Reweighting Oranges to Apples: Transported RE-LY Trial Versus Nonexperimental Effect Estimates of Anticoagulation in Atrial Fibrillation

Enrollment of Black, Indigenous and People of Color (BIPOC) and female participants in the US diabetes trials spanning 2000 to 2020: A chronological survey

AIMS

Little is known about the enrollment practice of both Black, Indigenous and People of Color (BIPOC) and females in the US diabetes trials. We aimed to perform a chronological survey to evaluate the enrollment of BIPOC and female participants in the US diabetes randomized controlled trials (RCTs) over the past two decades.

METHODS

We searched databases to systematically include the US diabetes RCTs from 2000 January 1st to 2020 December 31st. Primary outcome was the adequate enrollment of both BIPOC and females, defined by the participation to prevalence ratio (PPR) > 0.8. We tested the temporal trend in adequate enrollment over time and used logistic regression analysis to explore the relationship between adequate enrollment and trial characteristics.

RESULTS

A total of 69 US diabetes trials were included for analyses, with a median BIPOC and female enrollment percentage of 29.0 % and 45.4 % respectively. There were 22 (31.9 %) trials with adequate enrollment of both BIPOC and females. No significant trend of adequate enrollment percentage of BIPOC and females over time was observed (P = 0.16). Of trial types, those with medication interventions were significantly related to decreased odds of adequate enrollment, when compared to trials with non-drug interventions (odds ratio = 0.29, 95 % confidence interval: 0.11-0.84).

CONCLUSIONS

Less than one third of the US diabetes trials adequately enrolled both BIPOC and females over the past two decades, and no temporal improvement in BIPOC and female participant enrollment was observed. These results highlight the need for more endeavors to mitigate inadequate representation regarding BIPOC and female enrollment in diabetes trials.

Assessing the Benefits and Harms of Pharmacotherapy in Older Adults with Frailty: Insights from Pharmacoepidemiologic Studies of Routine Health Care Data

The objective of this review is to summarize and appraise the research methodology, emerging findings, and future directions in pharmacoepidemiologic studies assessing the benefits and harms of pharmacotherapies in older adults with different levels of frailty. Older adults living with frailty are at elevated risk for poor health outcomes and adverse effects from pharmacotherapy. However, current evidence is limited due to the under-enrollment of frail older adults and the lack of validated frailty assessments in clinical trials. Recent advancements in measuring frailty in administrative claims and electronic health records (database-derived frailty scores) have enabled researchers to identify patients with frailty and to evaluate the heterogeneity of treatment effects by patients' frailty levels using routine health care data. When selecting a database-derived frailty score, researchers must consider the type of data (e.g., different coding systems), the length of the predictor assessment period, the extent of validation against clinically validated frailty measures, and the possibility of surveillance bias arising from unequal access to care. We reviewed 13 pharmacoepidemiologic studies published on PubMed from 2013 to 2023 that evaluated the benefits and harms of cardiovascular medications, diabetes medications, anti-neoplastic agents, antipsychotic medications, and vaccines by frailty levels. These studies suggest that, while greater frailty is positively associated with adverse treatment outcomes, older adults with frailty can still benefit from pharmacotherapy. Therefore, we recommend routine frailty subgroup analyses in pharmacoepidemiologic studies. Despite data and design limitations, the findings from such studies may be informative to tailor pharmacotherapy for older adults across the frailty spectrum.

Performance of a Claims-Based Frailty Proxy Using Varying Frailty Ascertainment Lookback Windows

BACKGROUND

Frailty is an aging-related syndrome of reduced physiological reserve to maintain homeostasis. The Faurot frailty index has been validated as a Medicare claims-based proxy for predicting frailty using billing information from a user-specified ascertainment window.

OBJECTIVES

We assessed the validity of the Faurot frailty index as a predictor of the frailty phenotype and 1-year mortality using varying frailty ascertainment windows.

RESEARCH DESIGN

We identified older adults (66+ y) in Round 5 (2015) of the National Health and Aging Trends Study with Medicare claims linkage. Gold standard frailty was assessed using the frailty phenotype. We calculated the Faurot frailty index using 3, 6, 8, and 12 months of claims prior to the survey or all-available lookback. Model performance for each window in predicting the frailty phenotype was assessed by quantifying calibration and discrimination. Predictive performance for 1-year mortality was assessed by estimating risk differences across claims-based frailty strata.

