Martingale residual‐based method to control for confounders measured only in a validation sample in time‐to‐event analysis

Burne, Rebecca; Abrahamowicz, Michał

doi:10.1002/sim.7012

Cited by 11 publications

(33 citation statements)

References 51 publications

(71 reference statements)

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“…To date, various analytical methods could be used for such subsequent sensitivity analysis. These methods include, but are not limited to: Bayesian twin regression modeling, 39,40 difference in difference, 41,42 empirical distribution calibration, 43,44 high-dimensional propensity score, 45 Manski's partial identification, 46 martingale residual-based method, 47,48 missing cause approach, 49 multiple imputation, 50,51 negative control, 52,53 perturbation variable, 54 propensity score calibration, 55,56 pseudo treatment, 57 Rosenbaum sensitivity analysis, 58,59 Rosenbaum-Rubin sensitivity analysis, 60,61 and the trend-in-trend method. 62,63 However, these methods are more complicated in implementing, and require additional assumptions.…”

Section: Key Pointsmentioning

confidence: 99%

Assessing the impact of unmeasured confounders for credible and reliable real‐world evidence

Zhang

Stamey

Mathur

2020

Pharmacoepidemiology and Drug

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Purpose: We review statistical methods for assessing the possible impact of bias due to unmeasured confounding in real world data analysis and provide detailed recommendations for choosing among the methods. Methods: By updating an earlier systematic review, we summarize modern statistical best practices for evaluating and correcting for potential bias due to unmeasured confounding in estimating causal treatment effect from non-interventional studies. Results: We suggest a hierarchical structure for assessing unmeasured confounding. First, for initial sensitivity analyses, we strongly recommend applying a recently developed method, the E-value, that is straightforward to apply and does not require prior knowledge or assumptions about the unmeasured confounder(s). When some such knowledge is available, the E-value could be supplemented by the rule-out or array method at this step. If these initial analyses suggest results may not be robust to unmeasured confounding, subsequent analyses could be conducted using more specialized statistical methods, which we categorize based on whether they require access to external data on the suspected unmeasured confounder(s), internal data, or no data. Other factors for choosing the subsequent sensitivity analysis methods are also introduced and discussed, including the types of unmeasured confounders and whether the subsequent sensitivity analysis is intended to provide a corrected causal treatment effect. Conclusion: Various analytical methods have been proposed to address unmeasured confounding, but little research has discussed a structured approach to select appropriate methods in practice. In providing practical suggestions for choosing appropriate initial and, potentially, more specialized subsequent sensitivity analyses, we hope to facilitate the widespread reporting of such sensitivity analyses in non-interventional studies. The suggested approach also has the potential to inform pre-specification of sensitivity analyses before executing the analysis, and therefore increase the transparency and limit selective study reporting.

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Section: Key Pointsmentioning

confidence: 99%

Assessing the impact of unmeasured confounders for credible and reliable real‐world evidence

Zhang

Stamey

Mathur

2020

Pharmacoepidemiology and Drug

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“…Though a few correction and sensitivity-analysis techniques exist, the easier-to-use tools we have described have yet to be translated to these settings. [109, 110]…”

Section: Bias Correction For Unobserved Confoundingmentioning

confidence: 99%

Propensity Scores in Pharmacoepidemiology: Beyond the Horizon

2017

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Purpose of review Propensity score methods have become commonplace in pharmacoepidemiology over the past decade. Their adoption has confronted formidable obstacles that arise from pharmacoepidemiology's reliance on large healthcare databases of considerable heterogeneity and complexity. These include identifying clinically meaningful samples, defining treatment comparisons, and measuring covariates in ways that respect sound epidemiologic study design. Additional complexities involve correctly modeling treatment decisions in the face of variation in healthcare practice, and dealing with missing information and unmeasured confounding. In this review, we examine the application of propensity score methods in pharmacoepidemiology with particular attention to these and other issues, with an eye towards standards of practice, recent methodological advances, and opportunities for future progress. Recent findings Propensity score methods have matured in ways that can advance comparative effectiveness and safety research in pharmacoepidemiology. These include natural extensions for categorical treatments, matching algorithms that can optimize sample size given design constraints, weighting estimators that asymptotically target matched and overlap samples, and the incorporation of machine learning to aid in covariate selection and model building. Summary These recent and encouraging advances should be further evaluated through simulation and empirical studies, but nonetheless represent a bright path ahead for the observational study of treatment benefits and harms.

