SEM_BAYES_CMAMA
Mauricio Hernandez Carrillo , coaut. Ronaldo
17/5/2022
Paquetes utilizados
Se hará uso de la base de datos recolectada la cual consta de 80 observaciones y 105 variables de mujeres con cáncer de mama en la ciudad de Cali, concretando algunos parámetros para las simulaciones y el MCMC para obtener la convergencia de los parámetros esperados. Donde se parametrizara el numero de muestras de calentamiento (BURNIN1), el número de iteraciones(SAMPLE1) y el numero de cadenas (NCHAINS1).
options(mc.cores = parallel::detectCores())
set.seed(535535)
datos2 <- readRDS("data/datos2.RDS")
datos <- datos2
set.seed(535535)
BURNIN1 = 8000 #3000
SAMPLE1 = 12000 #6500
NCHAINS1 = 12 #6
#para cargar con menos numero de iteraciones y cadenas
BURNIN = 3000 # 2500
SAMPLE = 7000 # 6500
CHAINS = 6 # 6MODELOS PLANTEADOS
Modelos finales con 8000 muestras, 5000 de calentamiento y 10 cadenas, Todo usado para SEM BAYESIANO por medio de Cadenas de Markov de Monte Carlo. A partir del análisis de los modelos propuestos por Wilson & Cleary para predimiento confirmatorio basados en los estudios realizado la RELACIÓN ENTRE SÍNTOMAS, FUNCIONALIDAD, PERCEPCIÓN DE SALUD Y CALIDAD DE VIDA EN MUJERES CON CÁNCER DE MAMA SOMETIDAS A QUIMIOTERAPIA. CALI-COLOMBIA.
set.seed(535535)
model_ref_bio_ind_sinedad <- '
# measurement model
funcionalidad =~ prior("normal(0,15)")*f_br23 + prior("normal(0,15)")*f_c30
sintomas =~ prior("normal(0,15)")*s_br23 + prior("normal(0,15)")*s_c30
biologicas =~ her2_pos + comorb + estadio_avz
# regressions
cv_gral ~ salud + sintomas + funcionalidad
salud ~ funcionalidad
funcionalidad ~ sintomas
sintomas ~ biologicas
# residual correlations
'
model_ref_bio_ind_sinedad_sinher2 <- '
# measurement model
funcionalidad =~ prior("normal(0,15)")*f_br23 + prior("normal(0,15)")*f_c30
sintomas =~ prior("normal(0,15)")*s_br23 + prior("normal(0,15)")*s_c30
biologicas =~ comorb + estadio_avz
# regressions
cv_gral ~ salud + sintomas + funcionalidad
salud ~ funcionalidad
funcionalidad ~ sintomas
sintomas ~ biologicas
# residual correlations
'Exploracion de funcion bsem
De el paquete Blavaan que ayuda a ejecutar la estimacion de los modelos bayesianos para ecuaciones de modelos estructurales, por medio Markov Chain Monte Carlo (MCMC) el cual es un metodo de simulación para generar muestras de las distribuciones a posteriori y estimar cantidades de interes a posteriori.
#MODELO FINAL
#EFECTOS SOBRE CALIDAD DE VIDA,BIOLOGICAS,SIN EDAD
set.seed(535535)
fitref_bio_ind_sinedad <- bsem(
model = model_ref_bio_ind_sinedad,
data = datos,
auto.var = TRUE,
auto.fix.first = TRUE,
auto.cov.lv.x = TRUE,
save.lvs = TRUE,
inits = "prior",
sample = BURNIN1,
burnin = SAMPLE1,
n.chains = NCHAINS1)Computing posterior predictives...set.seed(535535)
fitref_bio_ind_sinedad_sinher2 <- bsem(
model = model_ref_bio_ind_sinedad_sinher2,
data = datos,
auto.var = TRUE,
auto.fix.first = TRUE,
auto.cov.lv.x = TRUE,
save.lvs = TRUE,
inits = "prior",
sample = BURNIN1,
burnin = SAMPLE1,
n.chains = NCHAINS1)Computing posterior predictives...Resumen del modelo