RESULTS

Among 4253 older adults, the 6 and 8-month windows had the best frailty phenotype calibration (calibration slopes: 0.88 and 0.87). All-available lookback had the best discrimination (C-statistic=0.780), but poor calibration. Mortality associations were strongest using a 3-month window and monotonically decreased with longer windows. Subgroup analyses revealed worse performance in Black and Hispanic individuals than counterparts.

CONCLUSIONS

The optimal ascertainment window for the Faurot frailty index may depend on the clinical context, and researchers should consider tradeoffs between discrimination, calibration, and mortality. Sensitivity analyses using different durations can enhance the robustness of inferences. Research is needed to improve prediction across racial and ethnic groups.

Translation of a Claims-Based Frailty Index From the International Classification of Diseases, Ninth Revision, Clinical Modification to the Tenth Revision

The Faurot frailty index (FFI) is a validated algorithm that uses enrollment and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-based billing information from Medicare claims data as a proxy for frailty. In October 2015, the US health-care system transitioned from the ICD-9-CM to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). Applying the Centers for Medicare and Medicaid Services General Equivalence Mappings, we translated diagnosis-based frailty indicator codes from the ICD-9-CM to the ICD-10-CM, followed by manual review. We used interrupted time-series analysis of Medicare data to assess the comparability of the pre- and posttransition FFI scores. In cohorts of beneficiaries enrolled in January 2015-2017 with 8-month frailty look-back periods, we estimated associations between the FFI and 1-year risk of aging-related outcomes (mortality, hospitalization, and admission to a skilled nursing facility). Updated indicators had similar prevalences as pretransition definitions. The median FFI scores and interquartile ranges (IQRs) for the predicted probability of frailty were similar before and after the International Classification of Diseases transition (pretransition: median, 0.034 (IQR, 0.02-0.07); posttransition: median, 0.038 (IQR, 0.02-0.09)). The updated FFI was associated with increased risks of mortality, hospitalization, and skilled nursing facility admission, similar to findings from the ICD-9-CM era. Studies of medical interventions in older adults using administrative claims should use validated indices, like the FFI, to mitigate confounding or assess effect-measure modification by frailty.

Predicting Treatment Effects of a New-to-Market Drug in Clinical Practice Based on Phase III Randomized Trial Results

Trial results may not be generalizable to target populations treated in clinical practice with different distributions of baseline characteristics that modify the treatment effect. We used outcome models developed with trial data to predict treatment effects in Medicare populations. We used data from the Randomized Evaluation of Long-Term Anticoagulation Therapy trial (RE-LY), which investigated the effect of dabigatran vs. warfarin on stroke or systemic embolism (stroke/SE) among patients with atrial fibrillation. We developed outcome models by fitting proportional hazards models in trial data. Target populations were trial-eligible Medicare beneficiaries who initiated dabigatran or warfarin in 2010-2011 ("early") and 2010-2017 ("extended"). We predicted 2-year risk ratios (RRs) and risk differences (RDs) for stroke/SE, major bleeding, and all-cause death in the Medicare populations using the observed baseline characteristics. The trial and early target populations had similar mean (SD) CHADS2 scores (2.15 (SD 1.13) vs. 2.15 (SD 0.91)) but different mean ages (71 vs. 79 years). Compared with RE-LY, the early Medicare population had similar predicted benefit of dabigatran vs. warfarin for stroke/SE (trial RR = 0.63, 95% confidence interval (CI) = 0.50 to 0.76 and RD = -1.37%, -1.96% to -0.77%, Medicare RR = 0.73, 0.65 to 0.82 and RD = -0.92%, -1.26% to -0.59%) and risks for major bleeding and all-cause death. The time-extended target population showed similar results. Outcome model-based prediction facilitates estimating the average treatment effects of a drug in different target populations when treatment and outcome data are unreliable or unavailable. The predicted effects may inform payers' coverage decisions for patients, especially shortly after a drug's launch when observational data are scarce.

