Meta-análisis - Ferran Torresferran.torres.name/edu/mrsm_/Meta-analysis.pps.pdf · Meta-análisis...

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Meta-análisis

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Definición El meta-análisis es una revisión sistemática de un gran número de estudios que utiliza métodos estadísticos para combinar, sintetizar e integrar la información de varios estudios independientes que son considerados por el análista como “combinables” Adaptado de: - Glass GV. Primary and Meta-analysis of research. Educational Researcher 1976;5:3-8. - Huque MF. Experiences with meta-analysis in NDA submissions. Proc

Biopharmaceutical Section of the American Statistical Association 1988;28-33. - Cook DJ, Sackett DL, Spitzer WO. Methodologic guidelines for systematic

reviews of randomized control trials in health care from the Potsdam consultation meta-analysis. J Clin Epidemiol 1995;48:168-71.

- D’Agostino RB, Weintraub M. Meta-analysis: a method for sythesizing research. Clin Pharmacol Ther 1995; 58:605-616.

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OBJETIVOS u Conclusión:

–  Ganancia en precisión –  Comparación crítica de los resultados –  Diferencias en magnitud o sentido –  Posibilidad de generalizar

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EVOLUCIÓN DEL USO DEL MÉTODO META-ANALÍTICO

Ratio articulos/mes

0,0005,000

10,00015,00020,00025,00030,00035,00040,000

1950-1959

1960-1969

1970-1979

1980-1984

1985-1989

1990-1992

(Junio)

1992(Julio)-1995

Ratio articulos/mes

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CARACTERÍSTICAS u Datos:

–  Tipos de medida de los efectos –  Escalas de medida –  Extensión de la información

u Datos originales u Estadísticos de resumen u Estimación del efecto y errores estándar u Valores de significación

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CARACTERÍSTICAS u No independencia de los estudios

–  Tiempo: momento en que se realiza el estudio

–  Centro o investigador – Múltiple publicación de los resultados – Mismos sujetos (en distintos estudios)

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Ventajas y limitaciones

u Ventajas u Consideración sistemática (evaluación no

sesgada) u Cuantificación de los resultados u Aumento de precisión de los resultados u Mayor capacidad de estudiar efectos en

subgrupos u Mayor facilidad para evaluar las discrepancias

entre estudios u Mayor generalización de las conclusiones

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Ventajas y limitaciones u Limitaciones:

u La calidad está limitada por los estudios individuales

u Dificultad para establecer los criterios de inclusión u Sesgo de selección (publicación, lengua, calidad ...)

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Análisis de la heterogeneidad • Respuesta a la siguiente pregunta:

¿SON COMBINABLES LOS ESTUDIOS? • Pruebas de homogeneidad de los resultados de los

estudios individuales: - Prueba ji-cadrado Q de Cochran - Prueba ji-cuadrado de Breslow-Day

• Las pruebas de homogeneidad tienen baja potencia

para detectar la heterogeneidad • Un valor p de la prueba de homogeneidad 0.10,

sugiere heterogeneidad entre estudios y, por tanto, podría no ser válido combinar los estudios

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Egger et al. Systematic reviews in health care. London: BMJ books, 2001.

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Magnitud del efecto (1) • Los métodos estadísticos utilizados para estimar

el tamaño del efecto global de diferentes estudios se basan en Modelos de Efectos Fijos y Efectos Aleatorios

• Los Modelos de Efectos Fijos asumen un efecto

constante del tratamiento entre estudios, es decir, los tamaños de los efectos entre estudios son homogéneos o similares. - Los diferentes estudios pertenecen a una misma población - Consideran la variabilidad intra-estudio


Magnitud del efecto (2) • Los Modelos de Efectos Aleatorios consideran que

existe una variación entre estudios - Los estudios provienen de poblaciones diferentes - Consideran la variabilidad intra e inter-estudio

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Magnitud del efecto (3) • Ninguno de los dos modelos se puede considerar

“correcto”:

- Si los estudios son homogéneos La elección entre un modelo de efectos fijos y efectos aleatorios no es importante, ya que los resultados serán idénticos

- Si los estudios no son homogéneos Es más apropiado elegir un modelo de efectos aleatorios

