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7 - Estimated true prevalence using two tests (unpooled) with a Gibbs sampler

This method uses a Bayesian approach and Gibbs sampling to estimate the true animal-level prevalence of infection based on testing of individual (not pooled) samples using two independent tests with imperfect sensitivity and/or specificity. The analysis requires prior estimates of true prevalence and test sensitivity and test specificity for both tests, as Beta probability distributions. It outputs posterior distributions for prevalence, sensitivity and specificity of both tests and several other parameters of interest. This method is similar to the one-test method, except that it allows incorporation of data from two tests used concurrently, and finds the best estimate that fits the combination of the prior information and the observed data. It also allows for uncertainty about the true values for sensitivity and specificity when calculating probability limits for the true prevalence estimate and the incorporation of prior information on the likely true prevalence based on pre-existing estimates or expert opinion. Because of the use of two tests, this method will often produce narrower probability limits about the prevalence estimate than the one-test method, particularly where there is considerable uncertainty about prior estimates.

For this analysis, the original values for stool sampling and serology for Strongyloides infection in Cambodian refugees from Joseph et al. (1996) were used, as listed in the table below, and 95% probability limits were calculated about the estimated prevalence.

Input Value
a (T1+/T2+) 38
b (T1+/T2-) 87
c (T1-/T2+) 2
d (T1-/T2-) 35
P alpha 1
P beta 1
Se 1 alpha 21.96
Se 1 beta 5.49
Sp 1 alpha 4.1
Sp 1 beta 1.76
Se 2 alpha 4.44
Se 2 beta 13.31
Sp 2 alpha 71.25
Sp 2 beta 3.75
Y1 start 35
Y2 start 30
Y3 start 2
Y4 start 10
Iterations 25000
Discard 5000

The prior Beta distributions defined above are equivalent to:

Distribution Alpha value Beta value 2.5% percentile Median 97.5% percentile Mean Mode Standard deviation
Prevalence 1 1 0.025 0.5 0.975 0.5 0.2887
Sensitivity (serology) 21.96 5.49 0.6346 0.8073 0.9242 0.8 0.8236 0.075
Specificity (serology) 4.1 1.76 0.3123 0.7235 0.9621 0.6997 0.8031 0.175
Sensitivity (stool) 4.44 13.31 0.0843 0.2406 0.469 0.2501 0.2184 0.1
Specificity (stool) 71.25 3.75 0.8909 0.954 0.9868 0.95 0.9623 0.025

The simulation was run for 25,000 iterations, with 5,000 iterations discarded to allow for convergence. Posterior probability distributions for prevalence, sensitivity, specificity and other parameters from the analysis are summarised below.

Prevalence Test 1 Se Test 1 Sp Test 1 PPV Test 1 NPV Test 2 Se Test 2 Sp Test 2 PPV Test 2 NPV Y1 Y2 Y3 Y4
Minimum 0.275 0.689 0.129 0.402 0.021 0.135 0.823 0.565 0.292 26 16 0 0
0.025 0.528 0.791 0.376 0.679 0.277 0.222 0.906 0.775 0.389 34 45 0 2
Median 0.772 0.889 0.695 0.918 0.639 0.305 0.96 0.907 0.519 38 77 2 10
0.975 0.921 0.954 0.955 0.992 0.858 0.425 0.989 0.973 0.705 38 87 2 25
Maximum 0.998 0.984 0.999 1 0.959 0.615 0.998 0.997 0.895 38 87 2 35
Mean 0.761 0.885 0.688 0.9 0.624 0.308 0.958 0.9 0.525 37 74 2 10
SD 0.099 0.042 0.16 0.084 0.15 0.051 0.021 0.051 0.079 1 11 1 6
Iterations 20000 20000 20000 20000 20000 20000 20000 20000 20000 20000 20000 20000 20000

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Contents
1 Fixed pool size and perfect tests
2 Fixed pool size and tests with known sensitivity and specificity
3 Fixed pool size and tests with uncertain sensitivity and specificity
4 Variable pool size and perfect test
5 Pooled prevalence using a Gibbs sampler
6 Estimated true prevalence using one test (unpooled) with a Gibbs sampler
7 Estimated true prevalence using two tests (unpooled) with a Gibbs sampler
8 Sample size calculation for fixed pool size and perfect tests
9 Sample size calculation for fixed pool size and tests with known sensitivity and specificity
10 Sample size calculation for fixed pool size and tests with uncertain sensitivity and specificity
11 Simulate sampling for fixed pool size and assumed perfect test
12 Simulate sampling for fixed pool size and test with known sensitivity and specificity
13 Simulate sampling for fixed pool size and test with uncertain sensitivity and specificity
14 Simulate sampling for variable pool size and assumed perfect test
15 Demonstration of freedom using pooled testing with tests of known sensitivity and fixed pool size
16 Estimation of alpha and beta Parameters for Prior Beta distributions
17 Estimation of Beta probability distributions for specified alpha and beta parameters