2.11 | ||
| ICC CGID | 0.39 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.024 / 0.406 | ||
MuMIn::r.squaredGLMM(gm_a)## R2m R2c
## theoretical 0.02444184 0.4060142
## delta 0.02161844 0.3591135## Results
lev <- c(lev, "Provider")
mod <- c(mod, "Model 1a")
icc <- c(icc, as.numeric(sjstats::icc(gm_a)))
std <- c(std, as.numeric(attr(summary(gm_a)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_a)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_a)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_a)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_a)[2,2]) ## Conditional R^2Model 1b (Clinical Variables)
- SOFA
- Care Provider (Random)
gm_b <- glmer(NQF ~ SOFA +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_b)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ SOFA + (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 13717.3 13739.3 -6855.7 13711.3 11156
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2906 -0.8251 0.3866 0.7564 3.6627
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 2.138 1.462
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1633086 0.0801375 2.038 0.0416 *
## SOFA 0.0002795 0.0075712 0.037 0.9706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## SOFA -0.311tab_model(gm_b)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 1.18 | 1.01 – 1.38 | 0.042 |
| SOFA | 1.00 | 0.99 – 1.02 | 0.971 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 2.14 | ||
| ICC CGID | 0.39 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.000 / 0.394 | ||
## Results
lev <- c(lev, "Provider")
mod <- c(mod, "Model 1b")
icc <- c(icc, as.numeric(sjstats::icc(gm_b)))
std <- c(std, as.numeric(attr(summary(gm_b)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_b)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_b)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_b)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_b)[2,2]) ## Conditional R^2Model 2a (Demographics + ICU)
- Demographics (Model 1a)
- First Intensive Care Unit
- Care Provider (Random)
gm_2a <- glmer(NQF ~ GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
FIRST_CAREUNIT +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_2a)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## FIRST_CAREUNIT + (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 13425.3 13527.8 -6698.7 13397.3 11145
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.1752 -0.7884 0.3457 0.7332 4.8049
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 2.181 1.477
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.085238 0.136196 -0.626 0.531412
## GENDERM -0.317860 0.049575 -6.412 1.44e-10 ***
## AGE 0.261045 0.023680 11.024 < 2e-16 ***
## ETHNICITYOTHER 0.126978 0.112951 1.124 0.260936
## ETHNICITYWHITE 0.009285 0.081506 0.114 0.909307
## LANGUAGEOTHER -0.094923 0.071804 -1.322 0.186175
## MARITAL_STATUSSINGLE 0.406708 0.063452 6.410 1.46e-10 ***
## MARITAL_STATUSUNKNOWN -0.193297 0.125557 -1.540 0.123679
## MARITAL_STATUSWIDOWED 0.199316 0.058500 3.407 0.000657 ***
## FIRST_CAREUNITCSRU -0.455543 0.171368 -2.658 0.007854 **
## FIRST_CAREUNITMICU 0.304684 0.078879 3.863 0.000112 ***
## FIRST_CAREUNITSICU 0.343549 0.116794 2.941 0.003266 **
## FIRST_CAREUNITTSICU 0.762857 0.142810 5.342 9.20e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(gm_2a)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.92 | 0.70 – 1.20 | 0.531 |
| GENDERM | 0.73 | 0.66 – 0.80 | <0.001 |
| AGE | 1.30 | 1.24 – 1.36 | <0.001 |
| ETHNICITYOTHER | 1.14 | 0.91 – 1.42 | 0.261 |
| ETHNICITYWHITE | 1.01 | 0.86 – 1.18 | 0.909 |
| LANGUAGEOTHER | 0.91 | 0.79 – 1.05 | 0.186 |
| MARITAL_STATUSSINGLE | 1.50 | 1.33 – 1.70 | <0.001 |
| MARITAL_STATUSUNKNOWN | 0.82 | 0.64 – 1.05 | 0.124 |
| MARITAL_STATUSWIDOWED | 1.22 | 1.09 – 1.37 | 0.001 |
| FIRST_CAREUNITCSRU | 0.63 | 0.45 – 0.89 | 0.008 |
| FIRST_CAREUNITMICU | 1.36 | 1.16 – 1.58 | <0.001 |
| FIRST_CAREUNITSICU | 1.41 | 1.12 – 1.77 | 0.003 |
| FIRST_CAREUNITTSICU | 2.14 | 1.62 – 2.84 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 2.18 | ||
| ICC CGID | 0.40 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.032 / 0.418 | ||
## Results
lev <- c(lev, "Provider")
mod <- c(mod, "Model 2a")
icc <- c(icc, as.numeric(sjstats::icc(gm_2a)))
std <- c(std, as.numeric(attr(summary(gm_2a)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_2a)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_2a)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_2a)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_2a)[2,2]) ## Conditional R^2Model 2b (Clinical Variables + ICU)
- Clinical Variables (Model 1b)
- First Intensive Care Unit
- Care Provider (Random)
gm_2b <- glmer(NQF ~ SOFA +
FIRST_CAREUNIT +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_2b)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ SOFA + FIRST_CAREUNIT + (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 13673.1 13724.3 -6829.5 13659.1 11152
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3161 -0.8161 0.3780 0.7567 4.2014
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 2.243 1.498
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.077708 0.103960 -0.747 0.454771
## SOFA 0.003185 0.007625 0.418 0.676141
## FIRST_CAREUNITCSRU -0.466875 0.168642 -2.768 0.005633 **
## FIRST_CAREUNITMICU 0.241646 0.077318 3.125 0.001776 **
## FIRST_CAREUNITSICU 0.382926 0.115541 3.314 0.000919 ***
## FIRST_CAREUNITTSICU 0.734990 0.140761 5.222 1.77e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SOFA FIRST_CAREUNITC FIRST_CAREUNITM
## SOFA -0.245
## FIRST_CAREUNITC -0.270 -0.020
## FIRST_CAREUNITM -0.570 -0.018 0.302
## FIRST_CAREUNITS -0.474 0.051 0.290 0.536
## FIRST_CAREUNITT -0.421 0.047 0.268 0.438
## FIRST_CAREUNITS
## SOFA
## FIRST_CAREUNITC
## FIRST_CAREUNITM
## FIRST_CAREUNITS
## FIRST_CAREUNITT 0.557tab_model(gm_2b)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.93 | 0.75 – 1.13 | 0.455 |
| SOFA | 1.00 | 0.99 – 1.02 | 0.676 |
