Evidence for Detection Bias by Medication Use in a Cohort Study of Breast Cancer Survivors

RoBANS 2: A Revised Risk of Bias Assessment Tool for Nonrandomized Studies of Interventions

Assessment of the risk of bias is an essential component of any systematic review. This is true for both nonrandomized studies and randomized trials, which are the main study designs of systematic reviews. The Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS) was developed in 2013 and has gained wide usage as a risk-of-bias assessment tool for nonrandomized studies. Four risk-of-bias assessment experts revised it by reviewing existing assessment tools and user surveys. The main modifications included additional domains of selection and detection bias susceptible to nonrandomized studies of interventions, a more detailed consideration of the comparability of participants, and more reliable and valid outcome measurements. A psychometric assessment of the revised RoBANS (RoBANS 2) revealed acceptable inter-rater reliability (weighted kappa, 0.25 to 0.49) and construct validity in which intervention effects of studies with an unclear or high risk of bias were overestimated. The RoBANS 2 has acceptable feasibility, fair-to-moderate reliability, and construct validity. It provides a comprehensive framework for allowing authors to assess and understand the plausible risk of bias in nonrandomized studies of interventions.

The Application of Lag Times in Cancer Pharmacoepidemiology: A Narrative Review

With the increasing utilization of medications worldwide, coupled with the increasing availability of long-term data, there is a growing opportunity and need for robust studies evaluating drug-cancer associations. One methodology of importance in such studies is the application of lag times. In this review, we discuss the main reasons for using lag times. Namely, we discuss the typically long latency period of cancer concerning both tumor promoter and initiator effects and outline why cancer latency is a key consideration when choosing a lag time. We also discuss how the use of lag times can help reduce protopathic and detection bias. Finally, we present practical advice for implementing lag periods. In general, we recommend that researchers consider the information that generated the hypothesis as well as clinical and biological knowledge to inform lag period selection. In addition, given that latency periods are usually unknown, we also advocate that researchers examine multiple lag periods in sensitivity analyses as well as duration analyses and flexible modeling approaches.

Concordance between clinical outcomes in the Systolic Blood Pressure Intervention Trial and in the electronic health record

BACKGROUND

Randomized trials are the gold standard for generating clinical practice evidence, but follow-up and outcome ascertainment are resource-intensive. Electronic health record (EHR) data from routine care can be a cost-effective means of follow-up, but concordance with trial-ascertained outcomes is less well-studied.

METHODS

We linked EHR and trial data for participants of the Systolic Blood Pressure Intervention Trial (SPRINT), a randomized trial comparing intensive and standard blood pressure targets. Among participants with available EHR data concurrent to trial-ascertained outcomes, we calculated sensitivity, specificity, positive predictive value, and negative predictive value for EHR-recorded cardiovascular disease (CVD) events, using the gold standard of SPRINT-adjudicated outcomes (myocardial infarction (MI)/acute coronary syndrome (ACS), heart failure, stroke, and composite CVD events). We additionally compared the incidence of non-CVD adverse events (hyponatremia, hypernatremia, hypokalemia, hyperkalemia, bradycardia, and hypotension) in trial versus EHR data.

RESULTS

2468 SPRINT participants were included (mean age 68 (SD 9) years; 26% female). EHR data demonstrated ≥80% sensitivity and specificity, and ≥ 99% negative predictive value for MI/ACS, heart failure, stroke, and composite CVD events. Positive predictive value ranged from 26% (95% CI; 16%, 38%) for heart failure to 52% (95% CI; 37%, 67%) for MI/ACS. EHR data uniformly identified more non-CVD adverse events and higher incidence rates compared with trial ascertainment.

CONCLUSIONS

These results support a role for EHR data collection in clinical trials, particularly for capturing laboratory-based adverse events. EHR data may be an efficient source for CVD outcome ascertainment, though there is clear benefit from adjudication to avoid false positives.

Increasing Pancreatic Cancer Incidence in Young Women in the United States: A Population-Based Time-Trend Analysis, 2001-2018

BACKGROUND & AIMS

Previous studies have shown an increasing incidence of pancreatic cancer (PC), especially in younger women; however, this has not been externally validated. In addition, there are limited data about contributing factors to this trend. We report age and sex-specific time-trend analysis of PC age-adjusted incidence rates (aIRs) using the National Program of Cancer Registries database without Surveillance Epidemiology and End Results data.

