Naslov
Developing prediction models using a small number of datasets with overlapping variables

Autori
Kovačić, Jelena

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
21st Young Statisticians Meeting: Programme - Abstracts - Participants / Batagelj, Vladimir ; Ferligoj, Anuška - Ljubljana : CMI, FDV, University of Ljubljana, 2016, 17-17

Skup
21st Young Statisticians Meeting

Mjesto i datum
Piran, Slovenija, 04.11.2016. - 06.11.2016

Vrsta sudjelovanja
Pozvano predavanje

Vrsta recenzije
Nije recenziran

Ključne riječi
prediction models ; meta-analysis ; unmeasured confounding

Sažetak
Using multiple data sources to develop clinical prediction models increases sample size and precision. However, when some datasets include only a part of the relevant predictors, a common regression analysis cannot be applied unless one part of the data is discarded. To overcome this issue, a recent study proposed to estimate a regression coefficient from a model with all relevant predictors (fully adjusted estimate, available from at least one dataset) using the correlations and conditional independencies between fully and partially adjusted estimates. To validate the proposed method for the prediction of risk of allergic diseases in Croatian population using 4 datasets, we consider the problem of developing a prediction model when the number of datasets is too small to estimate these correlations reliably. The proposed method, modified to include plausible correlation values in advance, was compared to the complete-case estimator in a simulation study. Although both approaches showed similarly low bias, the mean squared error of the complete-case estimator was larger. These results suggest that the proposed method may be better suited for population prediction models even when the number of datasets is small.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Javno zdravstvo i zdravstvena zaštita

POVEZANOST RADA