Abstract 189: Expressing Continuous Cardiovascular Outcome Data in Absolute Terms for use in Patient Treatment Decision Aids: Validation of a Proposed Method

Background: Patient decision aids (DA) supplement advice from health care professionals through provision of information on the likelihood of risks and benefits of treatment options. Ideally, these data are expressed in both visual and written form to communicate absolute risk or benefit (i.e., X/100). However, this well-accepted methodology lends itself to outcomes which are binary only. Valid methods for conversion of continuous outcomes for presentation in DAs are not well-established. The difficulty in converting these outcomes is compounded by the need to extract and pool aggregate standardized score data from multiple published sources to generate a single estimate of treatment effect. Our team was met with this challenge when developing a DA for refractory angina; the main outcome was angina frequency (AF) as measured by the AF subscale of the Seattle Angina Questionnaire. We aimed to develop and test the validity of a proposed method, based on statistical theory, for estimating absolute angina reduction based on AF scores. Methods: AF summary statistics, M(SD), were extracted from 2 distinct intervention studies for which raw data were accessible. A clinically important difference in AF scores was identified through expert consultation. Based on normal distribution theory, aggregate data from both studies were used to estimate the proportion of those who experienced a clinically significant change in AF. Chi-square comparisons of proportions was then used to determine if these estimates were dissimilar from true numbers reflected in the raw data. We then generated 500,000 simulated datasets using the same AF summary statistics, but with widely varying distribution characteristics (e.g., positive skew). Multiple comparisons of estimates to actual proportions of those experiencing a clinically significant change in AF were generated under simulated scenarios and expressed graphically. The effect of summary statistic on these comparisons was examined by conducting the simulations using both M(SD) and medians and interquartile ranges (IQR). Results: Overall agreement between estimated and actual proportions of those experiencing a clinically significant change in AF was excellent for the real study data; there were no significant differences (p >0.45), and no difference exceeded 5%. These results remained stable when data were pooled from the 2 studies using meta-analysis. For the simulated data sets, concordance between the estimated and actual proportions was also moderate to good in cases where data were not highly skewed (i.e. skewness <2.5); agreement improved in the context of medians and IQRs. Conclusion: Our results suggest that standard statistical theory can be used to estimate continuous outcomes in absolute terms with reasonable accuracy for use in DAs; caution is advised if outcomes are expected to be highly skewed in distribution.