Multidimensional sensitivity analysis for different sources of bias
Source:R/multidimBias.R
multidimBias.RdMultidimensional sensitivity analysis for different sources of bias, where the bias analysis is repeated within a range of values for the bias parameter(s).
Usage
multidimBias(
case,
exposed,
type = c("exposure", "outcome", "confounder", "selection"),
se = NULL,
sp = NULL,
bias_parms = NULL,
OR.sel = NULL,
OR_sel = NULL,
alpha = 0.05,
dec = 4,
print = TRUE
)Arguments
- case
Outcome variable. If a variable, this variable is tabulated against.
- exposed
Exposure variable.
- type
Implement analysis for exposure misclassification, outcome misclassification, unmeasured confounder, or selection bias.
- se
Numeric vector of sensitivities. Parameter used with exposure or outcome misclassification.
- sp
Numeric vector of specificities. Parameter used with exposure or outcome misclassification. Should be the same length as `se`.
- bias_parms
List of bias parameters used with unmeasured confounder. The list is made of 3 vectors of the same length:
Prevalence of Confounder in Exposure+ population,
Prevalence of Confounder in Exposure- population, and
Relative risk between Confounder and Outcome.
- OR.sel
Deprecated; please use OR_sel instead.
- OR_sel
Selection odds ratios, for selection bias implementation.
- alpha
Significance level.
- dec
Number of decimals in the printout.
A logical scalar. Should the results be printed?
Value
A list with elements:
- obs.data
The analyzed 2 x 2 table from the observed data.
- obs.measures
A table of odds ratios and relative risk with confidence intervals.
- adj.measures
Multidimensional corrected relative risk and/or odds ratio data.
- bias.parms
Bias parameters.
Examples
multidimBias(matrix(c(45, 94, 257, 945),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "exposure",
se = c(1, 1, 1, .9, .9, .9, .8, .8, .8),
sp = c(1, .9, .8, 1, .9, .8, 1, .9, .8))
#> Multidimensional Exposure Misclassification
#> Observed Data:
#> ---------------------------------------------------
#> Outcome : HIV+
#> Comparing : Circ+ vs. Circ-
#>
#> Circ+ Circ-
#> HIV+ 45 94
#> HIV- 257 945
#>
#> 95% conf. interval
#> Observed Relative Risk: 1.6470 1.1824 2.2941
#> Observed Odds Ratio: 1.7603 1.2025 2.5769
#>
#> Multidimensional Corrected Relative Risk Data:
#> ----------------------------------------------
#> Outcome + -->
#> Outcome - |
#> V
#> Se: 1 Sp: 1 Se: 1 Sp: 0.9 Se: 1 Sp: 0.8 Se: 0.9 Sp: 1
#> Se: 1 Sp: 1 1.646999 1.1908873 0.6980786 1.892179
#> Se: 1 Sp: 0.9 2.779998 2.0473750 1.2312833 3.167761
#> Se: 1 Sp: 0.8 9.285050 7.6913317 5.6247010 10.086553
