Preparation of the data set
Before performing the function, we have to make sure that the dataset is well structured.
To do so, we have to check 2 elements:
The health event (outcome) must be in the same dataframe as all the exposures: this will avoid making a model where we will include other outcomes.
The variables must be classified in the right way: for this, use ‘str()’.
str(PimaIndiansDiabetes)
#> 'data.frame': 768 obs. of 9 variables:
#> $ pregnant: num 6 1 8 1 0 5 3 10 2 8 ...
#> $ glucose : num 148 85 183 89 137 116 78 115 197 125 ...
#> $ pressure: num 72 66 64 66 40 74 50 0 70 96 ...
#> $ triceps : num 35 29 0 23 35 0 32 0 45 0 ...
#> $ insulin : num 0 0 0 94 168 0 88 0 543 0 ...
#> $ mass : num 33.6 26.6 23.3 28.1 43.1 25.6 31 35.3 30.5 0 ...
#> $ pedigree: num 0.627 0.351 0.672 0.167 2.288 ...
#> $ age : num 50 31 32 21 33 30 26 29 53 54 ...
#> $ diabetes: Factor w/ 2 levels "neg","pos": 2 1 2 1 2 1 2 1 2 2 ...Diabetes, which is our target health event, stands alone with exposures. In addition, the variables all have the correct class associated.
Determine the type of model you want to use
According to the class of the outcome, one model will be preferred to another. It is therefore necessary to choose the right model for the type of variable chosen as the health event.
We have seen previously that our health event is binary categorical: Diabetes (Yes/No).
str(PimaIndiansDiabetes$diabetes)
#> Factor w/ 2 levels "neg","pos": 2 1 2 1 2 1 2 1 2 2 ...We can therefore use a logistic regression model.
Use of the ELJAlogistic function
The approach for the logistic regression is similar for the models linear models with ELJAlinear function and for Generalized Linear Models with ELJAglm function.
The dataset being prepared and the type of model chosen, we can proceed to the analysis.
To do so, the following information are needed:
var: Outcome / health event; it must be categorical for a logistic regression model
data: Dataframe that contains the outcome and all the exposures to be tested
Other information can be added to the output of the function:
manplot: Indicates if it is desired to display a Manhattan plot of the results in the output of the function
nbvalmanplot: Indicates the number of values to display in the Manhattan plot (in order not to overload the graphs)
Bonferroni: Indicates if we want to display the Bonferroni threshold on the Manhattan plot
FDR : Indicates if you want to display the False Discovery Rate threshold according to the Benjamini-Hochberg method on the Manhattan plot
manplotsign : Indicates if you want to display a Manhattan plot containing only significant only the significant values with a p-value > 0.05.
ELJAlogistic(var = 'diabetes',data = PimaIndiansDiabetes,manplot = TRUE,
Bonferroni = TRUE,FDR = TRUE, nbvalmanplot = 30, manplotsign = FALSE)
results
#> level odd_ratio ci_low ci_high p_value n
#> pregnant_pregnant pregnant 1.147008 1.0970869 1.200315 2.147445e-09 768
#> glucose_glucose glucose 1.038599 1.0321816 1.045439 2.378098e-31 768
#> pressure_pressure pressure 1.007452 0.9994922 1.015902 7.299362e-02 768
#> triceps_triceps triceps 1.009911 1.0005344 1.019455 3.881576e-02 768
#> insulin_insulin insulin 1.002301 1.0010311 1.003607 4.353455e-04 768
#> mass_mass mass 1.098044 1.0730012 1.124942 8.449577e-15 768
#> pedigree_pedigree pedigree 2.953073 1.8799770 4.713627 3.702926e-06 768
#> age_age age 1.042922 1.0296867 1.056659 1.773155e-10 768
#> AIC
#> pregnant_pregnant 960.2099
#> glucose_glucose 812.7196
#> pressure_pressure 994.1276
#> triceps_triceps 993.1890
#> insulin_insulin 984.8104
#> mass_mass 924.7142
#> pedigree_pedigree 974.8609
#> age_age 954.7203We observe a Manhattan plot showing the results of the EWAS analysis and a dataframe showing the more detailed results.
References
- Dunn OJ. Multiple Comparisons Among Means. Journal of the American Statistical Association. 1961;56(293):52‑64.
- Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289‑300.
- MLBench · Distributed Machine Learning Benchmark. Available from: https://mlbench.github.io/
- Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proc Annu Symp Comput Appl Med Care. 1988 Nov 9;261–5.