A review of the performance of different methods for propensity score matched subgroup analyses and a summary of their application in peer‐reviewed research studies

Wang, Shirley; He, Min; Jin, Yinzhu; Wyss, Richard; Shin, HoJin; Ma, Yong; Keeton, Stephine; Fireman, Bruce; Karami, Sara; Major, Jacqueline M.; Schneeweiß, Sebastian; Gagne, Joshua J.

doi:10.1002/pds.4328

Cited by 16 publications

(16 citation statements)

References 4 publications

(4 reference statements)

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“…In this study with prevalent new-user design, given that comparable warfarin users were selected within the exposure sets and the subgroup of interest was prior warfarin use status, subgroup analysis of incident and prevalent new user could be performed without breaking the matched sets, while retaining balanced characteristics from PS matching. A methodology review suggested that most propensity matched studies (33% of the 83 studies reviewed) appeared to perform subgroup analysis according to any variable of interest directly from the matched cohorts, which would break the PS-matched sets and patient characteristics may no longer be balanced within the subgroups [39]. We suspect that similar issues may also be present in subgroup analysis along with inverse probability of treatment weighting where the cohort was subdivided directly from the weighted cohort.…”

Section: Discussionmentioning

confidence: 99%

Re-evaluating Safety and Effectiveness of Dabigatran Versus Warfarin in a Nationwide Data Environment: A Prevalent New-User Design Study

Lin

Lai

Dong³

et al. 2019

Drugs - Real World Outcomes

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Introduction The new user cohort design is widely used to assess the effects of a new drug, such as dabigatran, but inherently excludes some users due to prior use of the comparator drug, for example warfarin. The prevalent new-user design offers a solution that includes all eligible users of the new drug. Objective To evaluate the safety and effectiveness of dabigatran versus warfarin in non-valvular atrial fibrillation (NVAF) patients with prevalent new-user design. Methods Taiwan National Health Insurance and mortality data from 2011 through 2015 were utilized. From an incident NVAF cohort, we identified dabigatran initiators as either incident or prevalent (switchers from warfarin) new users. Time- and prescription-based exposure sets were formed for dabigatran initiators to account for prior warfarin prescriptions. A comparable warfarin user was matched on the time-conditional propensity score to the dabigatran initiator in each set. The matched patients were followed for clinical outcomes, with Cox proportional hazards model used to estimate hazard ratios (HRs). Results There were 10,811 dabigatran initiators, including 22% prevalent new users (switchers), who formed the exposure sets and were matched 1:1 to warfarin users. Dabigatran use was associated with lower risks of intracranial hemorrhage (HR 0.51; 95% confidence interval [CI] 0.39, 0.66) and gastrointestinal bleeding (HR 0.81; 95% CI 0.70, 0.92), compared with warfarin use. These effects were similar between the incident and prevalent new users. Conclusion Using a design that includes both incident and prevalent new users of dabigatran, the use of dabigatran is associated with lower major bleeding risk than warfarin use among patients with incident NVAF. Electronic supplementary material The online version of this article (10.1007/s40801-019-0156-2) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Re-evaluating Safety and Effectiveness of Dabigatran Versus Warfarin in a Nationwide Data Environment: A Prevalent New-User Design Study

Lin

Lai

Dong³

et al. 2019

Drugs - Real World Outcomes

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“…In estimating treatment effects, there is often an interest to explore if the effect of treatment varies among different subgroups (for example, men versus women) of the population under study, often called “treatment effect modification.” There are many ways to utilize propensity score methods to adjust for confounding in a subgroup analysis; however, common implementation of propensity score matching in the medical literature is sub-optimal (Wang et al, 2017; Ali et al, 2018a). The use of propensity score matched (PSM) cohort for subgroup analysis breaks the matched sets and might result in imbalance of covariates (Ali et al, 2018a).…”