METHODS

PC aIR, mortality rates, annual percentage change, and average annual percentage change (AAPC) were calculated and assessed for parallelism and identicalness. Age-specific analyses were conducted in older (≥55 years) and younger (<55 years) adults. PC incidence based on demographics, tumor characteristics, and mortality were evaluated in younger adults.

RESULTS

A total of 454,611 patients were diagnosed with PC between 2001 and 2018 with significantly increasing aIR in women (AAPC = 1.27%) and men (AAPC = 1.14%) without a difference (P = .37). Similar results were seen in older adults. However, in younger adults (53,051 cases; 42.9% women), women experienced a greater increase in aIR than men (AAPCs = 2.36%, P < .001 vs 0.62%, P = 0.62) with nonparallel trends (P < .001) and AAPC difference of 1.74% (P < .001). This AAPC difference appears to be due to rising aIR in Blacks (2.23%; P < .001), adenocarcinoma histopathologic subtype (0.89%; P = .003), and location in the head-of-pancreas (1.64%; P < .001). PC mortality was found to be unchanged in women but decreasing in counterpart men (AAPC difference = 0.54%; P = .001).

CONCLUSION

Using nationwide data, covering ≈64.5% of the U.S. population, we externally validate a rapidly increasing aIR of PC in younger women. There was a big separation of the incidence trend between women and men aged 15-34 years between 2001 and 2018 (>200% difference), and it did not show slowing down.

Associations between frailty and cancer-specific mortality among older women with breast cancer

PURPOSE

Frailty is assessed when making treatment decisions among older women with breast cancer (BC), which in turn impacts survival. We evaluated associations between pre-diagnosis frailty and risks of BC-specific and all-cause mortality in older women.

METHODS

We conducted a retrospective cohort study of Medicare beneficiaries ages ≥ 65 years with stage I-III BC using the Surveillance, Epidemiology and End Results-Medicare Health Outcome Survey Data Resource. Frailty was measured using the deficit-accumulation frailty index, categorized as robust, pre-frail, or frail, at baseline and during follow-up. Fine and Gray competing risk and Cox proportional hazards models were used to estimate subdistribution hazard ratios (SHR) and hazard ratios (HR) with 95% confidence intervals (CI) for BC-specific and all-cause mortality, respectively.

RESULTS

Among 2411 women with a median age of 75 years at BC diagnosis, 49.5% were categorized as robust, 29.4% were pre-frail and 21.1% were frail. Fewer frail women compared to robust women received breast-conserving surgery (52.8% vs. 61.5%, frail vs. robust, respectively) and radiation (43.5% vs. 51.8%). In multivariable analyses, degree of frailty was not associated with BC-specific mortality (frail vs robust SHR 1.47, 95% CI 0.97-2.24). However, frail women with BC had higher risks of all-cause mortality compared to robust women with BC (HR 2.32, 95% CI 1.84-2.92).

CONCLUSION

Among a cohort of older women with BC, higher degrees of frailty were associated with higher risk of all-cause mortality, but not BC-specific mortality. Future study should examine if preventing progression of frailty may improve all-cause mortality.

Population-based recurrence rates among older women with HR-positive, HER2-negative early breast cancer: Clinical risk factors, frailty status, and differences by race

BACKGROUND

Multiple independent risk factors are associated with the prognosis of hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) breast cancer (BC), the most common BC subtype. This study describes U.S. population-based recurrence rates among older, resected women with HR+/HER2- early BC.

METHODS

We conducted a retrospective cohort study of older women diagnosed with incident, invasive stages I-III HR+/HER2- BC who underwent surgery to remove the primary tumor using the Surveillance, Epidemiology, and End Results (SEER)-Medicare Linked Database (2007-2015). SEER records and administrative health claims data were used to ascertain patient and tumor-specific characteristics, treatment, and frailty status. Cumulative incidences of BC recurrence were estimated using a validated algorithm for administrative claims data. Multivariable Fine-Gray competing risk models estimated adjusted subdistribution hazards ratios and 95 % confidence intervals for associations with BC recurrence risk.

