Paradoxical relations of drug treatment with mortality in older persons

Methodological guidance for the use of real-world data to measure exposure and utilization patterns of osteoporosis medications

Observational studies of osteoporosis medications can provide critical real-world evidence (RWE) that fills knowledge gaps left by clinical trials. However, careful consideration of study design is needed to yield reliable estimates of association. In particular, obtaining valid measurements of exposure to osteoporosis medications from real-world data (RWD) sources is complicated due to different medication classes, formulations, and routes of administration, each with different pharmacology. Extended half-lives of bisphosphonates and extended dosing of denosumab and zoledronic acid require particular attention. In addition, prescribing patterns and medication taking behavior often result in gaps in therapy, switching, and concomitant use of osteoporosis therapies. In this review, we present important considerations and provide specialized guidance for measuring osteoporosis drug exposures in RWD. First, we compare different sources of RWD used for osteoporosis drug studies and provide guidance on identifying osteoporosis medication use in these data sources. Next, we provide an overview of osteoporosis pharmacology and how it can influence decisions on exposure measurement within RWD. Finally, we present considerations for the measurement of osteoporosis medication exposure, adherence, switching, long-term exposures, and drug holidays using RWD. Ultimately, a thorough understanding of the differences in RWD sources and the pharmacology of osteoporosis medications is essential to obtain valid estimates of the relationship between osteoporosis medications and outcomes, such as fractures, but also to improve the critical appraisal of published studies.

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials

Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis. There are increasing examples demonstrating the application of EC in precision oncology trials. The prospective application of EC in conducting comparative studies is associated with distinct methodological challenges, the specific considerations for EC use in biomarker-defined subpopulations have not been adequately discussed, and a formal framework is yet to be established. In this review, we present a framework for conducting a prospective comparative analysis using EC. Key steps are (1) defining the purpose of using EC to address the study question, (2) determining if the external data are fit for purpose, (3) developing a transparent study protocol and a statistical analysis plan, and (iv) interpreting results and drawing conclusions on the basis of a prespecified hypothesis. We specify the considerations required for the biomarker-defined subpopulations, which include (1) specifying the comparator and biomarker status of the comparator group, (2) defining lines of treatment, (3) assessment of the biomarker testing panels used, and (4) assessment of cohort stratification in tumor-agnostic studies. We further discuss novel clinical trial designs and statistical techniques leveraging EC to propose future directions to advance evidence generation and facilitate drug development in precision oncology.

Immeasurable Time Bias in Self-controlled Designs: Case-crossover, Case-time-control, and Case-case-time-control Analyses

BACKGROUND

Impact of immeasurable time bias (IMTB) is yet to be examined in self-controlled designs.

METHODS

We conducted case-crossover, case-time-control, and case-case-time-control analyses using Korea's healthcare database. Two empirical examples among elderly patients were used: 1) benzodiazepines-hip fracture; 2) benzodiazepines-mortality. For cases, the date of hip fracture diagnosis or death was defined as the index date, and the inherited date of their matched cases for controls or future cases. Exposure was assessed in the 1-30 day (hazard) and 61-90 day (control) windows preceding the index date. A non-missing exposure setting included in- and outpatient prescriptions and the pseudo-outpatient setting included only the outpatients. Conditional logistic regression was done to estimate odds ratios (ORs) with 95% confidence intervals (CIs), where the relative difference in OR among the two settings was calculated to quantify the IMTB.

RESULTS

The IMTB had negligible impacts in the hip fracture example in the case-crossover (non-missing exposure setting OR 1.27; 95% CI, 1.12-1.44; pseudo-outpatient setting OR 1.21; 95% CI, 1.06-1.39; magnitude 0.05), case-time-control (OR 1.18; 95% CI, 0.98-1.44; OR 1.13; 95% CI, 0.92-1.38; 0.04, respectively), and case-case-time-control analyses (OR 0.99; 95% CI, 0.80-1.23; OR 0.94; 95% CI, 0.75-1.18; 0.05, respectively). In the mortality example, IMTB had significant impacts in the case-crossover (non-missing exposure setting OR 1.44; 95% CI, 1.36-1.52; pseudo-outpatient setting OR 0.72; 95% CI, 0.67-0.78; magnitude 1.00), case-time-control (OR 1.38; 95% CI, 1.26-1.51; OR 0.68; 95% CI, 0.61-0.76; 1.03, respectively), and case-case-time-control analyses (OR 1.27; 95% CI, 1.15-1.40; OR 0.62; 95% CI, 0.55-0.69; 1.05, respectively).

CONCLUSION

Although IMTB had negligible impacts on the drug's effect on acute events, as these are unlikely to be accompanied with hospitalizations, it negatively biased the drug's effect on mortality, an outcome with prodromal phases, in the three self-controlled designs.

