The role of coping in the survival of cancer is a controversial topic. To specifiy the influence of coping on survival time, we conducted a longitudinal, prospective and observational study. In a preoperative intervie...The role of coping in the survival of cancer is a controversial topic. To specifiy the influence of coping on survival time, we conducted a longitudinal, prospective and observational study. In a preoperative interview, 105 patients with primary bladder cancer were asked about their active and depressive coping strategies. Ten years later, the survival rate was recorded;in cases of death, it was noted whether or not it was in consequence of the bladder cancer. Kaplan-Meier analyses of the collected data revealed a mean survival rate of about 60% after 10 years. Cox regression demonstrated no significant effect for active or depressive coping when tumour stage was controlled for. Patients who presented with high values for either of the coping strategies lived only slightly longer than those with low values. Therefore, it can be concluded that preoperative coping does not seem to demonstrate an important role for survival in bladder cancer.展开更多
Currently, a growing number of programs become available in statistical software for multiple imputation of missing values. Among others, two algorithms are mainly implemented: Expectation Maximization (EM) and Multip...Currently, a growing number of programs become available in statistical software for multiple imputation of missing values. Among others, two algorithms are mainly implemented: Expectation Maximization (EM) and Multiple Imputation by Chained Equations (MICE). They have been shown to work well in large samples or when only small proportions of missing data are to be imputed. However, some researchers have begun to impute large proportions of missing data or to apply the method to small samples. A simulation was performed using MICE on datasets with 50, 100 or 200 cases and four or eleven variables. A varying proportion of data (3% - 63%) was set as missing completely at random and subsequently substituted using multiple imputation by chained equations. In a logistic regression model, four coefficients, i.e. non-zero and zero main effects as well as non-zero and zero interaction effects were examined. Estimations of all main and interaction effects were unbiased. There was a considerable variance in the estimates, increasing with the proportion of missing data and decreasing with sample size. The imputation of missing data by chained equations is a useful tool for imputing small to moderate proportions of missing data. The method has its limits, however. In small samples, there are considerable random errors for all effects.展开更多
文摘The role of coping in the survival of cancer is a controversial topic. To specifiy the influence of coping on survival time, we conducted a longitudinal, prospective and observational study. In a preoperative interview, 105 patients with primary bladder cancer were asked about their active and depressive coping strategies. Ten years later, the survival rate was recorded;in cases of death, it was noted whether or not it was in consequence of the bladder cancer. Kaplan-Meier analyses of the collected data revealed a mean survival rate of about 60% after 10 years. Cox regression demonstrated no significant effect for active or depressive coping when tumour stage was controlled for. Patients who presented with high values for either of the coping strategies lived only slightly longer than those with low values. Therefore, it can be concluded that preoperative coping does not seem to demonstrate an important role for survival in bladder cancer.
基金supported by the Stiftung Rheinland-Pfalz fur Innovation(959).
文摘Currently, a growing number of programs become available in statistical software for multiple imputation of missing values. Among others, two algorithms are mainly implemented: Expectation Maximization (EM) and Multiple Imputation by Chained Equations (MICE). They have been shown to work well in large samples or when only small proportions of missing data are to be imputed. However, some researchers have begun to impute large proportions of missing data or to apply the method to small samples. A simulation was performed using MICE on datasets with 50, 100 or 200 cases and four or eleven variables. A varying proportion of data (3% - 63%) was set as missing completely at random and subsequently substituted using multiple imputation by chained equations. In a logistic regression model, four coefficients, i.e. non-zero and zero main effects as well as non-zero and zero interaction effects were examined. Estimations of all main and interaction effects were unbiased. There was a considerable variance in the estimates, increasing with the proportion of missing data and decreasing with sample size. The imputation of missing data by chained equations is a useful tool for imputing small to moderate proportions of missing data. The method has its limits, however. In small samples, there are considerable random errors for all effects.