#> Se: 0.9 Sp: 1 1.463365 1.0551404 0.6161420 1.683347
#> Se: 0.9 Sp: 0.9 2.493351 1.8275497 1.0916938 2.847120
#> Se: 0.9 Sp: 0.8 8.863847 7.2832297 5.2439447 9.658349
#> Se: 0.8 Sp: 1 1.274090 0.9160976 0.5329205 1.467484
#> Se: 0.8 Sp: 0.9 2.191376 1.5982876 0.9481515 2.507815
#> Se: 0.8 Sp: 0.8 8.355700 6.7995802 4.8076884 9.138255
#> Se: 0.9 Sp: 0.9 Se: 0.9 Sp: 0.8 Se: 0.8 Sp: 1 Se: 0.8 Sp: 0.9
#> Se: 1 Sp: 1 1.371475 0.8079398 2.230237 1.620214
#> Se: 1 Sp: 0.9 2.339504 1.4160818 3.697461 2.737415
#> Se: 1 Sp: 0.8 8.344028 6.1388867 11.158630 9.195756
#> Se: 0.9 Sp: 1 1.216587 0.7137754 1.987126 1.439353
#> Se: 0.9 Sp: 0.9 2.092559 1.2576463 3.331489 2.454552
#> Se: 0.9 Sp: 0.8 7.930458 5.7488347 10.719779 8.775295
#> Se: 0.8 Sp: 1 1.057519 0.6179382 1.734959 1.252999
#> Se: 0.8 Sp: 0.9 1.833908 1.0941550 2.942147 2.156718
#> Se: 0.8 Sp: 0.8 7.436350 5.2976259 10.182874 8.268464
#> Se: 0.8 Sp: 0.8
#> Se: 1 Sp: 1 0.9598438
#> Se: 1 Sp: 0.9 1.6686882
#> Se: 1 Sp: 0.8 6.7907766
#> Se: 0.9 Sp: 1 0.8490046
#> Se: 0.9 Sp: 0.9 1.4851742
#> Se: 0.9 Sp: 0.8 6.3917770
#> Se: 0.8 Sp: 1 0.7358953
#> Se: 0.8 Sp: 0.9 1.2949529
#> Se: 0.8 Sp: 0.8 5.9250970
#>
#> Multidimensional Corrected Odds Ratio Data:
#> -------------------------------------------
#> Cases -->
#> Controls |
#> V
#> Se: 1 Sp: 1 Se: 1 Sp: 0.9 Se: 1 Sp: 0.8 Se: 0.9 Sp: 1
#> Se: 1 Sp: 1 1.760286 1.2165535 0.6728206 2.065754
#> Se: 1 Sp: 0.9 3.306971 2.2854843 1.2639978 3.880840
#> Se: 1 Sp: 0.8 27.252628 18.8345937 10.4165599 31.981860
#> Se: 0.9 Sp: 1 1.536385 1.0618131 0.5872407 1.802999
#> Se: 0.9 Sp: 0.9 2.886338 1.9947804 1.1032226 3.387213
#> Se: 0.9 Sp: 0.8 23.786209 16.4389131 9.0916175 27.913903
#> Se: 0.8 Sp: 1 1.312484 0.9070726 0.5016607 1.540244
#> Se: 0.8 Sp: 0.9 2.465705 1.7040765 0.9424474 2.893587
#> Se: 0.8 Sp: 0.8 20.319790 14.0432325 7.7666752 23.845946
#> Se: 0.9 Sp: 0.9 Se: 0.9 Sp: 0.8 Se: 0.8 Sp: 1 Se: 0.8 Sp: 0.9
#> Se: 1 Sp: 1 1.427666 0.7895772 2.499500 1.727432
#> Se: 1 Sp: 0.9 2.682091 1.4833432 4.695699 3.245250
#> Se: 1 Sp: 0.8 22.103019 12.2241776 38.697084 26.743985
#> Se: 0.9 Sp: 1 1.246073 0.6891463 2.181575 1.507710
#> Se: 0.9 Sp: 0.9 2.340941 1.2946682 4.098426 2.832468
#> Se: 0.9 Sp: 0.8 19.291608 10.6693140 33.774979 23.342263
#> Se: 0.8 Sp: 1 1.064480 0.5887155 1.863649 1.287988
#> Se: 0.8 Sp: 0.9 1.999790 1.1059932 3.501153 2.419686
#> Se: 0.8 Sp: 0.8 16.480198 9.1144503 28.852874 19.940542
#> Se: 0.8 Sp: 0.8
#> Se: 1 Sp: 1 0.9553646
#> Se: 1 Sp: 0.9 1.7948004
#> Se: 1 Sp: 0.8 14.7908856
#> Se: 0.9 Sp: 1 0.8338463
#> Se: 0.9 Sp: 0.9 1.5665094
#> Se: 0.9 Sp: 0.8 12.9095476
