This has both direct care and supernumerary nurses. Not used in final paper.
summary(H1.icu <- lmer(diedicu ~ IMlo + prop.occ.c + mean.daily.transfer +
N.dc.perbed + N.supernum.perbed + num.consult.perbed + intensivist + meanap2probuk +
I(avyulos/100) + (1 | trust.code), data = dta, family = binomial(), na.action = na.omit,
subset = ss8), digits = 3)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedicu ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + N.supernum.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code)
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 23178 23272 -11578 23156
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.081 0.285
## Number of obs: 37969, groups: trust.code, 65
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.3386 0.2088 -6.4 1.4e-10 ***
## IMlo 0.9665 0.0123 78.8 < 2e-16 ***
## prop.occ.c 0.2362 0.0948 2.5 0.0127 *
## mean.daily.transfer 0.8840 0.3726 2.4 0.0177 *
## N.dc.perbed -0.0708 0.0264 -2.7 0.0074 **
## N.supernum.perbed 0.0293 0.0887 0.3 0.7412
## num.consult.perbed -0.2380 0.0983 -2.4 0.0155 *
## intensivistYes -0.1660 0.1076 -1.5 0.1228
## meanap2probuk -0.0647 0.1412 -0.5 0.6469
## I(avyulos/100) 0.3887 0.1483 2.6 0.0087 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p N.spr. nm.cn. intnsY mnp2pr
## IMlo 0.019
## prop.occ.c -0.291 -0.005
## mn.dly.trns -0.064 0.014 0.049
## N.dc.perbed -0.352 -0.018 -0.134 -0.154
## N.sprnm.prb 0.011 -0.008 -0.043 -0.124 -0.001
## nm.cnslt.pr -0.318 -0.021 -0.047 0.217 -0.172 -0.104
## intensvstYs -0.046 -0.013 -0.016 -0.180 -0.177 0.154 0.001
## meanap2prbk -0.233 -0.016 -0.016 -0.032 0.025 0.012 0.021 -0.031
## I(vyls/100) -0.658 -0.001 0.008 -0.033 -0.219 -0.156 0.273 0.103 0.027
summary(H1.hosp <- lmer(diedhosp ~ IMlo + prop.occ.c + mean.daily.transfer +
N.dc.perbed + N.supernum.perbed + num.consult.perbed + intensivist + meanap2probuk +
I(avyulos/100) + (1 | trust.code), data = dta, family = binomial(), na.action = na.omit,
subset = ss8), digits = 3)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedhosp ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + N.supernum.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code)
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 30196 30290 -15087 30174
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.0691 0.263
## Number of obs: 37390, groups: trust.code, 65
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.22930 0.18592 -1.2 0.2175
## IMlo 0.96148 0.01086 88.6 <2e-16 ***
## prop.occ.c 0.22216 0.08075 2.8 0.0059 **
## mean.daily.transfer 1.00813 0.34068 3.0 0.0031 **
## N.dc.perbed -0.05182 0.02353 -2.2 0.0276 *
## N.supernum.perbed 0.00432 0.08071 0.1 0.9573
## num.consult.perbed -0.20688 0.08846 -2.3 0.0193 *
## intensivistYes -0.12984 0.09756 -1.3 0.1832
## meanap2probuk 0.03261 0.12048 0.3 0.7866
## I(avyulos/100) 0.24648 0.13437 1.8 0.0666 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p N.spr. nm.cn. intnsY mnp2pr
## IMlo 0.056
## prop.occ.c -0.278 -0.004
## mn.dly.trns -0.065 0.018 0.044
## N.dc.perbed -0.343 -0.012 -0.127 -0.153
## N.sprnm.prb 0.004 -0.010 -0.041 -0.125 0.006
## nm.cnslt.pr -0.312 -0.013 -0.043 0.220 -0.197 -0.105
## intensvstYs -0.054 -0.010 -0.013 -0.177 -0.178 0.152 0.004
## meanap2prbk -0.229 -0.009 -0.012 -0.030 0.027 0.012 0.021 -0.032
## I(vyls/100) -0.664 -0.012 0.007 -0.034 -0.227 -0.152 0.270 0.106 0.027
summary(H2.icu <- lmer(diedicu ~ IMlo + prop.occ.c + mean.daily.transfer +
N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) +
(1 | trust.code), data = dta, family = binomial(), na.action = na.omit,
