Use foreign package to read Stata data files
library(foreign)
Load Stata data files directly from web site
strepto <- read.dta("http://www.blackwellpublishing.com/medicine/bmj/systreviews/datasets/strepto.dta")
magnes <- read.dta("http://www.blackwellpublishing.com/medicine/bmj/systreviews/datasets/magnes.dta")
bcgtrial <- read.dta("http://www.blackwellpublishing.com/medicine/bmj/systreviews/datasets/bcgtrial.dta")
## Page 349: Mortality due to MI: streptokinase users vs non-users
strepto
trial trialnam year pop1 deaths1 pop0 deaths0
1 1 Fletcher 1959 12 1 11 4
2 2 Dewar 1963 21 4 21 7
3 3 1st European 1969 83 20 84 15
4 4 Heikinheimo 1971 219 22 207 17
5 5 Italian 1971 164 19 157 18
6 6 2nd European 1971 373 69 357 94
7 7 2nd Frankfurt 1973 102 13 104 29
8 8 1st Australian 1973 264 26 253 32
9 9 NHLBI SMIT 1974 53 7 54 3
10 10 Valere 1975 49 11 42 9
11 11 Frank 1975 55 6 53 6
12 12 UK Collab 1976 302 48 293 52
13 13 Klein 1976 14 4 9 1
14 14 Austrian 1977 352 37 376 65
15 15 Lasierra 1977 13 1 11 3
16 16 N German 1977 249 63 234 51
17 17 Witchitz 1977 32 5 26 5
18 18 2nd Australian 1977 112 25 118 31
19 19 3rd European 1977 156 25 159 50
20 20 ISAM 1986 859 54 882 63
21 21 GISSI-1 1986 5860 628 5852 758
22 22 ISIS-2 1988 8592 791 8595 1029
## Load meta package
library(meta)
## Meta-analysis using metabin()
strepto_meta <-
metabin(data = strepto, # Data
event.e = deaths1, # Events in exposed
n.e = pop1, # Number in exposed
event.c = deaths0, # Events in control
n.c = pop0, # Number in control
sm = "RR", # Summary measure
studlab = trialnam, # Study labels
comb.fixed = TRUE, # Fixed effects yes
comb.random = FALSE # Random effects no
)
## Show results
strepto_meta
RR 95%-CI %W(fixed)
Fletcher 0.2292 [0.0300; 1.7499] 0.18
Dewar 0.5714 [0.1962; 1.6647] 0.30
1st European 1.3494 [0.7429; 2.4509] 0.64
Heikinheimo 1.2232 [0.6688; 2.2371] 0.75
Italian 1.0105 [0.5510; 1.8531] 0.78
2nd European 0.7026 [0.5338; 0.9247] 4.10
2nd Frankfurt 0.4571 [0.2522; 0.8282] 1.22
1st Australian 0.7786 [0.4780; 1.2683] 1.39
NHLBI SMIT 2.3774 [0.6490; 8.7086] 0.13
Valere 1.0476 [0.4809; 2.2821] 0.41
Frank 0.9636 [0.3316; 2.8005] 0.26
UK Collab 0.8956 [0.6261; 1.2809] 2.25
Klein 2.5714 [0.3394; 19.4813] 0.05
Austrian 0.6080 [0.4173; 0.8861] 2.68
Lasierra 0.2821 [0.0340; 2.3403] 0.14
N German 1.1609 [0.8403; 1.6038] 2.24
Witchitz 0.8125 [0.2634; 2.5062] 0.24
2nd Australian 0.8497 [0.5369; 1.3446] 1.29
3rd European 0.5096 [0.3327; 0.7805] 2.11
ISAM 0.8801 [0.6195; 1.2503] 2.65
GISSI-1 0.8274 [0.7491; 0.9138] 32.34
ISIS-2 0.7690 [0.7044; 0.8395] 43.86
