| 1. |
- Andersson, Eva M., 1968, et al.
(författare)
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Modeling influenza incidence for the purpose of on-line monitoring
- 2008
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Ingår i: Statistical Methods in Medical Research. - : SAGE Publications. - 0962-2802 .- 1477-0334. ; 17:4, s. 421-438
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Tidskriftsartikel (refereegranskat)abstract
- We describe and discuss statistical models of Swedish influenza data, with special focus on aspects which are important in on-line monitoring. Earlier suggested statistical models are reviewed and the possibility of using them to describe the variation in influenza-like illness (ILI) and laboratory diagnoses (LDI) is discussed. Exponential functions were found to work better than earlier suggested models for describing the influenza incidence. However, the parameters of the estimated functions varied considerably between years. For monitoring purposes we need models which focus on stable indicators of the change at the outbreak and at the peak. For outbreak detection we focus on ILI data. Instead of a parametric estimate of the baseline (which could be very uncertain,), we suggest a model utilizing the monotonicity property of a rise in the incidence. For ILI data at the outbreak, Poisson distributions can be used as a first approximation. To confirm that the peak has occurred and the decline has started, we focus on LDI data. A Gaussian distribution is a reasonable approximation near the peak. In view of the variability of the shape of the peak, we suggest that a detection system use the monotonicity properties of a peak.
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| 2. |
- Hansson, Lisbeth, et al.
(författare)
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Matched samples logistic regression in case-control studies with missing values : when to break the matches
- 2008
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Ingår i: Statistical Methods in Medical Research. - : SAGE. - 0962-2802 .- 1477-0334. ; 17:6, s. 595-607
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Tidskriftsartikel (refereegranskat)abstract
- Simulated data sets are used to evaluate conditional and unconditionalmaximum likelihood estimation in an individual case-controldesign with continuous covariates when there are different ratesof excluded cases and different levels of other design parameters.The effectiveness of the estimation procedures is measured bymethod bias, variance of the estimators, root mean square error(RMSE) for logistic regression and the percentage of explainedvariation. Conditional estimation leads to higher RMSE thanunconditional estimation in the presence of missing observations,especially for 1:1 matching. The RMSE is higher for the smallerstratum size, especially for the 1:1 matching. The percentageof explained variation appears to be insensitive to missingdata, but is generally higher for the conditional estimationthan for the unconditional estimation. It is particularly goodfor the 1:2 matching design. For minimizing RMSE, a high matchingratio is recommended; in this case, conditional and unconditionallogistic regression models yield comparable levels of effectiveness.For maximizing the percentage of explained variation, the 1:2matching design with the conditional logistic regression modelis recommended.
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