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Ben A. Dwamena, MDDepartment of Radiology, University of Michigan Medical
School
Nuclear Medicine Service, VA Ann Arbor Health Care System
Ann Arbor, Michigan
METAGRAPHITI
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Statistical Graphics For Interpretation, Exploration And Presentation Of Meta-analysis Data
METAGRAPHITI
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VISUOGRAPHIC FRAMEWORK FOR
Exploring distributional assumptions
Testing and correcting for
publication bias
Investigating heterogeneity
Summary of Data and Sensitivity
Analyses
METAGRAPHITI
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Avoid potential misrepresentation by faulty distributional and other statistical assumptions.
Facilitates greater interaction between the researcher and the data by highlighting interesting and unusual aspects of the quantitative data.
METAGRAPHITI
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User-friendlier summaries of large, complicated quantitative data sets
Preliminary exploration before definite data synthesis
Effective emphasis of important features rather than details of data
METAGRAPHITI
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CONTINGENCY TABLE FOR SINGLE STUDY
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True Positives =Experimental Group With Outcome Present (a).
False Positives = Control Group With Outcome Present (b).
False Negatives=Experimental Group With Outcome Absent (c).
True Negatives= Control Group With Outcome Absent (d).
DIAGNOSTIC VERSUS TREATMENT TRIAL
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Odds Ratio (OR) =(a x d)/(b x c).
Relative risk in experimental group {[a/(a +
c)]/[b/(b+ d)]} =Likelihood Ratio for a
Positive Test.
Relative Risk in Control Group = Likelihood
Ratio for a Negative Test.
DIAGNOSTIC VERSUS TREATMENT TRIAL
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Box plots
Normal quantile plots
Stem-and-Leaf plots
DISTRIBUTION PLOTS
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Displays important characteristics of the dataset based on the five-number summary of the data.
“Box” covers inter-quartile range. “Beltline” of box represents the
median value. “Whiskers” include all but outlier
observations.
BOX AND WHISKER PLOT
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12
34
56
BOX AND WHISKER PLOT
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1** | 47 1** | 81,93 2** | 20,48 2** | 51,86 3** | 04,22 3** | 59,81,85 4** | 10 4** | 59,67,67,68 5** | 24,34,48 5** | 57,58,67 6** | 06rounded to nearest multiple of .01 plot in units of .01
STEM-AND-LEAF PLOT
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Plot of standardized effect size, Ei/Vi vs. normal distribution.
Deviations from linearity deviations from normality.
Slope of regression line =standard deviation of data= 1 for effect size if the studies from a single population and have large samples.
The y-intercept of the regression =the
mean.
NORMAL QUANTILE PLOT
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NORMAL QUANTILE PLOT
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NORMAL QUANTILE PLOT
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NORMAL QUANTILE PLOT
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NORMAL QUANTILE PLOTS
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Selective publication of articles showing certain types of results over those of showing other types of results
Commonly, tendency to publish only studies with statistical significant results
PUBLICATION BIAS
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Published studies do not represent all studies on a specific topic.
Trend towards publishing statistically significant (p < 0.05) or clinically relevant results.
Publication bias assessed by examining asymmetry of funnel plots of estimates of odds ratios vs. precision.
INVESTIGATING PUBLICATION BIAS
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Funnel plot
Begg’s rank correlation plot
Egger’s regression plot
Harbord’s modified radial
plot
INVESTIGATING PUBLICATION BIAS
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A funnel diagram (a.k.a. funnel
plot, funnel graph, bias plot)
Special type of scatter plot with
an estimate of sample size on
one axis vs. effect-size estimate
on the other axis
FUNNEL PLOT
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Based on statistical principle that sampling error decreases as sample size increases
Used to search for publication bias and to test whether all studies come from a single population
FUNNEL PLOT
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FUNNEL PLOTS
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metafunnel ldor seldor, xlab(0(2)8) xtitle (Log odds ratio) ytitle(Standard error of log OR) saving(zfunnel, replace)
metafunnel ldor seldor, xlab(0(2)8) xtitle(Log odds ratio) ytitle (Standard error of log OR) egger saving (eggerfunnel, replace)
FUNNEL PLOT: STATA SYNTAX
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FUNNEL PLOT: STATA DIALOG
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0.5
11.5
2S
tand
ard
err
or
of lo
g O
R
0 2 4 6 8Log odds ratio
Funnel plot with pseudo 95% confidence limits
FUNNEL PLOT: EXAMPLE
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0.5
11.5
2S
tand
ard
err
or
of lo
g O
R
0 2 4 6 8Log odds ratio
Funnel plot with pseudo 95% confidence limits
FUNNEL PLOT: WITH REGRESSION LINE
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An adjusted rank correlation method to assess the correlation between effect estimates and their variances.
