The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The goal of metaSurvival is to …
You can install the released version of metaSurvival from CRAN with:
install.packages("metaSurvival")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("shubhrampandey/metaSurvival")library(metaSurvival)First start with loading the example dataset.
data(exampleData)
attach(exampleData)
head(exampleData)
#> Study FirstAuthor YearPub Time Survival NbRisk Location Design
#> 1 1 Lai 1988 1 0.8043 46 Asia Monocentric
#> 2 1 Lai 1988 2 0.4565 37 Asia Monocentric
#> 3 1 Lai 1988 3 0.2609 21 Asia Monocentric
#> 4 1 Lai 1988 4 0.1739 12 Asia Monocentric
#> 5 1 Lai 1988 5 0.1304 8 Asia Monocentric
#> 6 1 Lai 1988 6 0.0870 6 Asia MonocentricComputation of summary survival with continuity correction.
results<-msurv(Study, Time, NbRisk, Survival, confidence="Greenwood",correctionFlag = T,correctionVal = c(0.25,0.5))
results
#> $verif.data
#> Sstudy check
#> 1 1 1
#> 2 2 1
#> 3 3 1
#> 4 4 1
#> 5 5 1
#> 6 6 1
#> 7 7 1
#> 8 8 1
#> 9 9 1
#> 10 10 1
#> 11 11 1
#> 12 12 1
#> 13 13 1
#> 14 14 1
#> 15 15 1
#> 16 16 1
#> 17 17 1
#> 18 18 1
#> 19 19 1
#> 20 20 1
#> 21 21 1
#> 22 22 1
#> 23 23 1
#> 24 24 1
#> 25 25 1
#> 26 26 1
#> 27 27 1
#>
#> $summary.fixed
#> IndiceTimes PooledSurvivalFE PooledSurvivalICinfFE PooledSurvivalICsupFE
#> [1,] 1 0.94497450 0.93455418 0.95551100
#> [2,] 2 0.84347436 0.82683765 0.86044583
#> [3,] 3 0.73872717 0.71857893 0.75944035
#> [4,] 4 0.66245129 0.64073764 0.68490079
#> [5,] 5 0.58662304 0.56399133 0.61016291
#> [6,] 6 0.52144090 0.49844475 0.54549799
#> [7,] 9 0.40882896 0.38592475 0.43309252
#> [8,] 12 0.32461488 0.30222075 0.34866838
#> [9,] 15 0.26174091 0.23995379 0.28550624
#> [10,] 18 0.21983127 0.19855264 0.24339029
#> [11,] 21 0.18810271 0.16733508 0.21144777
#> [12,] 24 0.16086195 0.14062988 0.18400476
#> [13,] 27 0.13906070 0.11917994 0.16225783
#> [14,] 30 0.12241028 0.10252475 0.14615276
#> [15,] 33 0.09697105 0.07643349 0.12302702
#> [16,] 36 0.08104941 0.06104672 0.10760623
#> [17,] 39 0.07799121 0.05761503 0.10557365
#> [18,] 42 0.06960868 0.04863084 0.09963571
#> [19,] 45 0.06635655 0.04527093 0.09726311
#> [20,] 48 0.06295069 0.04180072 0.09480195
#>
#> $median.fixed
#> 2.5% 97.5%
#> 6.571189 5.969119 7.163636
#>
#> $mean.fixed
#> 2.5% 97.5%
#> 12.06584 11.28009 12.76527
#>
#> $heterogeneity
#> [1] 731.217128 2.521438 60.340098
#>
#> $summary.random
#> IndiceTimes PooledSurvivalRE PooledSurvivalICinfRE PooledSurvivalICsupRE
#> [1,] 1 0.93524921 0.90753843 0.96380610
#> [2,] 2 0.82628083 0.77535470 0.88055185
#> [3,] 3 0.70123428 0.63503595 0.77433335
#> [4,] 4 0.61766270 0.54632825 0.69831133
#> [5,] 5 0.53060766 0.45992592 0.61215181
#> [6,] 6 0.44916320 0.37511275 0.53783184
#> [7,] 9 0.32921715 0.26232834 0.41316134
#> [8,] 12 0.24431166 0.18426527 0.32392532
#> [9,] 15 0.18160515 0.13130502 0.25117417
#> [10,] 18 0.14534074 0.10284269 0.20540041
#> [11,] 21 0.11674507 0.07979975 0.17079517
#> [12,] 24 0.09822802 0.06593733 0.14633204
#> [13,] 27 0.08346181 0.05491511 0.12684802
#> [14,] 30 0.07097460 0.04554526 0.11060193
#> [15,] 33 0.05659698 0.03461920 0.09252720
#> [16,] 36 0.04869511 0.02927924 0.08098617
#> [17,] 39 0.04682745 0.02783640 0.07877493
#> [18,] 42 0.03866701 0.02088712 0.07158177
#> [19,] 45 0.03689963 0.01966964 0.06922256
#> [20,] 48 0.03516081 0.01850465 0.06680927
#>
#> $median.random
#> 2.5% 97.5%
#> 5.375810 4.469860 6.676145
#>
#> $mean.random
#> 2.5% 97.5%
#> 9.554813 7.764589 11.465606Plot the estimates summary survival
RandomEffectSummary<- results$summary.random
plot(Time, Survival, type="n", col="grey", ylim=c(0,1),xlab="Time",
ylab="Survival")
for (i in unique(sort(Study))){
lines(Time[Study==i], Survival[Study==i], type="l", col="grey")
points(max(Time[Study==i]),
Survival[Study==i & Time==max(Time[Study==i])], pch=15)
}
lines(RandomEffectSummary[,1], RandomEffectSummary[,2], type="l",
col="red", lwd=3)
points(RandomEffectSummary[,1], RandomEffectSummary[,3], type="l",
col="red", lty=3, lwd=3)
points(RandomEffectSummary[,1], RandomEffectSummary[,4], type="l",
col="red", lty=3, lwd=3)
Computation of summary survival without continuity correction.
