Refining estimates of prescription durations by using observed covariates in pharmacoepidemiologic databases: Necessary refinements to stimulate alternative approaches

Meid, Andreas D.; Haefeli, Walter E.

doi:10.1002/pds.4270

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Recently, Taipale et al compared treatment status derived with the “prescription drug purchases to drug use periods” (PRE2DUP) method in a cohort of older persons and found good agreement . Meid et al compared an individual estimation of drug coverage (COV), in which estimations are based on averaged fraction of prescribed doses from longitudinal prescription history, to real data and found it preferable over the DDD approach. It should, however, be noticed that estimation of treatment status is to a certain extent a simpler task than obtaining an unbiased estimate of daily dose.…”

Section: Discussionmentioning

confidence: 99%

Empirical validation of the reverse parametric waiting time distribution and standard methods to estimate prescription durations for warfarin

Thrane

Støvring

Hellfritzsch

et al. 2018

Pharmacoepidemiology and Drug

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Section: Discussionmentioning

confidence: 99%

Empirical validation of the reverse parametric waiting time distribution and standard methods to estimate prescription durations for warfarin

Thrane

Støvring

Hellfritzsch

et al. 2018

Pharmacoepidemiology and Drug

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show abstract

“…Dividing the dispensed package size by the estimated average dose yields the duration of drug exposure. A modification of the original formula was used for single or sporadic prescriptions [21]: Unless fixed dosing regimens were available (e.g., biologicals for the treatment of rheumatoid arthritis) [21], we estimated the individual daily dose using the study population's mean (intercept) dose and covariate effects based on indication (ATC code), comorbidities (Elixhauser groups [22]), sex, and age [21] as obtained from patients with multiple prescriptions.…”

Section: Medication Related To the Stopp/start Listmentioning

confidence: 99%

Prediction of Drug-Related Risks Using Clinical Context Information in Longitudinal Claims Data

et al. 2018

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To develop and internally validate prediction models for medication-related risks arising from overuse, misuse, and underuse that utilize clinical context information and are suitable for routine risk assessment in claims data (i.e., medication-based models predicting the risk for hospital admission apparent in routine claims data or MEDI-RADAR). Methods: Based on nationwide claims from healthinsured persons in Germany between 2010 and 2012, we drew a random sample of people aged 65 years (N ¼ 22,500 randomly allocated to training set, N ¼ 7500 to validation set). Individual duration of drug supply was estimated from prescription patterns to yield timevarying drug exposure windows. Together with concurrent medical conditions (ICD-10 diagnoses), exposure to the STOPP/START (screening tool of older persons' potentially inappropriate prescriptions/screening tool to alert doctors to the right treatment) criteria was derived. These were tested as time-dependent covariates together with time-constant covariates (patient demographics, baseline comorbidities) in regularized Cox regression models. Results: STOPP/START variables were iteratively refined and selected by regularization to include 2 up to 11 START variables and 8 up to 31 STOPP variables in parsimonious and liberal selections in the prediction modeling. The models discriminated well between patients with and without allcause hospitalizations, potentially drug-induced hospitalizations, and mortality (parsimonious model c-indices with 95% confidence intervals: 0.63 [0.62e0.64], 0.67 [0.65e0.68], and 0.78 [0.76e0.80]). Conclusions: The STOPP/START criteria proved to efficiently predict medication-related risk in models possessing good performance. Timely detection of such risks by routine monitoring in claims data can support tailored interventions targeting these modifiable risk factors. Their impact on older peoples' medication safety and effectiveness can now be explored in future implementation studies.

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Refining estimates of prescription durations by using observed covariates in pharmacoepidemiologic databases: Necessary refinements to stimulate alternative approaches

Cited by 2 publications

References 19 publications

Empirical validation of the reverse parametric waiting time distribution and standard methods to estimate prescription durations for warfarin

Empirical validation of the reverse parametric waiting time distribution and standard methods to estimate prescription durations for warfarin

Prediction of Drug-Related Risks Using Clinical Context Information in Longitudinal Claims Data

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