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In standard multivariable regression models including measures of public interest and scientific productivity, the total charity revenue for a given disease in 2006 (p = 0.04) was also predictive of funding in addition to DALYs (p = 0.006).
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Our models included measures of real-world networks and adjusted for baseline Facebook use.
Separate multivariable models included measures of public interest and scientific productivity.
Additional covariates in the models included measures of the characteristics of the pregnancy and delivery, maternal sociodemographic characteristics and the location in which the delivery occurred.
The final parsimonious logistic regression models included measures of infant behaviour, financial stress, mother's expectation of motherhood, emotional support, sole parenthood, social support and mother's country of birth.
Finally, our work found that many types of items were not good candidates for the final model, including measures of function, cognition, communication, and mood.
Covariates in the BC prediction model included measures of land use for each address (cumulative traffic density within 100 m, population density, percent urbanization), location (latitude and longitude), daily meteorological factors (apparent temperature, wind speed, and height of the planetary boundary layer), and temporal factors (day of week and day of season).
We augment Lintner's [Lintner, J., 1956. Distribution of incomes of corporations among dividends, retained earnings, and taxes. American Economic Review 46, 97 113.] partial adjustment model by including measures of politics and test the model using aggregate annual dividends and earnings data.
Output of the model includes standard measures of the incidence of infection and disease, the prevalence of infectious TB over time, and a count of cluster sizes.
Results for models including both measures are consistent with a contextual effect of SES on risk of breast cancer independent of individual SES.
We modeled the associations of % arsenic species biomarkers with body mass index, % body fat, fat free mass, and waist circumference categories in unadjusted regression models and in models including all measures of body composition.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com