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
The primary aim of clinical trials is to investigate whether a treatment is effective for a particular disease or condition. Randomized controlled clinical trials are considered to be the gold standard for evaluating ...The primary aim of clinical trials is to investigate whether a treatment is effective for a particular disease or condition. Randomized controlled clinical trials are considered to be the gold standard for evaluating the effect of a certain intervention. However, in clinical trials, even after randomization, there are situations where the patients differ substantially with respect to the baseline value of the outcome variable. Many a times the response to interventions depends on the baseline values of the outcome variable. When there are baseline-dependent treatment effects, differences among treatments vary as a function of baseline level. Although variation in outcome associated with baseline value is accounted for in ANCOVA, analysis of individual differences in treatment effect is precluded by the homogeneity of regression assumption. This assumption requires that expected differences in outcome among treatments be constant across all baseline levels. To overcome this difficulty, Weigel and Narvaez [7] proposed a regression model for two treatment groups to analyze individual response to treatments in randomized controlled clinical trials. The authors reviewed the model suggested by Weigel and Narvaez and extended further for three or more treatment groups. The utility of the model was demonstrated with real life data from a randomized controlled clinical trial of bronchial asthma.展开更多
Often many variables have to be analyzed for their importance in terms of significant contribution and predictability in medical research. One of the possible analytical tools may be the Multiple Linear Regression Ana...Often many variables have to be analyzed for their importance in terms of significant contribution and predictability in medical research. One of the possible analytical tools may be the Multiple Linear Regression Analysis. However, research papers usually report both univariate and multivariate regression analyses of the data. The biostatistician sometimes faces practical difficulties while selecting the independent variables for logical inclusion in the multivariate analysis. The selection criteria for inclusion of a variable in the multivariate regression is that the variable at the univariate level should have a regression coefficient with p 〈 0.20. However, there is a chance that an independent variable with p 〉 0.20 at univariate regression may become significant in the multivariate regression analysis and vice versa, provided the above criteria is not strictly adhered to. We undertook both univariate and multivariate linear regression analyses on data from two multi-centric clinical trials. We recommend that there is no need to restrict the p value of 〈= 0.20. Because of high speed computer and availability of statistical software, the desired results could be achieved by considering all relevant independent variables in multivariate regression analysis.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
文摘The primary aim of clinical trials is to investigate whether a treatment is effective for a particular disease or condition. Randomized controlled clinical trials are considered to be the gold standard for evaluating the effect of a certain intervention. However, in clinical trials, even after randomization, there are situations where the patients differ substantially with respect to the baseline value of the outcome variable. Many a times the response to interventions depends on the baseline values of the outcome variable. When there are baseline-dependent treatment effects, differences among treatments vary as a function of baseline level. Although variation in outcome associated with baseline value is accounted for in ANCOVA, analysis of individual differences in treatment effect is precluded by the homogeneity of regression assumption. This assumption requires that expected differences in outcome among treatments be constant across all baseline levels. To overcome this difficulty, Weigel and Narvaez [7] proposed a regression model for two treatment groups to analyze individual response to treatments in randomized controlled clinical trials. The authors reviewed the model suggested by Weigel and Narvaez and extended further for three or more treatment groups. The utility of the model was demonstrated with real life data from a randomized controlled clinical trial of bronchial asthma.
文摘Often many variables have to be analyzed for their importance in terms of significant contribution and predictability in medical research. One of the possible analytical tools may be the Multiple Linear Regression Analysis. However, research papers usually report both univariate and multivariate regression analyses of the data. The biostatistician sometimes faces practical difficulties while selecting the independent variables for logical inclusion in the multivariate analysis. The selection criteria for inclusion of a variable in the multivariate regression is that the variable at the univariate level should have a regression coefficient with p 〈 0.20. However, there is a chance that an independent variable with p 〉 0.20 at univariate regression may become significant in the multivariate regression analysis and vice versa, provided the above criteria is not strictly adhered to. We undertook both univariate and multivariate linear regression analyses on data from two multi-centric clinical trials. We recommend that there is no need to restrict the p value of 〈= 0.20. Because of high speed computer and availability of statistical software, the desired results could be achieved by considering all relevant independent variables in multivariate regression analysis.
