Literature DB >> 31758215

Methods for constructing treatment episodes and impact on exposure-outcome associations.

Laura Pazzagli1, Lena Brandt2, Marie Linder2, David Myers3, Panagiotis Mavros4, Morten Andersen2,5, Shahram Bahmanyar2,6.   

Abstract

PURPOSE: To assess the impact on exposure time and outcome misclassifications, and consequent impact on exposure-outcome associations from treatment episode construction. We investigated the dosage assumptions of 1 unit per day, and 1 DDD per day, versus actual prescribed dosage under different handling of gaps and overlaps of prescriptions.
METHODS: Data on mirtazapine and citalopram exposure (years 2006-2014) from the Swedish Prescribed Drug register were used. Via a within individuals design we compared method A, based on actual dosage, with methods B and C based on 1 unit of drug per day and 1 DDD per day assumptions, respectively, including consideration of gaps and overlaps. Four outcomes were used, hospitalizations and outpatient visits for all and for psychiatric causes.
RESULTS: Relative to method A, both alternative methods lead to misclassification of exposure time. With regard to outcome misclassifications, method B overestimates the effect of the exposure on the outcome in 77% and 100% of exposure definition comparisons for mirtazapine and citalopram respectively, while 23% of the comparisons for mirtazapine results in underestimation of exposure-outcome associations. Conversely, treatment episodes based on DDD (method C) result in underestimation of the exposure-outcome association in 100% and 87.5% of exposure definition comparisons for mirtazapine and citalopram respectively, while 12.5% of the comparisons for citalopram results in overestimation of the exposure-outcome associations.
CONCLUSIONS: The study provides results that have consistent clinical relevance. We have showed that a non-accurate construction of exposure time may lead to errors on outcome detection during exposed time, and consequently affect conclusions on safety or efficacy profile of a treatment.

Entities:  

Keywords:  Defined daily dose; Gaps; Overlaps; Pharmacoepidemiology; Treatment duration; Treatment episode

Mesh:

Substances:

Year:  2019        PMID: 31758215     DOI: 10.1007/s00228-019-02780-4

Source DB:  PubMed          Journal:  Eur J Clin Pharmacol        ISSN: 0031-6970            Impact factor:   2.953


  20 in total

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Authors:  L Bjerrum; J U Rosholm; J Hallas; J Kragstrup
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Authors:  Eva Lesén; Tatiana Z Sandström; Anders Carlsten; Anna K Jönsson; Ann-Charlotte Mårdby; Karolina Andersson Sundell
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6.  Comparison of different methods to estimate prevalence of drug use by using pharmacy records.

Authors:  A K Mantel-Teeuwisse; O H Klungel; W M Verschuren; A Porsius; A de Boer
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7.  Does the length of the first antidepressant treatment episode influence risk and time to a second episode?

Authors:  Helga Gardarsdottir; Erica C G van Geffen; Joost J Stolker; Toine C G Egberts; Eibert R Heerdink
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8.  Duration of antidepressant drug treatment and its influence on risk of relapse/recurrence: immortal and neglected time bias.

Authors:  Helga Gardarsdottir; Toine C Egberts; Joost J Stolker; Eibert R Heerdink
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9.  Persistence to antihypertensive drug classes: A cohort study using the Swedish Primary Care Cardiovascular Database (SPCCD).

Authors:  Miriam Qvarnström; Thomas Kahan; Helle Kieler; Lena Brandt; Jan Hasselström; Kristina Bengtsson Boström; Karin Manhem; Per Hjerpe; Björn Wettermark
Journal:  Medicine (Baltimore)       Date:  2016-10       Impact factor: 1.889

10.  Assumptions made when preparing drug exposure data for analysis have an impact on results: An unreported step in pharmacoepidemiology studies.

Authors:  Stephen R Pye; Thérèse Sheppard; Rebecca M Joseph; Mark Lunt; Nadyne Girard; Jennifer S Haas; David W Bates; David L Buckeridge; Tjeerd P van Staa; Robyn Tamblyn; William G Dixon
Journal:  Pharmacoepidemiol Drug Saf       Date:  2018-04-17       Impact factor: 2.890

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Review 2.  Pharmacoepidemiological methods for computing the duration of pharmacological prescriptions using secondary data sources.

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Journal:  Eur J Clin Pharmacol       Date:  2021-07-10       Impact factor: 2.953

3.  Rationale and performances of a data-driven method for computing the duration of pharmacological prescriptions using secondary data sources.

Authors:  Laura Pazzagli; David Liang; Morten Andersen; Marie Linder; Abdul Rauf Khan; Maurizio Sessa
Journal:  Sci Rep       Date:  2022-04-15       Impact factor: 4.379

4.  Impact of COVID-19 Lockdown, during the Two Waves, on Drug Use and Emergency Department Access in People with Epilepsy: An Interrupted Time-Series Analysis.

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Journal:  Int J Environ Res Public Health       Date:  2021-12-16       Impact factor: 3.390

  4 in total

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