Objective To assess whether the results of clinical trials on systematic reviews presented in different ways would influence postgraduates’ perception of risk and clinical decision after attending a research design course. Methods We distributed a questionnaire to all postgraduates who attended the final examination. The questionnaire presented the results of a systematic review. Data were presented in four different ways in the following order: as a relative risk reduction (RRR), as an absolute risk reduction (ARR), as the proportion of difference in event-free patients (EFP), and as the number of patients who needed to be treated to prevent one death (NNT). We asked all postgraduates to mark their decisions along a linear scale. Results We distributed and retrieved 342 questionnaires. Three were incomplete and excluded from our analyses. The results showed that the mean score and recommended level were significantly higher when data were expressed as NNT compared with RRR, ARR and EFP (Plt;0.01). There was no difference among RRR, ARR and EFP. However, 279 postgraduates’ score ranges were greater than 4 among the four different presentations. Conclusion The way of presenting data has significant influence on postgraduates’ perception of risk and their clinical decisions, even after a course teaching them about research design. Further improvements are needed for teachers on how to interprete different ways of presenting risk and their clinical importance.
ObjectivesTo explore the construction method of prediction model of absolute risk for breast cancer and provide personalized breast cancer management strategies based on the results.MethodsA case-control design was conducted with 2 747 individuals diagnosed as primary breast cancer by pathology in West China Hospital of Sichuan University from 2000 to 2017 and 6 307 healthy controls from Breast Cancer Screening Cohort in Sichuan Women and Children Center and Chengdu Shuangliu District Maternal and Child Health Hospital. Standardized questionnaires and information management systems in hospital were used to collect information. Decision trees, logistic regression, the formula in Gail model and registration data in China were used to estimate the probability of 5-year risk of breast cancer. Eventually a ROC (receiver operating characteristics) curve was drawn to identify optimal cut-off value, and the power was evaluated.ResultsThe decision tree exported 4 variables, which were urban or rural sources, number of live birth, age and age at menarche. The median 5-year risk and interquartile range of the controls was 0.027% and 0.137%, while the median 5-year risk and interquartile range of the cases was 0.219% and 0.256%. The ROC curve showed the cut-off value was 0.100%. Through verification, the sensitivity was 0.79, the specificity was 0.73, the accuracy was 0.75, and the AUC (area under the curve) was 0.79.ConclusionsThe methods used in our study based on 9 054 female individuals in Sichuan province could be used to predict the 5-year risk for breast cancer. Predictor variables include urban or rural sources, number of live birth, age, and age at menarche. If the 5-year risk is more than 0.100%, the person will be judged as a high risk individual.