1 e to settings where the exposure variable is
polytomous and where the assumption of independence betw
2 When one estimates the effects of a
polytomous exposure, it is common practice to express al
3 ted the biomarker panel to LV geometry using
polytomous logistic regression adjusting for clinical co
4 f a mutation in any of the LS genes by using
polytomous logistic regression analysis of clinical and
5 Polytomous logistic regression and Wald tests for hetero
6 In this paper, the authors describe how a
polytomous logistic regression method previously develop
7 A multivariable
polytomous logistic regression model (PREMM(1,2,6)) was
8 Polytomous logistic regression models assessed relations
9 Polytomous logistic regression models were used to asses
10 Polytomous logistic regression models were used to estim
11 Polytomous logistic regression models were used to evalu
12 In multivariate
polytomous logistic regression models, medically indicat
13 In
polytomous logistic regression models, parity and age at
14 Polytomous logistic regression procedures were used to d
15 This quantitative
polytomous logistic regression test allows for analysis
16 authors propose an extension to quantitative
polytomous logistic regression that allows testing for m
17 We performed
polytomous logistic regression to calculate odds ratios
18 We used multivariable
polytomous logistic regression to compare case groups wi
19 p but not enrollment (n = 688; 40%) and used
polytomous logistic regression to estimate odds ratios (
20 We used
polytomous logistic regression to estimate odds ratios (
21 to investigate etiologic heterogeneity or do
polytomous logistic regression to estimate odds ratios s
22 egorical outcome typically entails fitting a
polytomous logistic regression via maximum likelihood es
23 Unordered
polytomous logistic regression was used to calculate adj
24 Multivariate
polytomous logistic regression was used to calculate odd
25 Polytomous logistic regression was used to estimate odds
26 Multivariable
polytomous logistic regression was used to estimate odds
27 Polytomous logistic regression was used to estimate ORs
28 Polytomous logistic regression was used to estimate the
29 Logistic and
polytomous logistic regression were used to estimate odd
30 Factors associated with surgery use (from
polytomous logistic regression); overall and breast canc
31 analyses to support differences in risk: (1)
polytomous logistic regression, (2) homogeneity tests, o
32 By use of
polytomous logistic regression, factors possibly influen
33 articipants with concordant data via 2-sided
polytomous logistic regression.
34 the common control group through the use of
polytomous logistic regression.
35 25(OH)D using stepwise linear regression and
polytomous logistic regression.
36 % confidence intervals were calculated using
polytomous logistic regression.
37 This
polytomous model selection approach can be used to ident
38 e control group examined by colonoscopy in a
polytomous model with several case groups (newly diagnos
39 Using
polytomous multiple logistic regression, the authors fou
40 ype, for example, binary, count, continuous,
polytomous,
ordinal, time-to-onset, multivariate and oth
41 ment before and after rehabilitation, by the
polytomous rating scale measurement model of Wright and
42 In this paper, we show that standard
polytomous regression is ill equipped to detect outcome
43 We used
polytomous regression models adjusted for age, BMI, stat
44 Specifically, nonsaturated
polytomous regression will often a priori rule out the p
45 In
polytomous regression, odds ratios for BMI (P = 0.65), s
46 vals (CIs) were estimated using logistic and
polytomous regression.
47 atios were estimated by race/ethnicity using
polytomous regression.
48 bias away from the null; and, if exposure is
polytomous,
the bias produced by independent nondifferen