function nlcom  (STATA Corporation)

Journal: Scientific Reports

Article Title: Allometric fat mass index and alanine aminotransferase attenuate the associations of platelet parameters with lung cancer risk

doi: 10.1038/s41598-024-78281-x

Figure Lengend Snippet: Additive interactions of body composition indices or alanine aminotransferase with platelet parameters (men). AFI: allometric fat-mass index (cut-off: ≥13.703); ALT: alanine aminotransferase (cut-off: ≥28.65 IU/L); BMI: body mass index (cut-off: ≥28.982 kg/m 2 ); CI: confidence interval; HR: hazard ratio; MPV: mean platelet volume (cut-off: ≥9.17 fL); PLT: platelet count (cut-off: ≥234.0*10 9 /L); RERI: relative excess risk from interaction (additive interaction); cases: number of lung cancer cases; rate: incidence rate per 1*10 6 person years; p-value: p-value for RERI derived with the delta method or p-value from Wald test for the individual term. Cox proportional hazards models including a cross-classification between either PLT or MPV (dichotomised at the sex-specific median) and one of BMI, AFI, or ALT in men (dichotomised at the upper sex-specific tertile cut-off), stratified by age at recruitment, region, and smoking status and intensity, and adjusted for height, recent weight gain, alcohol consumption, physical activity, Townsend deprivation index, family history of lung cancer, time of blood collection, fasting time, diabetes, and use of lipid-lowering drugs, antihypertensive drugs, antiaggregant/anticoagulants, and paracetamol.

Article Snippet: We calculated the relative excess risk from interaction (RERI) and obtained confidence intervals and p-values with the delta method applied in function nlcom in STATA-13 : RERI = HR High−High – HR High−Low – HR Low−High + 1.

Techniques: Derivative Assay, Activity Assay

Journal: Nature Communications

Article Title: Identifying and characterizing pesticide use on 9,000 fields of organic agriculture

doi: 10.1038/s41467-021-25502-w

Figure Lengend Snippet: Lognormal hurdle models estimating the change in the probability of pesticide use ( a ) and the percent change in pesticide use for fields with positive use ( b ) for organic relative to conventional fields. The x -axis indicates different measures of pesticide use outcomes: kg ha −1 active ingredients (AI), kg ha − 1 products (Prd), kg ha −1 of products targeting insect pests only (Insect), kg ha −1 of products with a propensity to drift (Drift), kg ha −1 products of potential hazard to fish and bees (Fish, Bee), as well as products of higher (EPA signal word 1–2) and lower (EPA signal word 3–4) acute human toxicity (High, Low). Across all outcomes, organic fields have a significantly lower probability of using pesticides ( a ), though there is little difference between organic and conventional fields for those that do spray, with the exception of higher and lower toxicity chemicals ( b ). Symbols indicate point estimates (mean) and error bars represent the 95% CI. All models include cluster robust standard errors clustered at the farm-by-crop family level. For the second hurdle ( b ) in Figs. 2– , percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata. All models include covariates for field size, farm size, and soil quality as well farm-by-crop family random effects. N = 91,926 for all specifications in the first hurdle ( a ) and N = 68,704 (AI), N = 68,816 (Prd), N = 52,606 (Insect.), N = 67,988 (Drift), N = 60,653 (Fish), N = 48,254 (Bee), N = 61,883 (High), and N = 65,593 (Low) in the second hurdle ( b ), where abbreviations are as described above. Coefficient estimates for all covariates are provided in Supplementary Table .

Article Snippet: For the second hurdle ( b ), percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata.

Techniques: Pesticides, Derivative Assay

Journal: Nature Communications

Article Title: Identifying and characterizing pesticide use on 9,000 fields of organic agriculture

doi: 10.1038/s41467-021-25502-w

Figure Lengend Snippet: Correcting for yield gaps does not affect the first hurdle ( a ), but does shift the coefficient estimates in the second hurdle up ( b ) relative to the unadjusted model (Fig. ). Figure details are otherwise the same as Fig. . The x -axis indicates different pesticide use outcomes: kg ha −1 active ingredients (AI), kg ha −1 products (Prd), kg ha −1 of products targeting insect pests only (Insect), kg ha − 1 of products with a propensity to drift (Drift), kg ha − 1 products of potential hazard to fish and bees (Fish, Bee), as well as products of higher (EPA signal word 1–2) and lower (EPA signal word 3–4) acute human toxicity (High, Low). Symbols indicate point estimates (mean) and error bars represent the 95% CI. All models include cluster robust standard errors clustered at the farm-by-crop family level. For the second hurdle ( b ), percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata. All models include covariates for field size, farm size, and soil quality as well as farm-by-crop family random effects. N = 91,926 for all specifications in the first hurdle ( a ) and N = 68,704 (AI), N = 68,816 (Prd), N = 52,606 (Insect.), N = 67,988 (Drift), N = 60,653 (Fish), N = 48,254 (Bee), N = 61,883 (High), and N = 65,593 (Low) in the second hurdle ( b ). Coefficient estimates for all covariates are provided in Supplementary Table .

Article Snippet: For the second hurdle ( b ), percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata.

