In vitro quantitation of Theileria parva sporozoites for use - TropMed ...

120 Chapter 9: General Discussion
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yield all-positive results across dilution ranges making logistic regression analyses complicated.
However this would be limited only to situations where different stabilates are titrated in parallel.
The use of rows instead of columns for stabilate dilutions series in microtitration plates presents
more columns for test replications per microtitration plate. The overall range of stabilate dilutions
was still preserved by increasing the dilution factor to two-fold instead of 1.5-fold.
The statistical analyses of data were made more robust by taking random factors and any
interactions between variables into consideration. Multilevel logistic regression was introduced
whenever hierarchical data were encountered as presented in chapter 4. Most commonly, "stabilate"
and "session" of the experiment were used as random factors. GLLAMM ® (Rabe-Hesketh et al.,
2002) was run in Stata ® (StataCorp, Stata Statistical Software: Release 9. College Station, TX:
StataCorp LP) for this procedure as it takes care of several levels of random factors (latent
variables) which in this case mainly arise from experiment repetitions. Another source of random
variation is the "tick batch" in comparison of stabilates derived from different batches of ticks.
Repetitions of experiments narrows confidence intervals but concurrently adds the effect of a
random variable of the "session" because of a clustering effect described by Marcotty et al. (2004).
Such variables have potential of not only statistically interacting with the variables of interest but
might also result in inference of statistical significance where none exist (Speybroeck et al., 2003).
Comparison of stabilates or effects on stabilates is made easy through calculating ratios of ED's.
This approach approximates equivalence testing where the null hypothesis assumes a difference
between the groups being compared. In addition, a confidence interval is fitted by a process called
non-linear combination of estimators (NLCOM) in Stata (StataCorp, 2005). Therefore, factors by
which stabilate infectivities differ can be obtained thereby going beyond simply stating significant
differences.
Caution must be exercised in interpreting in vitro results as these conditions do not mimic exactly
those encountered by sporozoites in vivo. It is not inconceivable, for instance, that weakened
sporozoites may be rendered impotent by non-specific immune responses in vivo whereas in vitro,
they may still be infective for PBMC due to a less hostile environment. This would raise the
sensitivity of the in vitro assay in relation to in vivo titrations. Judicious interpretation of results is
therefore required when attempting to infer relationships between the two systems. However, within
the confines of in vitro quantitations, effects on sporozoite survival can be accurately compared
within and across different stabilates in a standardized way and precludes the wide variations
observed in in vivo titrations (Cunningham et al., 1974).