Deviation of Spearman's rho from zero=estimate of funnel plot asymmetry.
Positive values=a trend towards higher levels of effect sizes in studies with smaller sample sizes
BEGG’S BIAS TEST
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metabias LogOR seLogOR, graph(b) saving(beggplot, replace)
BEGG’S BIAS TEST: STATA SYNTAX
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BEGG’S BIAS TEST: STATA DIALOG
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Begg's funnel plot with pseudo 95% confidence limits
logOR
s.e. of: logOR0 .5 1 1.5 2
0
5
10
BEGG’S BIAS PLOT
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Adjusted Kendall's Score (P-Q) = 26 Std. Dev. of Score = 40.32 Number of Studies = 24 z = 0.64 Pr > |z| = 0.519 z = 0.62 (continuity corrected) Pr >|z| = 0.53(continuity corrected)
BEGG’S BIAS TEST: STATISTICS
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Assesses potential association b/n effect size and precision.
Regression equation: SND = A + B x SE(d)-1.
1. SND=standard normal deviate (effect, d divided by its standard error SE(d));
2. A =intercept
3. B=slope. .
EGGER’S REGRESSION TEST
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The intercept value (A) = estimate of asymmetry of funnel plot
Positive values (A > 0) indicate higher levels of effect size in studies with smaller sample sizes.
EGGER’S REGRESSION METHOD
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EGGER’S BIAS PLOT
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metabias logOR selogOR, graph(e) saving(eggerplot, replace)
EGGER’S BIAS TEST: STATA SYNTAX
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EGGER’S BIAS TEST: STATA DIALOG
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Egger's publication bias plot
standardized effect
precision0 1 2 3 4
0
2
4
6
8
EGGER’S BIAS PLOT: EXAMPLE
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------------------------------------------------------------- Std_Eff | Coef. P>|t| [95% CI]
-------------+-----------------------------------------------
slope | 1.737492 0.001 .8528166 2.622168
bias | 1.796411 0.002 .7487423 2.84408
-------------------------------------------------------------
EGGER’S BIAS TEST: STATISTICS
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Test for funnel-plot asymmetry Regresses Z/sqrt(V) vs. sqrt (V),
where Z is the efficient score and V is Fisher's information (the variance of Z under the null hypothesis).
Modified Galbraith plot of Z/sqrt(V) vs. sqrt(V) with the fitted regression line and a confidence interval around the intercept.
MODIFIED BIAS TEST(HARBORD)
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metamodbias tp fn fp tn, graph z(Z) v(V) mlabel(index) saving(HarbordPlot, replace)
MODIFIED BIAS TEST: STATA SYNTAX
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8
18
213
137
15
64
92211
10
12
24
17
1
2
1416 19
5
23
20
05
10
15
Z/sqrt(V)
0 1 2 3 4sqrt(V)
Study regression line 90% CI for intercept
MODIFIED BIAS PLOT
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----------------------------------------------------------------------------- ZoversqrtV | Coef. Std. Err. P>|t| [90% Conf. Interval]
--+--------------------------------------------------------------------------
sqrtV| 2.406756 .3464027 0.000 1.811933 3.00158
bias| .9965934 .6383554 0.133 -.0995549 2.092742-----------------------------------------------------------------------------
MODIFIED BIAS TEST: STATISTICS
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A rank-based data augmentation technique
used to estimate the number of missing studies and to produce an adjusted estimate of test accuracy by imputing suspected missing studies.
