A object of class metabias is the result of fitting one or
more models to a dataset with one row per study being meta-analyzed. These
models are either (1) a meta-analysis with a correction for one or more
within-study or across-study biases, or (2) a sensitivity analysis for
meta-analyses with respect to these biases. Examples of functions that
return such objects include:
PublicationBias::pubbias_meta()PublicationBias::pubbias_svalue()phacking::phacking_meta()multibiasmeta::multibias_meta()multibiasmeta::multibias_evalue()
Usage
metabias(
data = data.frame(),
values = list(),
stats = data.frame(),
fits = list()
)
new_metabias(x = list())
# S3 method for metabias
summary(object, ...)Arguments
- data
Dataframe containing data used to fit the model(s), with added columns for any values computed during model fitting.
- values
List of values of arguments passed to the function.
- stats
Dataframe of summary statistics from the model fit(s).
- fits
List of fitted objects (which have a class that depends on the underlying fitting methods, e.g.
robumeta::robuorrstan::stanfit).- x
List with elements "data", "values", "stats", "fits".
- object
Object of class
metabias.- ...
Not used.
Value
An object of class metabias, which consists of a list containing
the elements data, values, stats, fits (corresponding to the
arguments passed).
Examples
# example model from robumeta::robu()
hier_mod <- robumeta::robu(effectsize ~ binge + followup + sreport + age,
data = robumeta::hierdat, studynum = studyid,
var.eff.size = var, modelweights = "HIER",
small = TRUE)
ci <- 0.95 # example set value
hier_mb <- metabias(data = robumeta::hierdat, # data passed to model
values = list(ci_level = ci), # value used
stats = robu_ci(hier_mod, ci_level = ci), # stats from model
fits = list("robu" = hier_mod)) # model object
hier_mb
#> $data
#> esid studyid effectsize var binge sreport males age followup
#> 1 5035 1 0.950826585 0.139126107 1 1 90.0 16.00 51.42857
#> 2 15 1 -1.651862741 0.067085341 0 0 72.0 14.00 34.28571
#> 3 3028 1 0.258915693 0.060215075 0 1 80.0 15.90 17.14286
#> 4 4086 1 0.190914467 0.036034293 0 0 82.0 15.30 34.28571
#> 5 1297 2 -0.247202173 0.046234448 0 0 76.0 16.00 34.28571
#> 6 3721 2 0.301957160 0.020227946 0 1 79.0 16.00 17.14286
#> 7 2768 2 0.242364183 0.033295847 0 1 66.0 16.90 51.42857
#> 8 2841 2 0.269546747 0.009444829 0 1 82.0 16.00 34.28571
#> 9 2409 3 0.387382537 0.036139041 0 1 70.0 16.00 51.42857
#> 10 4959 3 -0.398471236 0.032931905 0 0 75.0 15.50 51.42857
#> 11 1185 4 0.452842206 0.051564503 0 1 79.0 15.70 208.57143
#> 12 2522 4 -0.271548301 0.057856761 0 0 83.0 15.20 51.42857
#> 13 913 5 -0.070886418 0.067858674 0 1 41.0 18.45 17.14286
#> 14 5 5 0.816636622 0.050794918 1 1 65.0 18.40 104.57143
#> 15 4376 5 0.525371492 0.150762171 1 1 66.0 19.13 17.14286
#> 16 5505 5 0.317641258 0.039656453 1 1 89.5 17.06 51.42857
#> 17 3043 5 0.334007829 0.043146599 1 1 51.0 18.00 17.14286
#> 18 4342 5 -0.134081945 0.067114107 0 0 85.0 19.10 34.28571
#> 19 1417 5 0.000000000 0.024250438 1 1 35.0 19.30 17.14286
#> 20 1068 6 -0.465214431 0.161737770 0 0 77.0 16.00 104.57143
#> 21 710 6 0.362018377 0.072689950 0 0 82.0 15.40 34.28571
#> 22 3528 7 1.176043868 0.090221912 1 1 62.0 15.40 104.57143
#> 23 13 7 0.566977739 0.099065043 0 1 76.2 16.05 34.28571
#> 24 4068 7 0.282816976 0.022944927 0 0 56.0 17.00 208.57143
#> 25 5526 8 0.004153749 0.050125420 0 0 67.0 15.90 34.28571
#> 26 3442 8 0.096900612 0.057789702 0 0 71.0 15.40 34.28571
#> 27 356 8 -0.020446168 0.176932156 0 1 60.0 15.40 34.28571
#> 28 576 9 1.091028214 0.176737353 1 1 77.0 16.20 51.42857
#> 29 530 9 1.091028214 0.176737353 1 1 69.0 16.90 51.42857
#> 30 491 9 0.724698544 0.163945928 1 1 50.0 16.00 51.42857
#> 31 1087 9 0.829267025 0.072397366 1 1 50.0 16.00 51.42857
#> 32 545 9 1.270201564 0.184873298 1 1 62.0 17.10 51.42857
#> 33 1237 10 1.039815664 0.075676806 1 1 80.0 15.43 68.57143
