STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2—More complex methods of adjustment and advanced topics

Shaw, Pamela A.; Gustafson, Paul; Carroll, Raymond J.; Deffner, Veronika; Keogh, Ruth H.; Kipnis, Victor; Tooze, Janet A.; Wallace, Michael P.; Küchenhoff, Helmut; Freedman, Laurence S.

doi:10.1002/sim.8531

Cited by 52 publications

(39 citation statements)

References 150 publications

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“…Whilst research may be interested solely in prevalence estimates, most frequently the interest is in exposure-outcome associations. MIME has been previously used in different study designs to correct odds ratios, risk ratios, and hazard ratios [ 15 , 17 ]. In our study, MIME correction was successfully applied to prevalence ratios estimated by Poisson regression.…”

Section: Discussionmentioning

confidence: 99%

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Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience

et al. 2021

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Background Accurate data on hypertension is essential to inform decision-making. Hypertension prevalence may be underestimated by population-based surveys due to misclassification of health status by participants. Therefore, adjustment for misclassification bias is required when relying on self-reports. This study aims to quantify misclassification bias in self-reported hypertension prevalence and prevalence ratios in the Portuguese component of the European Health Interview Survey (INS2014), and illustrate application of multiple imputation (MIME) for bias correction using measured high blood pressure data from the first Portuguese health examination survey (INSEF). Methods We assumed that objectively measured hypertension status was missing for INS2014 participants (n = 13,937) and imputed it using INSEF (n = 4910) as auxiliary data. Self-reported, objectively measured and MIME-corrected hypertension prevalence and prevalence ratios (PR) by sex, age group and education were estimated. Bias in self-reported and MIME-corrected estimates were computed using objectively measured INSEF data as a gold-standard. Results Self-reported INS2014 data underestimated hypertension prevalence in all population subgroups, with misclassification bias ranging from 5.2 to 18.6 percentage points (pp). After MIME-correction, prevalence estimates increased and became closer to objectively measured ones, with bias reduction to 0 pp - 5.7 pp. Compared to objectively measured INSEF, self-reported INS2014 data considerably underestimated prevalence ratio by sex (PR = 0.8, 95CI = [0.7, 0.9] vs. PR = 1.2, 95CI = [1.1, 1.4]). MIME successfully corrected direction of association with sex in bivariate (PR = 1.1, 95CI = [1.0, 1.3]) and multivariate analyses (PR = 1.2, 95CI = [1.0, 1.3]). Misclassification bias in hypertension prevalence ratios by education and age group were less pronounced and did not require correction in multivariate analyses. Conclusions Our results highlight the importance of misclassification bias analysis in self-reported hypertension. Multiple imputation is a feasible approach to adjust for misclassification bias in prevalence estimates and exposure-outcomes associations in survey data.

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Section: Discussionmentioning

confidence: 99%

“…Several methods, such as regression calibration, maximum likelihood and Bayesian approaches [ 12 – 16 ] and respective software solutions [ 14 , 15 ] have been put forward to account for misclassification in this context. However, these are complex and might not be intelligible for the average public health researcher.…”

Section: Introductionmentioning

confidence: 99%

Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience

et al. 2021

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“…In this situation, the risk of misclassification may increase for the "softer" endpoint, such as PFS (5). To control this type of bias, restricted and combined definition of survival time can be helpful, and can include identification mode, combined discrimination, and sensitivity analysis (45,46).…”

Section: Biasmentioning

confidence: 99%

The essence of real-world study in cancer research

Li¹,

Shi²,

Fan³

et al. 2020

Transl Breast Cancer Res

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BackgroundDespite the rapid development of gene engineering and other novel therapies, advanced cancer is still a largely intractable disease, and its high mortality necessitates the development of better treatment (1). In general, the regulatory approval of new cancer treatment is built on the objective criteria of efficacy and safety derived from randomized controlled trials (RCTs). However, with advances in genetic and molecular treatment, the number of patients available for a specific clinical trial may be too small for proper assessment of a benefit-risk paradigm (2). As a result, drug developers and regulators have become increasingly interested in data external from clinical trials, and there is growing need to rethink the traditional approaches to treatment development and approval (3). One potential solution to this may be to further extend and validate the results from RCTs with the wider experience of real-world data (RWD) or real-world evidence (RWE).Compared to the rigorous admission criteria and strict research conditions of RCTs, RWD is collected from the real world to provide evidence for clinical and policyrelevant questions that cannot be addressed with data from RCTs, such as patient health status, cancer incidence and mortality, quality of and access to care delivered in routine practice, rare cancers, and rare adverse events and toxicities

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“…Frequentist and Bayesian methods for bias adjustment of epidemiological risk estimates have *Correspondence: matthias.flor@bfr.bund.de 1 German Federal Institute for Risk Assessment, Max-Dohrn-Str. [8][9][10]10589 Berlin, Germany Full list of author information is available at the end of the article been reviewed in Keogh et al [2] and Shaw et al [3]. Estimation of prevalence is always based on the application of a diagnostic test to classify samples with respect to the binary trait under investigation.…”

Section: Introductionmentioning

confidence: 99%

“…However, to obtain an accurate estimation of prevalence, misclassification and measurement errors should be considered as part of bias analysis in epidemiological research [ 1 ]. Frequentist and Bayesian methods for bias adjustment of epidemiological risk estimates have been reviewed in Keogh et al [ 2 ] and Shaw et al [ 3 ]. Estimation of prevalence is always based on the application of a diagnostic test to classify samples with respect to the binary trait under investigation.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification

et al. 2020

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Background: Various methods exist for statistical inference about a prevalence that consider misclassifications due to an imperfect diagnostic test. However, traditional methods are known to suffer from truncation of the prevalence estimate and the confidence intervals constructed around the point estimate, as well as from under-performance of the confidence intervals' coverage. Methods: In this study, we used simulated data sets to validate a Bayesian prevalence estimation method and compare its performance to frequentist methods, i.e. the Rogan-Gladen estimate for prevalence, RGE, in combination with several methods of confidence interval construction. Our performance measures are (i) error distribution of the point estimate against the simulated true prevalence and (ii) coverage and length of the confidence interval, or credible interval in the case of the Bayesian method. Results: Across all data sets, the Bayesian point estimate and the RGE produced similar error distributions with slight advantages of the former over the latter. In addition, the Bayesian estimate did not suffer from the RGE's truncation problem at zero or unity. With respect to coverage performance of the confidence and credible intervals, all of the traditional frequentist methods exhibited strong under-coverage, whereas the Bayesian credible interval as well as a newly developed frequentist method by Lang and Reiczigel performed as desired, with the Bayesian method having a very slight advantage in terms of interval length. Conclusion: The Bayesian prevalence estimation method should be prefered over traditional frequentist methods. An acceptable alternative is to combine the Rogan-Gladen point estimate with the Lang-Reiczigel confidence interval.

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STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2—More complex methods of adjustment and advanced topics

Cited by 52 publications

References 150 publications

Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience

Combining self-reported and objectively measured survey data to improve hypertension prevalence estimates: Portuguese experience

The essence of real-world study in cancer research

Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification

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