PURPOSE: Implementing a cohort study in longitudinal healthcare databases requires looking back over some covariate assessment period (CAP) preceding cohort entry to measure confounders. We used simulations to compare fixed-duration versus all-available CAPs for confounder adjustment in the presence of differences in available baseline time between exposure groups.
METHODS: We simulated cohorts of 10 000 patients with binary variables for a single confounder, exposure, and outcome. Baseline time was simulated based on the observed distribution in a claims-based comparison of statin users versus nonusers. We compared bias after measuring confounders using fixed-duration and all-available CAPs, both when exposure groups had similar and discrepant amounts of available baseline time.
RESULTS: When the comparison groups had similar amounts of baseline time, an all-available CAP was less biased than a fixed-duration CAP. When baseline time differed between comparison groups, the preferable CAP approach depended on the direction of confounding and which exposure group had higher covariate sensitivity. These findings were consistent in direction across sensitivity analyses.
CONCLUSION: In certain settings of differential available baseline time between exposure groups, the all-available CAP was more biased than the fixed-duration CAP. The relative directions and strengths of confounding and misclassification biases are an important consideration when choosing between a fixed-duration or all-available CAP, but they are often unknown. Therefore, we recommend comparing the amount of available baseline time between exposure groups. When there is a large discrepancy, despite appropriate design choices, we recommend a fixed-duration approach to avoid potential increases in bias because of differential data availability.
BACKGROUND: Generic versions of a drug can vary in appearance, which can impact adherence.
OBJECTIVE: To assess the preferences, perceptions, and responses of patients who experienced a change in the appearance of a generic medication.
DESIGN: Cross-sectional survey of patients from a large commercial health plan.
PARTICIPANTS: Adults receiving generic versions of lisinopril, fluoxetine, lamotrigine, or simvastatin who experienced a change in the color or shape of their pills between March 2014 and November 2015.
MAIN MEASURES: Likert-scale responses to questions concerning perceptions of generic drug safety and effectiveness, reliance on and preferences for pill appearance, and responses to pill appearance changes. Multivariable logistic regression-modeled predictors of seeking advice and adjusting use following a pill appearance change.
KEY RESULTS: Of 814 respondents (response rate = 41%), 72% relied on pill appearance to ensure they took the correct medication. A similar percentage wanted their pills to remain the same color (72%), shape (71%), and size (75%) upon refill, but 58% would not have paid a $1 premium on a $5 co-pay to ensure such consistency. Most respondents (86%) wanted their pharmacists to notify them about pill appearance changes, but only 37% recalled such notification; 21% thought they received the wrong medication, and 8% adjusted medication use. Younger respondents (18-33 vs. 50-57 years) were more likely to seek advice (odds ratio [OR] = 1.91; 95% confidence interval [CI],1.02-3.59), and respondents with lower household income (< $30,000 vs. > $100,000) were more likely to adjust medication use (OR = 3.40; 95% CI,1.09-10.67).
CONCLUSIONS: Requiring uniform pill appearance may help increase adherence but presents challenges. Standardized pharmacy notification and education policies may be a more feasible short-term solution.
Randomized controlled trials (RCTs) provide evidence for regulatory agencies, shape clinical practice, influence formulary decisions, and have important implications for patients. However, many patient groups that are major consumers of drugs are under-represented in randomized trials. We review three methods to extrapolate evidence from trial participants to different target populations following market approval and discuss how these could be implemented in practice to support regulatory and health technology assessment decisions. Although these methods are not a substitute for less restrictive pre-approval RCTs or rigorous observational studies when sufficient data are available in the post-approval setting, they can help to fill the evidence gap that exists in the early marketing period. Early evidence using real-world data and methods for extrapolating evidence should be reported with clear explanation of assumptions and limitations especially when used to support regulatory and health technology assessment decisions.
BACKGROUND: The approval of new oral disease-modifying drugs (DMDs), such as fingolimod, dimethyl fumarate (DMF), and teriflunamide, has considerably expanded treatment options for relapsing forms of multiple sclerosis (MS). However, data describing the use of these agents in routine clinical practice are limited.
OBJECTIVE: To describe time trends and identify factors associated with oral DMD treatment initiation and switching among individuals with MS.
METHODS: Using data from a large sample of commercially insured patients, we evaluated changes over time in the proportion of MS patients who initiated treatment with an oral DMD and who switched from an injectable DMD to an oral DMD between 2009 and 2014 in the United States. We evaluated predictors of oral DMD use using conditional logistic regression in 2 groups matched on calendar time: oral DMD initiators matched to injectable DMDs initiators and oral DMD switchers matched to those who switched to a second injectable DMD.
RESULTS: Our cohort included 7,576 individuals who initiated a DMD and 1,342 who switched DMDs, of which oral DMDs accounted for 6% and 39%, respectively. Oral DMD initiation and switching steadily increased from 5% to 16% and 35% to 84%, respectively, between 2011 and 2014, with DMF being the most commonly used agent. Of the potential predictors with clinical significance, a recent neurologist consultation (OR = 1.60; 95% CI = 1.20-2.15) and emergency department visit (OR = 1.43; 95% CI = 1.01-2.01) were significantly associated with oral DMD initiation. History of depression was noted to be a potential predictor of oral DMD initiation; however, the estimate for this predictor did not reach statistical significance (OR = 1.35; 95% CI = 0.99-1.84). No clinically relevant factors measured in our data were associated with switching to an oral DMD.
CONCLUSIONS: Oral DMDs were found to be routinely used as second-line treatment. However, we identified few factors predictive of oral DMD initiation or switching, which implies that their selection is driven by patient and/or physician preferences.
DISCLOSURES: This study was funded by CVS Caremark through an unrestricted research grant to Brigham and Women's Hospital. Shrank and Matlin were employees of, and shareholders in, CVS Health at the time of the study; they report no financial interests in products or services that are related to the subject of this study. Spettell is an employee of, and shareholder in, Aetna. Chitnis serves on clinical trial advisory boards for Novartis and Genzyme-Sanofi; has consulted for Bayer, Biogen Idec, Celgene, Novartis, Merck-Serono, and Genentech-Roche; and has received research support from NIH, National Multiple Sclerosis Society, Peabody Foundation, Consortium for MS Centers, Guthy Jackson Charitable Foundation, EMD-Serono, Novartis Biogen, and Verily. Desai reports receiving a research grant from Merck for unrelated work. Gagne is principal investigator of a research grant from Novartis Pharmaceuticals Corporation to the Brigham and Women's Hospital and has received grant support from Eli Lilly, all for unrelated work. He is also a consultant to Aetion and Optum. Minden reports grants from Biogen and other fees from Genentech, EMD Serano, Avanir, and Novartis, unrelated to this study. The other authors have no conflicts to report. This study was presented as a poster at the International Society for Pharmacoepidemiology 32nd Annual Meeting; August 25-28, 2016; Dublin, Ireland.