| FIRST_CAREUNITCSRU | 0.63 | 0.45 – 0.87 | 0.006 |
| FIRST_CAREUNITMICU | 1.27 | 1.09 – 1.48 | 0.002 |
| FIRST_CAREUNITSICU | 1.47 | 1.17 – 1.84 | 0.001 |
| FIRST_CAREUNITTSICU | 2.09 | 1.58 – 2.75 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 2.24 | ||
| ICC CGID | 0.41 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.008 / 0.410 | ||
## Results
lev <- c(lev, "Provider")
mod <- c(mod, "Model 2b")
icc <- c(icc, as.numeric(sjstats::icc(gm_2b)))
std <- c(std, as.numeric(attr(summary(gm_2b)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_2b)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_2b)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_2b)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_2b)[2,2]) ## Conditional R^2Model 3 (Demographics + Clinical Variables + ICU)
- Demographics (as in Model 1a)
- SOFA Score (As Model 1b)
- First Intensive Care Unit
- Care Provider (Random)
gm_icu <- glmer(NQF ~
GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
SOFA +
FIRST_CAREUNIT +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_icu)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## SOFA + FIRST_CAREUNIT + (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 13420.6 13530.4 -6695.3 13390.6 11144
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0518 -0.7934 0.3424 0.7329 5.0031
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 2.191 1.48
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.154490 0.138983 -1.112 0.266319
## GENDERM -0.332127 0.049908 -6.655 2.84e-11 ***
## AGE 0.265132 0.023754 11.162 < 2e-16 ***
## ETHNICITYOTHER 0.124913 0.113119 1.104 0.269480
## ETHNICITYWHITE 0.016891 0.081662 0.207 0.836133
## LANGUAGEOTHER -0.099402 0.071854 -1.383 0.166548
## MARITAL_STATUSSINGLE 0.412472 0.063514 6.494 8.35e-11 ***
## MARITAL_STATUSUNKNOWN -0.183439 0.125660 -1.460 0.144344
## MARITAL_STATUSWIDOWED 0.203645 0.058553 3.478 0.000505 ***
## SOFA 0.020376 0.007853 2.595 0.009463 **
## FIRST_CAREUNITCSRU -0.464589 0.171368 -2.711 0.006707 **
## FIRST_CAREUNITMICU 0.301162 0.078934 3.815 0.000136 ***
## FIRST_CAREUNITSICU 0.358239 0.117041 3.061 0.002207 **
## FIRST_CAREUNITTSICU 0.780900 0.143031 5.460 4.77e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(gm_icu)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 0.86 | 0.65 – 1.13 | 0.266 |
| GENDERM | 0.72 | 0.65 – 0.79 | <0.001 |
| AGE | 1.30 | 1.24 – 1.37 | <0.001 |
| ETHNICITYOTHER | 1.13 | 0.91 – 1.41 | 0.269 |
| ETHNICITYWHITE | 1.02 | 0.87 – 1.19 | 0.836 |
| LANGUAGEOTHER | 0.91 | 0.79 – 1.04 | 0.167 |
| MARITAL_STATUSSINGLE | 1.51 | 1.33 – 1.71 | <0.001 |
| MARITAL_STATUSUNKNOWN | 0.83 | 0.65 – 1.06 | 0.144 |
| MARITAL_STATUSWIDOWED | 1.23 | 1.09 – 1.37 | 0.001 |
| SOFA | 1.02 | 1.00 – 1.04 | 0.009 |
| FIRST_CAREUNITCSRU | 0.63 | 0.45 – 0.88 | 0.007 |
| FIRST_CAREUNITMICU | 1.35 | 1.16 – 1.58 | <0.001 |
| FIRST_CAREUNITSICU | 1.43 | 1.14 – 1.80 | 0.002 |
| FIRST_CAREUNITTSICU | 2.18 | 1.65 – 2.89 | <0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 2.19 | ||
| ICC CGID | 0.40 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.033 / 0.419 | ||
## Results
lev <- c(lev, "Provider")
mod <- c(mod, "Model 3")
icc <- c(icc, as.numeric(sjstats::icc(gm_icu)))
std <- c(std, as.numeric(attr(summary(gm_icu)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_icu)[1,1]) ## Warning: The null model is correct only if all variables used by the
## original model remain unchanged.t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_icu)[1,2]) ## Conditional R^2## Warning: The null model is correct only if all variables used by the
## original model remain unchanged.d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_icu)[2,1]) ## Marginal R_glmm^2 ## Warning: The null model is correct only if all variables used by the
## original model remain unchanged.d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_icu)[2,2]) ## Conditional R^2## Warning: The null model is correct only if all variables used by the
## original model remain unchanged.Results
results <- as.data.frame(cbind(mod, icc, std, lev, t_r2m, t_R2c, d_r2m, d_R2c))
## Correct variables
results$mod <- as.character(results$mod)
results$icc <- as.numeric(as.character(results$icc))
results$std <- as.numeric(as.character(results$std))
results$lev <- as.character(results$lev)
print(results)## mod icc std lev t_r2m
## 1 Model 0 0.3938457 1.462045 Provider 0
## 2 Model 1a 0.3911323 1.453750 Provider 0.0244418379564992
## 3 Model 1b 0.3938446 1.462042 Provider 1.29522660237321e-07
## 4 Model 2a 0.3986534 1.476810 Provider 0.0322298072241591
## 5 Model 2b 0.4053823 1.497624 Provider 0.0076058384804099
## 6 Model 3 0.3997552 1.480206 Provider 0.0327984422997686
## t_R2c d_r2m d_R2c
## 1 0.393845690742604 0 0.34752823595739
## 2 0.406014175521916 0.0216184415541336 0.359113489717591
## 3 0.393844701204199 1.14290378300496e-07 0.347527296071577
## 4 0.418034637784055 0.0285735799337614 0.370611777313085
## 5 0.409904842781096 0.0067323447518925 0.362829255995969
## 6 0.419442255159451 0.0290856883952417 0.37196177250928ROC Curves
par(mfrow=c(2,3))
rocplot(as.numeric(predict(gm_initial, type="response")), temp$NQF, col="blue", main = "Initial Model")
rocplot(as.numeric(predict(gm_a, type="response")), temp$NQF, col="blue", main = "Model 1a (Demographics)")
rocplot(as.numeric(predict(gm_b, type="response")), temp$NQF, col="blue", main = "Model 1b (Clinical Variables)")
rocplot(as.numeric(predict(gm_2a, type="response")), temp$NQF, col="blue", main = "Model 2a (Demographics + ICU)")
rocplot(as.numeric(predict(gm_2b, type="response")), temp$NQF, col="blue", main = "Model 2b (Clinical Variables + ICU)")
rocplot(as.numeric(predict(gm_icu, type="response")), temp$NQF, col="blue", main = "Model 3 (All Covariates)")
Generalized Logistic Regression Models
Baseline Model (Provider Random Effects)
Not meaningful.
