Analyzing time-to-event data in a clinical trial when an unknown proportion of subjects has experienced the event at entry

In some clinical trials, where the outcome is the time until development of a silent event, an unknown proportion of subjects who have already experienced the event will be unknowingly enrolled due to the imperfect nature of the diagnostic tests used to screen potential subjects. For example, commonly used diagnostic tests for evaluating HIV infection status in infants, such as DNA PCR and HIV Culture, have low sensitivity when given soon after infection. This can lead to the inclusion of an unknown proportion of HIV-infected infants into clinical trials aimed at the prevention of transmission from HIV-positive mothers to their infants through breastfeeding. The infection status of infants at the end of the trial, when they are more than a year of age, can be determined with certainty. For those infants found to be infected with HIV at the end of the trial, it cannot be determined whether this occurred during the study or whether they were already infected when they were enrolled. In these settings, estimates of the cumulative risk of the event by the end of the study will overestimate the true probability of event during the study period and hypothesis tests comparing two or more intervention strategies can also be biased. We present inference methods for the distribution of time until the event of interest in these settings, and investigate issues in the design of such trials when there is a choice of using both imperfect and perfect diagnostic tests.     

Bayesian Meta‐Analysis of the Accuracy of a Test for Tuberculous Pleuritis in the Absence of a Gold Standard Reference

Summary Absence of a perfect reference test is an acknowledged source of bias in diagnostic studies. In the case of tuberculous pleuritis, standard reference tests such as smear microscopy, culture and biopsy have poor sensitivity. Yet meta‐analyses of new tests for this disease have always assumed the reference standard is perfect, leading to biased estimates of the new test’s accuracy. We describe a method for joint meta‐analysis of sensitivity and specificity of the diagnostic test under evaluation, while considering the imperfect nature of the reference standard. We use a Bayesian hierarchical model that takes into account within‐ and between‐study variability. We show how to obtain pooled estimates of sensitivity and specificity, and how to plot a hierarchical summary receiver operating characteristic curve. We describe extensions of the model to situations where multiple reference tests are used, and where index and reference tests are conditionally dependent. The performance of the model is evaluated using simulations and illustrated using data from a meta‐analysis of nucleic acid amplification tests (NAATs) for tuberculous pleuritis. The estimate of NAAT specificity was higher and the sensitivity lower compared to a model that assumed that the reference test was perfect.     

Estimating prevalence and test accuracy in disease ecology: How Bayesian latent class analysis can boost or bias imperfect test results

1. Obtaining accurate estimates of disease prevalence is crucial for the monitoring and management of wildlife populations but can be difficult if different diagnostic tests yield conflicting results and if the accuracy of each diagnostic test is unknown. Bayesian latent class analysis (BLCA) modeling offers a potential solution, providing estimates of prevalence levels and diagnostic test accuracy under the realistic assumption that no diagnostic test is perfect.
2. In typical applications of this approach, the specificity of one test is fixed at or close to 100%, allowing the model to simultaneously estimate the sensitivity and specificity of all other tests, in addition to infection prevalence. In wildlife systems, a test with near‐perfect specificity is not always available, so we simulated data to investigate how decreasing this fixed specificity value affects the accuracy of model estimates.
3. We used simulations to explore how the trade‐off between diagnostic test specificity and sensitivity impacts prevalence estimates and found that directional biases depend on pathogen prevalence. Both the precision and accuracy of results depend on the sample size, the diagnostic tests used, and the true infection prevalence, so these factors should be considered when applying BLCA to estimate disease prevalence and diagnostic test accuracy in wildlife systems. A wildlife disease case study, focusing on leptospirosis in California sea lions, demonstrated the potential for Bayesian latent class methods to provide reliable estimates under real‐world conditions.
4. We delineate conditions under which BLCA improves upon the results from a single diagnostic across a range of prevalence levels and sample sizes, demonstrating when this method is preferable for disease ecologists working in a wide variety of pathogen systems.

