Estimate the true sero prevalence using Frequentist/Bayesian estimation
Source:R/correct_prevalence.R
correct_prevalence.RdEstimate the true sero prevalence using Frequentist/Bayesian estimation
Usage
correct_prevalence(
data,
bayesian = TRUE,
init_se = 0.95,
init_sp = 0.8,
study_size_se = 1000,
study_size_sp = 1000,
chains = 1,
warmup = 1000,
iter = 2000
)Arguments
- data
the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)
- bayesian
whether to adjust sero-prevalence using the Bayesian or frequentist approach. If set to `TRUE`, true sero-prevalence is estimated using MCMC.
- init_se
sensitivity of the serological test
- init_sp
specificity of the serological test
- study_size_se
(applicable when `bayesian=TRUE`) study size for sensitivity validation study (i.e., number of confirmed infected patients in the study)
- study_size_sp
(applicable when `bayesian=TRUE`) study size for specificity validation study (i.e., number of confirmed non-infected patients in the study)
- chains
(applicable when `bayesian=TRUE`) number of Markov chains
- warmup
(applicable when `bayesian=TRUE`) number of warm up runs
- iter
(applicable when `bayesian=TRUE`) number of iterations
Value
a list of 3 items
- info
estimated parameters (when `bayesian = TRUE`) or formula to compute corrected prevalence (when `bayesian = FALSE`)
- df
data.frame of input data (in aggregated form)
- corrected_sero
data.frame containing age, the corresponding estimated seroprevalance with 95% confidence/credible interval, and adjusted tot and pos
Examples
data <- rubella_uk_1986_1987
correct_prevalence(data)
#>
#> SAMPLING FOR MODEL 'prevalence_correction' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000244 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.44 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
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#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
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#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 1.656 seconds (Warm-up)
#> Chain 1: 1.233 seconds (Sampling)
#> Chain 1: 2.889 seconds (Total)
#> Chain 1:
#> $info
#> mean se_mean sd 2.5% 25%
#> est_se 9.579691e-01 0.0001720772 0.005234571 9.468221e-01 9.545560e-01
#> est_sp 8.070363e-01 0.0003593900 0.010998038 7.851243e-01 8.001180e-01
#> theta[1] 1.747732e-02 0.0004012933 0.015518658 3.397982e-04 5.556424e-03
#> theta[2] 4.521156e-02 0.0009719303 0.032103002 3.497362e-03 1.967008e-02
#> theta[3] 3.934172e-02 0.0008073821 0.027757712 1.900997e-03 1.793613e-02
#> theta[4] 1.439986e-01 0.0014055351 0.046463549 5.425350e-02 1.123683e-01
#> theta[5] 3.158280e-01 0.0011278041 0.045792461 2.305850e-01 2.838526e-01
#> theta[6] 4.452249e-01 0.0012812842 0.047140675 3.545909e-01 4.120292e-01
#> theta[7] 4.636578e-01 0.0013137642 0.049670685 3.671684e-01 4.296514e-01
#> theta[8] 6.077679e-01 0.0012322838 0.051758945 5.034081e-01 5.746207e-01
#> theta[9] 7.173174e-01 0.0011569343 0.042494430 6.248539e-01 6.900703e-01
#> theta[10] 6.414107e-01 0.0013163957 0.050844687 5.427184e-01 6.076674e-01
#> theta[11] 7.251551e-01 0.0012041984 0.046902001 6.257779e-01 6.937983e-01