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“…Until recently, the availability of methods to impute information on unmeasured confounders in time-to-event analyses has been limited. Burne and Abrahamowicz 15 developed a method, applicable to multivariable analyses with Cox proportional hazards models, to impute unmeasured confounders using martingale residuals. In this method, imputation of confounding variables not available in health administrative data requires that these unmeasured confounding variables be available in a validation data set, either a subset of the health administrative data or an external data set.…”

Section: Introductionmentioning

confidence: 99%

“…In simulations, this method provided more accurate results in adjusting for an unmeasured confounder than other methods applicable to time-to-event analyses. 15 Thus, the objective of our study was to evaluate the impact of asthma on the need for an intestinal resection in patients with Crohn’s disease, while adjusting for smoking status, despite the limitation of smoking status being unmeasured in the health administrative data. We did this by using a validation data set in which smoking status was measured and applying the martingale residual-based imputation method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Asthma is not associated with the need for surgery in Crohn’s disease when controlling for smoking status: a population-based cohort study

Kuenzig

Sadatsafavi

Aviña‐Zubieta

et al. 2018

CLEP

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PurposeGrowing evidence suggests asthma and Crohn’s disease commonly cooccur. However, the impact of asthma on the prognosis of Crohn’s disease is unknown. The aim of our study was to assess the effect of asthma on the need for intestinal resection in patients with Crohn’s disease while adjusting for smoking status, imputed from a smaller, secondary data set.Patients and methodsUsing health administrative data from a universally funded healthcare plan in Alberta, Canada, we conducted a cohort study to assess the effect of asthma on the need for surgery in patients with Crohn’s disease diagnosed between 2002 and 2008 (N=2,113). Validated algorithms were used to identify incident cases of Crohn’s disease, cooccurring asthma, and intestinal resection. The association between asthma and intestinal resection was estimated using multivariable Cox proportional hazards regression. Smoking status was imputed using a novel method using martingale residuals, derived from a data set of 485 patients enrolled in the Alberta Inflammatory Bowel Disease Consortium (2007 to 2014) who completed environmental questionnaires. All analyses were adjusted for age, sex, rural/urban status, and mean neighborhood income quintile.ResultsAsthma did not increase the risk of surgery in the health administrative data when not adjusting for smoking status (HR 1.03, 95% CI 0.81 to 1.29). The association remained nonsignificant after imputing smoking status in the health administrative data (HR 1.03, 95% CI 0.81 to 1.29).ConclusionAlthough asthma is associated with an increased risk of Crohn’s disease, co-occurring asthma is not associated with the risk of surgery in these patients. This null association persisted after adjusting for smoking status. This study described a novel method to adjust for confounding (smoking status) in time-to-event analyses, even when the confounding variable is unmeasured in health administrative data.

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Martingale residual‐based method to control for confounders measured only in a validation sample in time‐to‐event analysis

Cited by 11 publications

References 51 publications

Assessing the impact of unmeasured confounders for credible and reliable real‐world evidence

Assessing the impact of unmeasured confounders for credible and reliable real‐world evidence

Propensity Scores in Pharmacoepidemiology: Beyond the Horizon

Asthma is not associated with the need for surgery in Crohn’s disease when controlling for smoking status: a population-based cohort study

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Martingale residual‐based method to control for confounders measured only in a validation sample in time‐to‐event analysis

Cited by 11 publications

References 51 publications

Assessing the impact of unmeasured confounders for credible and reliable real‐world evidence

Assessing the impact of unmeasured confounders for credible and reliable real‐world evidence

Propensity Scores in Pharmacoepidemiology: Beyond the Horizon

Asthma is not associated with the need for surgery in Crohn&rsquo;s disease when controlling for smoking status: a population-based cohort study

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Asthma is not associated with the need for surgery in Crohn’s disease when controlling for smoking status: a population-based cohort study