Patient perceptions of balance in prostate cancer screening decision aids

Purpose. Recent reports reveals that studies of decision aids reported concern about the balance and accuracy of information included in decision aids. This study explores measures of balance in patient decision aids through a review of prostate cancer screening decision aid studies and analysis of patients’ rating of a patient decision aid for prostate cancer screening. ^ Methods. A data-abstraction form was used to collect the key characteristics, pertaining to balance, of studies included in the review. The key characteristics included (1) sample characteristics (age, race, family history of prostate cancer, and education), (2) description of the decision aid and how it was implemented, and (3) if a measure of balance was used for process evaluation and the rating. A summary table was used to report the findings. Deidentified data was received from a decision aid control trial and logistic regression analysis was used to test the association between the dependent variable (balance) and the independent variables (age, family history, race, screening preference at baseline, education, health insurance status). ^ Conclusion. Three sociodemographic variables remained significant in the final regression model: African American race, education and PSA history. Further research is needed to determine if these variables can predict a man’s perception of balance in prostate cancer screening decision aids. If a patient’s perceptions of balance can be predicted based on specific characteristics, patient report may not be the most objective method of evaluating the acceptability of a decision.^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^