We are using cookies This website uses cookies in order to offer you the most relevant information. By browsing this website, you accept these cookies.
A zero-inflated Poissonmodel clustered by school tested the prediction of school-level variables.
2
Methods: Quasi- Poissonmodels were fitted separately to weekly numbers of deaths from various causes.
3
The final analysis used autoregressive Poissonmodels allowing for overdispersion.
4
Zero-inflated Poissonmodels were used to assess differences in absenteeism between cyclists and non-cyclists.
5
Standardized incidence ratios were calculated by a Poissonmodel.
6
Group-based trajectory analyses were conducted with zero-inflated Poissonmodels.
7
Most of existing sample size calculation methods for count outcomes are developed under the Poissonmodel.
8
The assumption of a Gaussian noise model yielded only slightly higher errors than the Poissonmodel.
9
Poissonmodels with natural splines were used to control for time-varying covariates such as season and weather.
10
We used a two-parameter hurdle Poissonmodel to simultaneously analyse the zero counts and the frequency of occurrence.
11
The effects of the new DWTP and fecal contamination levels on infections were tested using logistic and Poissonmodels.
12
A modified Poissonmodel was used to identify factors associated with failure to initiate ART rapidly under treat all.
13
We used Poissonmodels to obtain rate ratios (RRs) for incident IPD associated with HIV infection and other risk factors.
14
Two of these (the GS and PS models) are better than the Poissonmodel for the clinically relevant cases tested here.
15
The identification was conducted using a retrospective space-time analysis scan for statistically significant clusters with high or low rates by a Discrete PoissonModel.
16
The result of the statistical slope test can then be graphed to visualize whether the data are compatible or not with the single-hit Poissonmodel.