Calculates relative effects/mean differences at a particular time-point

Uses mbnma time-course parameter estimates to calculate treatment differences between treatments or classes at a particular time-point. Can be used to compare treatments evaluated in studies at different follow-up times, or even to compare treatments in different MBNMA models via a common comparator.

Usage

get.relative(
  mbnma,
  mbnma.add = NULL,
  time = max(mbnma$model.arg$jagsdata$time, na.rm = TRUE),
  treats = unique(c(mbnma$network$treatments, mbnma.add$network$treatments)),
  classes = NULL,
  lim = "cred"
)

Arguments

mbnma: An S3 object of class "mbnma" generated by running a time-course MBNMA model
mbnma.add: An S3 object of class("mbnma") generated by running a time-course MBNMA model. This should only be specified if results from two different MBNMA models are to be combined to perform a 2-stage MBNMA (see Details).
time: A numeric value for the time at which to estimate relative effects/mean differences.
treats: A character vector of treatment names for which to calculate relative effects/mean differences. Must be a subset of mbnma$network$treatments.
classes: A character vector of class names for which to calculate relative effects/mean differences. Must be a subset of mbnma$network$classes. Only works for class effect models.
lim: Specifies calculation of either 95% credible intervals (lim="cred") or 95% prediction intervals (lim="pred").

Value

An object of class "relative.array" list containing:

The time-point for which results are estimated
Matrices of posterior means, medians, SDs and upper and lower 95% credible intervals for the differences between each treatment
An array containing MCMC results for the differences between all treatments specified in treats or all classes specified in classes.

Results are reported in tables as the row-defined treatment minus the column-defined treatment.

Details

get.relative() can also be used to perform a 2-stage MBNMA that allows synthesis of results from two different MBNMA models via a single common comparator. In an MBNMA model, all treatments must share the same time-course function. However, a 2-stage approach can enable fitting of different time-course functions to different sets ("subnetworks") of treatments. For example, some treatments may have rich time-course information, allowing for a more complex time-course function to be used, whereas others may be sparse, requiring a simpler time-course function.

Relative comparisons between treatments in the two datasets at specific follow-up times can then be estimated from MBNMA predicted effects versus a common comparator using the Bucher method and assuming consistency. See the MBNMAtime vignette for further details.

Examples

# \donttest{
# Create an mb.network object from a dataset
alognet <- mb.network(alog_pcfb)
#> Reference treatment is `placebo`
#> Studies reporting change from baseline automatically identified from the data

# Run a quadratic time-course MBNMA using the alogliptin dataset
mbnma <- mb.run(alognet,
  fun=tpoly(degree=2,
  pool.1="rel", method.1="random",
  pool.2="rel", method.2="common"
  )
)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 233
#>    Unobserved stochastic nodes: 71
#>    Total graph size: 4375
#> 
#> Initializing model
#> 

# Calculate differences between all treatments at 20 weeks follow-up
allres <- get.relative(mbnma, time=20)

# Calculate difference between a subset of treatments at 10 weeks follow-up
subres <- get.relative(mbnma, time=10,
  treats=c("alog_50", "alog_25", "placebo"))



###########################
##### 2-stage MBNMA #####
###########################

# Using the osteoarthritis dataset
# With placebo (Pl_0) as common comparator between subnetworks

#### Sparse model ####

# Treatments on which time-course data is limited
sparse.trt <- c("Ce_100", "Ce_400", "Du_90", "Lu_200", "Lu_400",
  "Lu_NA", "Et_5", "Ox_44")

# Create a subnetwork of studies comparing these treatments
sparse.df <- osteopain %>% dplyr::group_by(studyID) %>%
  dplyr::filter(any(treatment %in% sparse.trt)) %>%
  dplyr::ungroup() %>%
  subset(treatment %in% c("Pl_0", sparse.trt))

sparse.net <- mb.network(sparse.df)
#> Reference treatment is `Pl_0`
#> Studies reporting change from baseline automatically identified from the data

