Co-calibration of two self-reported measures of adherence to antiretroviral therapy
Adherence to antiretroviral therapy (ART) is an important determinant of clinical success assessed in many HIV studies. Harmonizing adherence data from studies that use different measures is difficult without a co-calibration equation to convert between validated instruments. Our purpose was to co-calibrate two commonly used adherence measures: the AIDS Clinical Trials Group (ACTG) questionnaire and the Visual Analog Scale (VAS). We used robust linear regression to develop a co-calibration equation in a clinical care cohort. The outcome was the 30-day VAS percentage of ART taken and the predictors were ACTG questions. We evaluated the equation's goodness of fit in five STTR (Seek, Test, Treat, Retain) consortium studies where individuals completed both measures: 2 criminal justice; 2 international; and 1 other high-risk vulnerable population. We developed a three-phase decision rule to convert ACTG to VAS in 1045 participants. First, when the last missed dose on the ACTG was reported as > 30 days ago, the VAS was set to 100% (N = 582). Second, if "doses missed" was zero for all items, VAS was 100% (N = 104). Third, among remaining participants (N = 359), VAS was estimated as 96.8% minus 2.9% times the number of missed doses ("doses per day" was non-significant). Correlation between predicted and reported VAS was r = 0.80 in the criminal justice group (N = 446), r = 0.46 in the international group (N = 311), r = 0.32 in the other vulnerable population (N = 63), and r = 0.66 overall. When outliers due to inversion of the VAS scale were excluded (n = 25), these correlations were 0.88, 0.78, 0.80, and 0.86, respectively. We concluded that a simple decision rule and equation allowed us to co-calibrate between two widely used adherence measures thus combining data from studies with different instruments. This study highlighted issues with VAS inversions and its limitations as a single item. Combining studies using different instrument facilitates larger pooled datasets to address key research questions.