subset = ss8), digits = 3)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedicu ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code)
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 23176 23261 -11578 23156
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.0809 0.284
## Number of obs: 37969, groups: trust.code, 65
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.3394 0.2087 -6.4 1.4e-10 ***
## IMlo 0.9665 0.0123 78.8 < 2e-16 ***
## prop.occ.c 0.2375 0.0947 2.5 0.0121 *
## mean.daily.transfer 0.8991 0.3695 2.4 0.0149 *
## N.dc.perbed -0.0708 0.0264 -2.7 0.0074 **
## num.consult.perbed -0.2346 0.0977 -2.4 0.0163 *
## intensivistYes -0.1714 0.1063 -1.6 0.1067
## meanap2probuk -0.0653 0.1412 -0.5 0.6436
## I(avyulos/100) 0.3964 0.1464 2.7 0.0068 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p nm.cn. intnsY mnp2pr
## IMlo 0.019
## prop.occ.c -0.291 -0.005
## mn.dly.trns -0.063 0.013 0.044
## N.dc.perbed -0.352 -0.018 -0.134 -0.156
## nm.cnslt.pr -0.319 -0.022 -0.052 0.206 -0.173
## intensvstYs -0.049 -0.012 -0.009 -0.164 -0.179 0.017
## meanap2prbk -0.234 -0.016 -0.016 -0.030 0.025 0.022 -0.034
## I(vyls/100) -0.664 -0.002 0.002 -0.054 -0.222 0.261 0.130 0.029
summary(H2.hosp <- lmer(diedhosp ~ IMlo + prop.occ.c + mean.daily.transfer +
N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) +
(1 | trust.code), data = dta, family = binomial(), na.action = na.omit,
subset = ss8), digits = 3)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedhosp ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code)
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 30194 30280 -15087 30174
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.0691 0.263
## Number of obs: 37390, groups: trust.code, 65
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2293 0.1859 -1.2 0.2173
## IMlo 0.9615 0.0109 88.6 <2e-16 ***
## prop.occ.c 0.2223 0.0807 2.8 0.0059 **
## mean.daily.transfer 1.0103 0.3379 3.0 0.0028 **
## N.dc.perbed -0.0518 0.0235 -2.2 0.0276 *
## num.consult.perbed -0.2064 0.0879 -2.3 0.0189 *
## intensivistYes -0.1306 0.0964 -1.4 0.1754
## meanap2probuk 0.0325 0.1205 0.3 0.7874
## I(avyulos/100) 0.2476 0.1328 1.9 0.0622 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p nm.cn. intnsY mnp2pr
## IMlo 0.056
## prop.occ.c -0.278 -0.005
## mn.dly.trns -0.065 0.017 0.039
## N.dc.perbed -0.344 -0.012 -0.126 -0.154
## nm.cnslt.pr -0.313 -0.014 -0.048 0.210 -0.197
## intensvstYs -0.055 -0.009 -0.007 -0.161 -0.181 0.021
## meanap2prbk -0.229 -0.009 -0.011 -0.029 0.026 0.022 -0.034
## I(vyls/100) -0.671 -0.013 0.001 -0.054 -0.229 0.259 0.132 0.029
H3.icu <- update(H2.icu, . ~ . + I(N.dc.perbed^2))
summary(H3.icu)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedicu ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code) + I(N.dc.perbed^2)
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 23178 23272 -11578 23156
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.0798 0.282
## Number of obs: 37969, groups: trust.code, 65
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.27683 0.31532 -4.0 5.1e-05 ***