# Run a ITP MBNMA with a known rate
sparse.mbnma <- mb.run(sparse.net, fun=titp(method.rate=0.8, pool.rate="abs"))
#> 'rate' parameters must take positive values.
#>  Default half-normal prior restricts posterior to positive values.
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 76
#>    Unobserved stochastic nodes: 28
#>    Total graph size: 1447
#> 
#> Initializing model
#> 


#### Complex model ####

# Treatments on which time-course data is rich
rich.trt <- levels(osteopain$treatment)[!levels(osteopain$treatment) %in%
  c("Pl_0", "Ce_100", "Ce_400", "Du_90", "Lu_200",
    "Lu_400", "Lu_NA", "Et_5", "Ox_44")]

# Create a subnetwork of studies comparing these treatments
rich.df <- osteopain %>% dplyr::group_by(studyID) %>%
  dplyr::filter(any(treatment %in% rich.trt)) %>%
  dplyr::ungroup() %>%
  subset(treatment %in% c("Pl_0", rich.trt))

rich.net <- mb.network(rich.df)
#> Reference treatment is `Pl_0`
#> Studies reporting change from baseline automatically identified from the data

# Run a Emax MBNMA
rich.mbnma <- mb.run(rich.net, temax(p.expon = FALSE))
#> 'et50' parameters must take positive values.
#>  Default half-normal prior restricts posterior to positive values.
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 363
#>    Unobserved stochastic nodes: 121
#>    Total graph size: 7221
#> 
#> Initializing model
#> 


#### Calculate relative effects between models ####

# At 10 weeks follow-up
rels.sparse <- get.relative(sparse.mbnma, time=10)
rels.rich <- get.relative(rich.mbnma, time=10)

rels.all <- get.relative(mbnma=rich.mbnma,
  mbnma.add=sparse.mbnma, time=10)