• Los modelos de efectos fijos y efectos aleatorios

utilizan diferentes métodos estadísticos para combinarlos resultados


Magnitud del efecto (4)

Efectos fijos

Efectos aleatorios

Método Efecto Método Efecto

V. Cualit. Mantel-Haenszel OR, RR DerSimonian- Laird

Ratios y Diferencias

Peto OR

Basado en la varianza general

Ratios y diferencias

V. Cuantit. Diferencias tipificadas entre

medias

Cuantitativas

Diferencias tipificadas

entre medias

Cuantitativas

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Unbiased Hedges’ g estimate

u  Corrections for small sample size will be made.

σµµ

δ ce −=

1431−

−=m

Jm

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛

−+−=⎟

⎠

⎞⎜⎝

⎛−

−=94

3114

31ee nn

gm

gd

2−+= ec nnm


Effect size interpretation

u Since effect sizes are non-dimensional measurements (no units), some conventions have been proposed[1],[2]: – small≈0.20, – medium≈0.50 – large≈0.80

–  [1] Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

–  [2] Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.

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Análisis se sensibilidad

EFFECTS MODEL

RELATIVE LIVE-BIRTH RATIO

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

FIXEDRANDOM FIXEDRANDOM

FAVOURS CONTROLFAVOURS TREATMENT

Homogeneity testP = 0.06 (<<0.10)

Homogeneity testP = 0.63 (>>0.10)

Data from Jeng GT et al. JAMA 1995; 274:830-836.


Análisis de la correlación CORRELATION BETWEEN THE EFFECT SIZE AND SAMPLE SIZE

SAMPLE SIZE

0.00.20.40.60.81.01.21.41.61.82.02.22.4

100140180220260300340380420

ODDS RATIO

r2 = 0.01b = 0.0009p = 0.70

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Análisis de la robustez

• Análisis del sesgo de publicación y/o inclusión selectiva de estudios positivos

• Correlación entre la magnitud del efecto y

el tamaño muestral de los estudios.


u  Los métodos utilizados para detectar la introducción del sesgo de publicación en un meta-análisis son:

–  Funnel plot –  El análisis de la asimetria del funnel plot

u  Si el número de estudios incluidos en el meta-análisis es pequeño, el funnel plot es poco útil. En este caso, el mejor método es comparar el tamaño del efecto global entre los estudios publicados y no publicados

u  En un meta-análisis basado en todos los estudios originales, no es necesario analizar el sesgo de publicación

Análisis del sesgo de publicación y/o inclusión selectiva de estudios positivos (3)

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Funnel plot

PublishedUnpublishedOverall OR

DEMONSTRATION OF FUNNEL PLOT

ODDS RATIO

SAMPLE SIZE

0

20

40

60

80

100

120

140

160

180

200

220

0.20.40.60.81.01.21.41.61.82.0


Funnel plot

PublishedOverall OR

DEMONSTRATION OF FUNNEL PLOT

ODDS RATIO

SAMPLE SIZE

0

20

40

60

80

100

120

140

160

180

200

220

0.20.40.60.81.01.21.41.61.82.02.2

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Presentación gráfica


Conclusiones (1) 1. El meta-análisis es un herramienta valida y

poderosa para la síntesis de la investigación siempre y cuando se aplique de forma adecuada (justificación, protocolo, etc.)

2. La utilización no crítica del meta-análisis puede

llevar a conclusiones erróneas. Las principales críticas que se realizan a un meta-análisis son:

- Sesgo de publicación y/o inclusión selectiva de estudios positivos - Heterogeneidad entre estudios - Correlación entre el tamaño del efecto y el tamaño de la muestra

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Conclusiones (2) 3. Únicamente cuando estos problemas son

debidamente considerados y analizados por medio de:

- Análisis de la asimetría del funnel plot - Pruebas de homogeneidad - Análisis de la correlación entre el tamaño del efecto y el tamaño de la muestra de los estudios Es posible aplicar esta técnica estadística para combinar los estudios de forma que los resultados globales sean científicamente validos


Meta-análisis Ejemplo

Ferran Torres [email protected]

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u Effects of plantago ovata husk on lipid metabolism.

