| 49385 |
Creator |
0e2d1b90682e8c08761586c8975c7f13 |
| 49385 |
Creator |
538c0722d7d106c7098d06ea38e69498 |
| 49385 |
Creator |
cf4025dee845929a398330b0f5873116 |
| 49385 |
Date |
2017-08-30 |
| 49385 |
Is Part Of |
repository |
| 49385 |
Is Part Of |
p02776715 |
| 49385 |
abstract |
The self-controlled case series (SCCS) method is an alternative to study designs such
as cohort and case control methods and is used to investigate potential associations
between the timing of vaccine or other drug exposures and adverse events. It requires
information only on cases, individuals who have experienced the adverse event at least
once, and automatically controls all fixed confounding variables that could modify
the true association between exposure and adverse event. Time-varying confounders
such as age, on the other hand, are not automatically controlled and must be allowed
for explicitly. The original SCCS method used step functions to represent risk periods
(windows of exposed time) and age effects. Hence, exposure risk periods and/or age
groups have to be prespecified a priori, but a poor choice of group boundaries may
lead to biased estimates. In this paper, we propose a nonparametric SCCS method in
which both age and exposure effects are represented by spline functions at the same
time. To avoid a numerical integration of the product of these two spline functions
in the likelihood function of the SCCS method, we defined the first, second, and third
integrals of I-splines based on the definition of integrals of M-splines. Simulation
studies showed that the new method performs well. This new method is applied to data
on pediatric vaccines. |
| 49385 |
authorList |
authors |
| 49385 |
issue |
19 |
| 49385 |
status |
peerReviewed |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/623530 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/623536 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/623537 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/623538 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/623539 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/623540 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/652442 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/652456 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/652457 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/652458 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/652459 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/652460 |
| 49385 |
uri |
http://data.open.ac.uk/oro/document/667674 |
| 49385 |
volume |
36 |
| 49385 |
type |
AcademicArticle |
| 49385 |
type |
Article |
| 49385 |
label |
Ghebremichael-Weldeselassie, Yonas ; Whitaker, Heather J. and Farrington, C. Paddy
(2017). Spline-based self-controlled case series method. Statistics in Medicine,
36(19) pp. 3022–3038. |
| 49385 |
label |
Ghebremichael-Weldeselassie, Yonas ; Whitaker, Heather J. and Farrington, C. Paddy
(2017). Spline-based self-controlled case series method. Statistics in Medicine,
36(19) pp. 3022–3038. |
| 49385 |
Title |
Spline-based self-controlled case series method |
| 49385 |
in dataset |
oro |