#> Se: 0.8 Sp: 1 0.7123279
#> Se: 0.8 Sp: 0.9 1.3382184
#> Se: 0.8 Sp: 0.8 11.0282095
#>
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#> Sensitivities: 1 1.0 1.0 0.9 0.9 0.9 0.8 0.8 0.8
#> Specificities: 1 0.9 0.8 1.0 0.9 0.8 1.0 0.9 0.8
multidimBias(matrix(c(45, 94, 257, 945),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "outcome",
se = c(1, 1, 1, .9, .9, .9, .8, .8, .8),
sp = c(1, .9, .8, 1, .9, .8, 1, .9, .8))
#> Multidimensional Outcome Misclassification
#>
#> Observed Data:
#> ---------------------------------------------------
#> Outcome : HIV+
#> Comparing : Circ+ vs. Circ-
#>
#> Circ+ Circ-
#> HIV+ 45 94
#> HIV- 257 945
#>
#> 95% conf. interval
#> Observed Relative Risk: 1.6470 1.1824 2.2941
#> Observed Odds Ratio: 1.7603 1.2025 2.5769
#>
#> Multidimensional Corrected Relative Risk Data:
#> ----------------------------------------------
#> Outcome + -->
#> Outcome - |
#> V
#> Se: 1 Sp: 1 Se: 1 Sp: 0.9 Se: 1 Sp: 0.8 Se: 0.9 Sp: 1
#> Se: 1 Sp: 1 1.6469987 0.6018662 -0.7045495 1.8299986
#> Se: 1 Sp: 0.9 -14.0743528 -5.1432203 6.0206954 -15.6381698
#> Se: 1 Sp: 0.8 -1.0883507 -0.3977183 0.4655722 -1.2092785
#> Se: 0.9 Sp: 1 1.4822989 0.5416796 -0.6340945 1.6469987
#> Se: 0.9 Sp: 0.9 -12.5105358 -4.5717514 5.3517292 -13.9005954
#> Se: 0.9 Sp: 0.8 -0.9523068 -0.3480035 0.4073757 -1.0581187
#> Se: 0.8 Sp: 1 1.3175990 0.4814930 -0.5636396 1.4639989
#> Se: 0.8 Sp: 0.9 -10.9467188 -4.0002824 4.6827631 -12.1630209
#> Se: 0.8 Sp: 0.8 -0.8162630 -0.2982887 0.3491792 -0.9069589
#> Se: 0.9 Sp: 0.9 Se: 0.9 Sp: 0.8 Se: 0.8 Sp: 1 Se: 0.8 Sp: 0.9
#> Se: 1 Sp: 1 0.6770995 -0.8051994 2.058748 0.7738280
#> Se: 1 Sp: 0.9 -5.7861228 6.8807947 -17.592941 -6.6127118
#> Se: 1 Sp: 0.8 -0.4474330 0.5320825 -1.360438 -0.5113521
#> Se: 0.9 Sp: 1 0.6093895 -0.7246794 1.852874 0.6964452
#> Se: 0.9 Sp: 0.9 -5.1432203 6.1162620 -15.638170 -5.8779660
#> Se: 0.9 Sp: 0.8 -0.3915039 0.4655722 -1.190384 -0.4474330
#> Se: 0.8 Sp: 1 0.5416796 -0.6441595 1.646999 0.6190624
#> Se: 0.8 Sp: 0.9 -4.5003177 5.3517292 -13.683399 -5.1432203
#> Se: 0.8 Sp: 0.8 -0.3355748 0.3990619 -1.020329 -0.3835140
#> Se: 0.8 Sp: 0.8
#> Se: 1 Sp: 1 -0.9393993
#> Se: 1 Sp: 0.9 8.0275938
#> Se: 1 Sp: 0.8 0.6207630
#> Se: 0.9 Sp: 1 -0.8454593
#> Se: 0.9 Sp: 0.9 7.1356390
#> Se: 0.9 Sp: 0.8 0.5431676
#> Se: 0.8 Sp: 1 -0.7515194
#> Se: 0.8 Sp: 0.9 6.2436841
#> Se: 0.8 Sp: 0.8 0.4655722
#>
#> Multidimensional Corrected Odds Ratio Data:
#> -------------------------------------------
#> Cases -->
#> Controls |
#> V
#> Se: 1 Sp: 1 Se: 1 Sp: 0.9 Se: 1 Sp: 0.8 Se: 0.9 Sp: 1
