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The stratified and marginal methods will give the same results if the weights are chosen as the relative sizes of strata in the study population.
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For each stratum, the proportion of the reference population and the RR weighted according to the size of stratum population was estimated, giving the average adjusted risk (AR) for the population strata using the formula (Eq.1) described by Martin et al. [ 54]: (1) ∑ l L = A R l × P r P l = 1 where L is the number of risk strata and PrP is the proportion of farms in the lth stratum.
However, the sample sizes of specific strata are smaller than the total population, and interpretation of these results should be done with caution.
When the continuous variable is coarsened into bins, matching will take place on the respective strata and then observations are finally re-weighted according to the size of their strata.
Sample sizes for strata were determined proportional to the number of households residing in each stratum.
The low incidence rate also limits the minimum size of time strata for which sensibly precise estimates of protective efficacy can be calculated to around one month.
The selected sample was proportional to the size of the strata.
We distributed the total sample size proportionally to size of socioeconomic strata of the population from Lima and Callao cities. Demographic and clinical data were compared by age group using the chi-square test.
Given the moderate sample size of these strata, a likelihood-ratio statistics was used to test the null hypothesis that all the coefficients associated with the covariates were zero.
Since the intention is to have a small overall sample size the number of strata also needs to be small, at most two or three.
Assume that the number per stratum is Poisson, with mean X given by N/ k where N=sample size and k=number of strata and the minimum desired number per stratum is m.
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