print(rels.all$median)
#>                 Pl_0      Ce_200         Et_10       Et_30       Et_60
#> Pl_0              NA  0.96103461  0.4787077738  1.22821664  1.65238304
#> Ce_200  -0.961034609          NA -0.4856469514  0.26871815  0.69224079
#> Et_10   -0.478707774  0.48564695            NA  0.75366625  1.17201008
#> Et_30   -1.228216643 -0.26871815 -0.7536662512          NA  0.42188178
#> Et_60   -1.652383043 -0.69224079 -1.1720100841 -0.42188178          NA
#> Et_90   -1.728865224 -0.76896718 -1.2489730887 -0.49469318 -0.07415946
#> Lu_100  -0.918930675  0.04056182 -0.4391071842  0.31069220  0.73092671
#> Na_1000 -1.198118726 -0.23720961 -0.7173601881  0.03231904  0.45329833
#> Na_1500 -1.124594931 -0.16238080 -0.6429130349  0.10348977  0.52644337
#> Na_250  -0.006753349  0.95248989  0.4711502139  1.22220993  1.64120472
#> Na_750  -0.874043084  0.08597490 -0.3926811264  0.35459363  0.77521654
#> Ro_12   -0.944095355  0.01297419 -0.4668093742  0.28745657  0.70531622
#> Ro_125  -2.367597158 -1.40872129 -1.8922484293 -1.13483422 -0.71587707
#> Ro_25   -1.293198476 -0.33328318 -0.8115165344 -0.06321517  0.35762851
#> Tr_100  -0.474093934  0.48368456  0.0060812761  0.75649186  1.17393326
#> Tr_200  -0.480151024  0.48143975  0.0002301439  0.74842374  1.17021253
#> Tr_300  -0.838551367  0.12426541 -0.3555555801  0.39357270  0.81278235
#> Tr_400  -0.927857432  0.03127664 -0.4400428325  0.30167647  0.72426780
#> Va_10   -0.754420146  0.20747361 -0.2703618677  0.47818478  0.89821404
#> Va_20   -0.987110066 -0.02825168 -0.5065330838  0.24228177  0.66120937
#> Va_5    -0.843815624  0.11883824 -0.3634983589  0.38560680  0.80657641
#> Ce_100  -0.553353943  0.40498396 -0.0709626789  0.67967283  1.09789023
#> Ce_400  -0.904767724  0.05633500 -0.4240689290  0.33070009  0.74960799
#> Du_90   -0.553045575  0.41106117 -0.0659046497  0.68151734  1.10284851
#> Et_5    -1.028660625 -0.06951404 -0.5471234259  0.20362431  0.61995399
#> Lu_200  -0.545252568  0.41638080 -0.0677801612  0.68495098  1.10688678
#> Lu_400  -0.709315875  0.25130851 -0.2272174917  0.52303970  0.94588709
#> Lu_NA   -0.795898852  0.16745827 -0.3137188459  0.43599656  0.85594264
#> Ox_44   -1.074993057 -0.11685397 -0.6011510347  0.15899566  0.57412251
#>               Et_90       Lu_100     Na_1000     Na_1500       Na_250
#> Pl_0     1.72886522  0.918930675  1.19811873  1.12459493  0.006753349
#> Ce_200   0.76896718 -0.040561824  0.23720961  0.16238080 -0.952489889
#> Et_10    1.24897309  0.439107184  0.71736019  0.64291303 -0.471150214
#> Et_30    0.49469318 -0.310692199 -0.03231904 -0.10348977 -1.222209929
#> Et_60    0.07415946 -0.730926708 -0.45329833 -0.52644337 -1.641204717
#> Et_90            NA -0.806554348 -0.53181663 -0.60014842 -1.720970455
#> Lu_100   0.80655435           NA  0.27853508  0.20651608 -0.907105247
#> Na_1000  0.53181663 -0.278535084          NA -0.07250543 -1.187087241
#> Na_1500  0.60014842 -0.206516082  0.07250543          NA -1.114307386
#> Na_250   1.72097046  0.907105247  1.18708724  1.11430739           NA
#> Na_750   0.85605247  0.043800231  0.32286953  0.24828638 -0.868958054
#> Ro_12    0.77971146 -0.027172394  0.25056763  0.18088993 -0.932888322