Model 1a (Patient Demographics)
- Gender
- Age
- Ethnicity
- Language
- Marrital Status
- Care Provider (Random)
gm_a <- glm(NQF ~ GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_a)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS,
## family = binomial(link = "logit"), data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7122 -1.2237 0.8905 1.0774 1.5698
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.42029 0.07696 5.461 4.73e-08 ***
## GENDERM -0.31292 0.04191 -7.466 8.24e-14 ***
## AGE 0.25175 0.02012 12.515 < 2e-16 ***
## ETHNICITYOTHER 0.02276 0.09431 0.241 0.809266
## ETHNICITYWHITE -0.16037 0.06910 -2.321 0.020292 *
## LANGUAGEOTHER -0.06968 0.06123 -1.138 0.255150
## MARITAL_STATUSSINGLE 0.40719 0.05425 7.506 6.09e-14 ***
## MARITAL_STATUSUNKNOWN -0.39961 0.10615 -3.764 0.000167 ***
## MARITAL_STATUSWIDOWED 0.10637 0.04918 2.163 0.030538 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 15330 on 11158 degrees of freedom
## Residual deviance: 14979 on 11150 degrees of freedom
## AIC: 14997
##
## Number of Fisher Scoring iterations: 4
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 1.5224080 1.3097351 1.7710370
## GENDERM 0.7313053 0.6736128 0.7938953
## AGE 1.2862732 1.2366173 1.3380790
## ETHNICITYOTHER 1.0230253 0.8503155 1.2307284
## ETHNICITYWHITE 0.8518327 0.7435769 0.9749569
## LANGUAGEOTHER 0.9326962 0.8272891 1.0517552
## MARITAL_STATUSSINGLE 1.5025887 1.3512257 1.6714201
## MARITAL_STATUSUNKNOWN 0.6705842 0.5442147 0.8252715
## MARITAL_STATUSWIDOWED 1.1122330 1.0100302 1.2247760Model 1b (Clinical Variables)
- SOFA
- Care Provider (Random)
gm_b <- glm(NQF ~ SOFA,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_b)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ SOFA, family = binomial(link = "logit"),
## data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.276 -1.274 1.083 1.084 1.087
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2272396 0.0285897 7.948 1.89e-15 ***
## SOFA -0.0007278 0.0063508 -0.115 0.909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 15330 on 11158 degrees of freedom
## Residual deviance: 15330 on 11157 degrees of freedom
## AIC: 15334
##
## Number of Fisher Scoring iterations: 3
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 1.2551306 1.1867773 1.327527
## SOFA 0.9992725 0.9869175 1.011798Model 2a (Demographics + ICU)
- Demographics (Model 1a)
- First Intensive Care Unit
- Care Provider (Random)
gm_2a <- glm(NQF ~ GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
FIRST_CAREUNIT,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_2a)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## FIRST_CAREUNIT, family = binomial(link = "logit"), data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7363 -1.2211 0.8301 1.0551 1.7691
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.36799 0.09278 3.966 7.30e-05 ***
## GENDERM -0.29393 0.04241 -6.931 4.18e-12 ***
## AGE 0.25718 0.02029 12.672 < 2e-16 ***
## ETHNICITYOTHER 0.12885 0.09586 1.344 0.17890
## ETHNICITYWHITE -0.13000 0.06959 -1.868 0.06173 .
## LANGUAGEOTHER -0.13815 0.06191 -2.231 0.02565 *
## MARITAL_STATUSSINGLE 0.39887 0.05455 7.312 2.64e-13 ***
## MARITAL_STATUSUNKNOWN -0.29833 0.10814 -2.759 0.00580 **
## MARITAL_STATUSWIDOWED 0.15096 0.04983 3.029 0.00245 **
## FIRST_CAREUNITCSRU -1.08710 0.12548 -8.664 < 2e-16 ***
## FIRST_CAREUNITMICU 0.13879 0.05708 2.432 0.01503 *
## FIRST_CAREUNITSICU -0.36409 0.07958 -4.575 4.76e-06 ***
## FIRST_CAREUNITTSICU -0.28484 0.09373 -3.039 0.00237 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 15330 on 11158 degrees of freedom
## Residual deviance: 14799 on 11146 degrees of freedom
## AIC: 14825
##
## Number of Fisher Scoring iterations: 4
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 1.4448339 1.2050182 1.7336817
## GENDERM 0.7453311 0.6858651 0.8099145
## AGE 1.2932805 1.2429184 1.3458423
## ETHNICITYOTHER 1.1375187 0.9426591 1.3726823
## ETHNICITYWHITE 0.8780936 0.7657631 1.0059752
## LANGUAGEOTHER 0.8709679 0.7714980 0.9834440
## MARITAL_STATUSSINGLE 1.4901376 1.3392293 1.6585714
## MARITAL_STATUSUNKNOWN 0.7420538 0.5999307 0.9168668
## MARITAL_STATUSWIDOWED 1.1629461 1.0547389 1.2822969
## FIRST_CAREUNITCSRU 0.3371922 0.2628892 0.4300811
## FIRST_CAREUNITMICU 1.1488823 1.0271319 1.2847139
## FIRST_CAREUNITSICU 0.6948277 0.5943764 0.8120136
## FIRST_CAREUNITTSICU 0.7521322 0.6257859 0.9037148Model 2b (Clinical Variables + ICU)
- Clinical Variables (Model 1b)
- First Intensive Care Unit
- Care Provider (Random)
gm_2b <- glm(NQF ~ SOFA +
FIRST_CAREUNIT,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_2b)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ SOFA + FIRST_CAREUNIT, family = binomial(link = "logit"),
## data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.338 -1.323 1.027 1.036 1.579
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.273813 0.054575 5.017 5.24e-07 ***
## SOFA -0.003461 0.006437 -0.538 0.591
## FIRST_CAREUNITCSRU -1.159929 0.123438 -9.397 < 2e-16 ***
## FIRST_CAREUNITMICU 0.090876 0.055739 1.630 0.103
## FIRST_CAREUNITSICU -0.346994 0.077920 -4.453 8.46e-06 ***
## FIRST_CAREUNITTSICU -0.357304 0.091384 -3.910 9.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 15330 on 11158 degrees of freedom
## Residual deviance: 15143 on 11153 degrees of freedom
## AIC: 15155
##
## Number of Fisher Scoring iterations: 4
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 1.3149687 1.1817847 1.4637357
## SOFA 0.9965445 0.9840557 1.0092069
## FIRST_CAREUNITCSRU 0.3135084 0.2453797 0.3982432
## FIRST_CAREUNITMICU 1.0951331 0.9816228 1.2213724
## FIRST_CAREUNITSICU 0.7068092 0.6066043 0.8233301
## FIRST_CAREUNITTSICU 0.6995600 0.5847083 0.8366620Model 3 (Demographics + Clinical Variables + ICU)
- Demographics (as in Model 1a)
- SOFA Score (As Model 1b)
- First Intensive Care Unit
- Care Provider (Random)
gm_icu <- glm(NQF ~
GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
SOFA +
FIRST_CAREUNIT,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_icu)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## SOFA + FIRST_CAREUNIT, family = binomial(link = "logit"),
## data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7471 -1.2200 0.8355 1.0598 1.7589
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.328197 0.095340 3.442 0.000577 ***