## IMlo 0.96649 0.01227 78.8 < 2e-16 ***
## prop.occ.c 0.23588 0.09487 2.5 0.0129 *
## mean.daily.transfer 0.90581 0.36850 2.5 0.0140 *
## N.dc.perbed -0.09835 0.10837 -0.9 0.3641
## num.consult.perbed -0.22918 0.09969 -2.3 0.0215 *
## intensivistYes -0.17160 0.10573 -1.6 0.1046
## meanap2probuk -0.06717 0.14126 -0.5 0.6344
## I(avyulos/100) 0.39945 0.14601 2.7 0.0062 **
## I(N.dc.perbed^2) 0.00269 0.01028 0.3 0.7937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p nm.cn. intnsY mnp2pr I(/100
## IMlo 0.023
## prop.occ.c -0.242 -0.006
## mn.dly.trns 0.018 0.014 0.038
## N.dc.perbed -0.786 -0.018 0.032 -0.115
## nm.cnslt.pr -0.043 -0.019 -0.066 0.218 -0.251
## intensvstYs -0.052 -0.012 -0.007 -0.166 -0.017 0.010
## meanap2prbk -0.179 -0.016 -0.013 -0.033 0.038 0.014 -0.033
## I(vyls/100) -0.384 -0.001 -0.003 -0.048 -0.122 0.270 0.127 0.026
## I(N.dc.p^2) 0.752 0.014 -0.067 0.080 -0.970 0.217 -0.027 -0.033 0.070
H3.hosp <- update(H2.hosp, . ~ . + I(N.dc.perbed^2))
summary(H3.hosp)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedhosp ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code) + I(N.dc.perbed^2)
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 30196 30290 -15087 30174
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.069 0.263
## Number of obs: 37390, groups: trust.code, 65
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.22582 0.28521 -0.8 0.4285
## IMlo 0.96148 0.01086 88.6 <2e-16 ***
## prop.occ.c 0.22223 0.08080 2.8 0.0059 **
## mean.daily.transfer 1.01072 0.33894 3.0 0.0029 **
## N.dc.perbed -0.05339 0.09823 -0.5 0.5867
## num.consult.perbed -0.20603 0.08999 -2.3 0.0220 *
## intensivistYes -0.13066 0.09640 -1.4 0.1753
## meanap2probuk 0.03247 0.12053 0.3 0.7877
## I(avyulos/100) 0.24788 0.13301 1.9 0.0624 .
## I(N.dc.perbed^2) 0.00015 0.00926 0.0 0.9871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p nm.cn. intnsY mnp2pr I(/100
## IMlo 0.045
## prop.occ.c -0.222 -0.005
## mn.dly.trns 0.020 0.017 0.035
## N.dc.perbed -0.790 -0.013 0.022 -0.116
## nm.cnslt.pr -0.038 -0.011 -0.058 0.222 -0.253
## intensvstYs -0.055 -0.009 -0.005 -0.163 -0.019 0.015
## meanap2prbk -0.174 -0.010 -0.009 -0.032 0.038 0.014 -0.033
## I(vyls/100) -0.387 -0.013 -0.003 -0.048 -0.118 0.266 0.130 0.026
## I(N.dc.p^2) 0.759 0.011 -0.053 0.082 -0.971 0.213 -0.025 -0.033 0.065
H4.icu <- update(H2.icu, . ~ . + IMlo * N.dc.perbed)
summary(H4.icu, digits = 3)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedicu ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code) + IMlo:N.dc.perbed
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 23172 23266 -11575 23150
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.0813 0.285
## Number of obs: 37969, groups: trust.code, 65
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.32184 0.20900 -6.32 2.5e-10 ***
## IMlo 1.08366 0.04910 22.07 < 2e-16 ***
## prop.occ.c 0.23586 0.09463 2.49 0.0127 *
## mean.daily.transfer 0.90089 0.36999 2.43 0.0149 *
## N.dc.perbed -0.07242 0.02639 -2.74 0.0061 **
## num.consult.perbed -0.23794 0.09797 -2.43 0.0152 *
## intensivistYes -0.16182 0.10646 -1.52 0.1285
## meanap2probuk -0.06886 0.14129 -0.49 0.6260
## I(avyulos/100) 0.39142 0.14654 2.67 0.0076 **
## IMlo:N.dc.perbed -0.02334 0.00941 -2.48 0.0131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p nm.cn. intnsY mnp2pr I(/100
## IMlo 0.037
## prop.occ.c -0.290 -0.008
## mn.dly.trns -0.065 0.005 0.044
## N.dc.perbed -0.351 -0.029 -0.135 -0.154
## nm.cnslt.pr -0.320 -0.019 -0.052 0.207 -0.172
## intensvstYs -0.047 0.033 -0.009 -0.164 -0.180 0.016
## meanap2prbk -0.234 -0.014 -0.016 -0.031 0.025 0.022 -0.034
## I(vyls/100) -0.665 -0.013 0.002 -0.053 -0.221 0.261 0.129 0.029
## IMl:N.dc.pr -0.034 -0.968 0.007 -0.002 0.027 0.014 -0.037 0.011 0.013
H4.hosp <- update(H2.hosp, . ~ . + IMlo * N.dc.perbed)
summary(H4.hosp, digits = 3)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedhosp ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code) + IMlo:N.dc.perbed
