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Journal of Computer Science & Systems Biology | ISSN: 0974-7230 | Volume: 11
&
Biostatistics and Bioinformatics
Big Data Analytics & Data Mining
7
th
International Conference on
7
th
International Conference on
September 26-27, 2018 | Chicago, USA
Change-point regression models with unknown change-points: An application to Human papillomavirus
(HPV)-associated cancer incidence in Canada
Ibrahim A Watara
University of Saskatchewan, Canada
T
he identification of trends, determination of the optimal number of change-points and their locations is important is when
analyzing trend data. Very often, data exhibit non-linear trends over an entire study period but exhibit linear trends only within
sub-intervals. Most methods used to characterize these segmented relationships are not appropriate because the change-points are
either not considered at all (e.g. in polynomial regression) or are fixed a priori (e.g. regression splines). In addition, previous analyses
of time series data detecting change-points were based on the assumption that change-points occur only at discrete grid points
[Lerman, P. (1980). Fitting Segmented Regression Models by Grid Search. Journal of the Royal Statistical Society. Series C (Applied
Statistics), 29(1),77-84.]. However, it is more lifelike that change-points can assume any value in the range of observed data and so
are continuous. We fit a change-point linear regression model (made up of continuous linear segments) to determine the optimal
number and ideal location(s) of the continuous change-points on the basis of a statistical criterion. We also determine the number
of significant change-points through a serial permutation test using Monte Carlo simulation procedures. We maintained the global
asymptotic significance of the resulting p-values through a Bonferroni correction. The change-point linear regression model is applied
to national Human papillomavirus (HPV) - associated cancer incidence data in Canada from 1992-2013..
Ibrahim.watara@usask.caJ Comput Sci Syst Biol 2018, Volume: 11
DOI: 10.4172/0974-7230-C1-021