## GENDERM -0.302979 0.042710 -7.094 1.30e-12 ***
## AGE 0.260069 0.020365 12.770 < 2e-16 ***
## ETHNICITYOTHER 0.128288 0.095918 1.337 0.181070
## ETHNICITYWHITE -0.124975 0.069683 -1.793 0.072896 .
## LANGUAGEOTHER -0.142354 0.061977 -2.297 0.021626 *
## MARITAL_STATUSSINGLE 0.401902 0.054588 7.362 1.81e-13 ***
## MARITAL_STATUSUNKNOWN -0.293350 0.108211 -2.711 0.006710 **
## MARITAL_STATUSWIDOWED 0.153202 0.049852 3.073 0.002118 **
## SOFA 0.012153 0.006663 1.824 0.068142 .
## FIRST_CAREUNITCSRU -1.095251 0.125564 -8.723 < 2e-16 ***
## FIRST_CAREUNITMICU 0.135679 0.057109 2.376 0.017511 *
## FIRST_CAREUNITSICU -0.358829 0.079653 -4.505 6.64e-06 ***
## FIRST_CAREUNITTSICU -0.277514 0.093789 -2.959 0.003087 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 15330 on 11158 degrees of freedom
## Residual deviance: 14796 on 11145 degrees of freedom
## AIC: 14824
##
## Number of Fisher Scoring iterations: 4
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 1.3884625 1.1521673 1.6743439
## GENDERM 0.7386145 0.6792807 0.8030862
## AGE 1.2970196 1.2463410 1.3499227
## ETHNICITYOTHER 1.1368801 0.9420207 1.3720761
## ETHNICITYWHITE 0.8825186 0.7694794 1.0112434
## LANGUAGEOTHER 0.8673141 0.7681590 0.9794451
## MARITAL_STATUSSINGLE 1.4946647 1.3432087 1.6637255
## MARITAL_STATUSUNKNOWN 0.7457614 0.6028470 0.9215801
## MARITAL_STATUSWIDOWED 1.1655600 1.0570741 1.2852229
## SOFA 1.0122273 0.9991059 1.0255465
## FIRST_CAREUNITCSRU 0.3344556 0.2607124 0.4266638
## FIRST_CAREUNITMICU 1.1453145 1.0238761 1.2808048
## FIRST_CAREUNITSICU 0.6984937 0.5974332 0.8164097
## FIRST_CAREUNITTSICU 0.7576650 0.6303177 0.9104643par(mfrow=c(2,3))
rocplot(as.numeric(predict(gm_a, type="response")), temp$NQF, col="blue", main = "Model 1a (Demographics)")
rocplot(as.numeric(predict(gm_b, type="response")), temp$NQF, col="blue", main = "Model 1b (Clinical Variables)")
rocplot(as.numeric(predict(gm_2a, type="response")), temp$NQF, col="blue", main = "Model 2a (Demographics + ICU)")
rocplot(as.numeric(predict(gm_2b, type="response")), temp$NQF, col="blue", main = "Model 2b (Clinical Variables + ICU)")
rocplot(as.numeric(predict(gm_icu, type="response")), temp$NQF, col="blue", main = "Model 3 (All Covariates)")
Admission-Level Care Team Analysis
For this analysis, we will look at the clinical care team. If any care provider documented the patient care preferences during the admission, then the entire care team will get credit for the documentation.
temp <- team
lev <- vector()
mod <- vector()
icc <- vector()
std <- vector()
t_r2m <- vector() ## Marginal R_glmm^2
t_R2c <- vector() ## Conditional R^2
d_r2m <- vector() ## Marginal R_glmm^2
d_R2c <- vector() ## Conditional R^2Data Standardization and Factoring (for modeling)
Continuous data, Age and SOFA, will be standardized in the form:
\[z = \frac{x - \mu}{\sigma}\]
temp$AGE <- (temp$AGE - mean(temp$AGE))/sd(temp$AGE)
temp$SOFA <- (temp$SOFA - mean(temp$SOFA)/sd(temp$SOFA))Baseline Model (Team Random Effects)
gm_initial <- glmer(NQF ~ (1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## as.numeric(attr(summary(gm_initial)$varcor$CGID, "stddev"))
## View
summary(gm_initial)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 11907.8 11922.4 -5951.9 11903.8 11157
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6384 -0.7102 0.4009 0.5699 2.2754
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 2.011 1.418
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.05472 0.07638 13.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(gm_initial)##
## Generalized linear mixed model
##
## Family : binomial (logit)
## Formula: NQF ~ (1 | CGID)
##
## ICC (CGID): 0.3794## Results
lev <- c(lev, "Team")
mod <- c(mod, "Model 0")
icc <- c(icc, as.numeric(sjstats::icc(gm_initial)))
std <- c(std, as.numeric(attr(summary(gm_initial)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_initial)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_initial)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_initial)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_initial)[2,2]) ## Conditional R^2
## Check assumption of normality:
plot_model(gm_initial, type = "diag")## $CGID
Model 1a (Patient Demographics)
- Gender
- Age
- Ethnicity
- Language
- Marrital Status
- Care Provider (Random)
gm_a <- glmer(NQF ~ GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_a)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 11664.4 11737.6 -5822.2 11644.4 11149
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3957 -0.6490 0.3757 0.5826 2.7628
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 1.938 1.392
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.07216 0.12272 8.737 < 2e-16 ***
## GENDERM -0.36439 0.05429 -6.712 1.92e-11 ***
## AGE 0.25466 0.02611 9.754 < 2e-16 ***
## ETHNICITYOTHER 0.52036 0.12715 4.092 4.27e-05 ***
## ETHNICITYWHITE 0.01858 0.09069 0.205 0.8377
## LANGUAGEOTHER -0.09035 0.07904 -1.143 0.2530
## MARITAL_STATUSSINGLE 0.50454 0.07127 7.079 1.45e-12 ***
## MARITAL_STATUSUNKNOWN -0.38865 0.12853 -3.024 0.0025 **
## MARITAL_STATUSWIDOWED 0.14116 0.06339 2.227 0.0260 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GENDER AGE ETHNICITYO ETHNICITYW LANGUA
## GENDERM -0.275
## AGE 0.111 -0.061
## ETHNICITYOT -0.479 -0.050 -0.016
## ETHNICITYWH -0.675 -0.078 -0.067 0.649
## LANGUAGEOTH -0.049 0.003 -0.055 -0.243 -0.011
## MARITAL_STATUSS -0.287 0.226 0.035 0.097 0.087 -0.032
## MARITAL_STATUSU -0.134 0.068 -0.079 -0.086 0.053 0.028
## MARITAL_STATUSW -0.372 0.361 -0.175 0.098 0.095 0.001
## MARITAL_STATUSS MARITAL_STATUSU
## GENDERM
## AGE
## ETHNICITYOT
## ETHNICITYWH
## LANGUAGEOTH
## MARITAL_STATUSS
## MARITAL_STATUSU 0.149
## MARITAL_STATUSW 0.390 0.202tab_model(gm_a)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 2.92 | 2.30 – 3.72 | <0.001 |
| GENDERM | 0.69 | 0.62 – 0.77 | <0.001 |
| AGE | 1.29 | 1.23 – 1.36 | <0.001 |
| ETHNICITYOTHER | 1.68 | 1.31 – 2.16 | <0.001 |
| ETHNICITYWHITE | 1.02 | 0.85 – 1.22 | 0.838 |
| LANGUAGEOTHER | 0.91 | 0.78 – 1.07 | 0.253 |
| MARITAL_STATUSSINGLE | 1.66 | 1.44 – 1.90 | <0.001 |
| MARITAL_STATUSUNKNOWN | 0.68 | 0.53 – 0.87 | 0.002 |
| MARITAL_STATUSWIDOWED | 1.15 | 1.02 – 1.30 | 0.026 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 1.94 | ||
| ICC CGID | 0.37 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.032 / 0.391 | ||
MuMIn::r.squaredGLMM(gm_a)## R2m R2c
## theoretical 0.03168954 0.3905952
## delta 0.02549255 0.3142131## Results
lev <- c(lev, "Team")
mod <- c(mod, "Model 1a")
icc <- c(icc, as.numeric(sjstats::icc(gm_a)))
std <- c(std, as.numeric(attr(summary(gm_a)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_a)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_a)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_a)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_a)[2,2]) ## Conditional R^2Model 1b (Clinical Variables)
- SOFA
- Care Provider (Random)
gm_b <- glmer(NQF ~ SOFA +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_b)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ SOFA + (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 11898.9 11920.9 -5946.5 11892.9 11156
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.4794 -0.7124 0.3938 0.5783 2.4456
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 2.006 1.416
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.964591 0.080949 11.92 < 2e-16 ***
## SOFA 0.027701 0.008446 3.28 0.00104 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## SOFA -0.334tab_model(gm_b)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 2.62 | 2.24 – 3.07 | <0.001 |
| SOFA | 1.03 | 1.01 – 1.05 | 0.001 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 2.01 | ||
| ICC CGID | 0.38 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.001 / 0.380 | ||
## Results
lev <- c(lev, "Team")
mod <- c(mod, "Model 1b")
icc <- c(icc, as.numeric(sjstats::icc(gm_b)))
std <- c(std, as.numeric(attr(summary(gm_b)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_b)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_b)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_b)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_b)[2,2]) ## Conditional R^2Model 2a (Demographics + ICU)
- Demographics (Model 1a)
- First Intensive Care Unit
- Care Provider (Random)
gm_2a <- glmer(NQF ~ GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
FIRST_CAREUNIT +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_2a)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## FIRST_CAREUNIT + (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 11620.3 11722.7 -5796.1 11592.3 11145
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4094 -0.6266 0.3749 0.5797 3.1576
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 1.796 1.34
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.98383 0.14257 6.901 5.17e-12 ***
