Sentence examples for binomial regression to examine from inspiring English sources

In addition, we used univariate negative binomial regression to examine associations between the number of heterozygous SNPs within MIs and each of the following covariates: log-transformed parasite density, patient age, sex, season and year of sampling.

In secondary analyses, we used multivariable log binomial regression to examine the effects of treatment on a dichotomous outcome, BMI at or exceeding the age- and sex-specific 85th percentile.

We used log linear binomial regression to examine the relative risk of alcohol consumption during pregnancy in relation to sociodemographic characteristics in each cohort and in each SCOPE country.

For our hospitalization outcome, we conducted logistic regression to estimate the probability of hospitalization at one year, and then conducted negative binomial regression to examine total hospitalizations and cumulative inpatient length-of-stay among the subset of veterans who were hospitalized.

We used negative binomial segmented regression to examine the effect of initiatives on trends and Joinpoint regression to evaluate temporal trends in FOBT participation.

Using the data from the same 5 years, this analysis uses negative binomial regression to closely examine 121 residential burglary and 163 residential theft from vehicle micro-time hot spots that received police response, specifically directed patrol, to understand the effect of response dosage on the reduction of crime.

We used Poisson regression to examine time trends in research publications and negative binomial regression to explore determinants of research publications.

We built a multivariable log binomial regression model to examine the simultaneous effect of race, sex, insurance, age, and age group on CRS.

We used zero-inflated negative binomial regression models to examine the associations of demographic and clinical characteristics with the rates of measurement during follow-up.

We used negative binomial regression models to examine the association between the predictors and the rate of positive and negative interactions.

Since the dependent variables used in our analyses are count measures and therefore represent a Poisson distribution, we employed negative binomial regression analyses to examine the independent and interactive effects of DRD2 and DRD4 on conduct disorder and antisocial behavior.