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 30196 30289 -15087 30174
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.0691 0.263
## Number of obs: 37390, groups: trust.code, 65
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.20029 0.18860 -1.06 0.2882
## IMlo 0.99991 0.04338 23.05 <2e-16 ***
## prop.occ.c 0.22174 0.08066 2.75 0.0060 **
## mean.daily.transfer 1.00750 0.33804 2.98 0.0029 **
## N.dc.perbed -0.05726 0.02424 -2.36 0.0182 *
## num.consult.perbed -0.20744 0.08802 -2.36 0.0184 *
## intensivistYes -0.12788 0.09647 -1.33 0.1850
## meanap2probuk 0.03175 0.12048 0.26 0.7922
## I(avyulos/100) 0.24735 0.13280 1.86 0.0625 .
## IMlo:N.dc.perbed -0.00770 0.00839 -0.92 0.3588
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p nm.cn. intnsY mnp2pr I(/100
## IMlo 0.177
## prop.occ.c -0.275 -0.009
## mn.dly.trns -0.067 -0.005 0.039
## N.dc.perbed -0.369 -0.240 -0.121 -0.146
## nm.cnslt.pr -0.312 -0.016 -0.048 0.210 -0.187
## intensvstYs -0.049 0.028 -0.007 -0.161 -0.183 0.020
## meanap2prbk -0.227 -0.009 -0.011 -0.029 0.027 0.022 -0.034
## I(vyls/100) -0.662 -0.006 0.001 -0.054 -0.222 0.259 0.132 0.029
## IMl:N.dc.pr -0.168 -0.968 0.008 0.009 0.245 0.013 -0.031 0.007 0.003
H5.icu <- update(H4.icu, . ~ . + ave.cost.nurse + ratio.pbq.wte)
summary(H5.icu)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedicu ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code) + ave.cost.nurse + ratio.pbq.wte + IMlo:N.dc.perbed
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 22404 22515 -11189 22378
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.0797 0.282
## Number of obs: 36760, groups: trust.code, 63
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.31730 0.31566 -4.17 3e-05 ***
## IMlo 1.08494 0.05001 21.69 <2e-16 ***
## prop.occ.c 0.28074 0.09684 2.90 0.0037 **
## mean.daily.transfer 0.90085 0.37054 2.43 0.0150 *
## N.dc.perbed -0.07844 0.02748 -2.85 0.0043 **
## num.consult.perbed -0.26982 0.10105 -2.67 0.0076 **
## intensivistYes -0.16805 0.10816 -1.55 0.1203
## meanap2probuk -0.04808 0.14329 -0.34 0.7372
## I(avyulos/100) 0.40048 0.14757 2.71 0.0067 **
## ave.cost.nurse 0.00502 0.00905 0.55 0.5791
## ratio.pbq.wte -0.16013 0.14383 -1.11 0.2656
## IMlo:N.dc.perbed -0.02358 0.00963 -2.45 0.0144 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p nm.cn. intnsY mnp2pr I(/100