     

Testing inflated zeros in binomial regression models

Binomial regression models are commonly applied to proportion data such as those relating to the mortality and infection rates of diseases. However, it is often the case that the responses may exhibit excessive zeros; in such cases a zero‐inflated binomial (ZIB) regression model can be applied instead. In practice, it is essential to test if there are excessive zeros in the outcome to help choose an appropriate model. The binomial models can yield biased inference if there are excessive zeros, while ZIB models may be unnecessarily complex and hard to interpret, and even face convergence issues, if there are no excessive zeros. In this paper, we develop a new test for testing zero inflation in binomial regression models by directly comparing the amount of observed zeros with what would be expected under the binomial regression model. A closed form of the test statistic, as well as the asymptotic properties of the test, is derived based on estimating equations. Our systematic simulation studies show that the new test performs very well in most cases, and outperforms the classical Wald, likelihood ratio, and score tests, especially in controlling type I errors. Two real data examples are also included for illustrative purpose.     

Penalized survival models for the analysis of alternating recurrent event data

Recurrent event data are widely encountered in clinical and observational studies. Most methods for recurrent events treat the outcome as a point process and, as such, neglect any associated event duration. This generally leads to a less informative and potentially biased analysis. We propose a joint model for the recurrent event rate (of incidence) and duration. The two processes are linked through a bivariate normal frailty. For example, when the event is hospitalization, we can treat the time to admission and length-of-stay as two alternating recurrent events. In our method, the regression parameters are estimated through a penalized partial likelihood, and the variance-covariance matrix of the frailty is estimated through a recursive estimating formula. Moreover, we develop a likelihood ratio test to assess the dependence between the incidence and duration processes. Simulation results demonstrate that our method provides accurate parameter estimation, with a relatively fast computation time. We illustrate the methods through an analysis of hospitalizations among end-stage renal disease patients.     

Evaluation of an indirect serum ELISA and a bacteriological faecal culture test for diagnosis of Salmonella serotype Dublin in cattle using latent class models

AIMS: To evaluate a conventional bacteriological test based on faecal culture and an indirect serum ELISA for detection of S. Dublin infected cattle. To compare the predictive values of the two tests in relation to the prevalence. METHODS AND RESULTS: A total of 4531 paired samples from cattle in 29 dairy herds were analysed for presence of S. Dublin bacteria in faeces and immunoglobulins directed against S. Dublin lipopolysaccharide in an indirect serum ELISA. Sensitivity and specificity were estimated at two ELISA cut-off values using a validation method based on latent class models, which presumably provides less biased results than traditional validation methods. Stratification of data into three age groups gave significantly better estimates of test performance of the ELISA. Receiver operating characteristic (ROC) curves were constructed for comparison of overall performance of the ELISA between the three age groups. The sensitivity of the faecal culture test was low (6-14%). ELISA appeared to have a higher validity for animals aged 100-299 days of age than older or younger animals. Overall, the negative predictive value of the ELISA was 2-10 times higher than for the faecal culture test at realistic prevalence of infection in the test population. CONCLUSIONS: The diagnostic sensitivity of the faecal culture test for detection of S. Dublin is poor, the specificity is 1. The superior sensitivity and negative predictive value of the serum ELISA makes this test preferable to faecal culture as an initial screening test and for certification of herds not infected with S. Dublin. SIGNIFICANCE AND IMPACT OF THE STUDY: A quantitative estimate of the sensitivity of a faecal culture test for S. Dublin in a general population was provided. ELISA was shown to be an appropriate alternative diagnostic test. Preferably, samples from animals aged 100-299 days of age should be used as these give the best overall performance of the ELISA. Plots of ROC curves and predictive values in relation to prevalence facilitates optimisation of the ELISA cut-off value.     

Estimating the distribution of a renewal process from times at which events from an independent process are detected

The analysis of length-biased data has been mostly limited to the interarrival interval of a renewal process covering a specific time point. Motivated by a surveillance problem, we consider a more general situation where this time point is random and related to a specific event, for example, status change or onset of a disease. We also consider the problem when additional information is available on whether the event intervals (interarrival intervals covering the random event) end within or after a random time period (which we call a window period) following the random event. Under the assumptions that the occurrence rate of the random event is low and the renewal process is independent of the random event, we provide formulae for the estimation of the distribution of interarrival times based on the observed event intervals. Procedures for testing the required assumptions are also furnished. We apply our results to human immunodeficiency virus (HIV) test data from public test sites in Seattle, Washington, where the random event is HIV infection and the window period is from the onset of HIV infection to the time at which a less sensitive HIV test becomes positive. Results show that the estimator of the intertest interval length distribution from event intervals ending within the window period is less biased than the estimator from all event intervals; the latter estimator is affected by right truncation. Finally, we discuss possible applications to estimating HIV incidence and analyzing length-biased samples with right or left truncated data.     

