Non-differential measurement error does not always bias diagnostic likelihood ratios towards the null

Diagnostic test evaluations are susceptible to random and systematic error. Simulated non-differential random error for six different error distributions was evaluated for its effect on measures of diagnostic accuracy for a brucellosis competitive ELISA. Test results were divided into four categories: <0.25, 0.25 – 0.349, 0.35 – 0.499, and ≥ 0.50 proportions inhibition for calculation of likelihood ratios and diagnostic odds ratios. Larger variance components of the error structure resulted in larger accuracy attenuations as measured by the area under the receiver-operating characteristic curve and systematic components appeared to cause little bias. Added error caused point estimates of likelihood ratios to be biased towards the null value (1.0) for all categories except 0.25 – 0.349. Results for the 0.35 – 0.499 category also extended beyond the null value for some error structures. Diagnostic odds ratios were consistently biased towards the null when the <0.25 category was considered the reference level. Non-differential measurement error can lead to biased results in the quantitative evaluation of ELISA and the direction is not always towards the null value

A Bayesian latent class model to estimate the accuracy of pregnancy diagnosis by transrectal ultrasonography and laboratory detection of pregnancy-associated glycoproteins in dairy cows

Accurate diagnosis of pregnancy is an essential component of an effective reproductive management plan for dairy cattle. Indirect methods of pregnancy detection can be performed soon after breeding and offer an advantage over traditional direct methods in not requiring an experienced veterinarian and having potential for automation. The objective of this study was to estimate the sensitivity and specificity of pregnancy-associated glycoprotein (PAG) detection ELISA and transrectal ultrasound (TRUS) in dairy cows of South Africa using a Bayesian latent class approach. Commercial dairy cattle from the five important dairy regions in South Africa were enrolled in a short-term prospective cohort study. Cattle were examined at 28–35 days after artificial insemination (AI) and then followed up 14 days later. At both sampling times, TRUS was performed to detect pregnancy and commercially available PAG detection ELISAs were performed on collected serum and milk. A total of 1236 cows were sampled and 1006 had complete test information for use in the Bayesian latent class model. The estimated sensitivity (95% probability interval) and specificity for PAG detection serum ELISA were 99.4% (98.5, 99.9) and 97.4% (94.7, 99.2), respectively. The estimated sensitivity and specificity for PAG detection milk ELISA were 99.2% (98.2, 99.8) and 93.4% (89.7, 96.1), respectively. Sensitivity of veterinarian performed TRUS at 28–35 days post-AI varied between 77.8% and 90.5% and specificity varied between 94.7% and 99.8%. In summary, indirect detection of pregnancy using PAG ELISA is an accurate method for use in dairy cattle. The method is descriptively more sensitive than veterinarian-performed TRUS and therefore could be an economically viable addition to a reproductive management plan

Modified Exact Sample Size for Test Validation Studies Incorporating Adjustment for Clustered Data

ABSTRACT Design of epidemiologic studies for the validation of diagnostic tests necessitates accurate sample size calculations to allow for estimation of diagnostic sensitivity and specificity within a specified level of precision and with the desired level of confidence. The objective of this paper was to design and evaluate a computer algorithm for the calculation of sample sizes for diagnostic accuracy studies incorporating clustered sampling units using a beta-binomial model. A beta-binomial distribution can be used to model a proportion expected to vary across populations. The cluster-adjusted sample size is calculated as the product of the unadjusted sample size by the design effect, which is estimable after definition of the beta distribution. Design effect estimations varied depending upon the 90% limit inputs, hypothesized proportion, and evaluated number of clusters. Beta distributions representing larger variation (wider 90% probability intervals) resulted in larger design effects. Smaller number of clusters for sampling also caused larger design effects. Estimated design effects also tended to increase as the proportion approached 1. It is important to incorporate cluster adjustment to sample size calculations when designing epidemiologic studies for estimation of diagnostic accuracy in the situation of correlated data. Beta-binomial models can be used to account for clustering and design effects can be estimated by inducing beta distributions that encompass among herd variation

Ovarian function in pony mares undergoing porcine zona pellucida immunocontraception