#> theta[12] 8.200245e-01 0.0010676660 0.039122057 7.334985e-01 7.953583e-01
#> theta[13] 7.607357e-01 0.0010292102 0.041323713 6.779961e-01 7.336668e-01
#> theta[14] 8.589639e-01 0.0008862776 0.033589290 7.896023e-01 8.380959e-01
#> theta[15] 7.716052e-01 0.0017929505 0.065516404 6.349394e-01 7.285860e-01
#> theta[16] 8.381310e-01 0.0017053329 0.066149986 7.006723e-01 7.966934e-01
#> theta[17] 9.133985e-01 0.0014411494 0.044158724 8.153283e-01 8.879187e-01
#> theta[18] 8.803590e-01 0.0013704992 0.048219400 7.815409e-01 8.477237e-01
#> theta[19] 8.801362e-01 0.0009873217 0.041440481 7.940748e-01 8.510319e-01
#> theta[20] 8.516456e-01 0.0013062293 0.051569875 7.386624e-01 8.196764e-01
#> theta[21] 9.149110e-01 0.0014600247 0.046220606 8.144062e-01 8.874402e-01
#> theta[22] 8.248331e-01 0.0012972922 0.049342001 7.226267e-01 7.924436e-01
#> theta[23] 9.461869e-01 0.0012688504 0.035172720 8.654861e-01 9.237220e-01
#> theta[24] 9.458786e-01 0.0010783110 0.033650049 8.708283e-01 9.239385e-01
#> theta[25] 9.706552e-01 0.0006278715 0.023829751 9.115230e-01 9.589298e-01
#> theta[26] 9.400029e-01 0.0010821960 0.033457664 8.681502e-01 9.181518e-01
#> theta[27] 9.251679e-01 0.0012477057 0.043365359 8.198036e-01 9.011331e-01
#> theta[28] 9.578219e-01 0.0008736670 0.032194552 8.824005e-01 9.384708e-01
#> theta[29] 9.064031e-01 0.0012458483 0.043045202 8.166192e-01 8.788169e-01
#> theta[30] 9.164445e-01 0.0013366119 0.049352158 8.018241e-01 8.855027e-01
#> theta[31] 8.607109e-01 0.0017207072 0.062663082 7.114398e-01 8.265632e-01
#> theta[32] 9.407438e-01 0.0012121067 0.045559916 8.244369e-01 9.173058e-01
#> theta[33] 9.357270e-01 0.0013794554 0.054278925 7.888680e-01 9.085377e-01
#> theta[34] 9.458862e-01 0.0012548097 0.043638644 8.334385e-01 9.249963e-01
#> theta[35] 8.961572e-01 0.0018858060 0.062735450 7.444533e-01 8.625101e-01
#> theta[36] 9.553792e-01 0.0009240727 0.037492222 8.613046e-01 9.363582e-01
#> theta[37] 9.389160e-01 0.0014208188 0.049938528 8.041306e-01 9.158440e-01
#> theta[38] 8.990585e-01 0.0018333159 0.066536570 7.442794e-01 8.581524e-01
#> theta[39] 9.168747e-01 0.0018935154 0.062687616 7.562417e-01 8.885957e-01
#> theta[40] 9.371065e-01 0.0015140064 0.056832194 7.920169e-01 9.115977e-01
#> theta[41] 9.528511e-01 0.0010380880 0.045128543 8.275254e-01 9.341298e-01
#> theta[42] 9.173219e-01 0.0017494957 0.065112154 7.639822e-01 8.834364e-01
#> theta[43] 9.080154e-01 0.0019124502 0.072102410 7.300474e-01 8.720032e-01
#> theta[44] 9.386586e-01 0.0014204252 0.055390859 8.014757e-01 9.138788e-01
#> lp__ -2.763145e+03 0.3011237007 5.715746786 -2.775866e+03 -2.766777e+03
#> 50% 75% 97.5% n_eff Rhat
#> est_se 9.582485e-01 9.613921e-01 9.678490e-01 925.3698 0.9990461
#> est_sp 8.070742e-01 8.144335e-01 8.281304e-01 936.4798 1.0003010
#> theta[1] 1.325944e-02 2.580038e-02 5.764662e-02 1495.4931 0.9990388
#> theta[2] 3.926800e-02 6.313427e-02 1.179769e-01 1090.9906 0.9991753
#> theta[3] 3.510345e-02 5.608013e-02 1.059055e-01 1181.9772 0.9992290
#> theta[4] 1.436227e-01 1.740261e-01 2.363213e-01 1092.8017 1.0041265
#> theta[5] 3.135946e-01 3.461306e-01 4.051240e-01 1648.6201 1.0032065