A cautionary note on the robustness of latent class models for estimating diagnostic error without a gold standard

Modeling diagnostic error without a gold standard has been an active area of biostatistical research. In a majority of the approaches, model-based estimates of sensitivity, specificity, and prevalence are derived from a latent class model in which the latent variable represents an individual's true unobserved disease status. For simplicity, initial approaches assumed that the diagnostic test results on the same subject were independent given the true disease status (i.e., the conditional independence assumption). More recently, various authors have proposed approaches for modeling the dependence structure between test results given true disease status. This note discusses a potential problem with these approaches. Namely, we show that when the conditional dependence between tests is misspecified, estimators of sensitivity, specificity, and prevalence can be biased. Importantly, we demonstrate that with small numbers of tests, likelihood comparisons and other model diagnostics may not be able to distinguish between models with different dependence structures. We present asymptotic results that show the generality of the problem. Further, data analysis and simulations demonstrate the practical implications of model misspecification. Finally, we present some guidelines about the use of these models for practitioners.     

A score test for testing a zero-inflated Poisson regression model against zero-inflated negative binomial alternatives

Count data often show a higher incidence of zero counts than would be expected if the data were Poisson distributed. Zero-inflated Poisson regression models are a useful class of models for such data, but parameter estimates may be seriously biased if the nonzero counts are overdispersed in relation to the Poisson distribution. We therefore provide a score test for testing zero-inflated Poisson regression models against zero-inflated negative binomial alternatives.     

An Evaluation of Methods for the Estimation of Sensitivity and Specificity of Site-Specific Diagnostic Tests

The performance of diagnostic tests is often evaluated by estimating their sensitivity and specificity with respect to a traditionally accepted standard test regarded as a “gold standard” in making the diagnosis. Correlated samples of binary data arise in many fields of application. The fundamental unit for analysis is occasionally the site rather than the subject in site-specific studies. Statistical methods that take into account the within-subject corelation should be employed to estimate the sensitivity and the specificity of diagnostic tests since site-specific results within a subject can be highly correlated. I introduce several statistical methods for the estimation of the sensitivity and the specificity of sitespecific diagnostic tests. I apply these techniques to the data from a study involving an enzymatic diagnostic test to motivate and illustrate the estimation of the sensitivity and the specificity of periodontal diagnostic tests. I present results from a simulation study for the estimation of diagnostic sensitivity when the data are correlated within subjects. Through a simulation study, I compare the performance of the binomial estimator pCBE, the ratio estimator pCBE, the weighted estimator pCWE, the intracluster correlation estimator pCIC, and the generalized estimating equation (GEE) estimator PCGEE in terms of biases, observed variances, mean squared errors (MSE), relative efficiencies of their variances and 95 per cent coverage proportions. I recommend using PCBE when σ == 0. I recommend use of the weighted estimator PCWE when σ = 0.6. When σ == 0.2 or σ == 0.4, and the number of subjects is at least 30, PCGEE performs well.     

Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data

Background

In an emerging influenza pandemic, estimating severity (the probability of a severe outcome, such as hospitalization, if infected) is a public health priority. As many influenza infections are subclinical, sero-surveillance is needed to allow reliable real-time estimates of infection attack rate (IAR) and severity.

Methods and Findings

We tested 14,766 sera collected during the first wave of the 2009 pandemic in Hong Kong using viral microneutralization. We estimated IAR and infection-hospitalization probability (IHP) from the serial cross-sectional serologic data and hospitalization data. Had our serologic data been available weekly in real time, we would have obtained reliable IHP estimates 1 wk after, 1–2 wk before, and 3 wk after epidemic peak for individuals aged 5–14 y, 15–29 y, and 30–59 y. The ratio of IAR to pre-existing seroprevalence, which decreased with age, was a major determinant for the timeliness of reliable estimates. If we began sero-surveillance 3 wk after community transmission was confirmed, with 150, 350, and 500 specimens per week for individuals aged 5–14 y, 15–19 y, and 20–29 y, respectively, we would have obtained reliable IHP estimates for these age groups 4 wk before the peak. For 30–59 y olds, even 800 specimens per week would not have generated reliable estimates until the peak because the ratio of IAR to pre-existing seroprevalence for this age group was low. The performance of serial cross-sectional sero-surveillance substantially deteriorates if test specificity is not near 100% or pre-existing seroprevalence is not near zero. These potential limitations could be mitigated by choosing a higher titer cutoff for seropositivity. If the epidemic doubling time is longer than 6 d, then serial cross-sectional sero-surveillance with 300 specimens per week would yield reliable estimates when IAR reaches around 6%–10%.