summary(fitref_bio_ind_sinedad,standardized = TRUE, rsquare=TRUE)#esta## ** WARNING ** blavaan (0.4-1) did NOT converge after 12000 adapt+burnin iterations
## ** WARNING ** Proceed with caution
##
## Number of observations 80
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -916.499 0.064
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## funcionalidad =~
## f_br23 1.000 0.428 0.710
## f_c30 0.221 4.213 -14.876 1.794 0.094 0.324
## sintomas =~
## s_br23 1.000 0.294 0.543
## s_c30 2.365 0.478 1.681 3.522 0.696 0.934
## biologicas =~
## her2_pos 1.000 0.033 0.064
## comorb 0.105 6.184 -13.483 13.817 0.003 0.007
## estadio_avz -1.059 9.705 -19.347 18.309 -0.035 -0.078
## Rhat Prior
##
##
## 4.112 normal(0,15)
##
##
## 1.005 normal(0,15)
##
##
## 1.000 normal(0,10)
## 1.000 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## cv_gral ~
## salud 0.759 0.067 0.627 0.893 0.759 0.770
## sintomas -0.665 5.374 -13.124 11.416 -0.196 -0.153
## funcionalidad 0.290 4.016 -8.723 9.015 0.124 0.097
## salud ~
## funcionalidad 0.586 6.888 -24.151 3.418 0.250 0.193
## funcionalidad ~
## sintomas -1.396 0.571 -2.301 0.201 -0.961 -0.961
## sintomas ~
## biologicas 0.470 8.916 -17.712 17.857 0.052 0.052
## Rhat Prior
##
## 1.004 normal(0,10)
## 1.001 normal(0,10)
## 1.001 normal(0,10)
##
## 4.381 normal(0,10)
##
## 1.828 normal(0,10)
##
## 1.000 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 2.342 0.073 2.199 2.486 2.342 3.891
## .f_c30 2.438 0.088 2.264 2.610 2.438 8.371
## .s_br23 1.765 0.063 1.640 1.890 1.765 3.259
## .s_c30 2.164 0.090 1.986 2.341 2.164 2.904
## .her2_pos 0.438 0.057 0.327 0.549 0.438 0.860
## .comorb 0.337 0.055 0.230 0.444 0.337 0.704
## .estadio_avz 0.337 0.054 0.232 0.444 0.337 0.764
## .cv_gral 0.806 0.306 0.209 1.411 0.806 0.631
## .salud 4.337 0.206 3.931 4.743 4.337 3.349
## .funcionalidad 0.000 0.000 0.000
## .sintomas 0.000 0.000 0.000
## biologicas 0.000 0.000 0.000
## Rhat Prior
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.004 normal(0,10)
## 1.000 normal(0,10)
##
##
##
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 0.180 0.097 0.105 0.513 0.180 0.496
## .f_c30 0.076 0.029 0.018 0.137 0.076 0.895
## .s_br23 0.207 0.036 0.148 0.287 0.207 0.705
## .s_c30 0.071 0.041 0.001 0.148 0.071 0.128
## .her2_pos 0.258 0.042 0.188 0.354 0.258 0.996
## .comorb 0.229 0.043 0.156 0.320 0.229 1.000
## .estadio_avz 0.194 0.065 0.019 0.301 0.194 0.994
## .cv_gral 0.447 0.099 0.238 0.642 0.447 0.274
## .salud 1.614 0.288 1.138 2.261 1.614 0.963
## .funcionalidad 0.014 0.017 0.000 0.058 0.077 0.077
## .sintomas 0.086 0.047 0.003 0.188 0.997 0.997
## biologicas 0.001 0.003 0.000 0.007 1.000 1.000
## Rhat Prior
## 2.739 gamma(1,.5)[sd]
## 1.004 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.033 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.005 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.033 gamma(1,.5)[sd]
## 1.001 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
##
## R-Square:
## Estimate
## f_br23 0.504
## f_c30 0.105
## s_br23 0.295
## s_c30 0.872
## her2_pos 0.004
## comorb 0.000
## estadio_avz 0.006
## cv_gral 0.726
## salud 0.037