#> 34 1793 11 -0.211120903 0.108789451 0 0 78.0 17.00 34.28571
#> 35 1733 12 0.149576589 0.025931364 0 1 64.0 19.00 51.42857
#> 36 2099 12 0.000000000 0.061553031 1 1 41.0 15.14 51.42857
#> 37 509 13 0.744848609 0.063855089 0 0 82.0 15.80 34.28571
#> 38 3859 13 -0.034580503 0.037754927 0 1 67.4 17.16 17.14286
#> 39 2925 14 0.844063163 0.085446268 1 1 80.0 15.95 51.42857
#> 40 4265 15 0.347013086 0.019760976 0 0 84.0 15.90 313.71429
#> 41 3786 15 0.157813311 0.089209534 0 1 72.0 20.00 17.14286
#> 42 900 15 -0.028918153 0.047732249 0 1 81.0 16.00 17.14286
#> 43 1627 15 0.743935704 0.118523963 0 1 71.0 16.61 51.42857
#> 44 4283 15 -0.105550244 0.211404294 0 0 50.0 15.40 34.28571
#> 45 938 15 1.205112219 0.089183509 1 1 77.0 15.40 34.28571
#> 46 3618 15 0.176503450 0.065062061 0 1 96.0 20.00 34.28571
#> 47 3297 15 0.438628316 0.017596249 0 1 86.0 16.27 34.28571
#> 48 5278 15 0.277786195 0.045071550 1 0 57.0 15.51 112.00000
#> 49 4730 15 -1.036182284 0.021280514 0 0 54.0 15.40 200.57143
#> 50 5008 15 -0.284874618 0.020202883 0 0 100.0 17.03 34.28571
#> 51 5291 15 0.441509426 0.097775139 0 1 47.6 16.00 17.14286
#> 52 3117 15 -0.029882502 0.045091134 0 1 65.0 16.50 34.28571
#> 53 195 15 -0.134236827 0.027541475 0 0 54.7 17.40 17.14286
#> 54 1073 15 0.077441879 0.043696571 0 1 57.0 15.74 34.28571
#> 55 3918 15 0.445514768 0.102731667 0 1 0.0 19.20 34.28571
#> 56 885 15 0.367331475 0.045752995 0 0 72.0 16.10 313.71429
#> 57 3698 15 0.431943923 0.128405437 0 1 76.7 16.40 51.42857
#> 58 947 15 0.137708470 0.013434974 1 1 46.0 18.00 17.14286
#> 59 402 15 1.767375469 0.214831442 1 1 0.0 16.06 34.28571
#> 60 304 15 1.315015078 0.194841713 1 1 62.0 16.00 34.28571
#> 61 4119 15 0.481691241 0.106149293 1 1 48.0 19.00 17.14286
#> 62 4052 15 -0.074442938 0.078014299 0 1 35.0 18.99 17.14286
#> 63 2897 15 -0.862986565 0.014297741 0 0 69.8 15.48 104.57143
#> 64 144 15 1.618591547 0.265495986 1 1 50.0 16.00 34.28571
#> 65 4404 15 0.211723000 0.074897826 1 1 46.0 19.60 34.28571
#> 66 3904 15 0.454529226 0.024869366 0 1 76.0 16.00 51.42857
#> 67 4235 15 0.216540292 0.161194205 0 1 60.0 19.70 34.28571
#> 68 5272 15 -0.413202494 0.085111827 0 0 87.5 16.88 16.00000
#>
#> $values
#> $values$ci_level
#> [1] 0.95
#>
#>
#> $stats
#> param estimate se ci_lower ci_upper p_value
#> 1 X.Intercept. 0.39695130 0.6580061223 -1.675340360 2.469242958 0.58816304
#> 2 binge 0.45158407 0.1016414122 0.156275299 0.746892833 0.01438447
#> 3 followup 0.00133090 0.0007225729 -0.001731258 0.004393058 0.20481776
#> 4 sreport 0.53875845 0.1433975062 0.153235110 0.924281796 0.01696253
#> 5 age -0.04371413 0.0378744671 -0.172347635 0.084919375 0.34044554
#>
#> $fits
#> $fits$robu
#> RVE: Hierarchical Effects Model with Small-Sample Corrections
#>
#> Model: effectsize ~ binge + followup + sreport + age
#>
#> Number of clusters = 15
#> Number of outcomes = 68 (min = 1 , mean = 4.53 , median = 2 , max = 29 )
#> Omega.sq = 0.1086551
#> Tau.sq = 0.02362071
#>
#> Estimate StdErr t-value dfs P(|t|>) 95% CI.L 95% CI.U Sig
#> 1 X.Intercept. 0.39695 0.658006 0.603 3.06 0.5882 -1.67534 2.46924
#> 2 binge 0.45158 0.101641 4.443 3.59 0.0144 0.15628 0.74689 **
#> 3 followup 0.00133 0.000723 1.842 2.03 0.2048 -0.00173 0.00439
#> 4 sreport 0.53876 0.143398 3.757 4.36 0.0170 0.15324 0.92428 **
#> 5 age -0.04371 0.037874 -1.154 2.69 0.3404 -0.17235 0.08492
#> ---
#> Signif. codes: < .01 *** < .05 ** < .10 *
#> ---
#> Note: If df < 4, do not trust the results
#>
#> attr(,"class")
#> [1] "metabias" "list"
summary(hier_mb)
#> param estimate se ci_lower ci_upper p_value
#> 1 X.Intercept. 0.39695130 0.6580061223 -1.675340360 2.469242958 0.58816304
#> 2 binge 0.45158407 0.1016414122 0.156275299 0.746892833 0.01438447
#> 3 followup 0.00133090 0.0007225729 -0.001731258 0.004393058 0.20481776
#> 4 sreport 0.53875845 0.1433975062 0.153235110 0.924281796 0.01696253
#> 5 age -0.04371413 0.0378744671 -0.172347635 0.084919375 0.34044554