Weakness of the studies in this thesis

The study described in detail in chapter 3 relied on a Bayesian 3 in 1 population model. A non-informative prior of (1,1) was used in a sensitivity analysis to look at the potential bias of the expert opinion offered by the author of this thesis. Even though the sensitivity analysis did not show that the priors affected the outcome, a more thorough sensitivity analysis should have been done using different informative priors. The author of the paper provided the expert opinion after she did the tests, and hence was likely biased when she provided the priors. The researchers were also not blinded as to the

potential infection status of the 14 flocks tested. Because reading of the skin test is a subjective measurement, it is possible prior knowledge may have biased the skin test readings. Furthermore, as large numbers of sheep are palpated for an induration with no positive response, it is possible for the skin test reader to expect negative results and not palpate subsequent animals as carefully. When conducting the analysis and writing the paper, the amount of dependence between the antibody ELISA and the AGID should have been included. Another program, TAGS (v. 2.0 at bayes.math.montana.edu ) was initially considered (See Appendix D). Even though the results are similar between the models: point estimates for sensitivity for the ELISA, AGID and skin test were .083, 080, and .733 for the Bayesian model and .084, .083 and .700 for the TAGS program, there was enough correlation between the ELISA and AGID for TAGS to warn the model does not fit, and a model that allowed for dependence between 2 tests was needed.

The study described in chapter 4 used ROC analysis to compare 2 different antigens in the IFN-γ ELISA. The method used a non-parametric approach to calculate the curve and an algorithm based on DeLong’s paper in 1988 (see reference #6 on page 73) to compare the area under the curve (AUC) of the 2 tests. This method compares the entire AUC; however, if the shapes of the curves are visualized (see figure 2 on page 79) there is a point where the curves meet that may be in the region where sensitivity and specificity are maximized. If that is the case, then there would not be differences in the antigens when the tests were applied to clinical situations. A total of 150 sheep were used in the comparison with only 31 of those testing positive to the reference test. This is not a large enough sample size to definitively say that the difference in the curves at the

area of interest was or was not clinically significant. The study should be repeated with more sheep.

The study in Chapter 5 looks at the effect of time and temperature delays on whole blood used for the IFN-γ ELISA. Even though the paper states a randomized complete block design was used, this was not the case. There was no randomization involved. Every cow had a blood sample in every treatment.

In the statistical methods section it states that “A repeated measures mixed model Analyses of Variance (ANOVA) were conducted” and that “Time and temperature variables were considered to be random effects”. In fact, time and temperature were considered fixed effects, not random. Furthermore, confidence intervals were used to determine statistical significance rather than using a method that tests for significance directly. By using confidence intervals, some of the antigens and positive controls that were considered not significant may have been significant and were missed.

The regression equations that were used in the paper do not accurately reflect the data at lower temperatures. All 8 regression equations show that lower temperatures have a protective effect on whole blood. This was not the case. The likely problem is there were only 2 temperature points that were lower than the “ideal” temperature. Other higher order variables should have been considered when building this model. This may have improved the fit and better characterized the data at lower temperatures. Because of the unusual curvature of the data, (see Figure 2 on page 95) it is possible that one

equation is not sufficient to describe the survivability of the whole blood at both low and high temperatures. An equation modeling the decline in survivability should have been

calculated to look at the temperatures below the optimum range, and then the equation in the paper can be used to model the effects that higher temperatures have on survivability. The study detailed in Chapter 6 looks at CMI responses to dead organisms. In this study, 4 months post exposure there were 2 animals out of 15 that had positive skin test responses to the PPD. None of the 16 control sheep tested positive. A Fisher’s exact test was used to compare the 2 groups and while the results were not significant, the lack of significance may be due to a lack of power rather than artifact due to chance. Appendix A details the specificity of different PPD’s used in the papers included in this thesis. This particular paper used Johnin 0202 which has a point estimate specificity of 84%. Having 2 false positives out of 15 would not be unexpected. However, before a conclusion that the skin test is not affected by dead organisms, the trial should be repeated preferably with more animals. The raw data suggests a 13% higher response rate in the orally exposed sheep at 4 months post exposure. In order to determine if a 13% difference in response rate was significant using the Fisher’s exact test 30 animals per group should be used.

7.4 Preliminary Results of a longitudinal study evaluating the skin test and