Effect of with and without categorization of continuous variables on the number and nature of statistically significant predictors was examined while analyzing clinical trial data. The number of categories required to...Effect of with and without categorization of continuous variables on the number and nature of statistically significant predictors was examined while analyzing clinical trial data. The number of categories required to have consistent statistical inference was also explored. Multiple Logistic Regression Analysis was employed with the dependent variable in the model may be a dichotomous/multi-category in nature while the independent variables （predictors） may be either continuous or categorical or ordinal. Real-life clinical trial data was used to answer the objectives. It was found that there was no hard and fast rule to categorize the continuous variables. Sometimes, it was observed that the set of significant predictors identified might change with the criteria of categorization. Certain variables without categorization produced too large odds ratios to interpret meaningfully. The nature as well as number of significant predictors altered with classification criteria often forcing the authors to categorize variables, it is recommended that the independent variables need not be coded, unless otherwise warranted. Coding is needed when the odds ratio is extremely high. In this situation, two or more categories, including regression analysis. median cut off point, will be sufficient to undertake the logistic展开更多
文摘Effect of with and without categorization of continuous variables on the number and nature of statistically significant predictors was examined while analyzing clinical trial data. The number of categories required to have consistent statistical inference was also explored. Multiple Logistic Regression Analysis was employed with the dependent variable in the model may be a dichotomous/multi-category in nature while the independent variables （predictors） may be either continuous or categorical or ordinal. Real-life clinical trial data was used to answer the objectives. It was found that there was no hard and fast rule to categorize the continuous variables. Sometimes, it was observed that the set of significant predictors identified might change with the criteria of categorization. Certain variables without categorization produced too large odds ratios to interpret meaningfully. The nature as well as number of significant predictors altered with classification criteria often forcing the authors to categorize variables, it is recommended that the independent variables need not be coded, unless otherwise warranted. Coding is needed when the odds ratio is extremely high. In this situation, two or more categories, including regression analysis. median cut off point, will be sufficient to undertake the logistic
