Multivariate logistic regression model was developed to examine the association between maternal oral health sta- tus in particular specific periodontal conditions (perio- dontal pockets, gingival bleeding, gingival recession, calculus), combined periodontal conditions "periodontal disease threshold", and dentition status (decay-missing- filled teeth and open pulp pulpitis) as risk factors for pre- term low birth weight infant delivery. This was done with the understanding that PTLBW is multi-factorial in nature involving demographic, genetic, nutritional, obstetric, antenatal care, oral hygiene, professional dental care, den- tal plaque, poor oral health, periodontal disease, infec- tion, maternal morbidity and toxic exposure  whereby the interaction is not strictly in a hierarchical manner as described by Victoria and Coworkers  and presented by Bassani and Coworkers . In the multi-factorial con- ceptualization, demographic and social factors of impor- tance were age, education, parity, prenatal care, alcohol consumption, tobacco smoking and heavy duty during pregnancy. Environmental tobacco smoke (ETS) was con- sidered as an environmental risk factor for PTLBW. The logistic regression models used were the "Forward step- wise-conditional" based on the likelihood ratio criterion (p in = 0.05, p out = 0.10) and the "Enter" methods with both "continuous" and "categorical" variables in the model, and in a different phase, a model with "categori- cal" variables only was developed, accordingly.
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Abstract— This study is based on the development of multivariate logistic regression model to assess the effect of various risk factors like age, BMI, meal-regularity and fast-food consumption on the prevalence of diabetes spatially in urban and rural areas of India. The existence of non-multicollinearity, non-normality and non-linearity between the variables was studied. The test-of-association showed that age, BMI and fast-food were significantly associated with diabetes in rural(p<0.05). While, age and BMI were found to be associated with diabetes in urban area. The Wald test and Odds ratio (OR>1)showed that age and BMI were significant predictors of diabetes. The intake of fast-food has 4.08 times more effect on diabetes than those who do not take. Similarly, the persons with regular nutritional diet were at low risk of getting diabetic. The area under the ROC curve showed the better performance of the model. Hence, the developed logistic regression model can be a powerful statistical technique for identifying the association of most prominent risk factor to diabetes and thus timely notify to take needful actions to reduce the risk of getting diabetes.
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Results: In a multivariate logistic regression model, a diagnosis of cancer was significantly associated with TZD use, even after correcting for potential confounders including other oral anti- diabetic agents (sulfonylureas and biguanides), age, glycosylated hemoglobin A1C, body mass index, cigarette smoking, high comorbidity, and number of prescription medications (odds ratio = 1.59, P = 0.04). This association was particularly strong among patients using rosiglitazone (OR = 1.89, P = 0.02), and among women (OR = 2.07, P = 0.01).
In this study, we demonstrated that: 1) CSM-TACE group achieved better treatment response compared with cTACE group, and further multivariate logistic regression model analysis revealed that CSM-TACE independently correlated with better ORR; 2) although PFS and OS displayed no difference between the CSM-TACE and cTACE group, CSM-TACE was identi ﬁ ed as an independent predictive factor for more favorable OS in multivariate Cox ’ s propor- tional hazards regression model analysis; 3) abnormal ALP, history of alcohol intake and largest nodule size ≥ 7 cm were independently predicting factors for worse treatment response, and largest nodule size ≥ 7 cm, Child-Pugh stage B/C, abnormal ALB, ALP and AFP were independently associated with an unfavorable survival; 4) the majority of liver function indexes and AEs were similar between the two groups, except that ALP, TB, pain incidence during operation and occurrence of fever post treatment were elevated in the CSM-TACE compared with the cTACE group.
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the relationship between disease and SNP genotype, we established the univariate lo- gistic regression model. Adjusted the effects of the age, gender, smoking history in the multivariate logistic regression model, then evaluated the statistical association between the SNP and the susceptibility of lung cancer, which through doing the stratified analysis, according to the subsections of the age, the gender, the smoking history, and the patho- logical types. In this statistics, four kinds of biological models of coding SNP were consi- dered: additive model, co-dominant model, dominant model and recessive model (the partial results were exhibited in this paper). Results
Manual stepwise model-building strategy was used to develop the model that included all variables signifi- cantly associated with the main outcome variables. Pre- dictors were included in the model one at a time and removed if not significant at p <0.05. Asthma was the only predictor that fell out of the model. Parameter esti- mates and significance levels were reviewed during model building to assess the relative importance of each variable. Adjusted odds ratios (ORs) and confidence in- tervals (CIs) were reported for each multivariate logistic regression model. Variance inflation factor and tolerance values were evaluated to assess multicollinearity and model fitness. All variables were considered statistically significant at p <0.05.