Number of studies combined: k=22
RR 95%-CI z p.value
Fixed effect model 0.7988 [0.7546; 0.8455] -7.747 < 0.0001
Quantifying heterogeneity:
tau^2 = 0.0112; H = 1.2 [1; 1.56]; I^2 = 30.9% [0%; 58.9%]
Test of heterogeneity:
Q d.f. p.value
30.41 21 0.084
Details on meta-analytical method:
- Mantel-Haenszel method
## p352 Forest plot using forest.meta()
forest.meta(strepto_meta)
## Funnel plot using funnel()
funnel(strepto_meta)
## Test for funnel plot asymmetry
metabias(strepto_meta)
Linear regression test of funnel plot asymmetry
data: strepto_meta
t = 0.4681, df = 20, p-value = 0.6448
alternative hypothesis: asymmetry in funnel plot
sample estimates:
bias se.bias slope
0.1670 0.3567 -0.2389
## Cumulative meta-analysis using metacum()
strepto_meta.cuml <- metacum(strepto_meta)
strepto_meta.cuml
Cumulative meta-analysis (Fixed effect model)
RR 95%-CI p.value tau^2 I^2
Adding Fletcher (k=1) 0.2292 [0.0300; 1.7499] 0.1555 0
Adding Dewar (k=2) 0.4436 [0.1740; 1.1309] 0.0887 0 0.0%
Adding 1st European (k=3) 0.9614 [0.5905; 1.5651] 0.8741 0.3145 50.7%
Adding Heikinheimo (k=4) 1.0664 [0.7296; 1.5587] 0.7398 0.08 30.3%
Adding Italian (k=5) 1.0498 [0.7610; 1.4483] 0.7671 0.0128 7.8%
Adding 2nd European (k=6) 0.8387 [0.6812; 1.0327] 0.0976 0.0557 37.2%
Adding 2nd Frankfurt (k=7) 0.7800 [0.6415; 0.9485] 0.0128 0.082 47.6%
Adding 1st Australian (k=8) 0.7798 [0.6503; 0.9351] 0.0073 0.0525 38.9%
Adding NHLBI SMIT (k=9) 0.8012 [0.6697; 0.9585] 0.0154 0.0711 43.8%
Adding Valere (k=10) 0.8115 [0.6814; 0.9663] 0.019 0.0597 38.7%
Adding Frank (k=11) 0.8154 [0.6863; 0.9687] 0.0203 0.0482 32.3%
Adding UK Collab (k=12) 0.8299 [0.7106; 0.9693] 0.0186 0.0312 26.7%
Adding Klein (k=13) 0.8372 [0.7172; 0.9772] 0.0243 0.0325 26.0%
Adding Austrian (k=14) 0.7966 [0.6906; 0.9189] 0.0018 0.0351 29.7%
Adding Lasierra (k=15) 0.7919 [0.6868; 0.9132] 0.0013 0.0344 27.9%
Adding N German (k=16) 0.8392 [0.7368; 0.9558] 0.0082 0.0462 37.3%
Adding Witchitz (k=17) 0.8388 [0.7371; 0.9545] 0.0077 0.0404 33.1%
Adding 2nd Australian (k=18) 0.8395 [0.7413; 0.9508] 0.0059 0.0322 28.9%
Adding 3rd European (k=19) 0.8066 [0.7160; 0.9087] 0.0004 0.0459 37.6%
Adding ISAM (k=20) 0.8148 [0.7278; 0.9122] 0.0004 0.0376 34.6%
Adding GISSI-1 (k=21) 0.8220 [0.7629; 0.8857] < 0.0001 0.0201 31.2%
Adding ISIS-2 (k=22) 0.7988 [0.7546; 0.8455] < 0.0001 0.0112 30.9%
Pooled estimate 0.7988 [0.7546; 0.8455] < 0.0001 0.0112 30.9%
Details on meta-analytical method:
- Mantel-Haenszel method
## Cumulative forest plot using forest()
forest(strepto_meta.cuml, xlim = c(0.5, 2))
Reference
library(HSAUR2)
data(smoking)
