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The 95% confidence intervals (CI) of HPV prevalence were based on normal approximations.
We discuss various strategies to approach this problem including reparameterization, Langevin-Hastings updates, and updates based on normal approximations.
This tends to sustain a concern that power calculations based on normal approximations may not be accurate.> We first revisit the example of Brooker et al. [ 29], which was motivated by the Human Hookworm Vaccine Initiative HHVII).
Estimates of prevalence of urinary, sexual, bowel and hormone-related problems in prostate cancer survivors will be obtained and presented with their associated 95% CIs (based on normal approximations or exact methods if estimates are close to 0 or 1).
Several methods have been proposed for this problem [ 16], including a method based on normal approximations of a dichotomous outcome for pairwise meta-analysis [ 17] and a more general approach for multinomial outcomes [ 16, 18].
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In the case of LOOCV, approximate confidence bars based on normal approximation intervals [ 131, 132] are shown (see [ 132, 133] for critical assessment of the method).
This study uses computerized simulation of quasi-Bayesian Monte Carlo method based on normal approximation for variance estimation to test for mediation effect.
One should bear in mind that the error estimate is nevertheless conservative (i.e. likely to be underestimated), because (i) the method is based on normal approximation, (ii) we ignore time-dependent dynamics including public health interventions, and (iii) we ignore heterogeneous transmission (see Discussion for (ii) and (iii)).
*95% CI based on normal approximation to the binomial.
The confidence intervals of percentages were based on normal approximation.
We calculated proportions and their confidence intervals based on normal approximation.
More suggestions(19)
based on functional approximations
based on numerical approximations
based on various approximations
based on quadratic approximations
based on normal keratinocytes
based on sequential approximations
based on geometric approximations
based on asymptotic approximations
based on analytical approximations
based on different approximations
based on normal directions
based on geometrical approximations
based on local approximations
based on normal charges
based on experimental approximations
based on simplifying approximations
based on normal data
based on parabolic approximations
based on mathematical approximations
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