Supplementary materials: Comparing the performance of two-stage residual inclusion methods when using physician’s prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility

These are peer-reviewed supplementary materials for the article 'Supplementary materials: Comparing the performance of two-stage residual inclusion methods when using physician’s prescribing preference as an instrumental variable: unmeasured confounding and noncollapsibility' published in the Journal of Comparative Effectiveness Research.Figure S1. Results from prior 1 prescription as IVFigure S2. Results from prior 2 prescriptions as IVFigure S3. Results from prior 3 prescriptions as IVFigure S4. Results from prior 4 prescriptions as IVR CodeAim: The first objective is to compare the performance of two-stage residual inclusion (2SRI), two-stage least square (2SLS) with the multivariable generalized linear model (GLM) in terms of the reducing unmeasured confounding bias. The second objective is to demonstrate the ability of 2SRI and 2SPS in alleviating unmeasured confounding when noncollapsibility exists. Materials & methods: This study comprises a simulation study and an empirical example from a real-world UK population health dataset (Clinical Practice Research Datalink). The instrumental variable (IV) used is based on physicians’ prescribing preferences (defined by prescribing history). Results: The percent bias of 2SRI in terms of treatment effect estimates to be lower than GLM and 2SPS and was less than 15% in most scenarios. Further, 2SRI was found to be robust to mild noncollapsibility with the percent bias less than 50%. As the level of unmeasured confounding increased, the ability to alleviate the noncollapsibility decreased. Strong IVs tended to be more robust to noncollapsibility than weak IVs. Conclusion: 2SRI tends to be less biased than GLM and 2SPS in terms of estimating treatment effect. It can be robust to noncollapsibility in the case of the mild unmeasured confounding effect.</p

sj-docx-1-wso-10.1177_17474930231151847 – Supplemental material for Prevalence, measurement, and implications of frailty in stroke survivors: An analysis of three global aging cohorts

Supplemental material, sj-docx-1-wso-10.1177_17474930231151847 for Prevalence, measurement, and implications of frailty in stroke survivors: An analysis of three global aging cohorts by Peter Hanlon, Jennifer K Burton, Terence J Quinn, Frances S Mair, David McAllister, Jim Lewsey and Katie I Gallacher in International Journal of Stroke</p

Derivation and validation of a 10-year risk score for symptomatic abdominal aortic aneurysm

BACKGROUND: Abdominal aortic aneurysm (AAA) can occur in patients who are ineligible for routine ultrasound screening. A simple AAA risk score was derived and compared with current guidelines used for ultrasound screening of AAA. METHODS: United Kingdom Biobank participants without previous AAA were split into a derivation cohort (n=401820, 54.6% women, mean age 56.4 years, 95.5% White race) and validation cohort (n=83816). Incident AAA was defined as first hospital inpatient diagnosis of AAA, death from AAA, or an AAA-related surgical procedure. A multivariable Cox model was developed in the derivation cohort into an AAA risk score that did not require blood biomarkers. To illustrate the sensitivity and specificity of the risk score for AAA, a theoretical threshold to refer patients for ultrasound at 0.25% 10-year risk was modeled. Discrimination of the risk score was compared with a model of US Preventive Services Task Force (USPSTF) AAA screening guidelines. RESULTS: In the derivation cohort, there were 1570 (0.40%) cases of AAA over a median 11.3 years of follow-up. Components of the AAA risk score were age (stratified by smoking status), weight (stratified by smoking status), antihypertensive and cholesterol-lowering medication use, height, diastolic blood pressure, baseline cardiovascular disease, and diabetes. In the validation cohort, over 10 years of follow-up, the C-index for the model of the USPSTF guidelines was 0.705 (95% CI, 0.678–0.733). The C-index of the risk score as a continuous variable was 0.856 (95% CI, 0.837–0.878). In the validation cohort, the USPSTF model yielded sensitivity 63.9% and specificity 71.3%. At the 0.25% 10-year risk threshold, the risk score yielded sensitivity 82.1% and specificity 70.7% while also improving the net reclassification index compared with the USPSTF model +0.176 (95% CI, 0.120–0.232). A combined model, whereby risk scoring was combined with the USPSTF model, also improved prediction compared with USPSTF alone (net reclassification index +0.101 [95% CI, 0.055–0.147]). CONCLUSIONS: In an asymptomatic general population, a risk score based on patient age, height, weight, and medical history may improve identification of asymptomatic patients at risk for clinical events from AAA. Further development and validation of risk scores to detect asymptomatic AAA are needed