## IMlo 0.039
## prop.occ.c -0.175 -0.005
## mn.dly.trns 0.035 0.006 0.049
## N.dc.perbed -0.335 -0.033 -0.141 -0.182
## nm.cnslt.pr -0.092 -0.022 -0.047 0.222 -0.175
## intensvstYs -0.138 0.029 -0.012 -0.159 -0.180 0.004
## meanap2prbk -0.147 -0.013 -0.017 -0.029 0.021 0.023 -0.035
## I(vyls/100) -0.508 -0.015 0.001 -0.052 -0.225 0.232 0.157 0.029
## ave.cst.nrs -0.626 -0.003 -0.026 -0.125 0.167 -0.237 0.065 -0.010 0.063
## rati.pbq.wt -0.355 -0.030 -0.005 0.015 -0.007 0.108 0.159 -0.007 0.086
## IMl:N.dc.pr -0.039 -0.968 0.004 -0.003 0.031 0.016 -0.033 0.009 0.015
## av.cs. rt.pb.
## IMlo
## prop.occ.c
## mn.dly.trns
## N.dc.perbed
## nm.cnslt.pr
## intensvstYs
## meanap2prbk
## I(vyls/100)
## ave.cst.nrs
## rati.pbq.wt -0.090
## IMl:N.dc.pr 0.005 0.029
H5.hosp <- update(H4.hosp, . ~ . + ave.cost.nurse + ratio.pbq.wte)
summary(H5.hosp)
## Generalized linear mixed model fit by the Laplace approximation
## Formula: diedhosp ~ IMlo + prop.occ.c + mean.daily.transfer + N.dc.perbed + num.consult.perbed + intensivist + meanap2probuk + I(avyulos/100) + (1 | trust.code) + ave.cost.nurse + ratio.pbq.wte + IMlo:N.dc.perbed
## Data: dta
## Subset: ss8
## AIC BIC logLik deviance
## 29177 29287 -14575 29151
## Random effects:
## Groups Name Variance Std.Dev.
## trust.code (Intercept) 0.0703 0.265
## Number of obs: 36183, groups: trust.code, 63
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.26036 0.30069 -0.87 0.3865
## IMlo 0.99861 0.04416 22.61 <2e-16 ***
## prop.occ.c 0.25401 0.08266 3.07 0.0021 **
## mean.daily.transfer 0.99241 0.34440 2.88 0.0040 **
## N.dc.perbed -0.05723 0.02541 -2.25 0.0243 *
## num.consult.perbed -0.23438 0.09222 -2.54 0.0110 *
## intensivistYes -0.13265 0.09962 -1.33 0.1830
## meanap2probuk 0.02843 0.12230 0.23 0.8162
## I(avyulos/100) 0.25317 0.13563 1.87 0.0619 .
## ave.cost.nurse 0.00643 0.00914 0.70 0.4821
## ratio.pbq.wte -0.12827 0.13195 -0.97 0.3310
## IMlo:N.dc.perbed -0.00767 0.00859 -0.89 0.3719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) IMlo prp.c. mn.dl. N.dc.p nm.cn. intnsY mnp2pr I(/100
## IMlo 0.130
## prop.occ.c -0.156 -0.007
## mn.dly.trns 0.047 -0.003 0.044
## N.dc.perbed -0.339 -0.241 -0.126 -0.175
## nm.cnslt.pr -0.052 -0.016 -0.041 0.231 -0.200
## intensvstYs -0.150 0.025 -0.010 -0.159 -0.179 0.005
## meanap2prbk -0.136 -0.008 -0.014 -0.028 0.025 0.023 -0.035
## I(vyls/100) -0.496 -0.007 0.000 -0.054 -0.225 0.224 0.162 0.028
## ave.cst.nrs -0.664 -0.011 -0.025 -0.136 0.164 -0.258 0.079 -0.008 0.076
## rati.pbq.wt -0.339 -0.021 -0.005 0.018 -0.005 0.109 0.161 -0.007 0.082
## IMl:N.dc.pr -0.125 -0.968 0.006 0.007 0.246 0.013 -0.028 0.006 0.004
## av.cs. rt.pb.
## IMlo
## prop.occ.c
## mn.dly.trns
## N.dc.perbed
## nm.cnslt.pr
## intensvstYs
## meanap2prbk
## I(vyls/100)
## ave.cst.nrs
## rati.pbq.wt -0.086
## IMl:N.dc.pr 0.012 0.021
ggplot(death.dta[ix, ], aes(x = icu, y = prop.died.icu)) + geom_point(colour = "blue",
shape = 15) + labs(list(x = "", y = "Proportion died")) + scale_x_continuous(labels = NULL) +
opts(axis.ticks = theme_blank())
ggplot(death.dta, aes(x = icu, y = prop.died.hosp)) + geom_point(colour = "blue",
shape = 15) + labs(list(x = "", y = "Proportion died")) + scale_x_continuous(labels = NULL) +
opts(axis.ticks = theme_blank())
ggplot(dta, aes(x = IMlo)) + geom_density() + geom_hline(aes(yintercept = 0),
colour = "white") + xlim(c(-7, 7)) + ylab("Density") + xlab("IM log odds")
## Warning: Removed 96 rows containing non-finite values (stat_density).
ggplot(staff.dta2, aes(x = ICU, y = Number, fill = Staff)) + geom_bar(stat = "Identity",
position = "dodge") + labs(list(x = "", y = "Number")) + scale_x_continuous(labels = NULL) +
opts(axis.ticks = theme_blank())