#> Se: 1 Sp: 1 1.760286 0.5789387 -0.6024091 1.994681
#> Se: 1 Sp: 0.9 -16.713831 -5.4969933 5.7198444 -18.939394
#> Se: 1 Sp: 0.8 -1.454015 -0.4782094 0.4975963 -1.647627
#> Se: 0.9 Sp: 1 1.566748 0.5152860 -0.5361760 1.775371
#> Se: 0.9 Sp: 0.9 -14.876194 -4.8926149 5.0909641 -16.857063
#> Se: 0.9 Sp: 0.8 -1.294150 -0.4256317 0.4428870 -1.466476
#> Se: 0.8 Sp: 1 1.373210 0.4516334 -0.4699429 1.556062
#> Se: 0.8 Sp: 0.9 -13.038557 -4.2882365 4.4620839 -14.774731
#> Se: 0.8 Sp: 0.8 -1.134286 -0.3730540 0.3881778 -1.285324
#> Se: 0.9 Sp: 0.9 Se: 0.9 Sp: 0.8 Se: 0.8 Sp: 1 Se: 0.8 Sp: 0.9
#> Se: 1 Sp: 1 0.6560284 -0.6826241 2.301087 0.7568018
#> Se: 1 Sp: 0.9 -6.2289562 6.4814815 -21.848701 -7.1857949
#> Se: 1 Sp: 0.8 -0.5418864 0.5638547 -1.900722 -0.6251263
#> Se: 0.9 Sp: 1 0.5839000 -0.6075716 2.048089 0.6735936
#> Se: 0.9 Sp: 0.9 -5.5441006 5.7688615 -19.446500 -6.3957376
#> Se: 0.9 Sp: 0.8 -0.4823075 0.5018605 -1.691743 -0.5563955
#> Se: 0.8 Sp: 1 0.5117715 -0.5325190 1.795091 0.5903855
#> Se: 0.8 Sp: 0.9 -4.8592450 5.0562414 -17.044299 -5.6056804
#> Se: 0.8 Sp: 0.8 -0.4227287 0.4398663 -1.482764 -0.4876646
#> Se: 0.8 Sp: 0.8
#> Se: 1 Sp: 1 -0.7874830
#> Se: 1 Sp: 0.9 7.4771109
#> Se: 1 Sp: 0.8 0.6504692
#> Se: 0.9 Sp: 1 -0.7009015
#> Se: 0.9 Sp: 0.9 6.6550243
#> Se: 0.9 Sp: 0.8 0.5789520
#> Se: 0.8 Sp: 1 -0.6143200
#> Se: 0.8 Sp: 0.9 5.8329377
#> Se: 0.8 Sp: 0.8 0.5074348
#>
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#> Sensitivities: 1 1.0 1.0 0.9 0.9 0.9 0.8 0.8 0.8
#> Specificities: 1 0.9 0.8 1.0 0.9 0.8 1.0 0.9 0.8
multidimBias(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "confounder",
bias_parms = list(seq(.72, .92, by = .02),
seq(.01, .11, by = .01), seq(.13, 1.13, by = .1)))
#> Multidimensional Unmeasured Confounding
#> Observed Data:
#> ---------------------------------------------------
#> Outcome : HIV+
#> Comparing : Circ+ vs. Circ-
#>
#> Circ+ Circ-
#> HIV+ 105 85
#> HIV- 527 93
#>
#> 95% conf. interval
#> Observed Relative Risk: 0.3479 0.2757 0.4390
#> Observed Odds Ratio: 0.2180 0.1519 0.3128
#>
#> Multidimensional Relative Risk Exposure-Data Relationship Adjusted for Confounder:
#> ----------------------------------------------
#> RR(Conf-Outc): 0.13 RR(Conf-Outc): 0.23
#> p(Conf+|Exp+): 0.72 p(Conf+|Exp-): 0.01 0.9231484 0.7747670
#> p(Conf+|Exp+): 0.74 p(Conf+|Exp-): 0.02 0.9597456 0.7962743
#> p(Conf+|Exp+): 0.76 p(Conf+|Exp-): 0.03 1.0001019 0.8193787
#> p(Conf+|Exp+): 0.78 p(Conf+|Exp-): 0.04 1.0448278 0.8442647
#> p(Conf+|Exp+): 0.8 p(Conf+|Exp-): 0.05 1.0946737 0.8711468
#> p(Conf+|Exp+): 0.82 p(Conf+|Exp-): 0.06 1.1505720 0.9002752
