Conditions for confounding of interactions

Liu, Aihua; Abrahamowicz, Michał; Siemiatycki, Jack

doi:10.1002/pds.3924

Cited by 8 publications

(7 citation statements)

References 32 publications

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“…Results did not differ (all-cause mortality rate ratio: 0.96 (0.89 to 1.04) vs 0.98 (0.91 to 1.07)). To ensure baseline differences in RA duration across the two cohorts did not confound the interaction,72 we adjusted the exponential PH models for RA duration at index date. To avoid near-collinearity between RA/control status and RA duration (which, by definition, equals 0 for all controls), RA duration in RA cases was centred to a mean of 0 73.…”

Section: Resultsmentioning

confidence: 99%

Improvement in 5-year mortality in incident rheumatoid arthritis compared with the general population—closing the mortality gap

et al. 2016

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Section: Resultsmentioning

confidence: 99%

Improvement in 5-year mortality in incident rheumatoid arthritis compared with the general population—closing the mortality gap

et al. 2016

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“…Observational studies attempt to adjust for confounding of the treatment–outcome relationship but often do not consider any additional features required to unbiasedly evaluate TEM [ 5 ]. Here, we focus on the situation where the moderator not only influences the relationship between the treatment and outcome, but also the relationship between a confounder and either treatment receipt or the outcome.…”

Section: Introductionmentioning

confidence: 99%

The impact of moderator by confounder interactions in the assessment of treatment effect modification: a simulation study

Marsden

Dixon

Dunn

et al. 2022

BMC Med Res Methodol

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Background When performed in an observational setting, treatment effect modification analyses should account for all confounding, where possible. Often, such studies only consider confounding between the exposure and outcome. However, there is scope for misspecification of the confounding adjustment when estimating moderation as the effects of the confounders may themselves be influenced by the moderator. The aim of this study was to investigate bias in estimates of treatment effect modification resulting from failure to account for an interaction between a binary moderator and a confounder on either treatment receipt or the outcome, and to assess the performance of different approaches to account for such interactions. Methods The theory behind the reason for bias and factors that impact the magnitude of bias is explained. Monte Carlo simulations were used to assess the performance of different propensity scores adjustment methods and regression adjustment where the adjustment 1) did not account for any moderator-confounder interactions, 2) included moderator-confounder interactions, and 3) was estimated separately in each moderator subgroup. A real-world observational dataset was used to demonstrate this issue. Results Regression adjustment and propensity score covariate adjustment were sensitive to the presence of moderator-confounder interactions on outcome, whilst propensity score weighting and matching were more sensitive to the presence of moderator-confounder interactions on treatment receipt. Including the relevant moderator-confounder interactions in the propensity score (for methods using this) or the outcome model (for regression adjustment) rectified this for all methods except propensity score covariate adjustment. For the latter, subgroup-specific propensity scores were required. Analysis of the real-world dataset showed that accounting for a moderator-confounder interaction can change the estimate of effect modification. Conclusions When estimating treatment effect modification whilst adjusting for confounders, moderator-confounder interactions on outcome or treatment receipt should be accounted for.

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“…This measure will help gauge how well outpatient care is coordinated with hospital care as this is an important aspect of care coordination that may help reduce hospital readmissions. 67 68 In addition, we will capture whether patients had a follow-up visit within 1 week post-discharge with any physician with whom they had had at least two visits in the 12 months preceding the index hospitalisation (community physician) or at least one visit during the hospital stay (hospital physician). 10 Research studies have reported that seeing a physician who is familiar with the patient’s health post-hospitalisation may have a beneficial impact on follow-up rates and reduce risk of death or re-admissions.…”

Section: Methods and Analysismentioning

confidence: 99%

“…Bias in pharmacoepidemiology studies results from multiple sources of confounding. 67 71 72 DOAC users tend to be younger with fewer comorbidities than warfarin users. 73 To control for confounding, we will include variables such as age, sex, presence of specific comorbidities, concomitant medications, remote residence, neighbourhood income quintile and physician specialty among other independent variables in the model as potential risk factors.…”

Section: Methods and Analysismentioning

confidence: 99%

Predictors of oral anticoagulant-associated adverse events in seniors transitioning from hospital to home: a retrospective cohort study protocol

et al. 2020

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IntroductionOral anticoagulants (OACs) are widely prescribed in older adults. High OAC-related adverse event rates in the early period following hospital discharge argue for an analysis to identify predictors. Our objective is to identify and validate clinical and continuity of care variables among seniors discharged from hospital on an OAC, which are independently associated with OAC-related adverse events within 30 days.Methods and analysisWe propose a population-based retrospective cohort study of all adults aged 66 years or older who were discharged from hospital on an OAC from September 2010 to March 2015 in Ontario, Canada. The primary outcome is a composite of the first hospitalisation or emergency department visit for a haemorrhage or thromboembolic event or mortality within 30 days of hospital discharge. A Cox proportional hazards model will be used to determine the association between the composite outcome and a set of prespecified covariates. A split sample method will be adopted to validate the variables associated with OAC-related adverse events.Ethics and disseminationThe use of data in this project was authorised under section 45 of Ontario’s Personal Health Information Protection Act, which does not require review by a research ethics board. Results will be disseminated via peer-reviewed publications and presentations at conferences and will determine intervention targets to improve OAC management in upcoming randomised trials.Trial registration numberClinicalTrials.gov Identifier: NCT02777047; Pre-results.

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Conditions for confounding of interactions

Abstract: Studies of interactions should attempt to identify-as potential confounders-those risk factors whose associations with one of the exposures in the interaction term may be modified by the other exposure.

Cited by 8 publications

References 32 publications

Improvement in 5-year mortality in incident rheumatoid arthritis compared with the general population—closing the mortality gap

Improvement in 5-year mortality in incident rheumatoid arthritis compared with the general population—closing the mortality gap

The impact of moderator by confounder interactions in the assessment of treatment effect modification: a simulation study

Predictors of oral anticoagulant-associated adverse events in seniors transitioning from hospital to home: a retrospective cohort study protocol

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