## funcionalidad 0.923
## sintomas 0.003summary(fitref_bio_ind_sinedad_sinher2,standardized = TRUE, rsquare=TRUE)### blavaan (0.4-1) results of 8000 samples after 12000 adapt/burnin iterations
##
## Number of observations 80
##
## Number of missing patterns 1
##
## Statistic MargLogLik PPP
## Value -653.562 0.087
##
## Latent Variables:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## funcionalidad =~
## f_br23 1.000 0.443 0.750
## f_c30 1.453 0.161 1.179 1.810 0.644 0.919
## sintomas =~
## s_br23 1.000 0.279 0.523
## s_c30 2.338 0.456 1.674 3.422 0.651 0.924
## biologicas =~
## comorb 1.000 0.049 0.102
## estadio_avz -0.538 8.767 -17.855 17.028 -0.027 -0.063
## Rhat Prior
##
##
## 1.000 normal(0,15)
##
##
## 1.000 normal(0,15)
##
##
## 1.001 normal(0,10)
##
## Regressions:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## cv_gral ~
## salud 0.758 0.068 0.625 0.889 0.758 0.780
## sintomas -0.451 5.515 -12.915 12.280 -0.126 -0.075
## funcionalidad 0.435 3.649 -7.722 8.863 0.193 0.116
## salud ~
## funcionalidad 2.609 0.394 1.901 3.451 1.156 0.674
## funcionalidad ~
## sintomas -1.525 0.320 -2.271 -1.037 -0.959 -0.959
## sintomas ~
## biologicas -0.333 7.825 -16.094 15.709 -0.059 -0.059
## Rhat Prior
##
## 1.000 normal(0,10)
## 1.000 normal(0,10)
## 1.000 normal(0,10)
##
## 1.000 normal(0,10)
##
## 1.000 normal(0,10)
##
## 1.001 normal(0,10)
##
## Intercepts:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 2.343 0.072 2.200 2.485 2.343 3.964
## .f_c30 2.437 0.088 2.264 2.611 2.437 3.477
## .s_br23 1.765 0.064 1.640 1.889 1.765 3.313
## .s_c30 2.164 0.090 1.986 2.341 2.164 3.068
## .comorb 0.338 0.054 0.231 0.444 0.338 0.694
## .estadio_avz 0.337 0.055 0.230 0.445 0.337 0.804
## .cv_gral 0.813 0.306 0.221 1.420 0.813 0.487
## .salud 4.337 0.206 3.934 4.741 4.337 2.526
## .funcionalidad 0.000 0.000 0.000
## .sintomas 0.000 0.000 0.000
## biologicas 0.000 0.000 0.000
## Rhat Prior
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,32)
## 1.000 normal(0,10)
## 1.000 normal(0,10)
##
##
##
##
## Variances:
## Estimate Post.SD pi.lower pi.upper Std.lv Std.all
## .f_br23 0.153 0.029 0.104 0.217 0.153 0.437
## .f_c30 0.077 0.028 0.025 0.135 0.077 0.156
## .s_br23 0.206 0.036 0.147 0.286 0.206 0.726
## .s_c30 0.073 0.040 0.001 0.147 0.073 0.146
## .comorb 0.234 0.039 0.170 0.322 0.234 0.990
## .estadio_avz 0.175 0.073 0.005 0.292 0.175 0.996
## .cv_gral 0.444 0.101 0.222 0.638 0.444 0.159
## .salud 1.610 0.285 1.137 2.252 1.610 0.546
## .funcionalidad 0.016 0.017 0.000 0.059 0.080 0.080
## .sintomas 0.077 0.048 0.001 0.180 0.997 0.997
## biologicas 0.002 0.005 0.000 0.014 1.000 1.000
## Rhat Prior
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
## 1.000 gamma(1,.5)[sd]
##
## R-Square:
## Estimate
## f_br23 0.563
## f_c30 0.844
## s_br23 0.274
## s_c30 0.854
## comorb 0.010
## estadio_avz 0.004
## cv_gral 0.841
## salud 0.454
## funcionalidad 0.920
## sintomas 0.003Informacion del BRMSEA
Para la evaciacion de ajuste para ecuacuones de modelos estructurales en un análisis factorial confirmatorio bayesiano con tamaños de muestras grandes.