Both random and fixed effect models may be used to assess the impact of model choice on publication bias.
TRIM-AND-FILL METHOD
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•metatrim LogOR seLogOR, eform funnel print graph id(author)saving(tweedieplot, replace)
TRIM-AND-FILL TEST: STATA SYNTAX
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TRIM AND FILL: STATA DIALOG
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TRIM-AND-FILL BIAS PLOTFilled funnel plot with pseudo 95% confidence limits
th
eta
, fil
led
s.e. of: theta, filled0 .5 1 1.5 2
-2
0
2
4
6
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When effect sizes differences are attributable to only sampling error, studies are homogeneous.
Heterogeneity means that there is between-study variation and variability in effect sizes exceeds that expected from sampling error.
HETEROGENEITY
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Potential sources of heterogeneity:
1. Characteristics of study population
2. Variation in study design
3. Statistical methods
4. Covariates adjusted for (if relevant)
HETEROGENEITY
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DEALING WITH HETEROGENEITY
Use analysis of variance with
the log odds ratio as dependent
variable and categorical
variables for subgroups as
factors to look for differences
among subgroups
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DEALING WITH HETEROGENEITY
Repeat analysis after excluding
outliers
Conduct analysis with predefined
subgroups
Construct multivariate models to
search for the independent effect of
study characteristics
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Standardized effect vs. reciprocal of the standard error.
Small studies/less precise results appear on the left side and the largest trials on the right end .
GALBRAITH’S PLOT
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A regression line , through the origin, represents the overall log-odds ratio.
Lines +/- 2 above regression line =95 per cent boundaries of the overall log-odds ratio.
The majority of points within area of +/- 2 in the absence of heterogeneity.
GALBRAITH’S PLOT
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galbr LogOR seLogOR, id(index) yline(0) saving(gallplot, replace)
GALBRAITH’S PLOT: STATA SYNTAX
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GALBRAITH PLOT: STATA DIALOG
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b/se(b)
1/se(b)
b/se(b) Fitted values
0 3.80729
-2-2
0
2
13.1045
.
7 8131511
18321
1222
1419
17
69
2
24
1
4
10
23
16
5
20
GALBRAITH PLOT: EXAMPLE
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This plots the event rate in the experimental (intervention) group against the event rate in the control group
Visual aid to exploring the heterogeneity of effect estimates within a meta-analysis.
L’ABBE PLOT
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labbe tp fn fp tn, s(O) xlab(0,0.25,0.50,0.75,1) ylab(0,0.25,0.50,0.75,1) l1("TPR) b2("FPR") saving(flabbeplot, replace)
L’ABBE PLOT: STATA SYNTAX
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L’ABBE PLOT: STATA DIALOG
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TPR
FPR0 .25 .5 .75 1
0
.25
.5
.75
1
L’ABBE PLOT: EXAMPLE
