Tao, Pan,
Galiullin, D,
Chen, X.L.,
Zhang, W.H.,
Yang, K,
Liu, K,
Zhao, L.Y.,
Chen, X.Z.,
Hu, J.K. (2021) . The performance of the
model was assessed with its discrimination, calibration, and clinical usefulness. A total
).
Logistic regression was used to detect the association of SNPs and haplotypes of linked loci in different
Timasheva, Y,
Nasibullin, TR,
Tuktarova, IA,
Erdman, VV,
Galiullin, TR,
Zaplakhova, OV,
Bakhtiiarova, KZ (2022) tested using
logistic regression analysis under additive genetic
model adjusted for sex. Meta
YANG, G.,
SUN, J.,
ZHANG, D.,
ZHANG, R.,
ZHONG, Y.,
WANG, X.,
CHEN, X.,
LIU, B.,
LI, L.,
ZHAO, S.,
WANG, L.,
YUAN, C.,
LONG, M.,
JIANG, H.,
LI, C.,
ZHOU, Q.,
LIAN, A.,
GAREEV, I. (2020) were investigated using univariable and multivariable
logistic regression
models. Results: Depressed
KORYTINA, G.F.,
AKHMADISHINA, L.Z.,
KOCHETOVA, O.V.,
VIKTOROVA, T.V.,
AZNABAEVA, Y.G.,
ZAGIDULLIN, N.SH.,
ZAGIDULLIN, SH.Z.,
KZHYSHKOWSKA, J.G (2019) ).
Logistic regression was used to detect the association of SNPs in different
models. Linear regression
Korytina, G.F.,
Akhmadishina, L.Z.,
Kochetova, O.V.,
Aznabaeva, Y.G.,
Zagidullin, Sh.Z.,
Victorova, T.V. (2016) , Russia).
Logistic regression was used to detect the association of SNPs in different
models. Linear
Korneyev, I.A.,
Alexeeva, T.A.,
Al-Shukri, S.H.,
Bernikov, A.N.,
Erkovich, A.A.,
Kamalov, A.A.,
Kogan, M.I.,
Pavlov, V.N.,
Zhuravlev, V.N.,
Pushkar, D.Y. (2016) -adjusted odds ratio between 1.2 and 5.2. In
logistic regression
model (R2=0.361), the strongest associated
Zagidullin, N.,
Plechev, V.,
Badykova, E.,
Badykov, M.,
Akhmadishina, L.,
Korytina, G.,
Sagitov, I. (2019) -time polymerase chain reaction.
Logistic regression was used to detect the association of SNPs with SSS
was performed using
two-tailed Fisher’s exact test, odds ratio, 95% confidence interval and
logistic
regression
logistic regression with age and body mass
index as covariates under additive genetic
model implemented