An advantage of the porcine zona pellucida (pZP) vaccine over other immunocontraceptives is the preservation of reproductive cyclicity and associated behaviors. Few studies have investigated ovarian function following pZP vaccination in the mare despite reported ovarian dysfunction in other species. The objectives of this study were to investigate ovarian function and estrous cyclicity in pony mares during immunocontraception with the conventional pZP vaccine. Fourteen mares were randomized into two groups of seven. Group I received 100 µg of pZP with Freund’s complete modified adjuvant (FCMA; V1), followed after five weeks by booster vaccination with 100 µg of pZP with Freund’s incomplete adjuvant (FIA; V2). Group II (controls) received two treatments five weeks apart of saline with FCMA (V1) and saline with FIA (V2) respectively. Treatments were administered via intramuscular injection into the gluteal muscles. Data were collected by an investigator blinded to treatment group over a period of 24 weeks during the physiological breeding season. All mares underwent estrus monitoring via trans-rectal palpation and ultrasound examination of the internal reproductive tract, on D0 (day of ovulation), D7, and D14 of consecutive estrous cycles, with daily monitoring between D14 and D0 of the following cycle. Artificial insemination was performed using fresh semen for up to two consecutive estrous cycles, commencing five weeks post-V2. Serum samples were collected weekly for the analysis of antibody titres and ovarian steroid (progesterone and estradiol) levels. Data were compared using Mann-Whitney U tests using commercially available software (IBM SPSS Statistics Version 22, International Business Machines Corp., Armonk, NY). Statistical significance was set as P < 0.05. All Group II mares showed normal estrous cyclicity throughout the study. Four Group I mares showed signs of anestrus within seven weeks of V2, characterised by small, inactive ovaries and baseline progesterone and estradiol levels. One Group I mare entered anestrus within 11 weeks of V2 and a second showed estrus with ovulation between variable periods of anestrus. Ovarian volumes, follicle counts and maximal follicle diameters in Group I were significantly lower than Group II. Per-cycle pregnancy proportions in Groups I and II were 0% and 78% respectively. This study demonstrated suppression of ovarian function in six of seven (86%) mares following pZP immunocontraception. Further research into the mechanism of action of zona pellucida-based vaccines is warranted

Accuracy of end-on fluoroscopy in predicting implant position in relation to the vertebral canal in dogs.

Objective To evaluate the accuracy of end-on fluoroscopy in predicting implant position in relation to the vertebral canal in the canine thoracolumbar vertebral column. Study design In vitro imaging and anatomic study. Animals Canine cadaveric thoracolumbar vertebral columns (n = 5). Methods Smooth Steinmann pins were inserted bicortically into the thoracolumbar vertebral columns between T10 and L7 using recommended insertion angles. Penetration of the spinal canal was not strictly avoided. After pin placement, end-on fluoroscopy images were obtained of each pin. Pin position was subsequently assessed by four evaluators and determined to either being out of the vertebral canal or in, with the latter being additionally divided into partially or completely penetrating the canal. To assess potential differences in modalities, fluoroscopy images were gray-scale inverted and evaluated again later by the same four individuals. Correct identification of pin position in relationship to the vertebral canal was assessed for both fluoroscopy images. Anatomic preparation of the spines was used for verification of pin position in relation to the spinal canal. Some data from this study were compared with historical data on accuracy using orthogonal radiography and computed tomography (CT). Results Overall sensitivity and specificity of F to detect vertebral canal penetration was 98.8 % (95% confidence interval (CI), 96.0-99.6) and 98.0% (95% CI, 77.0-99.9), respectively. For Fi, sensitivity and specificity were 97.0% (95% CI, 91.5-99.0) and 98.5% (95% CI, 81.5-99.9) respectively. F exceeded Fi for the sensitivity of detecting pin penetration into the vertebral canal (p = 0.039) but specificities were not different (p = 0.585). When comparing to historical data, the overall accuracy of end-on fluoroscopy (F) and inverted fluoroscopy (Fi) was statistical better than conventional radiographic assessment (p < 0.001). Conclusion End-on fluoroscopy is a highly accurate method for the assessment of pin position in relationship to the thoracolumbar spinal canal in cadaveric dogs. Clinical significance End-on fluoroscopy, with or without inversion, is accurate in identifying vertebral canal violation by bicortically placed Steinmann pins. When CT is not available, end-on fluoroscopy might be a valuable imaging modality to determine pin position in the canine vertebral column

Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa

The population density of wildlife reservoirs contributes to disease transmission risk for domestic animals. The objective of this study was to model the African buffalo distribution of the Kruger National Park. A secondary objective was to collect field data to evaluate models and determine environmental predictors of buffalo detection. Spatial distribution models were created using buffalo census information and archived data from previous research. Field data were collected during the dry (August 2012) and wet (January 2013) seasons using a random walk design. The fit of the prediction models were assessed descriptively and formally by calculating the root mean square error (rMSE) of deviations from field observations. Logistic regression was used to estimate the effects of environmental variables on the detection of buffalo herds and linear regression was used to identify predictors of larger herd sizes. A zero-inflated Poisson model produced distributions that were most consistent with expected buffalo behavior. Field data confirmed that environmental factors including season (P = 0.008), vegetation type (P = 0.002), and vegetation density (P = 0.010) were significant predictors of buffalo detection. Bachelor herds were more likely to be detected in dense vegetation (P = 0.005) and during the wet season (P = 0.022) compared to the larger mixed-sex herds. Static distribution models for African buffalo can produce biologically reasonable results but environmental factors have significant effects and therefore could be used to improve model performance. Accurate distribution models are critical for the evaluation of disease risk and to model disease transmission