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Finally, based on our model estimates we were able to determine the required number of eggs to count for each sample size, enabling investigators to optimize the probability of correctly classifying a theoretical TDE while minimizing both financial and technical resources.
From the model estimates we know that both tests perform better than average.
To judge the effect of missing data on model estimates, we conducted multiple imputation using chained equations (20) in a sensitivity analysis.
To account for the uncertainty in our model estimates we performed sensitivity analyses to test how changes in our model inputs affected our results.
Considering the possibility of a missing not at random pattern and its potential effects on model estimates we conducted a sensitivity analysis fitting a linear mixed model as specified above only on subjects that had a visit at 24 months.
To explore the effects of measurement error in AMNART and biomarkers on our ordinal regression model estimates, we performed a sensitivity analysis using the simulation-extrapolation method (Cook and Stefanski, 1994).
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As an alternative to the binomial mixture model estimate, we have also included the Chao lower-bound estimate [ 11] when fitting to real data.
For a best-case model estimate, we optimized λ coefficients for temperature, drought, and fires from the Northern Hemisphere and tropics simultaneously.
To correctly model risk estimates, we must measure genetics and environment together in the same studies.
Since results of the RDS-adjusted and unadjusted models yielded similar estimates, we present the unadjusted model herein.
Because the three models have similar estimates, we provide only the final model in our results section.
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