Section: Advantages and Limitations Of Propensity Score Methodsmentioning

confidence: 99%

“…To account for covariate imbalances, subgroup analyses of propensity score matched cohorts involve: i) adjusting for covariates in the outcome model or ii) re-matching within the subgroups either using the propensity score estimated in the full cohort or fitting new propensity score within subgroups ( Figure 4 ) (Rassen et al, 2012; Wang et al, 2017). The choice of a specific method should take into account several factors: prevalence of the treatment and the outcome; strength of association between pretreatment covariates and the treatment; the true effect size within subgroups; and the amount of confounding within the subgroups (Wang et al, 2018).…”

Section: Advantages and Limitations Of Propensity Score Methodsmentioning

confidence: 99%

Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances

et al. 2019

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Randomized clinical trials (RCT) are accepted as the gold-standard approaches to measure effects of intervention or treatment on outcomes. They are also the designs of choice for health technology assessment (HTA). Randomization ensures comparability, in both measured and unmeasured pretreatment characteristics, of individuals assigned to treatment and control or comparator. However, even adequately powered RCTs are not always feasible for several reasons such as cost, time, practical and ethical constraints, and limited generalizability. RCTs rely on data collected on selected, homogeneous population under highly controlled conditions; hence, they provide evidence on efficacy of interventions rather than on effectiveness. Alternatively, observational studies can provide evidence on the relative effectiveness or safety of a health technology compared to one or more alternatives when provided under the setting of routine health care practice. In observational studies, however, treatment assignment is a non-random process based on an individual’s baseline characteristics; hence, treatment groups may not be comparable in their pretreatment characteristics. As a result, direct comparison of outcomes between treatment groups might lead to biased estimate of the treatment effect. Propensity score approaches have been used to achieve balance or comparability of treatment groups in terms of their measured pretreatment covariates thereby controlling for confounding bias in estimating treatment effects. Despite the popularity of propensity scores methods and recent important methodological advances, misunderstandings on their applications and limitations are all too common. In this article, we present a review of the propensity scores methods, extended applications, recent advances, and their strengths and limitations.

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“…33 Moreover, it balances the distribution of characteristics between the compared exposure groups on average, thus there may be pairs that are discordant on special characteristics; finally, there is no clear understanding of the relative performance of matching strategies in subgroup analyses. 34 The propensity score cannot account for unmeasured confounders such as the healthier behaviour of patients prescribed a new medication, or the profiles of treating clinicians and their early confidence in a new effective drug. In this regard, quasi-experimental methods using instrumental variables (e.g.…”

Section: Data Analysis: the Threat Of Bias And Unmeasured Confoundersmentioning

confidence: 99%

Observational research on sodium glucose co‐transporter‐2 inhibitors: A real breakthrough?

Raschi

Poluzzi

Fadini

et al. 2018

Diabetes Obesity Metabolism

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Sodium glucose co‐transporter‐2 inhibitors have attracted the interest of the scientific community following the results from dedicated cardiovascular outcome trials, which demonstrated remarkable reduction in all‐cause mortality and other cardiovascular (CV) endpoints with empagliflozin and canagliflozin. These impressive results raised further expectations on real world data from large observational cohort studies. They were designed to address the possible existence of a class effect, and the uncertainty on whether this benefit can be extended from secondary to primary CV prevention of patients with type 2 diabetes. In this review, we collated data from existing observational studies (including the celebrated CVD‐REAL cohorts) and critically appraised results and methodological issues with the aim of providing clinical insight, including unsettled aspects, and proposing a research agenda for future investigations.

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A review of the performance of different methods for propensity score matched subgroup analyses and a summary of their application in peer‐reviewed research studies

Cited by 16 publications

References 4 publications

Re-evaluating Safety and Effectiveness of Dabigatran Versus Warfarin in a Nationwide Data Environment: A Prevalent New-User Design Study

Re-evaluating Safety and Effectiveness of Dabigatran Versus Warfarin in a Nationwide Data Environment: A Prevalent New-User Design Study

Propensity Score Methods in Health Technology Assessment: Principles, Extended Applications, and Recent Advances

Observational research on sodium glucose co‐transporter‐2 inhibitors: A real breakthrough?

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