#> Ro_125  -0.63826295 -1.451102785 -1.16956614 -1.24179308 -2.354017830
#> Ro_25    0.43173333 -0.375133123 -0.09335932 -0.16951779 -1.289460824
#> Tr_100   1.24964010  0.443914688  0.72014657  0.64809892 -0.468760144
#> Tr_200   1.24949595  0.442030150  0.71618094  0.64131857 -0.469052844
#> Tr_300   0.88878239  0.081171243  0.35990152  0.28644322 -0.830893938
#> Tr_400   0.79935306 -0.007738603  0.27030416  0.19513042 -0.920492036
#> Va_10    0.96661066  0.167161057  0.44681746  0.36877638 -0.745121819
#> Va_20    0.74153152 -0.069235031  0.21074905  0.13855282 -0.974153199
#> Va_5     0.88352930  0.077685490  0.35565086  0.28156233 -0.836516782
#> Ce_100   1.17173826  0.365904761  0.64660176  0.57134972 -0.539854990
#> Ce_400   0.82342205  0.018484809  0.29804223  0.22522565 -0.889730845
#> Du_90    1.17903938  0.369381957  0.64599140  0.57502588 -0.536488689
#> Et_5     0.69939513 -0.108347312  0.17028675  0.10028841 -1.016984585
#> Lu_200   1.18438179  0.376398195  0.65376853  0.57934066 -0.540932722
#> Lu_400   1.01792908  0.212983691  0.49185280  0.41999651 -0.702021088
#> Lu_NA    0.93435929  0.125341666  0.40375721  0.33152076 -0.782420737
#> Ox_44    0.65393213 -0.156135543  0.12459415  0.04962048 -1.064744103
#>              Na_750       Ro_12    Ro_125       Ro_25       Tr_100
#> Pl_0     0.87404308  0.94409535 2.3675972  1.29319848  0.474093934
#> Ce_200  -0.08597490 -0.01297419 1.4087213  0.33328318 -0.483684564
#> Et_10    0.39268113  0.46680937 1.8922484  0.81151653 -0.006081276
#> Et_30   -0.35459363 -0.28745657 1.1348342  0.06321517 -0.756491860
#> Et_60   -0.77521654 -0.70531622 0.7158771 -0.35762851 -1.173933264
#> Et_90   -0.85605247 -0.77971146 0.6382630 -0.43173333 -1.249640096
#> Lu_100  -0.04380023  0.02717239 1.4511028  0.37513312 -0.443914688
#> Na_1000 -0.32286953 -0.25056763 1.1695661  0.09335932 -0.720146571
#> Na_1500 -0.24828638 -0.18088993 1.2417931  0.16951779 -0.648098920
#> Na_250   0.86895805  0.93288832 2.3540178  1.28946082  0.468760144
#> Na_750           NA  0.06949145 1.4908282  0.41594776 -0.396444390
#> Ro_12   -0.06949145          NA 1.4185970  0.34539773 -0.468789555
#> Ro_125  -1.49082818 -1.41859704        NA -1.07122352 -1.893964521
#> Ro_25   -0.41594776 -0.34539773 1.0712235          NA -0.817636802
#> Tr_100   0.39644439  0.46878956 1.8939645  0.81763680           NA
#> Tr_200   0.39270734  0.46618627 1.8852669  0.81386057 -0.004636722
#> Tr_300   0.03462791  0.10932570 1.5252405  0.45348354 -0.365181616
#> Tr_400  -0.05478530  0.02000783 1.4400226  0.36292074 -0.453963647
#> Va_10    0.12231068  0.19295203 1.6193414  0.54231400 -0.274733281
#> Va_20   -0.11205902 -0.04378595 1.3827800  0.30678364 -0.507579392
#> Va_5     0.03109391  0.10190337 1.5263759  0.45163653 -0.362402210
#> Ce_100   0.32475499  0.39490586 1.8148446  0.74314603 -0.074585797
#> Ce_400  -0.02517588  0.04606319 1.4651194  0.39314442 -0.425829099
#> Du_90    0.32403674  0.39614262 1.8207118  0.74945606 -0.074434940
#> Et_5    -0.15579367 -0.07904749 1.3426891  0.27101803 -0.548136557
#> Lu_200   0.33064688  0.39953096 1.8235486  0.74760510 -0.069257180
#> Lu_400   0.16739912  0.23932303 1.6578847  0.58887788 -0.230742322