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to assess the association between gender and an outcome of bac- teremia (Table 1). Logistic regression was then used to develop a multivariate model for risk based on variables with P ⬍ .05 in the univariate analyses. Final models include variables significantly associated with an outcome of bacteremia (Table 2). The presence of interaction between variables was examined by inclusion of interaction terms. The predictive power of models 1 and 2 and models based on individual predictors was compared by con- structing receiver operator characteristic (ROC) curves and com- paring the areas under these curves (Fig 1). The final multivariate logistic regression model based on ANC, temperature, and gender was used to develop a formula for individual level risk that uses information from all significant predictors of bacteremia (Table 3). This model was then applied to a validation dataset derived from a large single-center study aimed at determining the risk of bac- teremia in children 3 to 36 months of age in the post-H influenzae type b era. 22 Data fields present in both datasets included: age,
Out-patient department of Paediatrics, ICMH. Before data collection, informed written consent was taken from the respondents. A pre-tested, semi- structured questionnaire was filled up by the principal investigator in a face-to-face interview of both the parents group of cases and controls. Information regarding cases were also taken from individual master files of CDC. Exclusion criteria were any diagnosed case of: Hearing impairment, Congenital oral anomalies (cleft lip/palate, tongue-tie), Autism and other pervasive disorders, Cerebral palsy, Genetic disorders (e.g. Down syndrome), Metabolic disorder (e.g. Hypothyroidism). Data were checked and edited before incorporating into statistical software (SPSS- Version17). Initially chi-square test was done to identify association of other variables with being case (having delayed speech).Those variables which were found to be significant during initial analysis were included in the multivariate regression model to test for the effect of arisk variable after adjustment for the remaining independent variables and to test for possible interactions among variables related to speech delay. Multivariate regression model accounted the presence of multiple factors and therefore it provided adjusted odds ratio.95% confidence intervals were calculated and p- value below 0.05 was considered as significant. Ethical clearance of this study was taken from the Ethical committee of Institute of Child and Mother Health (ICMH), Dhaka.
multivariate adjustment in both sexes. More treatment options for FI makes a proper pretreatment evaluation increasingly important, whether by means of subjective severity questionnaires, such as the FISI, or by objective measurements, such as ARM, anal endosonography, electromyography, and defecography . Bordeianou et al.  reported MRP was the only objective measure- ment that seems to correlate with both FISI and the presence of sphincteric defects on anal endosonography, while maximal squeeze pressure (MSP) generated by the EAS was not correlated. Our results confirm a strong association between low MRP and FI.
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variate regression combining all individual risk factors covariates confirmed the significant effect of the distance- to-forest and distance-to-health-facility (Table 3). Males had an increased risk of Plasmodium spp. infection in the three areas, the highest risk (OR = 2.306) being observed in Sampovloun. The age group at highest risk of malaria differed in each area, being 15–39 years in Sampovloun, 5–39 years in Koh Kong, and 0–39 years in Preah Vihear. In Sampovloun, we found a significantly increased risk for males aged 15–39 years, due to an interaction between age and gender. A protective effect of bednets was detected in Preah Vihear. Due to the large coverage and regular use of bednets by population in other provinces, such an effect was not detected in Koh Kong and Sampovloum by the multivariate analysis, though it was readily estab- lished by the univariate analysis in Sampovloun (Table 1). An increased distance to forest tended to reduce the infec- tion risk in Preah Vihear and in Koh Kong, whereas an increased distance to health facilities tended to increase the risk of malaria infection.