El estudio muestra que el error cuadrático medio de aproximación de la raíz bayesiana recientemente propuesto
por metodo ppp valor p predictivo posteriores de las cadenas de markov
set.seed(535535)
blavFitIndices(fitref_bio_ind_sinedad)## Posterior mean (EAP) of devm-based fit indices:
##
## BRMSEA BGammaHat adjBGammaHat BMc
## 0.120 0.927 0.830 0.840blavFitIndices(fitref_bio_ind_sinedad_sinher2)## Posterior mean (EAP) of devm-based fit indices:
##
## BRMSEA BGammaHat adjBGammaHat BMc
## 0.106 0.945 0.879 0.891summary(blavFitIndices(fitref_bio_ind_sinedad))##
## Posterior summary statistics and highest posterior density (HPD) 90% credible intervals for devm-based fit indices:
##
## EAP Median MAP SD lower upper
## BRMSEA 0.120 0.111 0.109 0.040 0.062 0.155
## BGammaHat 0.927 0.940 0.944 0.050 0.893 0.983
## adjBGammaHat 0.830 0.860 0.868 0.116 0.749 0.960
## BMc 0.840 0.867 0.875 0.105 0.763 0.961summary(blavFitIndices(fitref_bio_ind_sinedad_sinher2))##
## Posterior summary statistics and highest posterior density (HPD) 90% credible intervals for devm-based fit indices:
##
## EAP Median MAP SD lower upper
## BRMSEA 0.106 0.106 0.108 0.022 0.070 0.142
## BGammaHat 0.945 0.947 0.948 0.021 0.913 0.980
## adjBGammaHat 0.879 0.882 0.886 0.046 0.807 0.955
## BMc 0.891 0.894 0.897 0.041 0.826 0.960Informacion de R hat modelo de referencia
blavInspect(fitref_bio_ind_sinedad, 'rhat') funcionalidad=~f_c30 sintomas=~s_c30
4.1122761 1.0047093
biologicas=~comorb biologicas=~estadio_avz
1.0000765 1.0002811
cv_gral~salud cv_gral~sintomas
1.0041027 1.0013746
cv_gral~funcionalidad salud~funcionalidad
1.0011761 4.3812789
funcionalidad~sintomas sintomas~biologicas
1.8283956 1.0003418
f_br23~~f_br23 f_c30~~f_c30
2.7385768 1.0035679
s_br23~~s_br23 s_c30~~s_c30
1.0007631 1.0334320
her2_pos~~her2_pos comorb~~comorb
0.9999554 1.0002715
estadio_avz~~estadio_avz cv_gral~~cv_gral
1.0003113 1.0049023
salud~~salud funcionalidad~~funcionalidad
1.0010730 1.0325213
sintomas~~sintomas biologicas~~biologicas
1.0014568 1.0002228
f_br23~1 f_c30~1
1.0001791 1.0001641
s_br23~1 s_c30~1
1.0001126 1.0002002
her2_pos~1 comorb~1
0.9999440 0.9999778
estadio_avz~1 cv_gral~1
0.9999621 1.0039958
salud~1
1.0001298 blavInspect(fitref_bio_ind_sinedad_sinher2, 'rhat') funcionalidad=~f_c30 sintomas=~s_c30
1.0002123 1.0002248
biologicas=~estadio_avz cv_gral~salud
1.0009251 1.0000338
cv_gral~sintomas cv_gral~funcionalidad
1.0003094 1.0002915
salud~funcionalidad funcionalidad~sintomas
1.0000917 1.0003472
sintomas~biologicas f_br23~~f_br23
1.0010503 0.9999669
f_c30~~f_c30 s_br23~~s_br23
1.0000435 0.9999784
s_c30~~s_c30 comorb~~comorb
1.0000192 0.9999825
estadio_avz~~estadio_avz cv_gral~~cv_gral
1.0001141 1.0003690
salud~~salud funcionalidad~~funcionalidad
1.0000350 1.0001027
sintomas~~sintomas biologicas~~biologicas
1.0000603 1.0001345
f_br23~1 f_c30~1
1.0001157 1.0001305
s_br23~1 s_c30~1
1.0000474 1.0001609
comorb~1 estadio_avz~1
0.9999909 0.9999881
cv_gral~1 salud~1
1.0000479 1.0000663 Informacion del neff modelo de referencia
blavInspect(fitref_bio_ind_sinedad, 'neff') funcionalidad=~f_c30 sintomas=~s_c30
6.380909e+00 2.171555e+04