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twoway (rcap dorlo dorhi Study, horizontal blpattern(dash))(scatter Study dor, ms(O)msize(medium) mcolor(black))(scatter DOR_with_CIs eb_dor, yaxis(2) msymbol(i) msize(large) mcolor(black))(scatteri 26 83, msymbol(diamond) msize(large)), ylabel(1(1)25 26 "OVERALL", valuelabels angle(horizontal)) xlabel(0 10 100 1000 10000) xscale(log) ylabel(1(1)25 26 "Pooled Estimate", valuelabels angle(horizontal) axis(2)) legend(off) xtitle(Odds Ratio) xline(83, lstyle(foreground)) saving(OddsForest, replace)
DATA SUMMARY: STATA SYNTAX
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metan tp fn fp tn, or random nowt sortby(year) label(namevar=author, yearvar=year) t1(Summary DOR, Random Effects) b2(Diagnostic Odds Ratio) saving(SDORRE, replace) force xlabel(0,1,10,100,1000)
DATA SYNTHESIS: RANDOM EFFECTS
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• metan tp fn fp tn, or fixed nowt sortby(year) label(namevar=author, yearvar=year) t1(Summary DOR, Fixed Effects) b2(Diagnostic Odds Ratio) saving(SDORFE, replace) force xlabel(0,1,10,100,1000)
DATA SYNTHESIS: FIXED EFFECTS
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106.33(6.40 - 1765.75107.23(40.31 - 285.28107.67(6.57 - 1763.9912.33(2.04 - 74.41)17.50(2.65 - 115.66)175.00(28.22 - 1085.2189.00(11.36 - 3143.3191.67(12.02 - 3055.921.00(1.43 - 308.21)240.88(68.75 - 843.9625.00(1.72 - 364.11)262.66(24.39 - 2829.1266.00(33.46 - 2114.732.82(5.43 - 198.37)33.00(8.83 - 123.26)4.33(1.05 - 17.90)429.00(14.65 - 12562.45.00(3.75 - 539.38)46.93(17.48 - 125.96)56.08(4.62 - 680.33)6.10(3.95 - 9.42)6.86(1.81 - 26.01)7.09(1.06 - 47.42)9.00(0.58 - 140.71)98.80(15.24 - 640.50)Pooled Estimate
DO
R_w
ith_C
Is
Adler 1997Avril 1996
Bassa 1996Danforth 2002
Greco 2001Guller 2002
Hubner 2000Inoue 2004
Lin 2002Nakamoto(a) 2002Nakamoto(b) 2002
Noh 1998Ohta 2000
Palmedo 1997Rostom 1999
Scheidhauer 1996Schirrmeister 2001
Smith 1998Tse 1992
Utech 1996Wahl 2004Yang 2001
Yutani 2001Zornoza 2004
van Hoeven 2002OVERALL
0 10 100 1000 10000Odds Ratio
FOREST PLOT: STATA GRAPHICS
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Assumes homogeneity of effects across the studies being combined.
There is a common true effect size for all studies.
In the summary estimate, only the variance of each study is taken into account.
FIXED EFFECTS META-ANALYSIS
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Summary DOR, Fixed Effects
Odds ratio.1 1 100 1000 10000
Study
Odds ratio (95% CI)
36.27 (4.27,308.02) Adler (1997)
98.80 (10.66,916.11) Avril (1996)
21.00 (0.86,515.50) Bassa (1996)
4.33 (0.80,23.49) Danforth (2002)
107.23 (33.42,344.07) Greco (2001)
12.00 (1.23,117.41) Guller (2002)
429.00 (7.67,23982.81) Hubner (2000)
189.00 (6.63,5384.60) Lin (2002)
17.50 (1.84,166.04) Nakamoto (2002)
6.86 (1.40,33.57) Nakamoto (2002)
208.33 (7.72,5621.57) Noh (1998)
60.23 (3.09,1174.51) Ohta (2000)
106.33 (3.74,3023.90) Palmedo (1997)
289.00 (15.83,5276.04) Rostom (1999)
107.67 (3.85,3013.13) Scheidhauer (1996)
46.93 (14.47,152.17) Schirrmeister (2001)
266.00 (22.50,3145.19) Smith (1998)
9.00 (0.34,238.21) Tse (1992)
262.66 (15.47,4459.70) Utech (1996)
6.10 (3.64,10.24) Wahl (2004)
25.00 (1.03,608.09) Yang (2001)
45.00 (2.33,867.81) Yutani (2001)
240.88 (54.08,1072.94) Zornoza (2004)
12.33 (1.45,104.97) van Hoeven (2002)
19.85 (14.16,27.82) Overall (95% CI)
FIXED EFECTS META-ANALYSIS
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Heterogeneity is incorporated into the
pooled estimate by including a between study component of variance.
Assumes sample of studies included in the analysis is drawn from a population of studies.
Each sample of studies has a true effect size.