## GENDERM -0.34563 0.05450 -6.342 2.27e-10 ***
## AGE 0.25973 0.02619 9.915 < 2e-16 ***
## ETHNICITYOTHER 0.57812 0.12772 4.526 6.00e-06 ***
## ETHNICITYWHITE 0.03539 0.09058 0.391 0.69604
## LANGUAGEOTHER -0.11338 0.07922 -1.431 0.15239
## MARITAL_STATUSSINGLE 0.52265 0.07143 7.317 2.54e-13 ***
## MARITAL_STATUSUNKNOWN -0.40570 0.12905 -3.144 0.00167 **
## MARITAL_STATUSWIDOWED 0.17427 0.06369 2.736 0.00622 **
## FIRST_CAREUNITCSRU -0.76801 0.17089 -4.494 6.99e-06 ***
## FIRST_CAREUNITMICU 0.13875 0.08690 1.597 0.11033
## FIRST_CAREUNITSICU -0.21347 0.12372 -1.725 0.08444 .
## FIRST_CAREUNITTSICU 0.32692 0.14829 2.205 0.02748 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 13 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(gm_2a)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 2.67 | 2.02 – 3.54 | <0.001 |
| GENDERM | 0.71 | 0.64 – 0.79 | <0.001 |
| AGE | 1.30 | 1.23 – 1.36 | <0.001 |
| ETHNICITYOTHER | 1.78 | 1.39 – 2.29 | <0.001 |
| ETHNICITYWHITE | 1.04 | 0.87 – 1.24 | 0.696 |
| LANGUAGEOTHER | 0.89 | 0.76 – 1.04 | 0.152 |
| MARITAL_STATUSSINGLE | 1.69 | 1.47 – 1.94 | <0.001 |
| MARITAL_STATUSUNKNOWN | 0.67 | 0.52 – 0.86 | 0.002 |
| MARITAL_STATUSWIDOWED | 1.19 | 1.05 – 1.35 | 0.006 |
| FIRST_CAREUNITCSRU | 0.46 | 0.33 – 0.65 | <0.001 |
| FIRST_CAREUNITMICU | 1.15 | 0.97 – 1.36 | 0.110 |
| FIRST_CAREUNITSICU | 0.81 | 0.63 – 1.03 | 0.084 |
| FIRST_CAREUNITTSICU | 1.39 | 1.04 – 1.85 | 0.027 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 1.80 | ||
| ICC CGID | 0.35 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.041 / 0.379 | ||
## Results
lev <- c(lev, "Team")
mod <- c(mod, "Model 2a")
icc <- c(icc, as.numeric(sjstats::icc(gm_2a)))
std <- c(std, as.numeric(attr(summary(gm_2a)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_2a)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_2a)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_2a)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_2a)[2,2]) ## Conditional R^2Model 2b (Clinical Variables + ICU)
- Clinical Variables (Model 1b)
- First Intensive Care Unit
- Care Provider (Random)
gm_2b <- glmer(NQF ~ SOFA +
FIRST_CAREUNIT +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_2b)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ SOFA + FIRST_CAREUNIT + (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 11861.9 11913.1 -5923.9 11847.9 11152
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.5217 -0.6889 0.3914 0.5803 2.7499
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 1.913 1.383
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.947851 0.105952 8.946 < 2e-16 ***
## SOFA 0.028912 0.008497 3.403 0.000667 ***
## FIRST_CAREUNITCSRU -0.800718 0.168384 -4.755 1.98e-06 ***
## FIRST_CAREUNITMICU 0.068061 0.085470 0.796 0.425849
## FIRST_CAREUNITSICU -0.136505 0.122717 -1.112 0.265985
## FIRST_CAREUNITTSICU 0.308234 0.145968 2.112 0.034716 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) SOFA FIRST_CAREUNITC FIRST_CAREUNITM
## SOFA -0.266
## FIRST_CAREUNITC -0.321 -0.017
## FIRST_CAREUNITM -0.623 -0.006 0.358
## FIRST_CAREUNITS -0.501 0.060 0.352 0.557
## FIRST_CAREUNITT -0.438 0.051 0.324 0.467
## FIRST_CAREUNITS
## SOFA
## FIRST_CAREUNITC
## FIRST_CAREUNITM
## FIRST_CAREUNITS
## FIRST_CAREUNITT 0.596tab_model(gm_2b)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 2.58 | 2.10 – 3.18 | <0.001 |
| SOFA | 1.03 | 1.01 – 1.05 | 0.001 |
| FIRST_CAREUNITCSRU | 0.45 | 0.32 – 0.62 | <0.001 |
| FIRST_CAREUNITMICU | 1.07 | 0.91 – 1.27 | 0.426 |
| FIRST_CAREUNITSICU | 0.87 | 0.69 – 1.11 | 0.266 |
| FIRST_CAREUNITTSICU | 1.36 | 1.02 – 1.81 | 0.035 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 1.91 | ||
| ICC CGID | 0.37 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.007 / 0.372 | ||
## Results
lev <- c(lev, "Team")
mod <- c(mod, "Model 2b")
icc <- c(icc, as.numeric(sjstats::icc(gm_2b)))
std <- c(std, as.numeric(attr(summary(gm_2b)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_2b)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_2b)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_2b)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_2b)[2,2]) ## Conditional R^2Model 3 (Demographics + Clinical Variables + ICU)
- Demographics (as in Model 1a)
- SOFA Score (As Model 1b)
- First Intensive Care Unit
- Care Provider (Random)
gm_icu <- glmer(NQF ~
GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
SOFA +
FIRST_CAREUNIT +
(1 | CGID),
data = temp,
family = binomial,
control = glmerControl(optimizer = "bobyqa"),
nAGQ = 10)
## View
summary(gm_icu)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 10) [glmerMod]
## Family: binomial ( logit )
## Formula: NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## SOFA + FIRST_CAREUNIT + (1 | CGID)
## Data: temp
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 11594.9 11704.7 -5782.4 11564.9 11144
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4725 -0.6227 0.3728 0.5760 3.4682