## ‘qt’ the number of treated subjetcs who stopped smoking.
## ‘tt’ the totla number of treated subjects.
## ‘qc’ the number of subjetcs who stopped smoking without being treated.
## ‘tc’ the total number of subject not being treated.
smoking
qt tt qc tc
Blondal89 37 92 24 90
Campbell91 21 107 21 105
Fagerstrom82 30 50 23 50
Fee82 23 180 15 172
Garcia89 21 68 5 38
Garvey00 75 405 17 203
Gross95 37 131 6 46
Hall85 18 41 10 36
Hall87 30 71 14 68
Hall96 24 98 28 103
Hjalmarson84 31 106 16 100
Huber88 31 54 11 60
Jarvis82 22 58 9 58
Jensen91 90 211 28 82
Killen84 16 44 6 20
Killen90 129 600 112 617
Malcolm80 6 73 3 121
McGovern92 51 146 40 127
Nakamura90 13 30 5 30
Niaura94 5 84 4 89
Pirie92 75 206 50 211
Puska79 29 116 21 113
Schneider85 9 30 6 30
Tonnesen88 23 60 12 53
Villa99 11 21 10 26
Zelman92 23 58 18 58
## Perform meta-analysis
smoking_meta <-
metabin(data = smoking, # Dataset
event.e = qt, # Events in exposed
n.e = tt, # Number in exposed
event.c = qc, # Events in control
n.c = tc, # Number in control
sm = "OR", # Summary measure: Odds ratio
studlab = rownames(smoking) # Study labels
)
## Page 4: Mantel-Haenszel (Fixed effect)
## Page 6: DerSimonian-Laird (Random effect)
smoking_meta
OR 95%-CI %W(fixed) %W(random)
Blondal89 1.8500 [0.9892; 3.460] 3.96 4.82
Campbell91 0.9767 [0.4971; 1.919] 4.66 4.33
Fagerstrom82 1.7609 [0.7965; 3.893] 2.51 3.41
Fee82 1.5333 [0.7713; 3.048] 3.66 4.23
Garcia89 2.9489 [1.0094; 8.615] 1.21 2.08
Garvey00 2.4866 [1.4256; 4.337] 5.04 5.63
Gross95 2.6241 [1.0265; 6.708] 1.74 2.60
Hall85 2.0348 [0.7829; 5.289] 1.63 2.53
Hall87 2.8223 [1.3289; 5.994] 2.26 3.69
Hall96 0.8687 [0.4614; 1.636] 5.63 4.75
Hjalmarson84 2.1700 [1.1005; 4.279] 3.18 4.30
Huber88 6.0040 [2.5721; 14.015] 1.21 3.07
Jarvis82 3.3272 [1.3706; 8.077] 1.53 2.86
Jensen91 1.4345 [0.8429; 2.441] 6.32 5.95
Killen84 1.3333 [0.4279; 4.155] 1.43 1.88
Killen90 1.2349 [0.9310; 1.638] 23.69 10.55
Malcolm80 3.5224 [0.8531; 14.543] 0.57 1.26
McGovern92 1.1676 [0.7040; 1.937] 7.61 6.31
Nakamura90 3.8235 [1.1500; 12.713] 0.77 1.70
Niaura94 1.3449 [0.3487; 5.188] 1.00 1.38
Pirie92 1.8435 [1.2044; 2.822] 8.59 7.61
Puska79 1.4603 [0.7750; 2.752] 4.36 4.74
Schneider85 1.7143 [0.5228; 5.621] 1.15 1.74
Tonnesen88 2.1239 [0.9285; 4.858] 2.15 3.19
Villa99 1.7600 [0.5489; 5.643] 1.16 1.80
Zelman92 1.4603 [0.6791; 3.140] 2.97 3.60
Number of studies combined: k=26
OR 95%-CI z p.value
Fixed effect model 1.670 [1.468; 1.899] 7.819 < 0.0001
Random effects model 1.752 [1.483; 2.069] 6.604 < 0.0001
Quantifying heterogeneity:
