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glm command in stata 18  (STATA Corporation)


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    STATA Corporation glm command in stata 18
    Glm Command In Stata 18, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/glm+command/glm+command+in+stata+18/pm40572069-54-26-26
    Average 90 stars, based on 1 article reviews
    glm command in stata 18 - by Bioz Stars, 2026-10
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    Article Title: Impact of pausing elective hip and knee replacement surgery during winter 2017 on subsequent service provision at a major NHS Trust: a descriptive observational study using interrupted time series
    Article Snippet: For count or proportion outcomes (number of admissions, proportion 185 women, proportion with 2+ comorbidities, proportion in top two deprivation quintiles, bed 186 occupancy) segmented Poisson regression models were fit to the data, whilst for averages/ratios 187 (average age, average length of stay, ratio of elective to emergency admissions, ratio of public to 188 private provision) segmented linear regression models were fit, using the ‘glm’ command in Stata.

    Article Title: A common East-Asian ALDH2 mutation causes metabolic disorders and the therapeutic effect of ALDH2 activators
    Article Snippet: Energy expenditure (expressed as kcal/hr) was regressed on fat and lean mass using the command “glm” implemented in STATA 14.0 and the three genotypes were coded by allelic doses as 0,1, and 2 for WT, HE and KI mice, respectively , .

    Article Title: Environmental variables serve as predictors of the invasive Asian longhorned tick (Haemaphysalis longicornis Neumann): An approach for targeted tick surveillance.
    Article Snippet: First, the STATA glm command was used to investigate the univariable associations between each potential predictor and the outcome under a Poisson distribution, applying a relaxed alpha level of 0.10.

    Article Title: The Healthcare Cost Burden of Asthma in Children: A Longitudinal Population-Based Study.
    Article Snippet: Objectives: This study aimed to investigate the extent of healthcare cost increase at population level due to childhood asthma.. We aimed to investigate the cross-sectional relationship between asthma and healthcare costs among children aged 2 to 18 years and, in longitudinal analyses, whether costs increase with an increase in the duration of asthma prevalence.. Methods: Study participants are 4175 and 4482 children of birth and kindergarten cohorts from the nationally representative Longitudinal Study of Australian Children for whom the linked Medicare cost data are available.

    Article Title: A common East-Asian ALDH2 mutation causes metabolic disorders and the therapeutic effect of ALDH2 activators
    Article Snippet: Energy expenditure (expressed as kcal/hr) of each mouse was regressed on fat and lean mass using the command “glm” implemented in STATA 14.0 and the three genotypes were coded by allelic doses as 0,1, and 2 for wild-type, heterozygous, and homozygous knock-in mice, respectively.

    Article Title: Micronutrient deficiencies among preschool-aged children and women of reproductive age worldwide: a pooled analysis of individual-level data from population-representative surveys
    Article Snippet: Regressions were fit using the glm command, Stata version 16.1.

    Article Title: Impact of pausing elective hip and knee replacement surgery during winter 2017 on subsequent service provision at a major NHS Trust: a descriptive observational study using interrupted time series.
    Article Snippet: We explored seasonality in the data by including indicator variables for spring, summer and autumn19 compared with winter as a baseline, and adjusted for serial autocorrelation using Newey- West standard errors with a maximum lag of two.20–22 For count or proportion outcomes (number of admissions, proportion women, proportion with 2+ comorbidities, proportion in top two deprivation quintiles, bed occupancy), segmented Poisson regression models were fit to the data, while for averages/ratios (average age, average length of stay, ratio of elective to emergency admissions, ratio of public to private provision), segmented linear regression models were fit, using the ‘glm’ command in Stata.

    Modification:

    Article Title: Influence of case definitions on epidemiological estimates of periodontitis prevalence and its associations with smoking and OHRQoL.
    Article Snippet: First, we identified (using SPSS) those with periodontitis using a number of case definitions, after which weighted estimates were generated using Stata. .. Second, we examined the strength of the association between periodontitis and cigarette smoking, an acknowledged important risk factor for periodontitis;21 this used cross- tabulations (and Chi- squared statistics) followed by multivariate modelling which computed the smoking- associated prevalence ratios for periodontitis, using the GLM command in Stata with a modified Poisson approach using Periodontitis case definition Requirements Attachment level (CAL) Probing depth (PD) CDC ‘moderate’ ≥2 interproximal sites with CAL ≥4 mm (not on the same tooth) OR ≥2 interproximal sites with PD ≥5 mm (not on the same tooth) CDC ‘severe’ ≥2 interproximal sites with CAL ≥6 mm AND ≥1 interproximal site with PD ≥ 5mm NHANES 1999 ≥3 sites with CAL ≥4 mm AND ≥2 sites with PD ≥3 mm 2009 NZOHS ≥1 site with CAL ≥4 mm 5th European workshop ≥2 interproximal sites with CAL ≥3 mm (not on same tooth) WHO CPI 4 ≥1 site with PD ≥6 mm (all teeth) WHO CPI 4 index teeth ≥1 site with PD ≥6 mm (index teeth) Machtei et al., 1992 ≥2 teeth with CAL ≥6 mm AL AND ≥1 site with PD ≥5 mm 2018 New classification stage II ≥2 interproximal sites with CAL of 3- 4 mm (not on same or adjacent tooth) 2018 New classification stage III ≥2 interproximal sites with CAL ≥5 mm (not on same or adjacent tooth) TA B L E 1 Periodontitis case definitions used in this study nloaded from https://onlinelibrary.w iley.com /doi/10.1111/cdoe.12726 by C A PE S, W iley O nline L ibrary on [25/02/2024]. ..