biologicas=~comorb biologicas=~estadio_avz
3.854590e+04 1.970033e+04
cv_gral~salud cv_gral~sintomas
3.082389e+04 1.109612e+04
cv_gral~funcionalidad salud~funcionalidad
1.382521e+04 6.331666e+00
funcionalidad~sintomas sintomas~biologicas
8.511477e+00 1.985611e+04
f_br23~~f_br23 f_c30~~f_c30
6.901139e+00 2.911446e+04
s_br23~~s_br23 s_c30~~s_c30
9.171438e+04 1.201701e+02
her2_pos~~her2_pos comorb~~comorb
1.174701e+05 2.994556e+04
estadio_avz~~estadio_avz cv_gral~~cv_gral
2.305150e+04 6.651723e+03
salud~~salud funcionalidad~~funcionalidad
1.022827e+05 1.327859e+02
sintomas~~sintomas biologicas~~biologicas
3.542005e+04 4.109040e+04
f_br23~1 f_c30~1
4.425340e+04 3.701052e+04
s_br23~1 s_c30~1
5.870269e+04 3.748293e+04
her2_pos~1 comorb~1
1.195710e+05 1.222230e+05
estadio_avz~1 cv_gral~1
1.184604e+05 2.769064e+04
salud~1
4.787104e+04 blavInspect(fitref_bio_ind_sinedad_sinher2, 'neff') funcionalidad=~f_c30 sintomas=~s_c30
52290.62 30396.16
biologicas=~estadio_avz cv_gral~salud
10142.27 54200.74
cv_gral~sintomas cv_gral~funcionalidad
16490.58 15997.96
salud~funcionalidad funcionalidad~sintomas
66958.34 31794.80
sintomas~biologicas f_br23~~f_br23
11157.69 89125.96
f_c30~~f_c30 s_br23~~s_br23
47448.12 97837.92
s_c30~~s_c30 comorb~~comorb
32103.93 111163.87
estadio_avz~~estadio_avz cv_gral~~cv_gral
26361.63 16738.74
salud~~salud funcionalidad~~funcionalidad
106237.73 25899.59
sintomas~~sintomas biologicas~~biologicas
36732.18 50801.17
f_br23~1 f_c30~1
41769.21 35822.62
s_br23~1 s_c30~1
56867.41 36401.31
comorb~1 estadio_avz~1
116691.85 111626.27
cv_gral~1 salud~1
53676.03 45461.26 Graficos sem plot
semPaths(
fitref_bio_ind_sinedad,
intercepts = FALSE,
residuals = TRUE,
edge.label.cex = 1.5,
intStyle = "multi",
optimizeLatRes = TRUE,
title.color = "black",
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt = 5,
edge.color = "black",
esize = 6,
label.prop = 2,
sizeLat = 6,
"std"
)
semPaths(
fitref_bio_ind_sinedad_sinher2,
intercepts = FALSE,
residuals = TRUE,
edge.label.cex = 1.5,
intStyle = "multi",
optimizeLatRes = TRUE,
title.color = "black",
groups = "lat",
pastel = TRUE,
exoVar = FALSE,
sizeInt = 5,
edge.color = "black",
esize = 6,
label.prop = 2,
sizeLat = 6,
"std"
)
graficos de mcmc
plot(fitref_bio_ind_sinedad, par = 1:12, facet_args = list(ncol = 4))
plot(fitref_bio_ind_sinedad_sinher2, par = 1:12, facet_args = list(ncol = 4))
plot(fitref_bio_ind_sinedad, plot.type = "intervals")
plot(fitref_bio_ind_sinedad, plot.type = "parcoord")
describe_posterior(fitref_bio_ind_sinedad)Summary of Posterior Distribution
Parameter | Component | Median | 95% CI | pd | ROPE | % in ROPE | Rhat | ESS
------------------------------------------------------------------------------------------------------------------------------
funcionalidad=~f_c30 | latent | 1.42 | [-12.25, 2.10] | 91.67% | [-0.10, 0.10] | 0% | 4.112 | 6.00
sintomas=~s_c30 | latent | 2.29 | [ 1.57, 3.31] | 100% | [-0.10, 0.10] | 0% | 1.005 | 21716.00
biologicas=~comorb | latent | 0.11 | [-13.58, 13.71] | 51.42% | [-0.10, 0.10] | 2.57% | 1.000 | 38546.00
biologicas=~estadio_avz | latent | -1.87 | [-19.21, 18.42] | 56.17% | [-0.10, 0.10] | 0.57% | 1.000 | 19700.00