RANDOM EFFECTS META-ANALYSIS
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Summary DOR, Random Effects
Odds ratio.1 1 100 1000 10000
Study
Odds ratio (95% CI)
36.27 (4.27,308.02) Adler (1997)
98.80 (10.66,916.11) Avril (1996)
21.00 (0.86,515.50) Bassa (1996)
4.33 (0.80,23.49) Danforth (2002)
107.23 (33.42,344.07) Greco (2001)
12.00 (1.23,117.41) Guller (2002)
429.00 (7.67,23982.81) Hubner (2000)
189.00 (6.63,5384.60) Lin (2002)
17.50 (1.84,166.04) Nakamoto (2002)
6.86 (1.40,33.57) Nakamoto (2002)
208.33 (7.72,5621.57) Noh (1998)
60.23 (3.09,1174.51) Ohta (2000)
106.33 (3.74,3023.90) Palmedo (1997)
289.00 (15.83,5276.04) Rostom (1999)
107.67 (3.85,3013.13) Scheidhauer (1996)
46.93 (14.47,152.17) Schirrmeister (2001)
266.00 (22.50,3145.19) Smith (1998)
9.00 (0.34,238.21) Tse (1992)
262.66 (15.47,4459.70) Utech (1996)
6.10 (3.64,10.24) Wahl (2004)
25.00 (1.03,608.09) Yang (2001)
45.00 (2.33,867.81) Yutani (2001)
240.88 (54.08,1072.94) Zornoza (2004)
12.33 (1.45,104.97) van Hoeven (2002)
42.54 (20.88,86.68) Overall (95% CI)
RANDOM EFFECTS META-ANALYSIS
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Process of “prospectively” performing a new or updated analysis every time another trial is published
Provides answers regarding effectiveness of an intervention at the earliest possible date in time
CUMULATIVE META-ANALYSIS
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Studies are sequentially pooled by adding each time one new study according to an ordered variable.
For instance, the year of publication; then, a pooling analysis will be done every time a new article appears.
CUMULATIVE META-ANALYSIS
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In theory, the effect of any continuous or ordinal study-related variable can be assessed
Ex: sample size, study quality score, baseline risk etc
CUMULATIVE META-ANALYSIS
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metacum LogOR seLogOR, eform id(author) effect(f) graph cline saving(year_fcummplot, replace)
CUMULATIVE META-ANALYSIS: SYNTAX
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CUMULATIVE META-ANALYSIS: DIALOG 1
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CUMULATIVE META-ANALYSIS: DIALOG 2
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LogOR7.67387 23982.8
Wahl 2004
Yang 2001
Yutani 2001
Zornoza 2004
Inoue 2004
Nakamoto(b) 2002
Nakamoto(a) 2002
van Hoeven 2002
Adler 1997
Rostom 1999
Avril 1996
Guller 2002
Schirrmeister 2001
Smith 1998
Utech 1996
Greco 2001
Ohta 2000
Hubner 2000
Bassa 1996
Tse 1992
Noh 1998
Scheidhauer 1996
Palmedo 1997
Lin 2002
Danforth 2002
CUMULATIVE META-ANALYSIS: PLOT
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Studies are pooled according to influence of a trial on overall effect defined as the difference between the effect estimated with and without the trial
INFLUENCE ANALYSIS
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metaninf tp fn fp tn, id(author) saving(influplot, replace) save(infcoeff, replace)
INFLUENCE ANALYSIS: STATA SYNTAX
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INFLUENCE ANALYSIS: STATA DIALOG
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INFLUENCE ANALYSIS: STATA DIALOG
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INFLUENCE ANALYSIS: PLOT
4.75 6.53 5.41 7.88 10.30
1 2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Lower CI Limit Estimate Upper CI Limit Meta-analysis estimates, given named study is omitted
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INFLUENCE ANALYSIS: STATA DIALOG
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A scatter plot of true positive fraction (sensitivity) vs. false positive fraction (1-specificity)
Aids in visualization of range of results from primary studies
ROC PLANE
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twoway (scatter TPF FPF, sort ) (lfit uTPR FPF, sort range(0 1) clcolor(black) clpat(dash) clwidth(vthin) connect(direct)) (lfit sTPR FPF, sort range(0 1) clcolor(black) clpat(dot) clwidth(vthin) connect(direct)), ytitle(Sensitivity) ylabel(0(.1)1, grid) xtitle(1-Specificity) xlabel(0(.1)1, grid) title(ROC Plot of SENSITIVITY vs. 1-SPECIFICITY, size(medium)) legend(pos(3) col(1) lab(1 "Observed Data") lab(2 "Uninformative Test") lab(3 "Symmetry Line")) saving(ROCplot, replace) plotregion(margin(zero))
ROC PLANE: STATA SYNTAX
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0.1
.2.3
.4.5
.6.7
.8.9
1S
en
sitiv
ity
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 11-Specificity
Observed Data
Uninformative TestSymmetry Line
ROC Plot of SENSITIVITY vs. 1-SPECIFICITY
ROC PLANE: PLOT
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ORDINARY LEAST SQUARES METHOD:
Studies are weighted equally
WEIGHTED LEAST SQUARES METHOD:
Weighted by the inverse variance weights of the
odds ratio, or simply the sample size
ROBUST-RESISTANT METHOD:
Minimizes the influence of outliers
SROC: LINEAR REGRESSION MODELS
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Logit transformations of the TP rate (sensitivity)
and FP rate (1 - specificity).