A meta-analysis


Selection of studies

u  Identified 26 studies =>18 were valid according to the predefined criteria

u  Excluded studies. –  Most of them (7 of 8) insufficient information

on descriptive statistics to calculate the meta-analysis estimates from those studies;

–  the other one was erroneously pre-selected since only insoluble fiber was used in that trial.

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Statistical issues on the results (1) u  Poor quality of information

u  withdrawals and descriptive results: u  Complete available descriptive data, n, mean and

dispersion (SD or SE), for the baseline subtracted effect was found only for one study (Williams 1995).

–  SD (or SE to derive the SD): u  Estimated from baseline and final values by using

correlation coefficients from other published data. u  If baseline or final SD not available: SD for the baseline

difference was directly imputed from other published data. u  cross-over design

–  Intrasubject correlation for the estimation of the SD of the treatment differences has been ignored for the cross-over design because of (a) it was not available and (b) this is conservative since it leads to less significant results.


Statistical issues on the results (2)

u  Treatment arms u  2 per study except 1:

–  (MacMahon 1998): The mentioned study was a three arm trial with a control group (n=74) and 2 active doses of 7 G/d (n=101), and 10.5G/d (n=91).

–  Half the sample size of the control group (n=37; 74/2) was used for the comparison between each active group.

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u  Potential Publication Bias

–  (a) The biggest studies have the lowest magnitude effect for Total Cholesterol, although the direction of the effect is positive for all of them.

–  (b) There are probably some unpublished small studies with negative results, although the bigger studies are positive.



u  Heterogeneity and sensibility analysis

–  (a) The biggest studies have the lowest magnitude effect for Total Cholesterol, although the direction of the effect is positive for all of them.

–  (b) There are probably some unpublished small studies with negative results, although the bigger studies are positive.

=>

–  Pooled results too heterogeneous u  very cautious with the conclusions u  fixed effects not valid

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Meta-analysis estimations u Significant effect for

– Total Cholesterol (p<0.001) – LDL (p<0.001)

u “Marginally significant“ – Triglycerides (p=0.213 but p=0.021 for

the fixed effect approach) u No effect:

– HDL (p=0.886 and p=0.178 for the fixed effect approach)


-5 -4 -3 -2 -1 0 1 2 3 4 5

-0.80 ( -0.93 , -0.68 )

Study ANDERSON 1988

BELL 1989 BELL 1990 LEVIN 1990 NEAL 1990

ANDERSON 1991 ANDERSON 1992 EVERSON 1992

SPRECHER 1993 SPRECHERON 1993

MACIEJKO 1994 SUMMERBELL 1994

WOLEVER 1994 WILLIAMS 1995

WEINGAND 1997 MACMAHON 1998 MACMAHON 1998

RODRIGUEZ-MORAN 1998 ANDERSON 1999

Pooled(fixed effects)

Fibern: mean(SD)

13: -0.940(0.152) 40: -0.250(0.072) 19: -0.340(0.151) 30: -0.337(0.119) 27: -0.510(0.529) 27: -0.520(0.363) 21: -0.550(0.416) 20: -0.363(0.565) 59: -0.337(0.407) 20: 0.070(0.447) 18: -0.810(0.177) 19: -0.450(0.159) 18: -0.290(0.407) 26: -0.544(0.133) 23: -0.180(0.407)101: -0.670(0.407) 91: -0.700(0.407) 62: -0.673(0.169) 14: -0.119(0.407)

n=522

Controln: mean(SD)

13: -0.230(0.320) 35: 0.010(0.219) 19: -0.020(0.242) 28: 0.052(0.179) 27: -0.070(0.364) 25: -0.240(0.133) 23: 0.000(0.225) 20: -0.130(0.134) 59: -0.018(0.407) 20: 0.080(0.358) 18: -0.620(1.169) 18: -0.170(0.296) 18: 0.270(0.407) 24: -0.299(0.177) 23: 0.080(0.407) 37: -0.610(0.407) 37: -0.610(0.407) 63: -0.440(0.746) 15: 0.372(0.407)

n= 648

Fiber vs Placebo effect in lipid reduction. Total CholesterolHedges unbiased g, Fixed effects