Conclusions

Serial cross-sectional serologic data together with clinical surveillance data can allow reliable real-time estimates of IAR and severity in an emerging pandemic. Sero-surveillance for pandemics should be considered. Please see later in the article for the Editors'' Summary     

An individual level infectious disease model in the presence of uncertainty from multiple,imperfect diagnostic tests

Bayesian compartmental infectious disease models yield important inference on disease transmission by appropriately accounting for the dynamics and uncertainty of infection processes. In addition to estimating transition probabilities and reproductive numbers, these statistical models allow researchers to assess the probability of disease risk and quantify the effectiveness of interventions. These infectious disease models rely on data collected from all individuals classified as positive based on various diagnostic tests. In infectious disease testing, however, such procedures produce both false-positives and false-negatives at varying rates depending on the sensitivity and specificity of the diagnostic tests being used. We propose a novel Bayesian spatio-temporal infectious disease modeling framework that accounts for the additional uncertainty in the diagnostic testing and classification process that provides estimates of the important transmission dynamics of interest to researchers. The method is applied to data on the 2006 mumps epidemic in Iowa, in which over 6,000 suspected mumps cases were tested using a buccal or oral swab specimen, a urine specimen, and/or a blood specimen. Although all procedures are believed to have high specificities, the sensitivities can be low and vary depending on the timing of the test as well as the vaccination status of the individual being tested.     

The use of Bartonella henselae-specific age dependent IgG and IgM in diagnostic models to discriminate diseased from non-diseased in Cat Scratch Disease serology

Cat Scratch Disease (CSD) is caused by Bartonella henselae infection and is a common cause of regional lymphadenopathy. The diagnosis of CSD largely depends on serology, but is hampered by both low sensitivity and specificity of the applied IgG and IgM assays. Using an in-house ELISA, we detected a significant age-dependent increase in the IgG levels in the general population compared to CSD patients. With this knowledge, we developed diagnostic models to differentiate diseased from non-diseased persons. Evaluation of these models using samples from PCR-positive patients (n=155) and age-matched controls (n=244) showed an important increase in the assay performance if the combination of the IgG and IgM results were taken into account. If the specificity was set at 98% the sensitivity was only 45% and 32% for the IgM and IgG ELISA, respectively but increased to 59% when these results were combined. Also the use of age-dependent factors further improved the clinical relevance of the outcome raising the sensitivity to 64%. Although the sensitivity of the ELISA remains low we conclude that the use of models using the combination of both IgM and IgG test results and age-depending factors can be a useful diagnostic tool in the serodiagnosis of CSD.     

Joint Estimation of Diagnostic Accuracy Measures for Paired Organs – Application in Ophthalmology

Diagnostic studies in ophthalmology frequently involve binocular data where pairs of eyes are evaluated, through some diagnostic procedure, for the presence of certain diseases or pathologies. The simplest approach of estimating measures of diagnostic accuracy, such as sensitivity and specificity, treats eyes as independent, consequently yielding incorrect estimates, especially of the standard errors. Approaches that account for the inter‐eye correlation include regression methods using generalized estimating equations and likelihood techniques based on various correlated binomial models. The paper proposes a simple alternative statistical methodology of jointly estimating measures of diagnostic accuracy for binocular tests based on a flexible model for correlated binary data. Moments' estimation of model parameters is outlined and asymptotic inference is discussed. The resulting estimates are straightforward and easy to obtain, requiring no special statistical software but only elementary calculations. Results of simulations indicate that large‐sample and bootstrap confidence intervals based on the estimates have relatively good coverage properties when the model is correctly specified. The computation of the estimates and their standard errors are illustrated with data from a study on diabetic retinopathy.     