tau^2 = 0.0475; H = 1.18 [1; 1.5]; I^2 = 28.4% [0%; 55.8%]
Test of heterogeneity:
Q d.f. p.value
34.9 25 0.09
Details on meta-analytical method:
- Mantel-Haenszel method
## Forest plot
forest.meta(smoking_meta)
## Funnel plot
funnel(smoking_meta)
## Test for funnel plot asymmetry
metabias(smoking_meta)
Linear regression test of funnel plot asymmetry (efficient score)
data: smoking_meta
t = 2.326, df = 24, p-value = 0.02878
alternative hypothesis: asymmetry in funnel plot
sample estimates:
bias se.bias slope
1.2912 0.5551 0.1127
## Cumulative meta-analysis
smoking_meta.cuml <- metacum(smoking_meta)
smoking_meta.cuml
Cumulative meta-analysis (Fixed effect model)
OR 95%-CI p.value tau^2 I^2
Adding Blondal89 (k=1) 1.850 [0.9892; 3.460] 0.0541 0
Adding Campbell91 (k=2) 1.378 [0.8736; 2.175] 0.1678 0.0936 45.9%
Adding Fagerstrom82 (k=3) 1.465 [0.9866; 2.175] 0.0583 0.0078 5.8%
Adding Fee82 (k=4) 1.482 [1.0520; 2.087] 0.0244 0 0.0%
Adding Garcia89 (k=5) 1.593 [1.1515; 2.203] 0.0049 0 0.0%
Adding Garvey00 (k=6) 1.807 [1.3682; 2.386] < 0.0001 0.0123 8.8%
Adding Gross95 (k=7) 1.869 [1.4325; 2.440] < 0.0001 0.0022 1.6%
Adding Hall85 (k=8) 1.881 [1.4550; 2.430] < 0.0001 0 0.0%
Adding Hall87 (k=9) 1.960 [1.5376; 2.499] < 0.0001 0 0.0%
Adding Hall96 (k=10) 1.770 [1.4134; 2.216] < 0.0001 0.0549 28.3%
Adding Hjalmarson84 (k=11) 1.806 [1.4586; 2.236] < 0.0001 0.0399 22.6%
Adding Huber88 (k=12) 1.944 [1.5820; 2.390] < 0.0001 0.1186 45.9%
Adding Jarvis82 (k=13) 2.000 [1.6359; 2.444] < 0.0001 0.1162 44.8%
Adding Jensen91 (k=14) 1.919 [1.5910; 2.316] < 0.0001 0.103 43.3%
Adding Killen84 (k=15) 1.901 [1.5798; 2.288] < 0.0001 0.0933 39.9%
Adding Killen90 (k=16) 1.675 [1.4354; 1.954] < 0.0001 0.1045 48.6%
Adding Malcolm80 (k=17) 1.690 [1.4496; 1.969] < 0.0001 0.1034 47.1%
Adding McGovern92 (k=18) 1.639 [1.4153; 1.897] < 0.0001 0.0978 46.9%
Adding Nakamura90 (k=19) 1.660 [1.4355; 1.920] < 0.0001 0.102 47.0%
Adding Niaura94 (k=20) 1.656 [1.4333; 1.914] < 0.0001 0.0947 44.1%
Adding Pirie92 (k=21) 1.674 [1.4602; 1.920] < 0.0001 0.0796 41.7%
Adding Puska79 (k=22) 1.664 [1.4560; 1.902] < 0.0001 0.0708 39.0%
Adding Schneider85 (k=23) 1.665 [1.4577; 1.901] < 0.0001 0.0646 36.1%
Adding Tonnesen88 (k=24) 1.675 [1.4692; 1.910] < 0.0001 0.0594 33.9%
Adding Villa99 (k=25) 1.676 [1.4713; 1.910] < 0.0001 0.0536 31.0%
Adding Zelman92 (k=26) 1.670 [1.4684; 1.899] < 0.0001 0.0475 28.4%
Pooled estimate 1.670 [1.4684; 1.899] < 0.0001 0.0475 28.4%
Details on meta-analytical method:
- Mantel-Haenszel method
## Cumulative forest plot
forest(smoking_meta.cuml)