RESULTS

Overall, 46,027 women age ≥65 years were included in our analysis. Over a median follow up of 7 years, 6531 women experienced BC recurrence with an estimated 3 and 5-year cumulative incidence rates of 10 % and 16 %, respectively. Higher 3- and 5-year cumulative incidences were observed in women with larger tumor size (5+ cm, 21 % and 28 %), lymph node involvement (4+ nodes, 27 % and 37 %), and with frail health status at diagnosis (13 % and 20 %). Independent of these clinical risk factors, Black, Hispanic and American Indian/Alaskan Native women had significantly increased BC recurrence risks.

CONCLUSIONS

Rates of recurrence in HR+/HER2- early BC differs by several patient and clinical factors, including high-risk tumor characteristics. Racial differences in BC outcomes deserve continued attention from clinicians and policymakers.

Tuberculosis-associated depression: a population-based cohort study of people immigrating to British Columbia, Canada, 1985-2015

PURPOSE

To estimate the risk of tuberculosis (TB)-associated depression. A second aim was to estimate the extent to which any increased risk of depression among TB patients may be mediated by the length of hospital length stay (LOS) METHODS: Retrospective cohort study of linked healthcare claims and public health surveillance data. Our primary outcome, time-to-depression, was analyzed using Cox proportional hazards (PH) regressions. Causal mediation analysis was used to estimate the natural direct and indirect effect of TB mediated by hospital LOS.

RESULTS

Among 755,836 participants (52.2% female, median age=35 years, median follow-up=8.75 years), 2295 were diagnosed with TB (exposure), and 128,963 were diagnosed with depression (outcome). We observed a covariate-adjusted hazard ratio (aHR) of 1.24 (95% CI, 1.14-1.34) for depression by TB. The total effect of TB on depression was decomposed into a natural direct effect of TB of aHR=1.11 (95% CI, 1.02-1.21) and an indirect effect through hospital LOS of aHR=1.11 (95% CI, 1.10-1.12), indicating that TB's total effect was mediated by 50% (95% CI, 35-82%) through hospital LOS.

CONCLUSIONS

TB patients had a 24% higher risk of developing depression. TB's effect was mediated substantially by hospital LOS, requiring further study. Depression screening among TB patients is warranted.

Challenges and Opportunities for Using Big Health Care Data to Advance Medical Science and Public Health

Methodological advancements in epidemiology, biostatistics, and data science have strengthened the research world's ability to use data captured from electronic health records (EHRs) to address pressing medical questions, but gaps remain. We describe methods investments that are needed to curate EHR data toward research quality and to integrate complementary data sources when EHR data alone are insufficient for research goals. We highlight new methods and directions for improving the integrity of medical evidence generated from pragmatic trials, observational studies, and predictive modeling. We also discuss needed methods contributions to further ease data sharing across multisite EHR data networks. Throughout, we identify opportunities for training and for bolstering collaboration among subject matter experts, methodologists, practicing clinicians, and health system leaders to help ensure that methods problems are identified and resulting advances are translated into mainstream research practice more quickly.

The importance and implications of comparator selection in pharmacoepidemiologic research

PURPOSE OF REVIEW

Pharmacoepidemiologic studies employing large databases are critical to evaluating the effectiveness and safety of drug exposures in large and diverse populations. Because treatment is not randomized, researchers must select a relevant comparison group for the treatment of interest. The comparator group can consist of individuals initiating: (1) a similarly indicated treatment (active comparator), (2) a treatment used for a different indication (inactive comparator) or (3) no particular treatment (non-initiators). Herein we review recent literature and describe considerations and implications of comparator selection in pharmacoepidemiologic studies.

RECENT FINDINGS

Comparator selection depends on the scientific question and feasibility constraints. Because pharmacoepidemiologic studies rely on the choice to initiate or not initiate a specific treatment, rather than randomization, they are at-risk for confounding related to the comparator choice including: by indication, disease severity and frailty. We describe forms of confounding specific to pharmacoepidemiologic studies and discuss each comparator along with informative examples and a case study. We provide commentary on potential issues relevant to comparator selection in each study, highlighting the importance of understanding the population in whom the treatment is given and how patient characteristics are associated with the outcome.

SUMMARY

Advanced statistical techniques may be insufficient for reducing confounding in observational studies. Evaluating the extent to which comparator selection may mitigate or induce systematic bias is a critical component of pharmacoepidemiologic studies.