A New Method for Assessing How Sensitivity and Specificity of Linkage Studies Affects Estimation

Background

While the importance of record linkage is widely recognised, few studies have attempted to quantify how linkage errors may have impacted on their own findings and outcomes. Even where authors of linkage studies have attempted to estimate sensitivity and specificity based on subjects with known status, the effects of false negatives and positives on event rates and estimates of effect are not often described.

Methods

We present quantification of the effect of sensitivity and specificity of the linkage process on event rates and incidence, as well as the resultant effect on relative risks. Formulae to estimate the true number of events and estimated relative risk adjusted for given linkage sensitivity and specificity are then derived and applied to data from a prisoner mortality study. The implications of false positive and false negative matches are also discussed.

Discussion

Comparisons of the effect of sensitivity and specificity on incidence and relative risks indicate that it is more important for linkages to be highly specific than sensitive, particularly if true incidence rates are low. We would recommend that, where possible, some quantitative estimates of the sensitivity and specificity of the linkage process be performed, allowing the effect of these quantities on observed results to be assessed.     

Testing for bias in weighted estimating equations

It is very common in regression analysis to encounter incompletely observed covariate information. A recent approach to analyse such data is weighted estimating equations (Robins, J. M., Rotnitzky, A. and Zhao, L. P. (1994), JASA, 89, 846-866, and Zhao, L. P., Lipsitz, S. R. and Lew, D. (1996), Biometrics, 52, 1165-1182). With weighted estimating equations, the contribution to the estimating equation from a complete observation is weighted by the inverse of the probability of being observed. We propose a test statistic to assess if the weighted estimating equations produce biased estimates. Our test statistic is similar to the test statistic proposed by DuMouchel and Duncan (1983) for weighted least squares estimates for sample survey data. The method is illustrated using data from a randomized clinical trial on chemotherapy for multiple myeloma.     

Comparison of two different stool antigen tests for the primary diagnosis of Helicobacter pylori infection in turkish patients with dyspepsia

AIM: To assess the reliability of two different enzyme immunoassays in detecting the Helicobacter pylori status in stool specimens of Turkish patients with dyspepsia. MATERIALS AND METHODS: One hundred and fifty-one patients [74 with nonulcer dyspepsia (NUD), 64 with duodenal ulcer (DU) and 13 with gastric cancer] who were admitted to the endoscopy unit of Istanbul University, Cerrahpasa Medical Faculty for upper gastrointestinal endoscopy because of dyspepsia were enrolled in the study. Helicobacter pylori infection was confirmed in all patients by histology, rapid urease test and culture. A patient was classified as being H. pylori-positive if the culture alone or both the histology and the rapid urease test were positive and as negative only if all of these tests remained negative. Stool samples were obtained from patients to assess the reliability of a monoclonal (FemtoLab H. pylori) and a polyclonal (Premier Platinum HpSA) stool antigen test and to compare the diagnostic accuracies of these two tests. A chi2 test was used for statistical comparisons. RESULTS: Using cut-off values of 0.19 for FemtoLab H. pylori and 0.16 for Premier Platinum HpSA, the sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy were 93%, 90%, 98%, 68% and 93% for the monoclonal test and 84%, 67%, 94%, 40% and 81% for the polyclonal test, respectively. The sensitivity, specificity, negative predictive value and diagnostic accuracy of the monoclonal test were significantly greater than those of the polyclonal test (chi2 = 3.98; p < .05 for sensitivity and chi2 = 15.67; p = .000 for specificity, chi2 = 15.78; p = .000 for negative predictive value and chi2 = 6.37; p = .012 for diagnostic accuracy). The bacterial load did not affect the sensitivity of either test. CONCLUSIONS: The monoclonal FemtoLab H pylori test, using a cut-off 0.19, is a very sensitive, specific and easy to perform diagnostic tool for the primary diagnosis of H. pylori infection in Turkish patients with dyspepsia.     

