60,262 research outputs found
Early estimates of seasonal influenza vaccine effectiveness in Europe among target groups for vaccination: results from the I-MOVE multicentre case-control study, 2011/12
Colaboração de: Baltazar Nunes, investigador do DEPTo provide an early estimate of 2011/12 influenza vaccine effectiveness (VE), we conducted a multicentre case–control study based on seven sentinel surveillance networks. We included influenza-like illness cases up to week 7/2012 from the vaccination target groups, swabbed less than eight days after symptom onset. Laboratory-confirmed influenza A(H3) cases were compared to negative controls. Adjusted VE was 43% (95% confidence interval: -0.4 to 67.7), suggesting low to moderate VE against influenza A(H3) in the early 2011/12 season
Accurate Liability Estimation Improves Power in Ascertained Case Control Studies
Linear mixed models (LMMs) have emerged as the method of choice for
confounded genome-wide association studies. However, the performance of LMMs in
non-randomly ascertained case-control studies deteriorates with increasing
sample size. We propose a framework called LEAP (Liability Estimator As a
Phenotype, https://github.com/omerwe/LEAP) that tests for association with
estimated latent values corresponding to severity of phenotype, and demonstrate
that this can lead to a substantial power increase
Case-control studies: basic concepts.
The purpose of this article is to present in elementary mathematical and statistical terms a simple way to quickly and effectively teach and understand case-control studies, as they are commonly done in dynamic populations-without using the rare disease assumption. Our focus is on case-control studies of disease incidence ('incident case-control studies'); we will not consider the situation of case-control studies of prevalent disease, which are published much less frequently
Analysis of matched case-control studies.
© BMJ Publishing Group Ltd 2015. There are two common misconceptions about case-control studies: that matching in itself eliminates (controls) confounding by the matching factors, and that if matching has been performed, then a "matched analysis" is required. However, matching in a case-control study does not control for confounding by the matching factors; in fact it can introduce confounding by the matching factors even when it did not exist in the source population. Thus, a matched design may require controlling for the matching factors in the analysis. However, it is not the case that a matched design requires a matched analysis. Provided that there are no problems of sparse data, control for the matching factors can be obtained, with no loss of validity and a possible increase in precision, using a "standard" (unconditional) analysis, and a "matched" (conditional) analysis may not be required or appropriate
Control Function Assisted IPW Estimation with a Secondary Outcome in Case-Control Studies
Case-control studies are designed towards studying associations between risk
factors and a single, primary outcome. Information about additional, secondary
outcomes is also collected, but association studies targeting such secondary
outcomes should account for the case-control sampling scheme, or otherwise
results may be biased. Often, one uses inverse probability weighted (IPW)
estimators to estimate population effects in such studies. However, these
estimators are inefficient relative to estimators that make additional
assumptions about the data generating mechanism. We propose a class of
estimators for the effect of risk factors on a secondary outcome in
case-control studies, when the mean is modeled using either the identity or the
log link. The proposed estimator combines IPW with a mean zero control function
that depends explicitly on a model for the primary disease outcome. The
efficient estimator in our class of estimators reduces to standard IPW when the
model for the primary disease outcome is unrestricted, and is more efficient
than standard IPW when the model is either parametric or semiparametric
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