Surveys were distributed and collected personally; of the 127 surveys distributed, 100 surveys were collected. This translates to a response rate of 78.7%. Of those who responded, most were male urologists (47%). Some statistics, such as medical school, residency school and program, fellowship school and program, were too stratified for analysis and therefore left out ().
Comparative differences between groups
Analysis was done to compare outcomes between groupings. The four factors that were analyzed based on adequate numbers were sex, level of training, location of work and specialty. In terms of sex, we found that there was no significant difference in predictive accuracy between men and women (p = 0.27). Similarly, level of training, when broken down into medical student, resident, and attending physician (including all physicians who have completed residency), yielded no significant difference in predictive accuracy (p = 0.46). Location of work did not affect the ability to predict life expectancy (p = 0.48), and no particular specialty did significantly better (p = 0.24) (). However, post-hoc power analyses indicated that the study was not adequately powered to detect differences between these groups, with power ranging from a low of 8% for the comparison of medical student, resident and attending, to 31% for the comparison of men and women. These findings therefore need to be interpreted within the context of the limited study power.
Predictive accuracy between groups
Accuracy of prediction overall
General trends in responses showed that medical professionals generally underestimate their patients’ life expectancy. Across all 700 responses to the clinical scenarios, the difference between the actual and estimated life expectancy ranged from −19.3 to 13.6, with a mean of −2.0 years (standard deviation [SD] 6.1), The coefficient relating actual survival time to estimated survival time was −0.57, (p < 0.001), suggesting that physicians are more likely to underestimate life expectancy as actual survival time increases.
To understand how well we do overall at estimating life expectancy in the context of screening guidelines, a dichotomous accuracy analysis was done, plotting the percentage of correct responses against actual life expectancy cut-offs (). For example, if we categorized answers as being ≥10 years or <10 years and compared them to the patients’ actual survival time, overall, medical professionals predicted correctly only 68.1% of the time; within the overall result, medical students scored 75.6%, residents 70.9% and attending physicians 64.8%. Interestingly, when graphed with a line of best fit, we found that the life expectancy cut-off at which medical professionals predict the least accurately was at exactly 10 years.
In investigating how accurate we are in our non-dichotomous life expectancy predictions versus actual survival, we considered each case response as an individual instance rather than as a case within a series (n=700). We found that, overall, if we considered only the absolute values of our error, we were 67.4% inaccurate on average compared to actual survival (SD=90.4%). Within that average, medical students were 77.5% inaccurate (SD=106.4%); residents were 68.6% inaccurate (SD=97.0%); attending physicians were 63.8% inaccurate (SD=81.9%). Overall, the range of inaccuracy per respondent ranged from 26.7% to 149.3% in the predictions of the actual survival over the 7 case scenarios, with both the best and worst predictors being attending physicians (). Another way of expressing this is in how many years off our predictions are from actual survival. We also stratified the predictions by group (). Overall, respondents underestimated life expectancy by an average of 2.0 years (SD=5.2).
Accuracy of life expectancy predictions vs. actual survival (calculated using absolute predictive error)
Finally, though the analysis did not carry enough power of response, there was a positive trend between years in practice and non-dichotomous accuracy of life expectancy prediction (p = 0.122).