Fiber better Control better

Estimates with 95% confidence intervals

Absolute Baseline Reduction

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-5 -4 -3 -2 -1 0 1 2 3 4 5

-1.01 ( -1.31 , -0.70 )

Study ANDERSON 1988








Pooled(random effects)

Fibern: mean(SD)

13: -0.940(0.152) 40: -0.250(0.072) 19: -0.340(0.151) 30: -0.337(0.119) 27: -0.510(0.529) 27: -0.520(0.363) 21: -0.550(0.416) 20: -0.363(0.565) 59: -0.337(0.407) 20: 0.070(0.447) 18: -0.810(0.177) 19: -0.450(0.159) 18: -0.290(0.407) 26: -0.544(0.133) 23: -0.180(0.407)101: -0.670(0.407) 91: -0.700(0.407) 62: -0.673(0.169) 14: -0.119(0.407)

n=522

Controln: mean(SD)

13: -0.230(0.320) 35: 0.010(0.219) 19: -0.020(0.242) 28: 0.052(0.179) 27: -0.070(0.364) 25: -0.240(0.133) 23: 0.000(0.225) 20: -0.130(0.134) 59: -0.018(0.407) 20: 0.080(0.358) 18: -0.620(1.169) 18: -0.170(0.296) 18: 0.270(0.407) 24: -0.299(0.177) 23: 0.080(0.407) 37: -0.610(0.407) 37: -0.610(0.407) 63: -0.440(0.746) 15: 0.372(0.407)

n= 648

Fiber vs Placebo effect in lipid reduction. Total CholesterolHedges unbiased g, Random effects





-1.0 -0.7 -0.4 -0.1 0.1 0.3 0.5 0.7 0.9

-0.31 ( -0.37 , -0.24 )

Study ANDERSON 1988









Fibern: mean(SD)

13: -0.940(0.152) 40: -0.250(0.072) 19: -0.340(0.151) 30: -0.337(0.119) 27: -0.510(0.529) 27: -0.520(0.363) 21: -0.550(0.416) 20: -0.363(0.565) 59: -0.337(0.407) 20: 0.070(0.447) 18: -0.810(0.177) 19: -0.450(0.159) 18: -0.290(0.407) 26: -0.544(0.133) 23: -0.180(0.407)101: -0.670(0.407) 91: -0.700(0.407) 62: -0.673(0.169) 14: -0.119(0.407)

n=522

Controln: mean(SD)

13: -0.230(0.320) 35: 0.010(0.219) 19: -0.020(0.242) 28: 0.052(0.179) 27: -0.070(0.364) 25: -0.240(0.133) 23: 0.000(0.225) 20: -0.130(0.134) 59: -0.018(0.407) 20: 0.080(0.358) 18: -0.620(1.169) 18: -0.170(0.296) 18: 0.270(0.407) 24: -0.299(0.177) 23: 0.080(0.407) 37: -0.610(0.407) 37: -0.610(0.407) 63: -0.440(0.746) 15: 0.372(0.407)

n= 648

Fiber vs Placebo effect in lipid reduction. Total CholesterolDifference of means (mmol/L), Random effects




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-5 -4 -3 -2 -1 0 1 2 3 4 5

-0.03 ( -0.39 , 0.33 )

Study ANDERSON 1988






WEINGAND 1997 RODRIGUEZ-MORAN 1998

ANDERSON 1999


Fibern: mean(SD)

13: -0.090(0.042) 40: 0.050(0.066) 19: -0.020(0.080) 30: 0.026(0.168) 27: 0.060(0.168) 27: -0.020(0.099) 21: -0.020(0.057) 20: 0.026(0.100) 59: -0.026(0.055) 20: -0.020(0.224) 18: 0.130(0.154) 19: -0.010(0.070) 18: -0.040(0.030) 26: 0.106(0.094) 23: 0.055(0.168) 62: 0.440(0.454) 14: 0.005(0.168)

n=448

Controln: mean(SD)