cv_gral~salud | regression | 0.76 | [ 0.63, 0.89] | 100% | [-0.10, 0.10] | 0% | 1.004 | 30824.00
cv_gral~sintomas | regression | -0.61 | [-13.10, 11.43] | 61.49% | [-0.10, 0.10] | 3.40% | 1.001 | 11096.00
cv_gral~funcionalidad | regression | 0.34 | [ -8.69, 9.03] | 58.36% | [-0.10, 0.10] | 4.73% | 1.001 | 13825.00
salud~funcionalidad | regression | 2.54 | [-20.04, 4.04] | 91.67% | [-0.10, 0.10] | 0% | 4.381 | 6.00
funcionalidad~sintomas | regression | -1.46 | [ -2.17, 0.25] | 91.67% | [-0.10, 0.10] | 0.45% | 1.828 | 9.00
sintomas~biologicas | regression | 1.18 | [-17.55, 17.99] | 55.51% | [-0.10, 0.10] | 0.68% | 1.000 | 19856.00
f_br23~~f_br23 | residual | 0.15 | [ 0.09, 0.45] | 100% | [-0.10, 0.10] | 1.10% | 2.739 | 7.00
f_c30~~f_c30 | residual | 0.08 | [ 0.02, 0.14] | 100% | [-0.10, 0.10] | 82.54% | 1.004 | 29114.00
s_br23~~s_br23 | residual | 0.20 | [ 0.14, 0.28] | 100% | [-0.10, 0.10] | 0% | 1.001 | 91714.00
s_c30~~s_c30 | residual | 0.07 | [ 0.00, 0.14] | 100% | [-0.10, 0.10] | 79.41% | 1.033 | 120.00
her2_pos~~her2_pos | residual | 0.25 | [ 0.18, 0.34] | 100% | [-0.10, 0.10] | 0% | 1.000 | 1.17e+05
comorb~~comorb | residual | 0.23 | [ 0.15, 0.32] | 100% | [-0.10, 0.10] | 0% | 1.000 | 29946.00
estadio_avz~~estadio_avz | residual | 0.20 | [ 0.03, 0.31] | 100% | [-0.10, 0.10] | 6.06% | 1.000 | 23052.00
cv_gral~~cv_gral | residual | 0.45 | [ 0.26, 0.66] | 100% | [-0.10, 0.10] | 0% | 1.005 | 6652.00
salud~~salud | residual | 1.58 | [ 1.09, 2.19] | 100% | [-0.10, 0.10] | 0% | 1.001 | 1.02e+05
funcionalidad~~funcionalidad | residual | 6.48e-03 | [ 0.00, 0.05] | 100% | [-0.10, 0.10] | 100% | 1.033 | 133.00
sintomas~~sintomas | residual | 0.08 | [ 0.00, 0.17] | 100% | [-0.10, 0.10] | 66.79% | 1.001 | 35420.00
biologicas~~biologicas | residual | 3.85e-04 | [ 0.00, 0.00] | 100% | [-0.10, 0.10] | 100% | 1.000 | 41090.00
f_br23~1 | intercept | 2.34 | [ 2.20, 2.48] | 100% | [-0.10, 0.10] | 0% | 1.000 | 44253.00
f_c30~1 | intercept | 2.44 | [ 2.26, 2.61] | 100% | [-0.10, 0.10] | 0% | 1.000 | 37011.00
s_br23~1 | intercept | 1.76 | [ 1.64, 1.89] | 100% | [-0.10, 0.10] | 0% | 1.000 | 58703.00
s_c30~1 | intercept | 2.16 | [ 1.98, 2.34] | 100% | [-0.10, 0.10] | 0% | 1.000 | 37483.00
her2_pos~1 | intercept | 0.44 | [ 0.33, 0.55] | 100% | [-0.10, 0.10] | 0% | 1.000 | 1.20e+05
comorb~1 | intercept | 0.34 | [ 0.23, 0.44] | 100% | [-0.10, 0.10] | 0% | 1.000 | 1.22e+05
estadio_avz~1 | intercept | 0.34 | [ 0.23, 0.44] | 100% | [-0.10, 0.10] | 0% | 1.000 | 1.18e+05
cv_gral~1 | intercept | 0.81 | [ 0.21, 1.41] | 99.59% | [-0.10, 0.10] | 0% | 1.004 | 27691.00
salud~1 | intercept | 4.34 | [ 3.93, 4.74] | 100% | [-0.10, 0.10] | 0% | 1.000 | 47871.00sexit(fitref_bio_ind_sinedad)# Following the Sequential Effect eXistence and sIgnificance Testing (SEXIT) framework, we report the median of the posterior distribution and its 95% CI (Highest Density Interval), along the probability of direction (pd), the probability of significance and the probability of being large. The thresholds beyond which the effect is considered as significant (i.e., non-negligible) and large are |0.05| and |0.30|.