##
## Random effects:
## Groups Name Variance Std.Dev.
## CGID (Intercept) 1.808 1.345
## Number of obs: 11159, groups: CGID, 493
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.830118 0.145771 5.695 1.24e-08 ***
## GENDERM -0.375047 0.054874 -6.835 8.21e-12 ***
## AGE 0.268603 0.026323 10.204 < 2e-16 ***
## ETHNICITYOTHER 0.582375 0.128231 4.542 5.58e-06 ***
## ETHNICITYWHITE 0.049426 0.090794 0.544 0.58618
## LANGUAGEOTHER -0.128255 0.079515 -1.613 0.10675
## MARITAL_STATUSSINGLE 0.535471 0.071580 7.481 7.39e-14 ***
## MARITAL_STATUSUNKNOWN -0.382603 0.129097 -2.964 0.00304 **
## MARITAL_STATUSWIDOWED 0.184334 0.063793 2.890 0.00386 **
## SOFA 0.045565 0.008774 5.193 2.07e-07 ***
## FIRST_CAREUNITCSRU -0.784574 0.171129 -4.585 4.55e-06 ***
## FIRST_CAREUNITMICU 0.135794 0.086988 1.561 0.11851
## FIRST_CAREUNITSICU -0.180827 0.124228 -1.456 0.14550
## FIRST_CAREUNITTSICU 0.361765 0.148627 2.434 0.01493 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need ittab_model(gm_icu)| NQF | |||
|---|---|---|---|
| Predictors | Odds Ratios | CI | p |
| (Intercept) | 2.29 | 1.72 – 3.05 | <0.001 |
| GENDERM | 0.69 | 0.62 – 0.77 | <0.001 |
| AGE | 1.31 | 1.24 – 1.38 | <0.001 |
| ETHNICITYOTHER | 1.79 | 1.39 – 2.30 | <0.001 |
| ETHNICITYWHITE | 1.05 | 0.88 – 1.26 | 0.586 |
| LANGUAGEOTHER | 0.88 | 0.75 – 1.03 | 0.107 |
| MARITAL_STATUSSINGLE | 1.71 | 1.48 – 1.97 | <0.001 |
| MARITAL_STATUSUNKNOWN | 0.68 | 0.53 – 0.88 | 0.003 |
| MARITAL_STATUSWIDOWED | 1.20 | 1.06 – 1.36 | 0.004 |
| SOFA | 1.05 | 1.03 – 1.06 | <0.001 |
| FIRST_CAREUNITCSRU | 0.46 | 0.33 – 0.64 | <0.001 |
| FIRST_CAREUNITMICU | 1.15 | 0.97 – 1.36 | 0.119 |
| FIRST_CAREUNITSICU | 0.83 | 0.65 – 1.06 | 0.146 |
| FIRST_CAREUNITTSICU | 1.44 | 1.07 – 1.92 | 0.015 |
| Random Effects | |||
| σ2 | 3.29 | ||
| τ00 CGID | 1.81 | ||
| ICC CGID | 0.35 | ||
| Observations | 11159 | ||
| Marginal R2 / Conditional R2 | 0.044 / 0.383 | ||
## Results
lev <- c(lev, "Team")
mod <- c(mod, "Model 3")
icc <- c(icc, as.numeric(sjstats::icc(gm_icu)))
std <- c(std, as.numeric(attr(summary(gm_icu)$varcor$CGID, "stddev")))
t_r2m <- c(t_r2m, MuMIn::r.squaredGLMM(gm_icu)[1,1])
t_R2c <- c(t_R2c, MuMIn::r.squaredGLMM(gm_icu)[1,2]) ## Conditional R^2
d_r2m <- c(d_r2m, MuMIn::r.squaredGLMM(gm_icu)[2,1]) ## Marginal R_glmm^2
d_R2c <- c(d_R2c, MuMIn::r.squaredGLMM(gm_icu)[2,2]) ## Conditional R^2ROC Curves
par(mfrow=c(2,3))
rocplot(as.numeric(predict(gm_initial, type="response")), temp$NQF, col="blue", main = "Initial Model")
rocplot(as.numeric(predict(gm_a, type="response")), temp$NQF, col="blue", main = "Model 1a (Demographics)")
rocplot(as.numeric(predict(gm_b, type="response")), temp$NQF, col="blue", main = "Model 1b (Clinical Variables)")
rocplot(as.numeric(predict(gm_2a, type="response")), temp$NQF, col="blue", main = "Model 2a (Demographics + ICU)")
rocplot(as.numeric(predict(gm_2b, type="response")), temp$NQF, col="blue", main = "Model 2b (Clinical Variables + ICU)")
rocplot(as.numeric(predict(gm_icu, type="response")), temp$NQF, col="blue", main = "Model 3 (All Covariates)")
Generalized Logistic Regression Models
Baseline Model (Provider Random Effects)
Not meaningful.
Model 1a (Patient Demographics)
- Gender
- Age
- Ethnicity
- Languagee
- Marrital Status
- Care Provider (Random)
gm_a <- glm(NQF ~ GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_a)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS,
## family = binomial(link = "logit"), data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0450 -1.3494 0.7126 0.8503 1.3895
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.14611 0.08677 13.209 < 2e-16 ***
## GENDERM -0.32538 0.04645 -7.005 2.47e-12 ***
## AGE 0.25832 0.02236 11.554 < 2e-16 ***
## ETHNICITYOTHER 0.28433 0.10866 2.617 0.00888 **
## ETHNICITYWHITE -0.17346 0.07844 -2.211 0.02701 *
## LANGUAGEOTHER -0.03501 0.06831 -0.513 0.60826
## MARITAL_STATUSSINGLE 0.52681 0.06216 8.475 < 2e-16 ***
## MARITAL_STATUSUNKNOWN -0.68721 0.10841 -6.339 2.31e-10 ***
## MARITAL_STATUSWIDOWED 0.05823 0.05395 1.079 0.28049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13281 on 11158 degrees of freedom
## Residual deviance: 12914 on 11150 degrees of freedom
## AIC: 12932
##
## Number of Fisher Scoring iterations: 4
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 3.1459451 2.6572706 3.7341953
## GENDERM 0.7222558 0.6593555 0.7910434
## AGE 1.2947534 1.2393381 1.3528639
## ETHNICITYOTHER 1.3288740 1.0740039 1.6445827
## ETHNICITYWHITE 0.8407523 0.7198356 0.9790896
## LANGUAGEOTHER 0.9655946 0.8452159 1.1047981
## MARITAL_STATUSSINGLE 1.6935260 1.5001033 1.9140858
## MARITAL_STATUSUNKNOWN 0.5029796 0.4068938 0.6225242
## MARITAL_STATUSWIDOWED 1.0599546 0.9536594 1.1782824Model 1b (Clinical Variables)
- SOFA
- Care Provider (Random)
gm_b <- glm(NQF ~ SOFA,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_b)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ SOFA, family = binomial(link = "logit"),
## data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7808 -1.5470 0.8029 0.8254 0.8717
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.826230 0.031302 26.395 < 2e-16 ***
## SOFA 0.032490 0.007181 4.525 6.05e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13281 on 11158 degrees of freedom
## Residual deviance: 13261 on 11157 degrees of freedom
## AIC: 13265
##
## Number of Fisher Scoring iterations: 4
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 2.284689 2.149072 2.429646
## SOFA 1.033024 1.018642 1.047725Model 2a (Demographics + ICU)
- Demographics (Model 1a)
- First Intensive Care Unit
- Care Provider (Random)
gm_2a <- glm(NQF ~ GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
FIRST_CAREUNIT,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_2a)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## FIRST_CAREUNIT, family = binomial(link = "logit"), data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1913 -1.1323 0.6659 0.8157 1.5564
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.18385 0.10583 11.186 < 2e-16 ***