Cost analysis in choosing group size when group testing for Potato virus Y in the presence of classification errors

In many areas of the world, Potato virus Y (PVY) is one of the most economically important disease problems in seed potatoes. In Taiwan, generation 2 (G2) class certified seed potatoes are required by law to be free of detectable levels of PVY. To meet this standard, it is necessary to perform accurate tests at a reasonable cost. We used a two‐stage testing design involving group testing which was performed in Taiwan's Seed Improvement and Propagation Station to identify plants infected with PVY. At the first stage of this two‐stage testing design, plants are tested in groups. The second stage involves no retesting for negative test groups and exhaustive testing of all constituent individual samples from positive test groups. In order to minimise costs while meeting government standards, it is imperative to estimate optimal group size. However, because of limited test accuracy, classification errors for diagnostic tests are inevitable; to get a more accurate estimate, it is necessary to adjust for these errors. Therefore, this paper describes an analysis of diagnostic test data in which specimens are grouped for batched testing to offset costs. The optimal batch size is determined by various cost parameters as well as test sensitivity, specificity and disease prevalence. Here, the Bayesian method is employed to deal with uncertainty in these parameters. Moreover, we developed a computer program to determine optimal group size for PVY tests such that the expected cost is minimised even when using imperfect diagnostic tests of pooled samples. Results from this research show that, compared with error free testing, when the presence of diagnostic testing errors is taken into account, the optimal group size becomes smaller. Higher diagnostic testing costs, lower costs of false negatives or smaller prevalence can all lead to a larger optimal group size. Regarding the effects of sensitivity and specificity, optimal group size increases as sensitivity increases; however, specificity has little effect on determining optimal group size. From our simulated study, it is apparent that the Bayesian method can truly update the prior information to more closely approximate the intrinsic characteristics of the parameters of interest. We believe that the results of this study will be useful in the implementation of seed potato certification programmes, particularly those which require zero tolerance for quarantine diseases in certified tubers.     

Impact of and Correction for Outcome Misclassification in Cumulative Incidence Estimation

Cohort studies and clinical trials may involve multiple events. When occurrence of one of these events prevents the observance of another, the situation is called “competing risks”. A useful measure in such studies is the cumulative incidence of an event, which is useful in evaluating interventions or assessing disease prognosis. When outcomes in such studies are subject to misclassification, the resulting cumulative incidence estimates may be biased. In this work, we study the mechanism of bias in cumulative incidence estimation due to outcome misclassification. We show that even moderate levels of misclassification can lead to seriously biased estimates in a frequently unpredictable manner. We propose an easy to use estimator for correcting this bias that is uniformly consistent. Extensive simulations suggest that this method leads to unbiased estimates in practical settings. The proposed method is useful, both in settings where misclassification probabilities are known by historical data or can be estimated by other means, and for performing sensitivity analyses when the misclassification probabilities are not precisely known.     

Occupancy Modeling for Improved Accuracy and Understanding of Pathogen Prevalence and Dynamics

Most pathogen detection tests are imperfect, with a sensitivity < 100%, thereby resulting in the potential for a false negative, where a pathogen is present but not detected. False negatives in a sample inflate the number of non-detections, negatively biasing estimates of pathogen prevalence. Histological examination of tissues as a diagnostic test can be advantageous as multiple pathogens can be examined and providing important information on associated pathological changes to the host. However, it is usually less sensitive than molecular or microbiological tests for specific pathogens. Our study objectives were to 1) develop a hierarchical occupancy model to examine pathogen prevalence in spring Chinook salmon Oncorhynchus tshawytscha and their distribution among host tissues 2) use the model to estimate pathogen-specific test sensitivities and infection rates, and 3) illustrate the effect of using replicate within host sampling on sample sizes required to detect a pathogen. We examined histological sections of replicate tissue samples from spring Chinook salmon O. tshawytscha collected after spawning for common pathogens seen in this population: Apophallus/echinostome metacercariae, Parvicapsula minibicornis, Nanophyetus salmincola/ metacercariae, and Renibacterium salmoninarum. A hierarchical occupancy model was developed to estimate pathogen and tissue-specific test sensitivities and unbiased estimation of host- and organ-level infection rates. Model estimated sensitivities and host- and organ-level infections rates varied among pathogens and model estimated infection rate was higher than prevalence unadjusted for test sensitivity, confirming that prevalence unadjusted for test sensitivity was negatively biased. The modeling approach provided an analytical approach for using hierarchically structured pathogen detection data from lower sensitivity diagnostic tests, such as histology, to obtain unbiased pathogen prevalence estimates with associated uncertainties. Accounting for test sensitivity using within host replicate samples also required fewer individual fish to be sampled. This approach is useful for evaluating pathogen or microbe community dynamics when test sensitivity is <100%.