D=ln(DOR) =logit(TPR) – logit(FPR)
Differences in logit transformations, D, regressed
on sums of logit transformations, S.
S=logit(TPR)+logit(FPR)
Logit(TPR)=natural log odds of a TP result and
logit(FPR) =natural log of the odds of a FP test
result.
SROC: LOGIT TRANSFORMATION
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twoway (scatter D S, sort msymbol(circle)) (lfit tfitted S, clcolor(black) clpat(solid) clwidth(thin) connect(direct))(lfit wfitted S, clcolor(black) clpat(dash) clwidth(thin) connect(direct)), ytitle(Discriminatory Power/D) xtitle(Diagnostic Threshold/S) title(REGRESSION PLOT) legend(lab(1 "Observed Data")lab(2 "EWLSR")lab(3 "VWLSR"))saving(regplot, replace) xline(0) yscale(noline)
ACCURACY/THRESHOLD PLOT
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12
34
56
Dis
crim
inato
ry P
ow
er/
D
-4 -2 0 2 4Diagnostic Threshold/S
Observed Data EWLSRVWLSR
REGRESSION PLOT
ACCURACY/THRESHOLD PLOT
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Back transformation of logistic regression
to conventional axes of sensitivity [TPR]
vs. (1 – specificity) [FPR]) with the equation
TPR = 1/{1 + exp[- a/(1 - b )]} [(1 -
FPR)/(FPR)](1 + b )/(1 - b ).
Slope b and intercept a are obtained from
the linear regression analyses
SUMMARY ROC CURVE
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twoway (scatter TPF FPF, sort msymbol(circle) msize(medium) mcolor(black))(fpfit tTPR FPF, clpat(dash)clwidth(medium) connect(direct ))(fpfit wTPR FPF, clpat(solid)clwidth(medium) connect(direct ))(lfit uTPR FPF, sort range(0 1) clcolor(black) clpat(dash) clwidth(thin) connect(direct)) (lfit sTPR FPF, sort range(0 1) clcolor(black) clpat(dot) clwidth(medium) connect(direct)), ytitle(Sensitivity/TPF) yscale(range(0 1)) ylabel( 0(.2)1,grid ) xtitle(1-Specificity/FPF) xscale(range(0 1)) xlabel(0(.2)1, grid) legend(lab(1 "Observed Data")lab(2 "EWLSR")lab(3 "VWLSR")lab(4 "RRLSR")lab(5 "Uninformative Test") lab(6 "Symmetry Line") pos(3) col(1)) title(SUMMARY ROC CURVES) graphregion(margin(zero)) saving(aSROCplot, replace)
SUMMARY ROC CURVE: STATA SYNTAX
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0.2
.4.6
.81
Sen
sitiv
ity/T
PF
0 .2 .4 .6 .8 11-Specificity/FPF
Observed Data
EWLSRVWLSRRRLSRUninformative Test
SUMMARY ROC CURVES
SUMMARY ROC CURVE: EXAMPLE
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SUMMARY ROC : SUBGROUP ANALYSIS
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STATA 8.2 (Stata Corp, College Station, Texas,
USA)
SOFTWARE