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    Antibody response <t>after</t> <t>SARS-CoV-2</t> vaccination in MTX-m ( n = 72), MTX-1ww ( n = 71) and MTX-2ww ( n = 73) groups separated in previously uninfected (no COVID-19) and previously infected (COVID-19) individuals. Results are represented as increment (Δ) of AU/mL between pre-vaccination and post-vaccination samples (1 month after complete vaccination), after adjusting for confounding variables by means of <t>glm</t> (left). Cuzick test was used to assess statistical significance. * p < 0.05, *** p < 0.001. Percentage of poor (below 25 th percentile response), moderate (25 th to 50 th percentile) and good responders (greater than 50 th percentile) to vaccination across groups (right). Fisher exact test was used to check statistical significance. Box plots represent the interquartile ranges, horizontal lines indicate the medians, and error bars extend to the minimum and maximum observed values.
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    Antibody response <t>after</t> <t>SARS-CoV-2</t> vaccination in MTX-m ( n = 72), MTX-1ww ( n = 71) and MTX-2ww ( n = 73) groups separated in previously uninfected (no COVID-19) and previously infected (COVID-19) individuals. Results are represented as increment (Δ) of AU/mL between pre-vaccination and post-vaccination samples (1 month after complete vaccination), after adjusting for confounding variables by means of <t>glm</t> (left). Cuzick test was used to assess statistical significance. * p < 0.05, *** p < 0.001. Percentage of poor (below 25 th percentile response), moderate (25 th to 50 th percentile) and good responders (greater than 50 th percentile) to vaccination across groups (right). Fisher exact test was used to check statistical significance. Box plots represent the interquartile ranges, horizontal lines indicate the medians, and error bars extend to the minimum and maximum observed values.
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    Antibody response after SARS-CoV-2 vaccination in MTX-m ( n = 72), MTX-1ww ( n = 71) and MTX-2ww ( n = 73) groups separated in previously uninfected (no COVID-19) and previously infected (COVID-19) individuals. Results are represented as increment (Δ) of AU/mL between pre-vaccination and post-vaccination samples (1 month after complete vaccination), after adjusting for confounding variables by means of glm (left). Cuzick test was used to assess statistical significance. * p < 0.05, *** p < 0.001. Percentage of poor (below 25 th percentile response), moderate (25 th to 50 th percentile) and good responders (greater than 50 th percentile) to vaccination across groups (right). Fisher exact test was used to check statistical significance. Box plots represent the interquartile ranges, horizontal lines indicate the medians, and error bars extend to the minimum and maximum observed values.

    Journal: NPJ Vaccines

    Article Title: Beneficial effect of temporary methotrexate interruption on B and T cell responses upon SARS-CoV-2 vaccination in patients with rheumatoid arthritis or psoriatic arthritis

    doi: 10.1038/s41541-024-00805-3

    Figure Lengend Snippet: Antibody response after SARS-CoV-2 vaccination in MTX-m ( n = 72), MTX-1ww ( n = 71) and MTX-2ww ( n = 73) groups separated in previously uninfected (no COVID-19) and previously infected (COVID-19) individuals. Results are represented as increment (Δ) of AU/mL between pre-vaccination and post-vaccination samples (1 month after complete vaccination), after adjusting for confounding variables by means of glm (left). Cuzick test was used to assess statistical significance. * p < 0.05, *** p < 0.001. Percentage of poor (below 25 th percentile response), moderate (25 th to 50 th percentile) and good responders (greater than 50 th percentile) to vaccination across groups (right). Fisher exact test was used to check statistical significance. Box plots represent the interquartile ranges, horizontal lines indicate the medians, and error bars extend to the minimum and maximum observed values.

    Article Snippet: To determine which variables could explain variability in humoral and cellular response to SARS-CoV-2 vaccination, we performed a multivariable linear regression model using generalised linear models (GLM) for each dependent variable by using the Stata glm command.

    Techniques: Infection