## GENDERM -0.31922 0.04750 -6.720 1.81e-11 ***
## AGE 0.27033 0.02279 11.861 < 2e-16 ***
## ETHNICITYOTHER 0.47616 0.11192 4.254 2.10e-05 ***
## ETHNICITYWHITE -0.13330 0.07985 -1.669 0.0950 .
## LANGUAGEOTHER -0.16040 0.06974 -2.300 0.0214 *
## MARITAL_STATUSSINGLE 0.50884 0.06306 8.069 7.10e-16 ***
## MARITAL_STATUSUNKNOWN -0.50575 0.11215 -4.510 6.49e-06 ***
## MARITAL_STATUSWIDOWED 0.11793 0.05527 2.134 0.0329 *
## FIRST_CAREUNITCSRU -1.19074 0.12032 -9.897 < 2e-16 ***
## FIRST_CAREUNITMICU 0.14864 0.06518 2.281 0.0226 *
## FIRST_CAREUNITSICU -0.84509 0.08508 -9.933 < 2e-16 ***
## FIRST_CAREUNITTSICU -0.66840 0.09909 -6.745 1.53e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13281 on 11158 degrees of freedom
## Residual deviance: 12540 on 11146 degrees of freedom
## AIC: 12566
##
## Number of Fisher Scoring iterations: 4
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 3.2669367 2.6582230 4.0251932
## GENDERM 0.7267132 0.6620572 0.7975655
## AGE 1.3103975 1.2532525 1.3703799
## ETHNICITYOTHER 1.6098768 1.2929677 2.0052989
## ETHNICITYWHITE 0.8752003 0.7472729 1.0220439
## LANGUAGEOTHER 0.8517989 0.7434865 0.9772923
## MARITAL_STATUSSINGLE 1.6633622 1.4707744 1.8833109
## MARITAL_STATUSUNKNOWN 0.6030541 0.4843306 0.7519202
## MARITAL_STATUSWIDOWED 1.1251604 1.0097372 1.2540385
## FIRST_CAREUNITCSRU 0.3039954 0.2399291 0.3846012
## FIRST_CAREUNITMICU 1.1602561 1.0203280 1.3174134
## FIRST_CAREUNITSICU 0.4295194 0.3634068 0.5072978
## FIRST_CAREUNITTSICU 0.5125257 0.4220521 0.6224501Model 2b (Clinical Variables + ICU)
- Clinical Variables (Model 1b)
- First Intensive Care Unit
- Care Provider (Random)
gm_2b <- glm(NQF ~ SOFA +
FIRST_CAREUNIT,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_2b)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ SOFA + FIRST_CAREUNIT, family = binomial(link = "logit"),
## data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8415 -1.2707 0.7305 0.7639 1.3155
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.997956 0.061864 16.131 < 2e-16 ***
## SOFA 0.025667 0.007342 3.496 0.000473 ***
## FIRST_CAREUNITCSRU -1.299375 0.117808 -11.030 < 2e-16 ***
## FIRST_CAREUNITMICU 0.076010 0.063712 1.193 0.232859
## FIRST_CAREUNITSICU -0.800513 0.083025 -9.642 < 2e-16 ***
## FIRST_CAREUNITTSICU -0.738141 0.096241 -7.670 1.72e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13281 on 11158 degrees of freedom
## Residual deviance: 12892 on 11153 degrees of freedom
## AIC: 12904
##
## Number of Fisher Scoring iterations: 4
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 2.7127318 2.4051091 3.0653430
## SOFA 1.0259989 1.0113926 1.0409289
## FIRST_CAREUNITCSRU 0.2727022 0.2162857 0.3433094
## FIRST_CAREUNITMICU 1.0789731 0.9515195 1.2215327
## FIRST_CAREUNITSICU 0.4490984 0.3815144 0.5282975
## FIRST_CAREUNITTSICU 0.4780018 0.3958215 0.5772770Model 3 (Demographics + Clinical Variables + ICU)
- Demographics (as in Model 1a)
- SOFA Score (As Model 1b)
- First Intensive Care Unit
- Care Provider (Random)
gm_icu <- glm(NQF ~
GENDER +
AGE +
ETHNICITY +
LANGUAGE +
MARITAL_STATUS +
SOFA +
FIRST_CAREUNIT,
data = temp,
family = binomial(link = "logit"))
## View
model_info(gm_icu)## Waiting for profiling to be done...## $`Model Summary`
##
## Call:
## glm(formula = NQF ~ GENDER + AGE + ETHNICITY + LANGUAGE + MARITAL_STATUS +
## SOFA + FIRST_CAREUNIT, family = binomial(link = "logit"),
## data = temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2894 -1.1498 0.6684 0.8166 1.5496
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.047484 0.108555 9.649 < 2e-16 ***
## GENDERM -0.349655 0.047894 -7.301 2.86e-13 ***
## AGE 0.281278 0.022939 12.262 < 2e-16 ***
## ETHNICITYOTHER 0.479055 0.112366 4.263 2.01e-05 ***
## ETHNICITYWHITE -0.119561 0.080072 -1.493 0.1354
## LANGUAGEOTHER -0.176088 0.070031 -2.514 0.0119 *
## MARITAL_STATUSSINGLE 0.521340 0.063216 8.247 < 2e-16 ***
## MARITAL_STATUSUNKNOWN -0.487552 0.112375 -4.339 1.43e-05 ***
## MARITAL_STATUSWIDOWED 0.126786 0.055320 2.292 0.0219 *
## SOFA 0.043068 0.007615 5.655 1.55e-08 ***
## FIRST_CAREUNITCSRU -1.223911 0.120647 -10.145 < 2e-16 ***
## FIRST_CAREUNITMICU 0.138801 0.065314 2.125 0.0336 *
## FIRST_CAREUNITSICU -0.828584 0.085265 -9.718 < 2e-16 ***
## FIRST_CAREUNITTSICU -0.646877 0.099174 -6.523 6.91e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 13281 on 11158 degrees of freedom
## Residual deviance: 12508 on 11145 degrees of freedom
## AIC: 12536
##
## Number of Fisher Scoring iterations: 4
##
##
## $`OR Summary`
## OR 2.5 % 97.5 %
## (Intercept) 2.8504691 2.3067742 3.5305457
## GENDERM 0.7049316 0.6417118 0.7742484
## AGE 1.3248217 1.2666855 1.3858749
## ETHNICITYOTHER 1.6145473 1.2956016 2.0129012
## ETHNICITYWHITE 0.8873098 0.7572923 1.0366430
## LANGUAGEOTHER 0.8385444 0.7314905 0.9626274
## MARITAL_STATUSSINGLE 1.6842835 1.4888322 1.9075766
## MARITAL_STATUSUNKNOWN 0.6141281 0.4930084 0.7660711
## MARITAL_STATUSWIDOWED 1.1351745 1.0186282 1.2653224
## SOFA 1.0440092 1.0285974 1.0597692
## FIRST_CAREUNITCSRU 0.2940779 0.2319516 0.3722915
## FIRST_CAREUNITMICU 1.1488952 1.0100659 1.3048646
## FIRST_CAREUNITSICU 0.4366674 0.3693252 0.5159274
## FIRST_CAREUNITTSICU 0.5236788 0.4311703 0.6361001par(mfrow=c(2,3))
rocplot(as.numeric(predict(gm_a, type="response")), temp$NQF, col="blue", main = "Model 1a (Demographics)")
rocplot(as.numeric(predict(gm_b, type="response")), temp$NQF, col="blue", main = "Model 1b (Clinical Variables)")
rocplot(as.numeric(predict(gm_2a, type="response")), temp$NQF, col="blue", main = "Model 2a (Demographics + ICU)")
rocplot(as.numeric(predict(gm_2b, type="response")), temp$NQF, col="blue", main = "Model 2b (Clinical Variables + ICU)")
