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This paper proposes an approach to interpreting the sky conditions using the variables that are readily accessible from meteorological stations for many years.
A multivariate predictive model for mortality was constructed by using the variables that had significant interactions with the rate of demise (P ≤.1).
The final models were obtained by using the variables that minimized Akaike's Information Criterion AICC) [20].
The models were built using the variables that were found to be associated with these in bivariate analysis.
We performed univariate analyses to determine the magnitude of unadjusted associations and subsequently performed multivariate analyses using the variables that were deemed significant in the univariate analyses.
A multivariate Cox proportional hazard model for PFS and OS was built using the variables that were found significant at the univariate analysis.
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Another alternative is to use the variables that will be used in the analysis model in the imputation model (Schafer, 1997, Raghunathan et al., 2001).
The procedure starts by building a one variable regression model using the variable that has the highest correlation with the response variable y.
A multiple linear regression model was constructed using the variable that multiplied their respective hazard ratios in a Cox proportional hazards model: (Y= X1 × HR1+ X2 × HR2+…+ Xn × HRn).
Logarithmic transformation (ln) was used for the variables that were not normally distributed to make their distribution normal.
The chi-square test was performed for the categorical variables and Fisher's exact test was used for the variables that had few observations.
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