Transporting observational study results to a target population of interest using inverse odds of participation weighting

Inverse odds of participation weighting (IOPW) has been proposed to transport clinical trial findings to target populations of interest when the distribution of treatment effect modifiers differs between trial and target populations. We set out to apply IOPW to transport results from an observational study to a target population of interest. We demonstrated the feasibility of this idea with a real-world example using a nationwide electronic health record derived de-identified database from Flatiron Health. First, we conducted an observational study that carefully adjusted for confounding to estimate the treatment effect of fulvestrant plus palbociclib relative to letrozole plus palbociclib as a second-line therapy among estrogen receptor (ER)-positive, human epidermal growth factor receptor (HER2)-negative metastatic breast cancer patients. Second, we transported these findings to the broader cohort of patients who were eligible for a first-line therapy. The interpretation of the findings and validity of such studies, however, rely on the extent that causal inference assumptions are met.

Exploring ethnic representativeness in diabetes clinical trial enrolment from 2000 to 2020: a chronological survey

AIMS/HYPOTHESIS

Ethnic representativeness of participant enrolment in diabetes RCTs involving multiple ethnicities remains unknown. The aims of this study were to evaluate the status and temporal trend of ethnic representativeness in enrolment to diabetes RCTs, and to assess under-enrolment of non-white ethnic groups and explore trial characteristics associated with under-enrolment.

METHODS

We conducted a chronological survey by systematically searching the literature to include eligible RCTs published between January 2000 and December 2020. We assessed temporal trends in enrolment of ethnic groups in the included trials. Univariable logistic regression was used to explore the association between trial characteristics and under-enrolment of non-white groups, using a participant to prevalence ratio of <0.8 to define under-enrolment. This study was registered in PROSPERO (CRD42021229100).

RESULTS

We included 405 RCTs for analysis (327 multi-country trials, 69 conducted in the USA and nine conducted in the UK). The median enrolment rate of all non-white groups was 24.0% in the overall RCTs. Trials conducted in the USA and the UK had median enrolment rates of 29.0% and 12.0% for all non-white groups, respectively. There was a temporal trend towards increased participation of non-white ethnic groups in the overall RCTs; however, no significant improvement over time was found in the US or UK trials. Non-white groups were under-enrolled in most included trials: 62.3% (43/69) in US trials and 77.8% (7/9) in UK trials. The US trials with a high female proportion were associated with lower odds of under-enrolment of all non-white groups (OR 0.22; 95% CI 0.07, 0.65), while trials receiving funding from industry showed increased odds of under-enrolment (OR 4.64; 95% CI 1.50, 14.35). Outpatient enrolment and intervention duration were significantly associated with under-enrolment of Black participants. Only a small proportion of trials reported subgroup results or explored the effect modification by ethnicity.

CONCLUSIONS/INTERPRETATION

A temporal trend towards increased non-white ethnic enrolment was found in diabetes RCTs globally, but not in the USA or the UK. Non-white ethnic groups were under-represented in the majority of diabetes trials conducted in the USA and the UK. Some trial characteristics may be associated with non-white under-enrolment in diabetes trials. These findings provide some evidence for non-white ethnic representativeness in diabetes trials over the past two decades, and highlight the need for more effective strategies and endeavours to alleviate under-enrolment of non-white ethnic groups.

Use of Linked Databases for Improved Confounding Control: Considerations for Potential Selection Bias

Pharmacoepidemiologic studies are increasingly conducted within linked databases, often to obtain richer confounder data. However, the potential for selection bias is frequently overlooked when linked data is available only for a subset of patients. We highlight the importance of accounting for potential selection bias by evaluating the association between antipsychotics and type 2 diabetes in youths within a claims database linked to a smaller laboratory database. We used inverse probability of treatment weights (IPTW) to control for confounding. In analyses restricted to the linked cohorts, we applied inverse probability of selection weights (IPSW) to create a population representative of the full cohort. We used pooled logistic regression weighted by IPTW only or IPTW and IPSW to estimate treatment effects. Metabolic conditions were more prevalent in linked cohorts compared with the full cohort. Within the full cohort, the confounding-adjusted hazard ratio was 2.26 (95% CI: 2.07, 2.49) comparing initiation of antipsychotics with initiation of control medications. Within the linked cohorts, a different magnitude of association was obtained without adjustment for selection, whereas applying IPSW resulted in point estimates similar to the full cohort's (e.g., an adjusted hazard ratio of 1.63 became 2.12). Linked database studies may generate biased estimates without proper adjustment for potential selection bias.