#> p(Conf+|Exp+): 0.84 p(Conf+|Exp-): 0.07 1.2136965 0.9319436
#> p(Conf+|Exp+): 0.86 p(Conf+|Exp-): 0.08 1.2855450 0.9664995
#> p(Conf+|Exp+): 0.88 p(Conf+|Exp-): 0.09 1.3680604 1.0043567
#> p(Conf+|Exp+): 0.9 p(Conf+|Exp-): 0.1 1.4638088 1.0460119
#> p(Conf+|Exp+): 0.92 p(Conf+|Exp-): 0.11 1.5762507 1.0920669
#> RR(Conf-Outc): 0.33 RR(Conf-Outc): 0.43
#> p(Conf+|Exp+): 0.72 p(Conf+|Exp-): 0.01 0.6676663 0.5867232
#> p(Conf+|Exp+): 0.74 p(Conf+|Exp-): 0.02 0.6807875 0.5948614
#> p(Conf+|Exp+): 0.76 p(Conf+|Exp-): 0.03 0.6946251 0.6033270
#> p(Conf+|Exp+): 0.78 p(Conf+|Exp-): 0.04 0.7092396 0.6121402
#> p(Conf+|Exp+): 0.8 p(Conf+|Exp-): 0.05 0.7246982 0.6213227
#> p(Conf+|Exp+): 0.82 p(Conf+|Exp-): 0.06 0.7410762 0.6308983
#> p(Conf+|Exp+): 0.84 p(Conf+|Exp-): 0.07 0.7584581 0.6408928
#> p(Conf+|Exp+): 0.86 p(Conf+|Exp-): 0.08 0.7769393 0.6513342
#> p(Conf+|Exp+): 0.88 p(Conf+|Exp-): 0.09 0.7966273 0.6622534
#> p(Conf+|Exp+): 0.9 p(Conf+|Exp-): 0.1 0.8176443 0.6736837
#> p(Conf+|Exp+): 0.92 p(Conf+|Exp-): 0.11 0.8401297 0.6856620
#> RR(Conf-Outc): 0.53 RR(Conf-Outc): 0.63
#> p(Conf+|Exp+): 0.72 p(Conf+|Exp-): 0.01 0.5233977 0.4725025
#> p(Conf+|Exp+): 0.74 p(Conf+|Exp-): 0.02 0.5284341 0.4755447
#> p(Conf+|Exp+): 0.76 p(Conf+|Exp-): 0.03 0.5336178 0.4786495
#> p(Conf+|Exp+): 0.78 p(Conf+|Exp-): 0.04 0.5389553 0.4818189
#> p(Conf+|Exp+): 0.8 p(Conf+|Exp-): 0.05 0.5444537 0.4850550
#> p(Conf+|Exp+): 0.82 p(Conf+|Exp-): 0.06 0.5501203 0.4883597
#> p(Conf+|Exp+): 0.84 p(Conf+|Exp-): 0.07 0.5559628 0.4917355
#> p(Conf+|Exp+): 0.86 p(Conf+|Exp-): 0.08 0.5619898 0.4951846
#> p(Conf+|Exp+): 0.88 p(Conf+|Exp-): 0.09 0.5682099 0.4987093
#> p(Conf+|Exp+): 0.9 p(Conf+|Exp-): 0.1 0.5746328 0.5023122
#> p(Conf+|Exp+): 0.92 p(Conf+|Exp-): 0.11 0.5812683 0.5059960
#> RR(Conf-Outc): 0.73 RR(Conf-Outc): 0.83
#> p(Conf+|Exp+): 0.72 p(Conf+|Exp-): 0.01 0.4307047 0.3957653
#> p(Conf+|Exp+): 0.74 p(Conf+|Exp-): 0.02 0.4324374 0.3966280
#> p(Conf+|Exp+): 0.76 p(Conf+|Exp-): 0.03 0.4341935 0.3974974
#> p(Conf+|Exp+): 0.78 p(Conf+|Exp-): 0.04 0.4359737 0.3983736
#> p(Conf+|Exp+): 0.8 p(Conf+|Exp-): 0.05 0.4377784 0.3992568
#> p(Conf+|Exp+): 0.82 p(Conf+|Exp-): 0.06 0.4396081 0.4001469
#> p(Conf+|Exp+): 0.84 p(Conf+|Exp-): 0.07 0.4414634 0.4010440
#> p(Conf+|Exp+): 0.86 p(Conf+|Exp-): 0.08 0.4433448 0.4019483
#> p(Conf+|Exp+): 0.88 p(Conf+|Exp-): 0.09 0.4452529 0.4028598
#> p(Conf+|Exp+): 0.9 p(Conf+|Exp-): 0.1 0.4471881 0.4037787
#> p(Conf+|Exp+): 0.92 p(Conf+|Exp-): 0.11 0.4491512 0.4047050
#> RR(Conf-Outc): 0.93 RR(Conf-Outc): 1.03
#> p(Conf+|Exp+): 0.72 p(Conf+|Exp-): 0.01 0.3661242 0.3406612
