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R a language and environment for statistical computing version 4

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R is an open-source programming language and software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more. R is designed to be highly extensible, with a large and active user community that provides a vast array of packages and libraries for specific applications.

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R: A Language and Environment for Statistical Computing R language and environment for statistical computing R environment for statistical computing version 4.1.2 R language for statistical computing R environment for statistical computing R statistical language and environment R statistical computing environment R Statistical language, version 4.3.0 R statistical environment, version 4.1.2 R language, version 4.1.0

4 protocols using r a language and environment for statistical computing version 4

1

ALS Risk Factors Analysis

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We started by testing for univariate associations between the response variable, ALS case–control status of participants, and categorical predictor variables by utilizing the chi-square test of independence and Fisher’s exact test. Multivariable modeling used case–control status as the outcome in an unconditional logistic regression analysis with adjustment for the potential confounders age, gender, family history, and smoking status. The index year was defined as the year of diagnosis for ALS patients or an equivalent year for controls. Bayesian Kernal Mixture Modeling (BKMR) was performed to assess the possible interactions between income, mercury, and omega-3 PUFA estimates [29 (link)]. These analyses were all performed using R: A Language and Environment for Statistical Computing, version 4.02 (R Foundation for Statistical Computing, Vienna, Austria).
Hoffman H.I., Bradley W.G., Chen C.Y., Pioro E.P., Stommel E.W, & Andrew A.S. (2021). Amyotrophic Lateral Sclerosis Risk, Family Income, and Fish Consumption Estimates of Mercury and Omega-3 PUFAs in the United States. International Journal of Environmental Research and Public Health, 18(9), 4528.
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2

Effectiveness of Household Stove Ownership

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The analysis consisted of crude and adjusted linear mixed-effects models using a log transformation of PM2.5 as the dependent variable, stove ownership as the independent variable, and a random intercept for subject. The model was adjusted for biomass stove type and biomass cooking location, other sources of exposure to HAP, and sociodemographic factors. The effectiveness of stove ownership was defined as the percent difference in personal PM2.5 calculated with the formula:

The determinants of the effectiveness of stove ownership were investigated by adding an interaction term for each sociodemographic factor in the models. We visually tested the mixed-model assumptions by plotting the standardized residuals vs. fitted values to evaluate variance homogeneity and a quantile-quantile plot of the residuals to assess normality. We did not find major deviations from model assumptions. We used R: A Language and Environment for Statistical Computing, version 4.0.2 released on 2020-06-22 (R Foundation for Statistical Computing, Vienna, Austria) and RStudio: Integrated Development Environment for R, version 1.3.1073 released on 2020 (RStudio, PBC, Boston, MA, USA) for all analyses.
Grajeda L.M., Thompson L.M., Arriaga W., Canuz E., Omer S.B., Sage M., Azziz-Baumgartner E., Bryan J.P, & McCracken J.P. (2020). Effectiveness of Gas and Chimney Biomass Stoves for Reducing Household Air Pollution Pregnancy Exposure in Guatemala: Sociodemographic Effect Modifiers. International Journal of Environmental Research and Public Health, 17(21), 7723.
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3

Identifying Contaminant Exposures Associated with ALS

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ALS patients and controls in the SYMPHONY Integrated Dataverse® were randomly broken into two groups based on their zip3 region of residence (863 total regions). One group served as the ‘discovery’ cohort (500 zip3 regions), which we used to identify contaminant exposures estimated from the 2008 NEI database associated with ALS risk. To select contaminants for further study, we used False Discovery Rate (FDR) correction to account for multiple comparisons (268 airborne contaminants), and a significance threshold of <0.2. We then performed a ‘validation’ of these top-hit contaminants in the other independent group (the remaining 363 zip3 regions). Logistic regression analysis assessed the association between the log-transformed level of each contaminant and ALS risk. Odds ratios (OR) reflect the change in ALS risk associated with an increase in the level of a contaminant, using the natural log of 1.0 as the unit. At the validation stage, we used a FDR with a significance threshold of <0.05. We also evaluated combinations of contaminants associated with ALS, testing all combinations with a main effect of FDR <0.2. These analyses were all performed using R: A Language and Environment for Statistical Computing, version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria).
Represa J., Avila M.A., Miner C., Giraldez F., Romero G., Clemente R., Mato J.M, & Varela-Nieto I. (1991). Glycosyl-phosphatidylinositol/inositol phosphoglycan: a signaling system for the low-affinity nerve growth factor receptor.. Proceedings of the National Academy of Sciences of the United States of America, 88(18), 8016-8019.
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4

Validating Exposure Levels in Case-Control Study

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The subset of contaminants identified by the ‘discovery phase’ entered the ‘validation phase’ analysis, which used the residential history epoch estimates of exposure for the case-control study subjects. The median exposure estimate of each contaminant over the residence prior to diagnosis was binned into categories based on the quartile distribution of each contaminant in the controls. Chi-square tests assessed the univariate difference in proportion of cases and controls by quantile, followed by logistic regression analysis that adjusted for age and gender. These analyses were all performed using R: A Language and Environment for Statistical Computing, version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria).
Giavedoni L., Jones L., Mebus C, & Yilma T. (1991). A vaccinia virus double recombinant expressing the F and H genes of rinderpest virus protects cattle against rinderpest and causes no pock lesions.. Proceedings of the National Academy of Sciences of the United States of America, 88(18), 8011-8015.
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