- funcionalidad=~f_c30 (Median = 1.42, 95% CI [-12.25, 2.10]) has a 91.67% probability of being positive (> 0), 91.67% of being significant (> 0.05), and 91.67% of being large (> 0.30)
- sintomas=~s_c30 (Median = 2.29, 95% CI [1.57, 3.31]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
- biologicas=~comorb (Median = 0.11, 95% CI [-13.58, 13.71]) has a 51.42% probability of being positive (> 0), 50.83% of being significant (> 0.05), and 47.79% of being large (> 0.30)
- biologicas=~estadio_avz (Median = -1.87, 95% CI [-19.21, 18.42]) has a 56.17% probability of being negative (< 0), 56.03% of being significant (< -0.05), and 55.33% of being large (< -0.30)
- cv_gral~salud (Median = 0.76, 95% CI [0.63, 0.89]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
- cv_gral~sintomas (Median = -0.61, 95% CI [-13.10, 11.43]) has a 61.49% probability of being negative (< 0), 60.64% of being significant (< -0.05), and 56.13% of being large (< -0.30)
- cv_gral~funcionalidad (Median = 0.34, 95% CI [-8.69, 9.03]) has a 58.36% probability of being positive (> 0), 57.22% of being significant (> 0.05), and 51.06% of being large (> 0.30)
- salud~funcionalidad (Median = 2.54, 95% CI [-20.04, 4.04]) has a 91.67% probability of being positive (> 0), 91.67% of being significant (> 0.05), and 91.67% of being large (> 0.30)
- funcionalidad~sintomas (Median = -1.46, 95% CI [-2.17, 0.25]) has a 91.67% probability of being negative (< 0), 91.67% of being significant (< -0.05), and 91.67% of being large (< -0.30)
- sintomas~biologicas (Median = 1.18, 95% CI [-17.55, 17.99]) has a 55.51% probability of being positive (> 0), 55.33% of being significant (> 0.05), and 54.43% of being large (> 0.30)
- f_br23~~f_br23 (Median = 0.15, 95% CI [0.09, 0.45]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 8.34% of being large (> 0.30)
- f_c30~~f_c30 (Median = 0.08, 95% CI [0.02, 0.14]) has a 100.00% probability of being positive (> 0), 82.28% of being significant (> 0.05), and 0.00% of being large (> 0.30)
- s_br23~~s_br23 (Median = 0.20, 95% CI [0.14, 0.28]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 1.39% of being large (> 0.30)
- s_c30~~s_c30 (Median = 0.07, 95% CI [5.74e-10, 0.14]) has a 100.00% probability of being positive (> 0), 69.14% of being significant (> 0.05), and 0.00% of being large (> 0.30)
- her2_pos~~her2_pos (Median = 0.25, 95% CI [0.18, 0.34]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 15.26% of being large (> 0.30)
- comorb~~comorb (Median = 0.23, 95% CI [0.15, 0.32]) has a 100.00% probability of being positive (> 0), 99.69% of being significant (> 0.05), and 5.34% of being large (> 0.30)
- estadio_avz~~estadio_avz (Median = 0.20, 95% CI [0.03, 0.31]) has a 100.00% probability of being positive (> 0), 95.29% of being significant (> 0.05), and 2.63% of being large (> 0.30)
- cv_gral~~cv_gral (Median = 0.45, 95% CI [0.26, 0.66]) has a 100.00% probability of being positive (> 0), 99.57% of being significant (> 0.05), and 94.67% of being large (> 0.30)
- salud~~salud (Median = 1.58, 95% CI [1.09, 2.19]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
- funcionalidad~~funcionalidad (Median = 6.48e-03, 95% CI [2.17e-13, 0.05]) has a 100.00% probability of being positive (> 0), 4.72% of being significant (> 0.05), and 0.00% of being large (> 0.30)
- sintomas~~sintomas (Median = 0.08, 95% CI [1.50e-09, 0.17]) has a 100.00% probability of being positive (> 0), 77.29% of being significant (> 0.05), and 0.04% of being large (> 0.30)