results<-msurv(Study, Time, NbRisk, Survival, confidence="Greenwood",correctionFlag = F)
results
#> $verif.data
#> Sstudy check
#> 1 1 1
#> 2 2 1
#> 3 3 1
#> 4 4 1
#> 5 5 1
#> 6 6 1
#> 7 7 1
#> 8 8 1
#> 9 9 1
#> 10 10 1
#> 11 11 1
#> 12 12 1
#> 13 13 1
#> 14 14 1
#> 15 15 1
#> 16 16 1
#> 17 17 1
#> 18 18 1
#> 19 19 1
#> 20 20 1
#> 21 21 1
#> 22 22 1
#> 23 23 1
#> 24 24 1
#> 25 25 1
#> 26 26 1
#> 27 27 1
#>
#> $summary.fixed
#> IndiceTimes PooledSurvivalFE PooledSurvivalICinfFE PooledSurvivalICsupFE
#> [1,] 1 0.9508837 0.93115618 0.9710293
#> [2,] 2 0.8542395 0.82202310 0.8877185
#> [3,] 3 0.7529489 0.71356234 0.7945096
#> [4,] 4 0.6801444 0.63752795 0.7256097
#> [5,] 5 0.6081467 0.56352148 0.6563058
#> [6,] 6 0.5449696 0.49936809 0.5947354
#> [7,] 9 0.4303415 0.38446782 0.4816886
#> [8,] 12 0.3452733 0.30011887 0.3972213
#> [9,] 15 0.2814425 0.23729653 0.3338012
#> [10,] 18 0.2411057 0.19773211 0.2939935
#> [11,] 21 0.2109529 0.16828161 0.2644443
#> [12,] 24 0.1858428 0.14380950 0.2401619
#> [13,] 27 0.1671095 0.12528742 0.2228921
#> [14,] 30 0.1549979 0.11269730 0.2131760
#> [15,] 33 0.1322367 0.08722757 0.2004703
#> [16,] 36 0.1201395 0.07462625 0.1934106
#> [17,] 39 0.1201395 0.07462625 0.1934106
#> [18,] 42 0.1144864 0.06665886 0.1966300
#> [19,] 45 0.1144864 0.06665886 0.1966300
#> [20,] 48 0.1144864 0.06665886 0.1966300
#>
#> $median.fixed
#> 2.5% 97.5%
#> 7.176926 5.923614 8.344025
#>
#> $mean.fixed
#> 2.5% 97.5%
#> 13.46370 11.45324 14.73451
#>
#> $heterogeneity
#> [1] 219.6071244 0.7572659 0.0000000
#>
#> $summary.random
#> IndiceTimes PooledSurvivalRE PooledSurvivalICinfRE PooledSurvivalICsupRE
#> [1,] 1 0.94768647 0.91731470 0.9790638
#> [2,] 2 0.84755282 0.78823478 0.9113348
#> [3,] 3 0.73182809 0.65606515 0.8163402
#> [4,] 4 0.65409564 0.56903170 0.7518757
#> [5,] 5 0.57872161 0.49473929 0.6769600
#> [6,] 6 0.50184228 0.41034422 0.6137425
#> [7,] 9 0.38269038 0.29711268 0.4929171
#> [8,] 12 0.29890154 0.22167479 0.4030324
#> [9,] 15 0.23629264 0.16877051 0.3308292
#> [10,] 18 0.20208143 0.14274382 0.2860853
#> [11,] 21 0.17082971 0.11580178 0.2520064
#> [12,] 24 0.15126442 0.10079984 0.2269937
#> [13,] 27 0.13757394 0.09019805 0.2098337
#> [14,] 30 0.12300453 0.07677350 0.1970747
#> [15,] 33 0.10536383 0.06049565 0.1835097
#> [16,] 36 0.10074068 0.05720945 0.1773952
#> [17,] 39 0.10074068 0.05720945 0.1773952
#> [18,] 42 0.08499342 0.03811229 0.1895420
#> [19,] 45 0.08499342 0.03811229 0.1895420
#> [20,] 48 0.08499342 0.03811229 0.1895420
#>
#> $median.random
#> 2.5% 97.5%
#> 6.046385 4.826019 8.540219
#>
#> $mean.random
#> 2.5% 97.5%
#> 11.929471 9.190071 14.485928Plot the estimates summary survival
RandomEffectSummary<- results$summary.random
plot(Time, Survival, type="n", col="grey", ylim=c(0,1),xlab="Time",
ylab="Survival")
for (i in unique(sort(Study))){
lines(Time[Study==i], Survival[Study==i], type="l", col="grey")
points(max(Time[Study==i]),
Survival[Study==i & Time==max(Time[Study==i])], pch=15)
}
lines(RandomEffectSummary[,1], RandomEffectSummary[,2], type="l",
col="red", lwd=3)
points(RandomEffectSummary[,1], RandomEffectSummary[,3], type="l",
col="red", lty=3, lwd=3)
points(RandomEffectSummary[,1], RandomEffectSummary[,4], type="l",
col="red", lty=3, lwd=3)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.