AEGIS study possesses unique information on longitudinal changes in circulating glucose from a random sample . However, some five-year outcomes, such as glycosylated hemoglobin (A1C) , are over 40%DIFDELCMD < \\%%% %DIF < missing data. To alleviate this problem, this article proposes a new data analysis framework based on learning in reproducing kernel Hilbert spaces (RKHS) with missing responses. In particular, we extend the Hilbert-Schmidt dependence measure to this context introducing a new bootstrap procedure in which we prove to be consistent. In addition, we adapt or use existing algorithms/models of variable selection, regression, or conformal inference to acquire new clinical findings with the AEGIS study data. The fitted models allow: i) to identify new factors associated with long-term glucose changes; ii) to highly the usefulness of CGM technology predictive capacity; and iii) to improve and optimize clinical interventions based on expected glucose changes according to patients' baseline characteristics.abstract 
AEGIS study possesses unique information on longitudinal changes in circulating glucose through continuous glucose monitoring technology (CGM) . However, as usual in longitudinal medical studies, there is a significant amount of missing data in the outcome variables. For example, 40 percent of glycosylated hemoglobin (A1C) %DIFDELCMD < \\%%% %DIF < missing data. To alleviate this problem, this article proposes a new data analysis framework based on learning in reproducing kernel Hilbert spaces (RKHS) with missing responses. In particular, we extend the Hilbert-Schmidt dependence measure to this context introducing a new bootstrap procedure in which we prove to be consistent. In addition, we adapt or use existing algorithms/models of variable selection, regression, or conformal inference to acquire new clinical findings with the AEGIS study data. The fitted models allow: i) to identify new factors associated with long-term glucose changes; ii) to highly the usefulness of CGM technology predictive capacity; and iii) to improve and optimize clinical interventions based on expected glucose changes according to patients' baseline characteristics.abstract biomarker data are missing five years ahead. With the purpose to reduce the impact of this issue, this article proposes a new data analysis framework based on learning in reproducing kernel Hilbert spaces (RKHS) with missing responses that allows to capture non-linear relations between variable studies in different supervised modeling tasks. First, we extend the Hilbert-Schmidt dependence measure to test statistical independence in this context introducing a new bootstrap procedure, for which we prove consistency. Next, we adapt or use existing models of variable selection, regression, and conformal inference to obtain new clinical findings about glucose changes five years ahead with the AEGIS data. The most relevant findings are summarized below: i) We identify new factors associated with long-term glucose evolution; ii) We show the clinical sensibility of CGM data to detect changes in glucose metabolism; iii) We can improve clinical interventions based on our algorithms' expected glucose changes according to patients' baseline characteristics.
R
from a random sample
through continuous glucose monitoring technology (CGM)
R
some five-year outcomes, such as
as usual in longitudinal medical studies, there is a significant amount of missing data in the outcome variables. For example, 40 percent of
D
, are over 40
A
biomarker data are missing five years ahead. With the purpose to reduce the impact of this issue, this article proposes a new data analysis framework based on learning in reproducing kernel Hilbert spaces (RKHS) with missing responses that allows to capture non-linear relations between variable studies in different supervised modeling tasks. First, we extend the Hilbert-Schmidt dependence measure to test statistical independence in this context introducing a new bootstrap procedure, for which we prove consistency. Next, we adapt or use existing models of variable selection, regression, and conformal inference to obtain new clinical findings about glucose changes five years ahead with the AEGIS data. The most relevant findings are summarized below: i) We identify new factors associated with long-term glucose evolution; ii) We show the clinical sensibility of CGM data to detect changes in glucose metabolism; iii) We can improve clinical interventions based on our algorithms' expected glucose changes according to patients' baseline characteristics.