13: -0.090(0.141) 35: 0.030(0.058) 19: -0.020(0.072) 28: 0.078(0.168) 27: 0.140(0.168) 25: 0.000(0.072) 23: -0.010(0.024) 20: 0.000(0.094) 59: -0.013(0.059) 20: -0.040(0.134) 18: 0.130(0.120) 18: 0.070(0.085) 18: 0.040(0.030) 24: 0.039(0.031) 23: -0.185(0.168) 63: 0.078(0.044) 15: 0.017(0.168)

n=456

Fiber vs Placebo effect in lipid reduction. HDLHedges unbiased g, Random effects

Control better Fiber better




-5 -4 -3 -2 -1 0 1 2 3 4 5

-1.02 ( -1.36 , -0.69 )

Study ANDERSON 1988









Fibern: mean(SD)

13: -0.840(0.264) 40: -0.310(0.081) 19: -0.160(0.100) 30: -0.337(0.224) 27: -0.480(0.448) 27: -0.590(0.283) 21: -0.560(0.385) 20: -0.389(0.372) 59: -0.332(0.096) 20: 0.040(0.358) 18: -1.090(0.216) 19: -0.440(0.194) 18: -0.260(0.016) 26: -0.613(0.093) 23: -0.320(0.427)101: -0.630(0.427) 91: -0.710(0.427) 62: -0.725(0.427) 14: -0.179(0.427)

n=522

Controln: mean(SD)

13: -0.110(0.321) 35: 0.000(0.232) 19: -0.140(0.252) 28: -0.104(0.144) 27: -0.140(0.523) 25: -0.200(0.147) 23: -0.130(0.211) 20: -0.130(0.099) 59: -0.057(0.200) 20: -0.060(0.358) 18: -0.850(0.941) 18: -0.250(0.261) 18: 0.280(0.316) 24: -0.221(0.238) 23: -0.020(0.427) 37: -0.580(0.427) 37: -0.580(0.427) 63: -0.440(0.427) 15: 0.281(0.427)

n=648

Fiber vs Placebo effect in lipid reduction. LDLHedges unbiased g, Random effects




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-5 -4 -3 -2 -1 0 1 2 3 4 5

-0.88 ( -1.01 , -0.75 )

Study ANDERSON 1988









Fibern: mean(SD)

13: -0.840(0.264) 40: -0.310(0.081) 19: -0.160(0.100) 30: -0.337(0.224) 27: -0.480(0.448) 27: -0.590(0.283) 21: -0.560(0.385) 20: -0.389(0.372) 59: -0.332(0.096) 20: 0.040(0.358) 18: -1.090(0.216) 19: -0.440(0.194) 18: -0.260(0.016) 26: -0.613(0.093) 23: -0.320(0.427)101: -0.630(0.427) 91: -0.710(0.427) 62: -0.725(0.427) 14: -0.179(0.427)

n=522

Controln: mean(SD)

13: -0.110(0.321) 35: 0.000(0.232) 19: -0.140(0.252) 28: -0.104(0.144) 27: -0.140(0.523) 25: -0.200(0.147) 23: -0.130(0.211) 20: -0.130(0.099) 59: -0.057(0.200) 20: -0.060(0.358) 18: -0.850(0.941) 18: -0.250(0.261) 18: 0.280(0.316) 24: -0.221(0.238) 23: -0.020(0.427) 37: -0.580(0.427) 37: -0.580(0.427) 63: -0.440(0.427) 15: 0.281(0.427)

n=648

Fiber vs Placebo effect in lipid reduction. LDLHedges unbiased g, Fixed effects





-5 -4 -3 -2 -1 0 1 2 3 4 5

-0.15 ( -0.39 , 0.09 )

Study

ANDERSON 1988 BELL 1989 BELL 1990 LEVIN 1990 NEAL 1990



MACIEJKO 1994 WILLIAMS 1995


ANDERSON 1999


Fibern: mean(SD)

13: -0.210(0.058) 40: 0.030(0.302) 19: -0.135(0.214) 30: -0.011(0.320) 27: -0.240(0.293) 27: 0.200(0.516) 21: 0.080(0.455) 20: 0.102(0.630) 59: 0.006(0.308) 20: 0.240(0.805) 18: 0.450(1.008) 26: -0.142(0.249) 23: 0.002(0.781) 62: -0.554(0.781) 14: 0.165(0.781)

n=412

Controln: mean(SD)