#> theta[6] 4.451716e-01 4.781418e-01 5.345672e-01 1353.6321 1.0045891
#> theta[7] 4.650392e-01 4.964724e-01 5.600045e-01 1429.4384 0.9991162
#> theta[8] 6.071917e-01 6.426559e-01 7.083871e-01 1764.2062 0.9996147
#> theta[9] 7.187469e-01 7.459264e-01 7.981012e-01 1349.1078 0.9994776
#> theta[10] 6.413189e-01 6.777565e-01 7.376091e-01 1491.8264 0.9999393
#> theta[11] 7.275840e-01 7.571155e-01 8.113803e-01 1517.0036 0.9990510
#> theta[12] 8.232098e-01 8.466952e-01 8.914191e-01 1342.6802 0.9990800
#> theta[13] 7.630979e-01 7.894624e-01 8.355361e-01 1612.0945 0.9994269
#> theta[14] 8.596282e-01 8.830362e-01 9.199369e-01 1436.3561 1.0028516
#> theta[15] 7.768558e-01 8.169429e-01 8.886224e-01 1335.2529 0.9990301
#> theta[16] 8.440784e-01 8.872369e-01 9.488487e-01 1504.6697 0.9990161
#> theta[17] 9.172955e-01 9.451696e-01 9.910868e-01 938.8907 0.9990891
#> theta[18] 8.832052e-01 9.150528e-01 9.691902e-01 1237.9014 1.0004986
#> theta[19] 8.835078e-01 9.116064e-01 9.480115e-01 1761.7010 0.9998162
#> theta[20] 8.551365e-01 8.881845e-01 9.406861e-01 1558.6669 1.0006793
#> theta[21] 9.202015e-01 9.487129e-01 9.903668e-01 1002.1918 1.0008532
#> theta[22] 8.264882e-01 8.589395e-01 9.131122e-01 1446.6315 0.9993236
#> theta[23] 9.535076e-01 9.739242e-01 9.950822e-01 768.4065 0.9994326
#> theta[24] 9.497731e-01 9.711970e-01 9.966858e-01 973.8305 0.9995677
#> theta[25] 9.762265e-01 9.886092e-01 9.990507e-01 1440.4476 1.0010816
#> theta[26] 9.439230e-01 9.641349e-01 9.964088e-01 955.8272 1.0003354
#> theta[27] 9.305549e-01 9.561449e-01 9.929416e-01 1207.9850 0.9998152
#> theta[28] 9.653058e-01 9.828475e-01 9.980652e-01 1357.9160 0.9997075
#> theta[29] 9.094284e-01 9.377292e-01 9.803109e-01 1193.7659 0.9990008
#> theta[30] 9.239625e-01 9.526448e-01 9.917734e-01 1363.3321 0.9990114
#> theta[31] 8.645073e-01 9.066013e-01 9.615028e-01 1326.2016 0.9995796
#> theta[32] 9.514552e-01 9.741705e-01 9.978432e-01 1412.8111 0.9994082
#> theta[33] 9.493927e-01 9.758949e-01 9.982326e-01 1548.2716 1.0000931
#> theta[34] 9.557844e-01 9.797785e-01 9.973173e-01 1209.4468 0.9998171
#> theta[35] 9.057973e-01 9.406812e-01 9.882563e-01 1106.7053 0.9990242
#> theta[36] 9.663073e-01 9.840566e-01 9.982648e-01 1646.1524 0.9992741
#> theta[37] 9.502159e-01 9.773138e-01 9.976396e-01 1235.3616 0.9992080
#> theta[38] 9.099911e-01 9.501315e-01 9.910139e-01 1317.1832 0.9990001
#> theta[39] 9.280455e-01 9.629734e-01 9.939965e-01 1096.0386 0.9992802
#> theta[40] 9.530193e-01 9.777640e-01 9.975286e-01 1409.0728 0.9993224
#> theta[41] 9.646571e-01 9.854371e-01 9.991666e-01 1889.8803 0.9991952
#> theta[42] 9.328743e-01 9.681655e-01 9.964167e-01 1385.1550 0.9990917
#> theta[43] 9.253118e-01 9.602544e-01 9.959770e-01 1421.4097 0.9991980
#> theta[44] 9.546272e-01 9.817446e-01 9.978124e-01 1520.6858 0.9991256
#> lp__ -2.762797e+03 -2.759032e+03 -2.753133e+03 360.2932 1.0126026
#>
#> $df
#> age pos tot
#> 1 1.5 31 206
#> 2 2.5 30 146
#> 3 3.5 34 168
#> 4 4.5 57 189
#> 5 5.5 95 219
#> 6 6.5 104 195
#> 7 7.5 90 164
#> 8 8.5 96 145
#> 9 9.5 134 180
#> 10 10.5 110 160
#> 11 11.5 111 148
#> 12 12.5 147 178
#> 13 13.5 138 177
#> 14 14.5 141 165
#> 15 15.5 53 67
#> 16 16.5 49 58
#> 17 17.5 73 81