Techniques: Derivative Assay

Journal: Nature Communications

Article Title: Identifying and characterizing pesticide use on 9,000 fields of organic agriculture

doi: 10.1038/s41467-021-25502-w

Figure Lengend Snippet: Across all five crops, organic fields have a lower probability of using any pesticide active ingredients ( a ). The effect of organic on pesticide use for fields that do spray is crop-dependent ( b ). For the second hurdle ( b ), percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata. Symbols indicate point estimates (mean) and error bars represent the 95% CI. All models include heteroskedasticity robust standard errors. All models include covariates for field size, farm size, and soil quality, as well as year random intercepts. For the first hurdle, N = 4289 (Carrot), N = 8760 (Grape), N = 4654 (Orange), N = 2804 (Potato), N = 1126 (Onion). For the second hurdle, N = 2766 (Carrot), N = 7678 (Grape), N = 4316 (Orange), N = 2059 (Potato), N = 814 (Onion). Coefficient estimates for all covariates are provided in Supplementary Table .

Article Snippet: For the second hurdle ( b ), percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata.

Techniques: Derivative Assay

Journal: Nature Communications

Article Title: Identifying and characterizing pesticide use on 9,000 fields of organic agriculture

doi: 10.1038/s41467-021-25502-w

Figure Lengend Snippet: Lognormal hurdle models estimating the change in the probability of pesticide use ( a ) and the percent change in pesticide use for fields with positive use ( b ) for organic relative to conventional fields. The x -axis indicates different measures of pesticide use outcomes: kg ha −1 active ingredients (AI), kg ha − 1 products (Prd), kg ha −1 of products targeting insect pests only (Insect), kg ha −1 of products with a propensity to drift (Drift), kg ha −1 products of potential hazard to fish and bees (Fish, Bee), as well as products of higher (EPA signal word 1–2) and lower (EPA signal word 3–4) acute human toxicity (High, Low). Across all outcomes, organic fields have a significantly lower probability of using pesticides ( a ), though there is little difference between organic and conventional fields for those that do spray, with the exception of higher and lower toxicity chemicals ( b ). Symbols indicate point estimates (mean) and error bars represent the 95% CI. All models include cluster robust standard errors clustered at the farm-by-crop family level. For the second hurdle ( b ) in Figs. 2– , percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata. All models include covariates for field size, farm size, and soil quality as well farm-by-crop family random effects. N = 91,926 for all specifications in the first hurdle ( a ) and N = 68,704 (AI), N = 68,816 (Prd), N = 52,606 (Insect.), N = 67,988 (Drift), N = 60,653 (Fish), N = 48,254 (Bee), N = 61,883 (High), and N = 65,593 (Low) in the second hurdle ( b ), where abbreviations are as described above. Coefficient estimates for all covariates are provided in Supplementary Table .

Article Snippet: For the second hurdle ( b ) in Figs. 2– , percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata.

Techniques: Pesticides, Derivative Assay

Journal: Nature Communications

Article Title: Identifying and characterizing pesticide use on 9,000 fields of organic agriculture

doi: 10.1038/s41467-021-25502-w

Figure Lengend Snippet: Correcting for yield gaps does not affect the first hurdle ( a ), but does shift the coefficient estimates in the second hurdle up ( b ) relative to the unadjusted model (Fig. ). Figure details are otherwise the same as Fig. . The x -axis indicates different pesticide use outcomes: kg ha −1 active ingredients (AI), kg ha −1 products (Prd), kg ha −1 of products targeting insect pests only (Insect), kg ha − 1 of products with a propensity to drift (Drift), kg ha − 1 products of potential hazard to fish and bees (Fish, Bee), as well as products of higher (EPA signal word 1–2) and lower (EPA signal word 3–4) acute human toxicity (High, Low). Symbols indicate point estimates (mean) and error bars represent the 95% CI. All models include cluster robust standard errors clustered at the farm-by-crop family level. For the second hurdle ( b ), percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata. All models include covariates for field size, farm size, and soil quality as well as farm-by-crop family random effects. N = 91,926 for all specifications in the first hurdle ( a ) and N = 68,704 (AI), N = 68,816 (Prd), N = 52,606 (Insect.), N = 67,988 (Drift), N = 60,653 (Fish), N = 48,254 (Bee), N = 61,883 (High), and N = 65,593 (Low) in the second hurdle ( b ). Coefficient estimates for all covariates are provided in Supplementary Table .

Article Snippet: For the second hurdle ( b ) in Figs. 2– , percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata.

Techniques: Derivative Assay

Journal: Nature Communications

Article Title: Identifying and characterizing pesticide use on 9,000 fields of organic agriculture

doi: 10.1038/s41467-021-25502-w

Figure Lengend Snippet: Across all five crops, organic fields have a lower probability of using any pesticide active ingredients ( a ). The effect of organic on pesticide use for fields that do spray is crop-dependent ( b ). For the second hurdle ( b ), percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata. Symbols indicate point estimates (mean) and error bars represent the 95% CI. All models include heteroskedasticity robust standard errors. All models include covariates for field size, farm size, and soil quality, as well as year random intercepts. For the first hurdle, N = 4289 (Carrot), N = 8760 (Grape), N = 4654 (Orange), N = 2804 (Potato), N = 1126 (Onion). For the second hurdle, N = 2766 (Carrot), N = 7678 (Grape), N = 4316 (Orange), N = 2059 (Potato), N = 814 (Onion). Coefficient estimates for all covariates are provided in Supplementary Table .

Article Snippet: For the second hurdle ( b ) in Figs. 2– , percent change is calculated from the log-level model as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100(e^{\beta }-1)$$\end{document} 100 ( e β − 1 ) and standard errors are derived using the delta-method implemented with the nlcom function in Stata.

Techniques: Derivative Assay