13: 0.190(0.479) 35: -0.050(0.102) 19: 0.030(0.201) 28: 0.000(0.088) 27: -0.140(0.313) 25: -0.120(0.185) 23: 0.300(0.791) 20: 0.000(0.224) 59: 0.150(0.345) 20: 0.920(1.163) 18: 0.410(0.351) 24: -0.232(0.137) 23: 0.002(0.781) 63: -0.181(0.781) 15: 0.342(0.781)

n=419

Fiber vs Placebo effect in lipid reduction. TriglyceridesHedges unbiased g, Random effects




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-5 -4 -3 -2 -1 0 1 2 3 4 5

-0.16 ( -0.30 , -0.02 )

Study

ANDERSON 1988 BELL 1989 BELL 1990 LEVIN 1990 NEAL 1990



MACIEJKO 1994 WILLIAMS 1995


ANDERSON 1999


Fibern: mean(SD)

13: -0.210(0.058) 40: 0.030(0.302) 19: -0.135(0.214) 30: -0.011(0.320) 27: -0.240(0.293) 27: 0.200(0.516) 21: 0.080(0.455) 20: 0.102(0.630) 59: 0.006(0.308) 20: 0.240(0.805) 18: 0.450(1.008) 26: -0.142(0.249) 23: 0.002(0.781) 62: -0.554(0.781) 14: 0.165(0.781)

n=412

Controln: mean(SD)

13: 0.190(0.479) 35: -0.050(0.102) 19: 0.030(0.201) 28: 0.000(0.088) 27: -0.140(0.313) 25: -0.120(0.185) 23: 0.300(0.791) 20: 0.000(0.224) 59: 0.150(0.345) 20: 0.920(1.163) 18: 0.410(0.351) 24: -0.232(0.137) 23: 0.002(0.781) 63: -0.181(0.781) 15: 0.342(0.781)

n=419

Fiber vs Placebo effect in lipid reduction. TriglyceridesHedges unbiased g, Fixed effects





Subgroups u  SS with ES>1

–  Cholesterol and LDL u  fiber food supplement compound u  intake duration between 4 and 8 weeks u  daily doses >10G/d

u  Moderate SS ES –  >8 weeks regimens

u  -0.7 cholesterol and -0.8 LDL

u  SS with ES≈1 –  periods of study publication: no clear trend

u  Adult population: replication of the main pooled effect because only 2 studies on childhood.

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Group P value

Hedges’ g estimator (random)

95% Lower Limit

95% Upper Limit

Main pooled effect 0.000 -1.007 -1.311 -0.703 Type of fiber

Fiber food supplement compound 0.000 -1.134 -1.561 -0.707 Diet or Cereals supplements 0.000 -0.869 -1.313 -0.426

Duration of fiber Intake <=4 weeks 0.310 -0.670 -1.966 0.625 >4 to <=8 weeks 0.000 -1.171 -1.539 -0.803 >8 weeks 0.024 -0.672 -1.255 -0.088

Daily dose < 5 G/d 0.939 -0.024 -0.644 0.596 >= 5 to 10 G/d 0.005 -0.908 -1.542 -0.275 >10 G/d 0.000 -1.123 -1.477 -0.769

Population Non Adults 0.303 -0.786 -2.282 0.711 Adults 0.000 -1.032 -1.350 -0.714

Year of publication 1988-1990 0.000 -1.807 -2.418 -1.196 1991-1993 0.001 -0.786 -1.234 -0.338 1994-1997 0.000 -0.971 -1.456 -0.486 >=1998 0.016 -0.370 -0.673 -0.068

Cholesterol


LDL Group

P value

Hedges’ g estimator (random)

95% Lower Limit

95% Upper Limit

Main pooled effect 0.000 -1.023 -1.356 -0.690 Type of fiber

Fiber food supplement compound 0.000 -1.191 -1.569 -0.813 Diet or Cereals supplements 0.002 -0.833 -1.360 -0.306

Duration of fiber Intake <=4 weeks 0.437 -1.023 -3.601 1.555 >4 to <=8 weeks 0.000 -1.115 -1.447 -0.783 >8 weeks 0.036 -0.765 -1.480 -0.051