#> Lu_NA    0.08299674  0.15319154 1.5769416  0.49714476 -0.317298050
#> Ox_44   -0.20174002 -0.13157444 1.2916954  0.21994702 -0.602325086
#>                Tr_200       Tr_300       Tr_400       Va_10       Va_20
#> Pl_0     0.4801510237  0.838551367  0.927857432  0.75442015  0.98711007
#> Ce_200  -0.4814397549 -0.124265407 -0.031276637 -0.20747361  0.02825168
#> Et_10   -0.0002301439  0.355555580  0.440042833  0.27036187  0.50653308
#> Et_30   -0.7484237439 -0.393572697 -0.301676472 -0.47818478 -0.24228177
#> Et_60   -1.1702125338 -0.812782350 -0.724267797 -0.89821404 -0.66120937
#> Et_90   -1.2494959501 -0.888782390 -0.799353064 -0.96661066 -0.74153152
#> Lu_100  -0.4420301499 -0.081171243  0.007738603 -0.16716106  0.06923503
#> Na_1000 -0.7161809434 -0.359901515 -0.270304162 -0.44681746 -0.21074905
#> Na_1500 -0.6413185691 -0.286443216 -0.195130416 -0.36877638 -0.13855282
#> Na_250   0.4690528438  0.830893938  0.920492036  0.74512182  0.97415320
#> Na_750  -0.3927073369 -0.034627912  0.054785303 -0.12231068  0.11205902
#> Ro_12   -0.4661862749 -0.109325697 -0.020007829 -0.19295203  0.04378595
#> Ro_125  -1.8852669335 -1.525240509 -1.440022571 -1.61934140 -1.38277998
#> Ro_25   -0.8138605698 -0.453483536 -0.362920740 -0.54231400 -0.30678364
#> Tr_100   0.0046367224  0.365181616  0.453963647  0.27473328  0.50757939
#> Tr_200             NA  0.357084806  0.444435476  0.27511578  0.50301032
#> Tr_300  -0.3570848062           NA  0.088205480 -0.08356090  0.15072347
#> Tr_400  -0.4444354757 -0.088205480           NA -0.17226463  0.05988759
#> Va_10   -0.2751157788  0.083560901  0.172264631          NA  0.23594913
#> Va_20   -0.5030103246 -0.150723471 -0.059887592 -0.23594913          NA
#> Va_5    -0.3612223577 -0.004645471  0.084402140 -0.08464596  0.14534351
#> Ce_100  -0.0705358524  0.285177982  0.376909990  0.20075845  0.43204536
#> Ce_400  -0.4223245710 -0.062355702  0.026386112 -0.14840452  0.08581500
#> Du_90   -0.0676254006  0.291480827  0.379084362  0.20371918  0.43508570
#> Et_5    -0.5430334473 -0.190763027 -0.096947060 -0.27080281 -0.04085513
#> Lu_200  -0.0623748718  0.294113729  0.383484965  0.20995134  0.44271526
#> Lu_400  -0.2261935627  0.129341133  0.217381923  0.04569202  0.28070707
#> Lu_NA   -0.3153875655  0.045525487  0.134389567 -0.03776895  0.19686019
#> Ox_44   -0.5938515461 -0.240485457 -0.147666893 -0.32435043 -0.08944222
#>                 Va_5       Ce_100      Ce_400        Du_90        Et_5
#> Pl_0     0.843815624  0.553353943  0.90476772  0.553045575  1.02866062
#> Ce_200  -0.118838236 -0.404983956 -0.05633500 -0.411061173  0.06951404
#> Et_10    0.363498359  0.070962679  0.42406893  0.065904650  0.54712343
#> Et_30   -0.385606802 -0.679672825 -0.33070009 -0.681517341 -0.20362431
#> Et_60   -0.806576407 -1.097890234 -0.74960799 -1.102848510 -0.61995399
#> Et_90   -0.883529295 -1.171738261 -0.82342205 -1.179039384 -0.69939513
#> Lu_100  -0.077685490 -0.365904761 -0.01848481 -0.369381957  0.10834731
#> Na_1000 -0.355650862 -0.646601757 -0.29804223 -0.645991397 -0.17028675
#> Na_1500 -0.281562333 -0.571349717 -0.22522565 -0.575025882 -0.10028841
#> Na_250   0.836516782  0.539854990  0.88973084  0.536488689  1.01698458