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There are several limitations to the interpretation of the results. First, because the number of articles was insufficient, the multivariate logistic analysis showed only the statistical tendency for the significance of baPWV after redefining the studies. Moreover, the analysis also demonstrated the most powerful effect of the number of the participants in each study. This implies that the success of the prognostic prediction strongly relies upon the quality of the study itself. However, it should be recognized that the statistical tendency for baPWV and exclusion criteria of LE-ASO/PAD emerged in the number of currently available articles. The results also imply that the reproducibility of baPWV as a prognostic predictor is superior to that of CAVI in the various clinical conditions. Moreover, the fact that baPWV already showed results similar to those for cfPWV in the meta-analyses would be consistent with the results of this study as a whole. Furthermore, we should recognize that only 40% of the studies proved the prognostic significance of CAVI. Second, publication bias was not considered in this study. As such, denying the existence of unpublished studies that could affect the statistical results is difficult. Nevertheless, the ratio of articles that clarified the exclusion criteria of LE-ASO/PAD was similar in the baPWV and CAVI studies. The success rates of the baPWV and CAVI studies declined in the absence of clarified exclusion criteria of LE-ASO/PAD. Moreover, the ratio of the articles studying the prognostic significance of these parameters did not differ significantly (baPWV 70/1,800; CAVI 15/550). Therefore, the prognostic studies of baPWV and CAVI were published without strong bias. Third, this study did not consider other criteria of the patient exclu-
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Though ordinal logistic regression can be applied, retain- ing all values of the LMUP, which has been recommended and retains all possible information, these values may also be aggregated for analysis. This may be because problems are found or expected with convergence or precision when fitting ordinal logistic regression models with all values of the LMUP score, due to small cell counts. We have been unable to find any guidance on how many cut points can be man- aged by an ordinal logistic regression and the pros and cons of this choice in terms of “power” or “sensitivity” to detect associations. Another reason to aggregate is for simplicity and to link the regression analysis to meaningful prevalence estimates. With this in mind, we explore the application of ordinal logistic regression retaining all values of the LMUP and also with LMUP scores aggregated into three categories that seem theoretically valid: a score of 0–3 is classed as “unplanned”, 4–9 as “ambivalent”, and ≥ 10 as “planned”. 7
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Similarly, studies on human hepatocellular carcinomas (HCC)  showed up-regulation of CEBPA at both mRNA and protein levels. A forced down- regulation of this gene led to reduced colony formation and cell growth. In line with these observations, Yin at al. have shown in their research on prostate cancer that up-regulation of CEBPA may cause the inactivation of the G1/S checkpoint, stimulation of a transition from the G1 to S and G2 phases, stimulation of cell proliferation and enhancement of the anchorage-independent formation of colonies . Inactivation of the G1/S checkpoint may protect cancer cells from apoptosis triggered off by DNA-damaging agents, causing a “replication by-pass”, Table 3: The CEBPA mRNA expression – statistical results of the multivariate analysis of prognosis (Cox
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Stroke patients benefit from neurovascular expertise as soon as they are admitted to emergencies, in collaboration between emergency doctors, via an operational penalty. Data entry and analysis was performed using the EPI INFO software in its English version 22.214.171.124. The Chi-square test and the student test were used as statistical tests with a 5% threshold of significance. An univariate analysis was first conducted to seek an association between the medical and neurological complications during hospitalization and the occurrence of intra- hospital deaths. Secondly, a multivariate analysis with step-by-step logistic regression was carried out to determine the effect of different medical and neurological complications on intra-hospital deaths of patients hospitalized for stroke, using independent variables that had a p ≤ 0.20. Proportions of deaths attributable to the various incident complications were calculated using gold from significant variables in univariate analysis. The following proportions were obtained: the proportion of deaths attributable in the exposures (PDAE) to each complication, which gives the proportion by which the mortality rate among the exposures would be reduced if the complication was absent (PDAE = [OR − 1]/ OR); the attributable proportion of deaths in the population (APDP) to each incident complication, which indicates the proportion by which the death rate would be reduced in the overall population of patients hospitalized for stroke if the complication was absent ( APDP = intra-hospital deaths in patients with strokes exposed to complications/all cases of intra-hospital deaths in patients hospitalisez for stroke).