- biologicas~~biologicas (Median = 3.85e-04, 95% CI [2.10e-12, 4.18e-03]) has a 100.00% probability of being positive (> 0), 0.02% of being significant (> 0.05), and 0.00% of being large (> 0.30)
- f_br23~1 (Median = 2.34, 95% CI [2.20, 2.48]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
- f_c30~1 (Median = 2.44, 95% CI [2.26, 2.61]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
- s_br23~1 (Median = 1.76, 95% CI [1.64, 1.89]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
- s_c30~1 (Median = 2.16, 95% CI [1.98, 2.34]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
- her2_pos~1 (Median = 0.44, 95% CI [0.33, 0.55]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 99.21% of being large (> 0.30)
- comorb~1 (Median = 0.34, 95% CI [0.23, 0.44]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 75.38% of being large (> 0.30)
- estadio_avz~1 (Median = 0.34, 95% CI [0.23, 0.44]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 75.74% of being large (> 0.30)
- cv_gral~1 (Median = 0.81, 95% CI [0.21, 1.41]) has a 99.59% probability of being positive (> 0), 99.30% of being significant (> 0.05), and 95.18% of being large (> 0.30)
- salud~1 (Median = 4.34, 95% CI [3.93, 4.74]) has a 100.00% probability of being positive (> 0), 100.00% of being significant (> 0.05), and 100.00% of being large (> 0.30)
Parameter | Median | 95% CI | Direction | Significance (> |0.05|) | Large (> |0.30|)
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funcionalidad=~f_c30 | 1.42 | [-12.25, 2.10] | 0.92 | 0.92 | 0.92
sintomas=~s_c30 | 2.29 | [1.57, 3.31] | 1.00 | 1.00 | 1.00
biologicas=~comorb | 0.11 | [-13.58, 13.71] | 0.51 | 0.51 | 0.48
biologicas=~estadio_avz | -1.87 | [-19.21, 18.42] | 0.56 | 0.56 | 0.55
cv_gral~salud | 0.76 | [0.63, 0.89] | 1.00 | 1.00 | 1.00
cv_gral~sintomas | -0.61 | [-13.10, 11.43] | 0.61 | 0.61 | 0.56
cv_gral~funcionalidad | 0.34 | [-8.69, 9.03] | 0.58 | 0.57 | 0.51
salud~funcionalidad | 2.54 | [-20.04, 4.04] | 0.92 | 0.92 | 0.92
funcionalidad~sintomas | -1.46 | [-2.17, 0.25] | 0.92 | 0.92 | 0.92
sintomas~biologicas | 1.18 | [-17.55, 17.99] | 0.56 | 0.55 | 0.54
f_br23~~f_br23 | 0.15 | [0.09, 0.45] | 1.00 | 1.00 | 0.08
f_c30~~f_c30 | 0.08 | [0.02, 0.14] | 1.00 | 0.82 | 0.00
s_br23~~s_br23 | 0.20 | [0.14, 0.28] | 1.00 | 1.00 | 0.01
s_c30~~s_c30 | 0.07 | [5.74e-10, 0.14] | 1.00 | 0.69 | 0.00
her2_pos~~her2_pos | 0.25 | [0.18, 0.34] | 1.00 | 1.00 | 0.15
comorb~~comorb | 0.23 | [0.15, 0.32] | 1.00 | 1.00 | 0.05
estadio_avz~~estadio_avz | 0.20 | [0.03, 0.31] | 1.00 | 0.95 | 0.03
cv_gral~~cv_gral | 0.45 | [0.26, 0.66] | 1.00 | 1.00 | 0.95
salud~~salud | 1.58 | [1.09, 2.19] | 1.00 | 1.00 | 1.00
funcionalidad~~funcionalidad | 6.48e-03 | [2.17e-13, 0.05] | 1.00 | 0.05 | 0.00
sintomas~~sintomas | 0.08 | [1.50e-09, 0.17] | 1.00 | 0.77 | 3.75e-04
biologicas~~biologicas | 3.85e-04 | [2.10e-12, 4.18e-03] | 1.00 | 2.40e-04 | 0.00
f_br23~1 | 2.34 | [2.20, 2.48] | 1.00 | 1.00 | 1.00
f_c30~1 | 2.44 | [2.26, 2.61] | 1.00 | 1.00 | 1.00
s_br23~1 | 1.76 | [1.64, 1.89] | 1.00 | 1.00 | 1.00
s_c30~1 | 2.16 | [1.98, 2.34] | 1.00 | 1.00 | 1.00
her2_pos~1 | 0.44 | [0.33, 0.55] | 1.00 | 1.00 | 0.99
comorb~1 | 0.34 | [0.23, 0.44] | 1.00 | 1.00 | 0.75
estadio_avz~1 | 0.34 | [0.23, 0.44] | 1.00 | 1.00 | 0.76
cv_gral~1 | 0.81 | [0.21, 1.41] | 1.00 | 0.99 | 0.95
salud~1 | 4.34 | [3.93, 4.74] | 1.00 | 1.00 | 1.00