rocplot(as.numeric(predict(gm_icu, type="response")), temp$NQF, col="blue", main = "Model 3 (All Covariates)")
Results
tmp <- rbind(results, as.data.frame(cbind(mod, icc, std, lev, t_r2m, t_R2c, d_r2m, d_R2c)))
tmp$index <- c(0, 1, 1, 2, 2, 3, 0, 1, 1, 2, 2, 3)
## Correct variables
tmp$mod <- as.character(tmp$mod)
tmp$icc <- as.numeric(as.character(tmp$icc))
tmp$std <- as.numeric(as.character(tmp$std))
tmp$lev <- as.character(tmp$lev)
tmp$t_r2m <- as.numeric(as.character(tmp$t_r2m))
tmp$t_R2c <- as.numeric(as.character(tmp$t_R2c))
tmp$std <- round(tmp$std, 4)
tmp$icc <- round(tmp$icc, 4)
tmp$t_r2m <- round(tmp$t_r2m, 4)
tmp$t_R2c <- round(tmp$t_R2c, 4)
colnames(tmp) <- c("Model", "ICC", "Standard Deviation", "Level", colnames(tmp)[5:ncol(tmp)])
tmp$Letter <- c("Model: No Covariates", "Model: a (Demographics)",
"Model: b (Clinical Variables)", "Model: a (Demographics)",
"Model: b (Clinical Variables)", "Model: All Covariates", "Model: No Covariates", "Model: a (Demographics)", "Model: b (Clinical Variables)", "Model: a (Demographics)", "Model: b (Clinical Variables)", "Model: All Covariates")
colnames(tmp)[which(colnames(tmp) == "t_r2m")] <- "Marginal Coefficient of Determination"
colnames(tmp)[which(colnames(tmp) == "t_R2c")] <- "Conditional Coefficient of Determination"
print(tmp)## Model ICC Standard Deviation Level
## 1 Model 0 0.3938 1.4620 Provider
## 2 Model 1a 0.3911 1.4537 Provider
## 3 Model 1b 0.3938 1.4620 Provider
## 4 Model 2a 0.3987 1.4768 Provider
## 5 Model 2b 0.4054 1.4976 Provider
## 6 Model 3 0.3998 1.4802 Provider
## 7 Model 0 0.3794 1.4181 Team
## 8 Model 1a 0.3707 1.3920 Team
## 9 Model 1b 0.3787 1.4162 Team
## 10 Model 2a 0.3532 1.3403 Team
## 11 Model 2b 0.3677 1.3832 Team
## 12 Model 3 0.3547 1.3447 Team
## Marginal Coefficient of Determination
## 1 0.0000
## 2 0.0244
## 3 0.0000
## 4 0.0322
## 5 0.0076
## 6 0.0328
## 7 0.0000
## 8 0.0317
## 9 0.0013
## 10 0.0406
## 11 0.0074
## 12 0.0436
## Conditional Coefficient of Determination d_r2m
## 1 0.3938 0
## 2 0.4060 0.0216184415541336
## 3 0.3938 1.14290378300496e-07
## 4 0.4180 0.0285735799337614
## 5 0.4099 0.0067323447518925
## 6 0.4194 0.0290856883952417
## 7 0.3794 0
## 8 0.3906 0.025492554357851
## 9 0.3795 0.00104391524155094
## 10 0.3795 0.0325798018098079
## 11 0.3724 0.00595304054668518
## 12 0.3828 0.0349931785928889
## d_R2c index Letter
## 1 0.34752823595739 0 Model: No Covariates
## 2 0.359113489717591 1 Model: a (Demographics)
## 3 0.347527296071577 1 Model: b (Clinical Variables)
## 4 0.370611777313085 2 Model: a (Demographics)
## 5 0.362829255995969 2 Model: b (Clinical Variables)
## 6 0.37196177250928 3 Model: All Covariates
## 7 0.304086725763605 0 Model: No Covariates
## 8 0.314213109198477 1 Model: a (Demographics)
## 9 0.304241721258049 1 Model: b (Clinical Variables)
## 10 0.304173223878352 2 Model: a (Demographics)
## 11 0.297849819413253 2 Model: b (Clinical Variables)
## 12 0.307211798782246 3 Model: All Covariates##theme_set(theme_classic())
ggplot(tmp, aes(x=Model, y=`Marginal Coefficient of Determination`,
label = Level,
colour = Level)) +
geom_point(size=5, aes(col=Level), stat = "identity") + # Draw points
#geom_text(color="black", size=3) +
geom_segment(aes(x=Model,
xend=Model,
y=min(`Marginal Coefficient of Determination`),
yend=max(`Marginal Coefficient of Determination`)+0.02),
linetype="dashed",
size=0.1) + # Draw dashed lines
labs(title="Dot Plot of Marginal Coefficient of Determination by Model", #,
subtitle="Care Team Level (Left), Provider Level (Right)") +#,
#caption="source: mpg")# +
# Change fontface. Allowed values : 1(normal),
# 2(bold), 3(italic), 4(bold.italic)
geom_text(size=4,aes(label=`Marginal Coefficient of Determination`, fontface = 2), position=position_dodge(width=0.9), hjust=0, vjust = -1,colour="black") +
#geom_text(size=2,aes(label=Level, fontface = 2),position=position_dodge(width=0.9), hjust=-0.45, vjust = 1,colour="black") +
coord_flip()
ggplot(tmp, aes(x=Model, y=`Conditional Coefficient of Determination`,
label = Level,
colour = Level)) +
geom_point(size=5, aes(col=Level), stat = "identity") + # Draw points
#geom_text(color="black", size=3) +
geom_segment(aes(x=Model,
xend=Model,
y=min(`Conditional Coefficient of Determination`),
yend=max(`Conditional Coefficient of Determination`)+0.02),
linetype="dashed",
size=0.1) + # Draw dashed lines
labs(title="Dot Plot of Conditional Coefficient of Determination by Model", #,
subtitle="Care Team Level (Left), Provider Level (Right)") +#,
#caption="source: mpg")# +
# Change fontface. Allowed values : 1(normal),
# 2(bold), 3(italic), 4(bold.italic)
geom_text(size=4,aes(label=`Conditional Coefficient of Determination`, fontface = 2),position=position_dodge(width=0.9), hjust=0, vjust = -1,colour="black") +
#geom_text(size=2,aes(label=Level, fontface = 2),position=position_dodge(width=0.9), hjust=-0.45, vjust = 1,colour="black") +
coord_flip()
References
Vincent JL, de Mendonça A, Cantraine F, Moreno R, Takala J, Suter PM, Sprung CL, Colardyn F, Blecher S. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on “sepsis-related problems” of the European Society of Intensive Care Medicine. Crit Care Med 1998 Nov;26(11):1793-800. PMID 9824069.
Moreno R, Vincent JL, Matos R, Mendonça A, Cantraine F, Thijs L, Takala J, Sprung C, Antonelli M, Bruining H, Willatts S. The use of maximum SOFA score to quantify organ dysfunction/failure in intensive care. Results of a prospective, multicentre study. Working Group on Sepsis related Problems of the ESICM. Intensive Care Med 1999 Jul;25(7):686-96. PMID 10470572.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57, 289–300.