#> Na_750  -0.031093907 -0.324754995  0.02517588 -0.324036737  0.15579367
#> Ro_12   -0.101903372 -0.394905862 -0.04606319 -0.396142623  0.07904749
#> Ro_125  -1.526375921 -1.814844620 -1.46511935 -1.820711818 -1.34268908
#> Ro_25   -0.451636533 -0.743146028 -0.39314442 -0.749456055 -0.27101803
#> Tr_100   0.362402210  0.074585797  0.42582910  0.074434940  0.54813656
#> Tr_200   0.361222358  0.070535852  0.42232457  0.067625401  0.54303345
#> Tr_300   0.004645471 -0.285177982  0.06235570 -0.291480827  0.19076303
#> Tr_400  -0.084402140 -0.376909990 -0.02638611 -0.379084362  0.09694706
#> Va_10    0.084645962 -0.200758452  0.14840452 -0.203719179  0.27080281
#> Va_20   -0.145343515 -0.432045355 -0.08581500 -0.435085703  0.04085513
#> Va_5              NA -0.289066653  0.05650033 -0.288311152  0.18260908
#> Ce_100   0.289066653           NA  0.35145365  0.001325617  0.47150412
#> Ce_400  -0.056500330 -0.351453647          NA -0.350958841  0.12536905
#> Du_90    0.288311152 -0.001325617  0.35095884           NA  0.48034190
#> Et_5    -0.182609076 -0.471504124 -0.12536905 -0.480341899          NA
#> Lu_200   0.299094225  0.009064791  0.35915856  0.009330239  0.48337385
#> Lu_400   0.135210912 -0.153030670  0.19460255 -0.155008322  0.31810196
#> Lu_NA    0.050133610 -0.241180210  0.10697699 -0.247954849  0.23756277
#> Ox_44   -0.229724544 -0.523443890 -0.17017884 -0.531055906 -0.04995463
#>               Lu_200      Lu_400       Lu_NA       Ox_44
#> Pl_0     0.545252568  0.70931588  0.79589885  1.07499306
#> Ce_200  -0.416380803 -0.25130851 -0.16745827  0.11685397
#> Et_10    0.067780161  0.22721749  0.31371885  0.60115103
#> Et_30   -0.684950977 -0.52303970 -0.43599656 -0.15899566
#> Et_60   -1.106886776 -0.94588709 -0.85594264 -0.57412251
#> Et_90   -1.184381785 -1.01792908 -0.93435929 -0.65393213
#> Lu_100  -0.376398195 -0.21298369 -0.12534167  0.15613554
#> Na_1000 -0.653768531 -0.49185280 -0.40375721 -0.12459415
#> Na_1500 -0.579340658 -0.41999651 -0.33152076 -0.04962048
#> Na_250   0.540932722  0.70202109  0.78242074  1.06474410
#> Na_750  -0.330646884 -0.16739912 -0.08299674  0.20174002
#> Ro_12   -0.399530957 -0.23932303 -0.15319154  0.13157444
#> Ro_125  -1.823548636 -1.65788468 -1.57694163 -1.29169541
#> Ro_25   -0.747605100 -0.58887788 -0.49714476 -0.21994702
#> Tr_100   0.069257180  0.23074232  0.31729805  0.60232509
#> Tr_200   0.062374872  0.22619356  0.31538757  0.59385155
#> Tr_300  -0.294113729 -0.12934113 -0.04552549  0.24048546
#> Tr_400  -0.383484965 -0.21738192 -0.13438957  0.14766689
#> Va_10   -0.209951340 -0.04569202  0.03776895  0.32435043
#> Va_20   -0.442715262 -0.28070707 -0.19686019  0.08944222
#> Va_5    -0.299094225 -0.13521091 -0.05013361  0.22972454
#> Ce_100  -0.009064791  0.15303067  0.24118021  0.52344389
#> Ce_400  -0.359158561 -0.19460255 -0.10697699  0.17017884
#> Du_90   -0.009330239  0.15500832  0.24795485  0.53105591
#> Et_5    -0.483373848 -0.31810196 -0.23756277  0.04995463
#> Lu_200            NA  0.16294203  0.24955334  0.53358212
#> Lu_400  -0.162942028          NA  0.08338881  0.36837114
#> Lu_NA   -0.249553339 -0.08338881          NA  0.28422431
#> Ox_44   -0.533582116 -0.36837114 -0.28422431          NA


# }