Daily dose < 5 G/d 0.389 0.274 -0.349 0.897 >= 5 to 10 G/d 0.022 -0.950 -1.761 -0.139 >10 G/d 0.000 -1.154 -1.457 -0.851

Population Non Adults 0.441 -0.942 -3.338 1.454 Adults 0.000 -1.029 -1.354 -0.704

Year of publication 1988-1990 0.001 -1.192 -1.891 -0.493 1991-1993 0.003 -1.102 -1.834 -0.370 1994-1997 0.001 -1.245 -2.008 -0.481 >=1998 0.008 -0.453 -0.788 -0.118

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Meta-análisis Ejemplo



Example u  Fleiss JL The statistical basis of meta-analysis.

Statistical Methods in Medical research 1993; 2: 121-145.

Results of seven placebo-controlled randomised studies of the effect of aspirin in preventing death after myocardial infarction

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StudyAAS Placebo AAS Placebo

MRC-1 49 67 615 624CDP 44 64 758 771MRC-2 102 126 832 850GASP 32 38 317 309PARIS 85 104 810 812AMIS 246 219 2267 2257ISIS-2 1570 1720 8587 8600

deaths Total patients

Studies of aspirin in myocardial infarction


Study OR y=ln(OR) se{lnOR} w Prop weightMRC-1 0.72 0.49 1.06 -0.33 0.19 26.3 2.9%CDP 0.68 0.46 1.01 -0.38 0.20 25.1 2.8%MRC-2 0.80 0.61 1.06 -0.22 0.14 49.3 5.4%GASP 0.80 0.49 1.32 -0.22 0.25 15.6 1.7%PARIS 0.79 0.54 1.16 -0.23 0.19 27.1 3.0%AMIS 1.13 0.94 1.37 0.12 0.10 104.3 11.4%ISIS-2 0.90 0.83 0.97 -0.11 0.04 665.1 72.9%

95% CI

8.910=∑ iw3.99yw ii −=∑Pooled estimate of ln(OR) =

OR = 0.90 (0.84 0.96)

11.08.9103.99

−=−

Meta-analysis of Aspirin trials

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Graphical representation


Study OR y=ln(OR) se{lnOR} w Prop weightMRC-1 0.72 0.49 1.06 -0.33 0.19 26.3 2.9%CDP 0.68 0.46 1.01 -0.38 0.20 25.1 2.8%MRC-2 0.80 0.61 1.06 -0.22 0.14 49.3 5.4%GASP 0.80 0.49 1.32 -0.22 0.25 15.6 1.7%PARIS 0.79 0.54 1.16 -0.23 0.19 27.1 3.0%AMIS 1.13 0.94 1.37 0.12 0.10 104.3 11.4%ISIS-2 0.90 0.83 0.97 -0.11 0.04 665.1 72.9%

95% CI

Fixed OR = 0.90 (0.84 0.96)

Study OR y=ln(OR) var Prop weightMRC-1 0.72 0.46 1.10 -0.33 0.05 8.0%CDP 0.68 0.43 1.05 -0.38 0.05 8.0%MRC-2 0.80 0.57 1.12 -0.22 0.03 13.0%GASP 0.80 0.46 1.36 -0.22 0.07 5.0%PARIS 0.79 0.52 1.20 -0.23 0.05 9.0%AMIS 1.13 0.86 1.48 0.12 0.02 21.0%ISIS-2 0.90 0.72 1.10 -0.11 0.01 36.0%

95% CI

Random OR= 0.88 95%CI : (0.77 ; 0.99)

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10.4%-14.2% = 3.8%

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Meta-análisis Normativas



Guias y Normativas u  ICH - E9: Statistical Principles for Clinical Trials Note

for Guidance on Statistical Principles for Clinical Trials. CMP/ICH/363/96. September 1998

u  CPMP/EWP/2330/99: Validity and Interpretation of Pooled Analyses, and one Pivotal study

u  The Potsdam International Consultation on Meta-analysis Potsdam, Germany, March 1994

u  Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. http://www.prisma-statement.org/

u  QUORUM. Statement. Lancet 1999; 354: 1896-1900.

Meta-análisis - Ferran Torresferran.torres.name/edu/mrsm_/Meta-analysis.pps.pdf · Meta-análisis...

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