#primero instalo los packages
#el analisis
Packages
Graph theme
Dataset (plantilla)
PRECISION
Precision scanner
Error: Problem with `filter()` input `..1`.
x object 'examen' not found
ℹ Input `..1` is `examen == "scanner"`.
#forest plot

Debido a la alta heterogeneidad (93.8%), es recomendable omitir el meta analisis de efectos aleatorios y dejar solo la predicción del intervalo de confianza de la precisión, que para los escáneres evalaudos va de 16 a 80. Si el umbral clínico es máximo 100, entonces todos están dentro del margen clínico.
Sesgo publicacion

Linear regression test of funnel plot asymmetry
data: precision_scanner
t = -0.89804, df = 16, p-value = 0.3825
alternative hypothesis: asymmetry in funnel plot
sample estimates:
bias se.bias intercept
-1.24594 1.38740 53.75269
Interpretación: no hay sesgo de publicación para la precisión de los scanners ## Precisión impresion
There were 16 warnings (use warnings() to see them)
#el forest-plot de impresion para precision

Sesgo publicacion

VERACIDAD
Veracidad scaners
mean 95%-CI %W(random)
Malik - 2018 - 3Shape TRIOS 3 87.1000 [80.1755; 94.0245] 16.7
Gan - 2016 - 3Shape TRIOS 3 POD 80.0100 [76.4533; 83.5667] 16.9
Ender - 2015 - Cadent iTero 32.4000 [26.1767; 38.6233] 16.8
Sim - 2019 - Carestream CS3500 28.0900 [26.6279; 29.5521] 16.9
Malik - 2018 - CEREC Omnicam 80.3000 [69.6941; 90.9059] 16.5
Ender - 2015 - CEREC Omnicam 37.3000 [24.7657; 49.8343] 16.3
Number of studies combined: k = 6
mean 95%-CI
Random effects model 57.5296 [31.2901; 83.7691]
Quantifying heterogeneity:
tau^2 = 1059.4818 [297.1007; 6432.5742]; tau = 32.5497 [17.2366; 80.2033];
I^2 = 99.5% [99.3%; 99.6%]; H = 13.93 [12.28; 15.79]
Test of heterogeneity:
Q d.f. p-value
969.75 5 < 0.0001
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Untransformed (raw) means

Sesgo publicacion

muy pocos estudios para hacer el análisis de meta bias (sesgo de publicación)
Veracidad impresion
mean 95%-CI %W(random)
Ender - 2015 - Alginato 37.7000 [ 7.1094; 68.2906] 1.0
Sim - 2019 - Polivinilsiloxano 28.4900 [27.2843; 29.6957] 72.7
Malik - 2018 - Silicona por adicion 24.3000 [19.3038; 29.2962] 26.2
Number of studies combined: k = 3
mean 95%-CI
Random effects model 27.4842 [24.3776; 30.5909]
Quantifying heterogeneity:
tau^2 = 3.0755 [0.0000; >100.0000]; tau = 1.7537 [0.0000; >10.0000];
I^2 = 31.5% [0.0%; 92.9%]; H = 1.21 [1.00; 3.75]
Test of heterogeneity:
Q d.f. p-value
2.92 2 0.2324
Details on meta-analytical method:
- Inverse variance method
- DerSimonian-Laird estimator for tau^2
- Jackson method for confidence interval of tau^2 and tau
- Untransformed (raw) means
Aquí si se puede hacer un ma debido a que la heterogeneidad es baja

Sesgo publicacion

muy pocos estudios para hacer el análisis de meta bias (sesgo de publicación)
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