#> 18 18.5 69 79
#> 19 19.5 97 111
#> 20 20.5 65 76
#> 21 21.5 74 82
#> 22 22.5 84 101
#> 23 23.5 82 88
#> 24 24.5 79 85
#> 25 25.5 90 94
#> 26 26.5 84 91
#> 27 27.5 81 89
#> 28 28.5 72 76
#> 29 29.5 71 79
#> 30 30.5 51 56
#> 31 31.5 45 52
#> 32 32.5 45 48
#> 33 33.5 35 37
#> 34 34.5 39 41
#> 35 35.5 36 40
#> 36 36.5 37 38
#> 37 37.5 37 39
#> 38 38.5 37 41
#> 39 39.5 28 30
#> 40 40.5 26 27
#> 41 41.5 25 25
#> 42 42.5 21 22
#> 43 43.5 18 19
#> 44 44.5 18 18
#>
#> $corrected_se
#> age sero pos tot sero_lwr sero_upr
#> 1 1.5 0.01325944 2.731444 206 0.0003397982 0.05764662
#> 2 2.5 0.03926800 5.733129 146 0.0034973616 0.11797694
#> 3 3.5 0.03510345 5.897379 168 0.0019009974 0.10590546
#> 4 4.5 0.14362274 27.144698 189 0.0542534972 0.23632128
#> 5 5.5 0.31359457 68.677211 219 0.2305849615 0.40512397
#> 6 6.5 0.44517156 86.808453 195 0.3545909437 0.53456725
#> 7 7.5 0.46503915 76.266421 164 0.3671684261 0.56000455
#> 8 8.5 0.60719173 88.042801 145 0.5034081474 0.70838708
#> 9 9.5 0.71874687 129.374436 180 0.6248538646 0.79810121
#> 10 10.5 0.64131891 102.611026 160 0.5427183649 0.73760906
#> 11 11.5 0.72758395 107.682425 148 0.6257778650 0.81138031
#> 12 12.5 0.82320978 146.531341 178 0.7334985417 0.89141914
#> 13 13.5 0.76309793 135.068334 177 0.6779961210 0.83553609
#> 14 14.5 0.85962824 141.838659 165 0.7896023026 0.91993691
#> 15 15.5 0.77685579 52.049338 67 0.6349393644 0.88862239
#> 16 16.5 0.84407836 48.956545 58 0.7006722751 0.94884869
#> 17 17.5 0.91729554 74.300939 81 0.8153282665 0.99108683
#> 18 18.5 0.88320521 69.773211 79 0.7815408701 0.96919022
#> 19 19.5 0.88350782 98.069368 111 0.7940747981 0.94801151
#> 20 20.5 0.85513649 64.990373 76 0.7386624226 0.94068610
#> 21 21.5 0.92020148 75.456521 82 0.8144062288 0.99036682
#> 22 22.5 0.82648821 83.475309 101 0.7226266670 0.91311217
#> 23 23.5 0.95350762 83.908671 88 0.8654860615 0.99508220
#> 24 24.5 0.94977310 80.730713 85 0.8708282569 0.99668585
#> 25 25.5 0.97622654 91.765295 94 0.9115229908 0.99905070
#> 26 26.5 0.94392302 85.896995 91 0.8681502083 0.99640880
#> 27 27.5 0.93055493 82.819389 89 0.8198035939 0.99294163
#> 28 28.5 0.96530578 73.363239 76 0.8824004970 0.99806516
#> 29 29.5 0.90942839 71.844843 79 0.8166192321 0.98031092
#> 30 30.5 0.92396246 51.741898 56 0.8018241060 0.99177343
#> 31 31.5 0.86450734 44.954381 52 0.7114398199 0.96150283
#> 32 32.5 0.95145521 45.669850 48 0.8244368835 0.99784321
#> 33 33.5 0.94939266 35.127529 37 0.7888680098 0.99823258
#> 34 34.5 0.95578440 39.187161 41 0.8334385481 0.99731726
#> 35 35.5 0.90579729 36.231892 40 0.7444533435 0.98825635
#> 36 36.5 0.96630732 36.719678 38 0.8613046445 0.99826479
#> 37 37.5 0.95021588 37.058419 39 0.8041305633 0.99763957
#> 38 38.5 0.90999106 37.309634 41 0.7442793553 0.99101392
#> 39 39.5 0.92804546 27.841364 30 0.7562417314 0.99399646
#> 40 40.5 0.95301930 25.731521 27 0.7920169109 0.99752857
#> 41 41.5 0.96465707 24.116427 25 0.8275253759 0.99916657
#> 42 42.5 0.93287433 20.523235 22 0.7639822308 0.99641675
#> 43 43.5 0.92531182 17.580925 19 0.7300474394 0.99597705
#> 44 44.5 0.95462715 17.183289 18 0.8014756977 0.99781240
#>
#> $method
#> [1] "bayesian"
#>