A Method to Model Season of Birth as a Surrogate Environmental Risk Factor
for Disease

Jimmy Efird, Ph.D., M.Sc.
* Candidate for Director Position in the Biostatistical Design and Analysis Center
John A. Burns School of Medicine

Monday, October 15, 2007
12:15pm
MoosT 2-520
Minneapolis Campus

Abstract:
Environmental exposures, including some that vary seasonally, may play a role in the development of many types of childhood diseases such as cancer. Those observed in children are unique in that the relevant period of exposure is inherently limited or perhaps even specific to a very short window during prenatal development or early infancy. As such, researchers have investigated whether specific childhood cancers are associated with season of birth. Typically a basic method for analysis has been used, for example categorization of births into one of four seasons, followed by simple comparisons between categories such as via logistic regression, to obtain odds ratios (ORs), confidence intervals (CIs) and p-values. In this paper we present an alternative method, based upon an iterative trigonometric logistic regression model used to analyze the cyclic nature of birth dates related to disease risk. Disease birth-date results are presented using a sinusoidal graph with a peak date of relative risk and a single p-value that tests whether an overall seasonal association is present. An OR and CI comparing children born in the 3 month period around the peak to the symmetrically opposite 3 month period also can be obtained. Advantages of this method include ease of use, increased statistical power to detect associations, and the ability to avoid potentially arbitrary, subjective demarcation of seasons.
All are Welcome.
Lunch will be provided for the first 30 attendees