Calendar time‐specific propensity score analysis for observational data: a case study estimating the effectiveness of inhaled long‐acting beta‐agonist on asthma exacerbations

Dilokthornsakul, P.; Chaiyakunapruk, Nathorn; Schumock, Glen T.; Lee, Todd A.

doi:10.1002/pds.3540

Cited by 7 publications

(11 citation statements)

References 20 publications

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“…Assessing asthma severity 18 studies used 20 different algorithms to assess asthma severity (supplementary table E10) as binary (i.e. severe versus nonsevere asthma) [15,23,[25][26][27][28][29][30][31][32][33][34][35][36][37][38] or ordinal variables (mild, moderate and severe asthma [39]; low-, moderate-and high-risk asthma [40]). The algorithms were based on one or more of the following asthma-related variables: number and/or dosage of prescriptions (i.e.…”

Section: Defining Asthmamentioning

confidence: 99%

“…OCS prescriptions were used as a marker for exacerbations in 17 studies, either alone [23,30,31,35,39,42,47,48,53] or with a concurrent asthma encounter (e.g. a general practice, outpatient or emergency department visit, or hospitalisation within 5 or 7 days) [16,17,32,37,38,46,52,54]. In one study, exacerbations were defined by a minimum of six SABA prescriptions per year [47].…”

Section: Assessing Asthma Controlmentioning

confidence: 99%

“…Other definitions included an outpatient code of "asthma exacerbation" [52], asthma hospitalisation [23,30,32,35,37,39,43,44,46,48,50,51,[53][54][55], asthma emergency department visit [16, 30-32, 35, 37, 38, 43, 44, 46, 48, 51-54], or hospitalisation with diagnosis of status asthmaticus or (in already diagnosed asthma patients) diagnosis of pneumonia, dyspnoea or respiratory insufficiency [30].…”

Section: Assessing Asthma Controlmentioning

confidence: 99%

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Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review

et al. 2017

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There is currently no consensus on approaches to defining asthma or assessing asthma outcomes using electronic health record-derived data. We explored these approaches in the recent literature and examined the clarity of reporting.We systematically searched for asthma-related articles published between January 1, 2014 and December 31, 2015, extracted the algorithms used to identify asthma patients and assess severity, control and exacerbations, and examined how the validity of these outcomes was justified.From 113 eligible articles, we found significant heterogeneity in the algorithms used to define asthma (n=66 different algorithms), severity (n=18), control (n=9) and exacerbations (n=24). For the majority of algorithms (n=106), validity was not justified. In the remaining cases, approaches ranged from using algorithms validated in the same databases to using nonvalidated algorithms that were based on clinical judgement or clinical guidelines. The implementation of these algorithms was suboptimally described overall.Although electronic health record-derived data are now widely used to study asthma, the approaches being used are significantly varied and are often underdescribed, rendering it difficult to assess the validity of studies and compare their findings. Given the substantial growth in this body of literature, it is crucial that scientific consensus is reached on the underlying definitions and algorithms.

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Section: Defining Asthmamentioning

confidence: 99%

Section: Assessing Asthma Controlmentioning

confidence: 99%

Section: Assessing Asthma Controlmentioning

confidence: 99%

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Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review

et al. 2017

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“…In these settings, calendar time is often associated with the dissemination (or restricted use) of the treatment of interest (Dusetzina et al, 2013). Examples include a newly approved treatment quickly diffusing through the market and the issuance of black box warnings (Dilokthornsakul et al, 2014). During these periods, the effects of covariates on treatment often change, or are modified over time (Mack et al, 2013, Dusetzina et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…One approach is to fit a complex model that includes interaction terms involving calendar time within the PS model (Mack et al, 2013, Dilokthornsakul et al, 2014). Changes over time in the treatment assignment mechanism can also be addressed by estimating the PS within given time periods (Seeger et al, 2005; Mack et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Model Misspecification When Excluding Instrumental Variables from PS Models in Settings Where Instruments Modify the Effects of Covariates on Treatment

Wyss

Ellis

Brookhart

et al. 2014

Epidemiologic Methods

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Theory and simulations show that variables affecting the outcome only through exposure, known as instrumental variables (IVs), should be excluded from propensity score (PS) models. In pharmacoepidemiologic studies based on automated healthcare databases, researchers will sometimes use a single PS model to control for confounding when evaluating the effect of a treatment on multiple outcomes. Because these “full” models are not constructed with a specific outcome in mind, they will usually contain a large number of IVs for any individual study or outcome. If researchers subsequently decide to evaluate a subset of the outcomes in more detail, they can construct reduced “outcome-specific” models that exclude IVs for the particular study. Accurate estimates of PSs that do not condition on IVs, however, can be compromised when simply excluding instruments from the full PS model. This misspecification may have a negligible impact on effect estimates in many settings, but is likely to be more pronounced for situations where instruments modify the effects of covariates on treatment (instrument-confounder interactions). In studies evaluating drugs during early dissemination, the effects of covariates on treatment are likely modified over calendar time and IV-confounder interaction effects on treatment are likely to exist. In these settings, refitting more flexible PS models after excluding IVs and IV-confounder interactions can work well. The authors propose an alternative method based on the concept of marginalization that can be used to remove the negative effects of controlling for IVs and IV-confounder interactions without having to refit the full PS model. This method fits the full PS model, including IVs and IV-confounder interactions, but marginalizes over values of the instruments. Fitting more flexible PS models after excluding IVs or using the full model to marginalize over IVs can prevent model misspecification along with the negative effects of balancing instruments in certain settings.

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Comparative Analysis of Calendar Time-Specific and Conventional Propensity Score Analysis for Thiazolidinedione Use in Diabetes

Dilokthornsakul

Chaiyakunapruk

Jeanpeerapong

et al. 2014

Value in Health Regional Issues

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Calendar time‐specific propensity score analysis for observational data: a case study estimating the effectiveness of inhaled long‐acting beta‐agonist on asthma exacerbations

Abstract: Focusing on a specific year, there was a difference between conventional PS estimated versus CTS-PS for that year. However, there was minimal effect of CTS-PS on the observed treatment effects compared with conventional PS approach.

Cited by 7 publications

References 20 publications

Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review

Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review

Model Misspecification When Excluding Instrumental Variables from PS Models in Settings Where Instruments Modify the Effects of Covariates on Treatment

Comparative Analysis of Calendar Time-Specific and Conventional Propensity Score Analysis for Thiazolidinedione Use in Diabetes

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