| 1. |
- Andersson, Mikael, et al.
(författare)
-
Modelling the spread of penicillin-resistant Streptococcus pneumoniae in day-care and evaluation of intervention.
- 2005
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 24:23, s. 3593-607
-
Tidskriftsartikel (refereegranskat)abstract
- In 1995, a disease control and intervention project was initiated in Malmöhus county in southern Sweden to limit the spread of penicillin-resistant pneumococci. Since most of the carriers of pneumococci are preschool children, and since most of the spread is believed to take place in day-care, a mathematical model, in the form of a stochastic process, for the spread in a day-care group was constructed. Effects of seasonal variation and size of the day-care group were particularly considered. The model was then used for comparing results from computer simulations without and with intervention. Results indicate that intervention is highly effective in day-care groups with more than ten children during the second half of the year.
|
|
| 2. |
- Berglund, Lars, et al.
(författare)
-
Correction for regression dilution bias using replicates from subjects with extreme first measurements
- 2007
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 26:10, s. 2246-2257
-
Tidskriftsartikel (refereegranskat)abstract
- The least squares estimator of the slope in a simple linear regression model will be biased towards zero when the predictor is measured with random error, i.e. intra-individual variation or technical measurement error. A correction factor can be estimated from a reliability study where one replicate is available on a subset of subjects from the main study. Previous work in this field has assumed that the reliability study constitutes a random subsample from the main study.We propose that a more efficient design is to collect replicates for subjects with extreme values on their first measurement. A variance formula for this estimator of the correction factor is presented. The variance for the corrected estimated regression coefficient for the extreme selection technique is also derived and compared with random subsampling. Results show that variances for corrected regression coefficients can be markedly reduced with extreme selection. The variance gain can be estimated from the main study data. The results are illustrated using Monte Carlo simulations and an application on the relation between insulin sensitivity and fasting insulin using data from the population-based ULSAM study.In conclusion, an investigator faced with the planning of a reliability study may wish to consider an extreme selection design in order to improve precision at a given number of subjects or alternatively decrease the number of subjects at a given precision.
|
|
| 3. |
- Berglund, Lars, et al.
(författare)
-
Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements
- 2008
-
Ingår i: Statistics in Medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 27:22, s. 4397-4407
-
Tidskriftsartikel (refereegranskat)abstract
- The least-squares estimator of the slope in a simple linear regression model is biased towards zero when the predictor is measured with random error. A corrected slope may be estimated by adding data from a reliability study, which comprises a subset of subjects from the main study. The precision of this corrected slope depends on the design of the reliability study and estimator choice.Previous work has assumed that the reliability study constitutes a random sample from the main study. A more efficient design is to use subjects with extreme values on their first measurement. Previously, we published a variance formula for the corrected slope, when the correction factor is the slope in the regression of the second measurement on the first. In this paper we show that both designs improve by maximum likelihood estimation (MLE). The precision gain is explained by the inclusion of data from all subjects for estimation of the predictor's variance and by the use of the second measurement for estimation of the covariance between response and predictor. The gain of MLE enhances with stronger true relationship between response and predictor and with lower precision in the predictor measurements. We present a real data example on the relationship between fasting insulin, a surrogate market, and true insulin sensitivity measured by a gold-standard euglycaemic insulin clamp, and simulations, where the behavior of profile-likelihood-based confidence intervals is examined. MLE was shown to be a robust estimator for non-normal distributions and efficient for small sample situations.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Fibrinogen Studies, Collaboration, et al.
(författare)
-
Systematically missing confounders in individual participant data meta-analysis of observational cohort studies.
- 2009
-
Ingår i: Statistics in medicine. - : Wiley. - 0277-6715 .- 1097-0258. ; 28:8, s. 1218-37
-
Tidskriftsartikel (refereegranskat)abstract
- One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154,012 participants in 31 cohorts
|
|
| 7. |
- Frigyesi, Attila, et al.
(författare)
-
Estimating the parameters of the operational model of pharmacological agonism
- 2006
-
Ingår i: Statistics in Medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 25:17, s. 2932-2945
-
Tidskriftsartikel (refereegranskat)abstract
- The aim of this work is practical. We show that the parameters of the widely used operational model of pharmacological agonism are difficult to estimate from single dose-response curves. The parameters can be estimated using pairs of dose-response curves (usually treatment and control) sharing some parameters. Confidence bands for the estimators are developed. In the case of multiple dose-response curve pairs one can employ a non-linear mixed effects model to allow for inter-individual variation. The point estimates and the confidence intervals thus obtained are similar to the more naive construction based on mean and standard errors of parameter estimates. To test for difference of certain parameters between treatment and control we employ a permutation test and Wald's test. Copyright (c) 2005 John Wiley & Sons, Ltd.
|
|
| 8. |
|
|
| 9. |
|
|
| 10. |
|
|