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Fourth, this study is not without limitations. For example, it did not address more complex multi-modal journeys, considered as a trip chain (Currie and Del- bosc 2011; Park 2017). The survey was designed and administered in the Eng- lish language, meaning that the current study might not have captured linguistic minorities similar to the Farag and Lyons (2012) study where linguistic minorities were excluded. The developed explanatory model could explain approximately 30.4% of the phenomenon (pre-travel information-seeking behaviour) suggesting that, like previous studies reviewed, more explanatory factors need to be explored in future studies. The indicators of representativeness of the primary sample need to be considered in terms of interpretation and generalisability. While gender statistics for bus users in the primary sample (2016) appeared to have a similar trend to the previous TfWM Bus survey (2014/2009) with females being more represented than males, gender statistics of metro/rail users in the primary sample appeared not to be the same as the previous Metro/Rail surveys (2014/2009 or 2013/2008).
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that age might have affected the dose administered by the physician. Patients aged < 70 years had higher dose (3.5±1.2mg/day) than patients aged ≥70 years (2.7±1.0mg/ day). However, after multivariate analysis, they showed that warfarin dose remained an independent predictor of TTR . Importantly, in our study, the variable age did not affect warfarin dose. Some studies showed that CYP2C9 and VKORC1 polymorphisms influenced TTR mean during initiation of therapy with warfarin [14, 15, 29]. We did not find this association probably because our patient group were anticoagulated for at least 12 months.
Another consideration that is taken into account is the sample size n. Actually, when comparing different estimation methods, increasing the n is supposed to have a positive effect on the MSE, as increasing the n leads to a lower variance of the estimated parameters. Therefore, it is interesting to investigate the gain of using LRR when n is both small and large. The sample size is increased with the number of independent variables (p). Many papers show that to obtain meaningful results from the LR model, the sample size is needed to be adjusted. Therefore, the number of observations that are used in this simulation study is depend on 20p+10, 30p, 40p, 60p, and 100p, respectively (Mansson and Shukur, 2011 and Peruzzi, 1996).
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Results were expressed as mean ± standard deviation (SD). Kolmogorov-Smirnov test was used to assess the normality for continuous variables. Independent t-test was used to compare the mean age and monthly income in two groups. To explore the relationship between the occupation and education level of women as well as husband’s job, the Chi-square test was used. Both univariable and multiple logistic regression analyses were used to indicate the association between the dependent (infertility compared to no fertility) and independent variables. P value less than 0.05 was considered as the level of significance. All the statistical analyses were performed by SPSS software 16.
Data analysis. Univariate and multivariate analyses were performed with Statistical Package for Social Sciences, version 18. Logistic regression was per- formed with the forward method. Antibiotic susceptibility was introduced as a dependent variable. The covariates were gender (male [reference] or female), age groups (0 to 17 years, 18 to 39 years [reference], 40 to 64 years, or ⱖ 65 years of age), period (6 periods for 1990 to 2007 by a range of 3 years and the period of 2008 to 2009 [reference]), previous eradication therapy (none [reference], 1, or 2 or more), and ethnic origin (Northern Europe, North Africa, Southern Europe, Middle East [reference], Central and West Africa, Eastern Europe, and Asia). As there was a strong interaction of previous H. pylori eradication therapy effects and period effects, a multivariate analysis for the period effect on antibi- otic susceptibility was used for measurements in strata classified according to the presence or absence of a previous eradication attempt. Estimated odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. A P value of ⬍0.05 indicated significant differences.
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The performance of the five-gene classifier on the de novo PD individuals alone composing the early PD cohort (n = 38), resulted in a similar ROC with an AUC of 0.95, indicating the stability of the model and also, that patient medication had no significant effect on the predictive probability (PP) of the classifier for PD risk. The predictive ability of the model was validated in an independent cohort of 30 patients at advanced stage of PD, classifying correctly all cases as PD (100% sensitivity). Notably, the nominal average value of the PP for PD (0.95 (SD = 0.09)) in this cohort was higher than that of the early PD group (0.83 (SD = 0.22)), suggesting a potential for the model to assess disease severity. Lastly, the gene panel fully discriminated between PD and Alzheimer ’ s disease (n = 29). Conclusions: The findings provide evidence on the ability of a five-gene panel to diagnose early/mild PD, with a possible diagnostic value for detection of asymptomatic PD before overt expression of the disorder.
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