#> p(Conf+|Exp+): 0.74 p(Conf+|Exp-): 0.02 0.3664080 0.3405634
#> p(Conf+|Exp+): 0.76 p(Conf+|Exp-): 0.03 0.3666925 0.3404656
#> p(Conf+|Exp+): 0.78 p(Conf+|Exp-): 0.04 0.3669779 0.3403680
#> p(Conf+|Exp+): 0.8 p(Conf+|Exp-): 0.05 0.3672642 0.3402705
#> p(Conf+|Exp+): 0.82 p(Conf+|Exp-): 0.06 0.3675513 0.3401731
#> p(Conf+|Exp+): 0.84 p(Conf+|Exp-): 0.07 0.3678393 0.3400758
#> p(Conf+|Exp+): 0.86 p(Conf+|Exp-): 0.08 0.3681281 0.3399787
#> p(Conf+|Exp+): 0.88 p(Conf+|Exp-): 0.09 0.3684178 0.3398816
#> p(Conf+|Exp+): 0.9 p(Conf+|Exp-): 0.1 0.3687083 0.3397847
#> p(Conf+|Exp+): 0.92 p(Conf+|Exp-): 0.11 0.3689998 0.3396879
#> RR(Conf-Outc): 1.13
#> p(Conf+|Exp+): 0.72 p(Conf+|Exp-): 0.01 0.3185510
#> p(Conf+|Exp+): 0.74 p(Conf+|Exp-): 0.02 0.3182081
#> p(Conf+|Exp+): 0.76 p(Conf+|Exp-): 0.03 0.3178667
#> p(Conf+|Exp+): 0.78 p(Conf+|Exp-): 0.04 0.3175270
#> p(Conf+|Exp+): 0.8 p(Conf+|Exp-): 0.05 0.3171889
#> p(Conf+|Exp+): 0.82 p(Conf+|Exp-): 0.06 0.3168524
#> p(Conf+|Exp+): 0.84 p(Conf+|Exp-): 0.07 0.3165174
#> p(Conf+|Exp+): 0.86 p(Conf+|Exp-): 0.08 0.3161841
#> p(Conf+|Exp+): 0.88 p(Conf+|Exp-): 0.09 0.3158522
#> p(Conf+|Exp+): 0.9 p(Conf+|Exp-): 0.1 0.3155219
#> p(Conf+|Exp+): 0.92 p(Conf+|Exp-): 0.11 0.3151932
#>
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> p(Confounder+|Exposure+): 0.72 0.74 0.76 0.78 0.80 0.82 0.84 0.86 0.88 0.90
#> p(Confounder+|Exposure-): 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10
#> RR(Confounder-Outcome) 0.13 0.23 0.33 0.43 0.53 0.63 0.73 0.83 0.93 1.03
#> [,11]
#> p(Confounder+|Exposure+): 0.92
#> p(Confounder+|Exposure-): 0.11
#> RR(Confounder-Outcome) 1.13
multidimBias(matrix(c(136, 107, 297, 165),
dimnames = list(c("Uveal Melanoma+", "Uveal Melanoma-"),
c("Mobile Use+", "Mobile Use -")),
nrow = 2, byrow = TRUE),
type = "selection",
OR_sel = seq(1.5, 6.5, by = .5))
#> Multidimensional Selection Bias
#> Observed Data:
#> ---------------------------------------------------
#> Outcome : Uveal Melanoma+
#> Comparing : Mobile Use+ vs. Mobile Use -
#>
#> Mobile Use+ Mobile Use -
#> Uveal Melanoma+ 136 107
#> Uveal Melanoma- 297 165
#>
#> 95% conf. interval
#> Observed Relative Risk: 0.7984 0.6518 0.9780
#> Observed Odds Ratio: 0.7061 0.5144 0.9693
#>
#> Observed and Selection Bias Corrected Measures:
#> -----------------------------------------------
#> OR selection: OR corrected:
#> [1,] 1.5 0.4707511
#> [2,] 2.0 0.3530633
#> [3,] 2.5 0.2824507
#> [4,] 3.0 0.2353756
#> [5,] 3.5 0.2017505
#> [6,] 4.0 0.1765317
#> [7,] 4.5 0.1569170
#> [8,] 5.0 0.1412253
#> [9,] 5.5 0.1283867
#> [10,] 6.0 0.1176878
#> [11,] 6.5 0.1086349
#>