1 ors associated with second opinion use were evaluated using

logistic regression.

2 was performed with the chi(2) test, the Student t test, and

logistic regression.

3 or death within 14 days of LVAD were assessed with stepwise

logistic regression.

4 Adjusted odds ratios were calculated using multivariable

logistic regression.

5 as compared with uptake in the non-incentivised group using

logistic regression,

adjusting for community and number of ch

6 We conducted conditional

logistic regression analyses adjusted for body mass index, sm

7 Logistic regression analyses examined the association between

8 chemokines were compared between groups using multivariate

logistic regression analyses, adjusting for maternal age, eth

9 e treatment of breast cancer were assessed in multivariable

logistic regression analyses.

10 Univariate and mixed-effects

logistic regression analysis controlling for center effect we

11 We used

logistic regression analysis for remission and zero-inflated

12 Introducing these variables to a

logistic regression analysis showed areas under the receiver-

13 We performed multinomial

logistic regression analysis to assess the weighting of histo

14 Multiple

logistic regression analysis was used to estimate adjusted od

15 Multivariable ordinal

logistic regression analysis with an interaction term was use

16 and identified factors associated with 30-day mortality in

logistic regression analysis.

17 nd its accuracy to predict MDD, using area under the curve,

logistic regression,

and linear mixed model analyses, was tes

18 This analysis demonstrates a simple

logistic regression approach for testing a priori hypotheses

19 In multivariable

logistic regression,

high safe patient handling behaviors wer

20 Logistic regression identified that dwell time was the only r

21 On multivariate

logistic regression,

lower baseline GDF-15 was associated wit

22 A multivariate

logistic regression model predicting referral to PC was creat

23 A multivariable

logistic regression model was constructed to quantify the adj

24 A propensity score-weighted

logistic regression model was used to adjust for confounders.

25 A hierarchical

logistic regression model was used to identify predictors of

26 Expected mortality was obtained from multilevel

logistic regression model, adjusting for demographics, mechan

27 In a

logistic regression model, more catatonia signs were associat

28 Conditional

logistic regression models adjusting for risk factors evaluat

29 We used published data to create

logistic regression models comparing annual trends in the rep

30 preguideline and postguideline periods in the hierarchical

logistic regression models for all of the risk groups.

31 Logistic regression models identified characteristics associa

32 We used

logistic regression models to estimate associations of PFASs

33 Conditional

logistic regression models were used to estimate odds ratios

34 Multiple linear and

logistic regression models were used to examine relations of

35 sis of differences in percent effect changes in conditional

logistic regression models with and without additional adjust

36 effect of treatment delay on treatment effectiveness using

logistic regression models.

37 maternal diseases and ADHD in offspring were analyzed using

logistic regression models.

38 On multivariate

logistic regression,

only age younger than 50 years, baseline

39 Firth's

logistic regression provides a concise statistical inference

40 We used

logistic regression to estimate the association between epile

41 We used

logistic regression to investigate factors associated with re

42 ree risk classes (low, moderate, and high) were created and

logistic regression was undertaken to evaluate the optimal pr

43 Time trends were identified and multivariable

logistic regression was used to determine sociodemographic fa

44 Logistic regression was used to evaluate the association betw

45 Multivariable

logistic regression was used to explore the association of di

46 Logistic regression was used to identify risk factors for new

47 Using multiple

logistic regression,

we identified significant associations b

48 Multivariable

logistic regression with generalized estimating equations was

49 We used multivariate

logistic regression with PCR-confirmed influenza infection as

50 Multivariate

logistic regression with restricted cubic splines was utilize