Using real-world data for supporting regulatory decision making: Comparison of cardiovascular and safety outcomes of an empagliflozin randomized clinical trial versus real-world data

Aims: In countries where a randomized clinical trial (RCT) is difficult to perform, a real-world evidence (RWE) study with a design similar to an RCT may be an option for drug regulatory decision-making. In this study, the objective was to find out to what extent the safety of empagliflozin from the RWE study in Korea is different from the one in RCT by emulating the design of foreign RCT. The outcome covers various safety outcomes including cardiovascular safety.

Methods: The EMPA-REG OUTCOME trial (NCT01131676) was selected for comparison. The inclusion/exclusion criteria and follow-up method for the RWE were matched to the comparison RCT. Major adverse cardiovascular events (MACEs) were used as a primary outcome and 15 other outcomes were also included for analysis.

Result: We followed 23,126 matched patients with type 2 diabetes mellitus (11,563 empagliflozin users and 11,563 sitagliptin users) for 2.7 years (median). Empagliflozin use was associated with a significantly decreased risk of MACEs [EMPA-REG DUPLICATE RWE: adjusted HR 0.87, 95% confidence interval (CI) 0.79–0.96]. The predefined estimate agreement, regulatory agreement, and standardized difference for RCT duplication were achieved [EMPA-REG OUTCOME RCT: adjusted HR 0.86, 95% (CI) 0.74–0.99]. According to the predefined criteria for 15 outcomes, 10 outcomes were evaluated as good, and three as moderate.

Conclusion: Our study results suggest that RWE in one country in comparison with an RCT has the potential for providing evidence for future regulatory decision-making in an environment where RCT could not be performed.

Cardiovascular Outcomes in Patients Initiating First-Line Treatment of Type 2 Diabetes With Sodium-Glucose Cotransporter-2 Inhibitors Versus Metformin : A Cohort Study

BACKGROUND

Evidence on the risk for cardiovascular events associated with use of first-line sodium-glucose cotransporter-2 inhibitors (SGLT-2i) compared with metformin is limited.

OBJECTIVE

To assess cardiovascular outcomes among adults with type 2 diabetes (T2D) who initiated first-line treatment with SGLT-2i versus metformin.

DESIGN

Population-based cohort study.

SETTING

Claims data from 2 large U.S. commercial and Medicare databases (April 2013 to March 2020).

PARTICIPANTS

Patients with T2D aged 18 years and older (>65 years in Medicare) initiating treatment with SGLT-2i or metformin during April 2013 to March 2020, without any use of antidiabetic medications before cohort entry, were identified. After 1:2 propensity score matching in each database, pooled hazard ratios (HRs) and 95% CIs were reported.

INTERVENTION

First-line SGLT-2i (canagliflozin, empagliflozin, or dapagliflozin) or metformin.

MEASUREMENTS

Primary outcomes were a composite of hospitalization for myocardial infarction (MI), hospitalization for ischemic or hemorrhagic stroke or all-cause mortality (MI/stroke/mortality), and a composite of hospitalization for heart failure (HHF) or all-cause mortality (HHF/mortality). Safety outcomes including genital infections were assessed.

RESULTS

Among 8613 first-line SGLT-2i initiators matched to 17 226 metformin initiators, SGLT-2i initiators had a similar risk for MI/stroke/mortality (HR, 0.96; 95% CI, 0.77 to 1.19) and a lower risk for HHF/mortality (HR, 0.80; CI, 0.66 to 0.97) during a mean follow-up of 12 months. Initiators receiving SGLT-2i showed a lower risk for HHF (HR, 0.78; CI, 0.63 to 0.97), a numerically lower risk for MI (HR, 0.70; CI, 0.48 to 1.00), and similar risk for stroke, mortality, and MI/stroke/HHF/mortality compared with metformin. Initiators receiving SGLT-2i had a higher risk for genital infections (HR, 2.19; CI, 1.91 to 2.51) and otherwise similar safety as those receiving metformin.

LIMITATION

Treatment selection was not randomized.

CONCLUSION

As first-line T2D treatment, initiators receiving SGLT-2i showed a similar risk for MI/stroke/mortality, lower risk for HHF/mortality and HHF, and a similar safety profile except for an increased risk for genital infections compared with those receiving metformin.

PRIMARY FUNDING SOURCE

Brigham and Women's Hospital and Harvard Medical School.

Evaluating Confounding Control in Estimations of Influenza Antiviral Effectiveness in Electronic Health Plan Data

Observational studies of oseltamivir use and influenza complications could suffer from residual confounding. Using negative control risk periods and a negative control outcome, we examined confounding control in a health-insurance-claims-based study of oseltamivir and influenza complications (pneumonia, all-cause hospitalization, and dispensing of an antibiotic). Within the Food and Drug Administration's Sentinel System, we identified individuals aged ≥18 years who initiated oseltamivir use on the influenza diagnosis date versus those who did not, during 3 influenza seasons (2014-2017). We evaluated primary outcomes within the following 1-30 days (the primary risk period) and 61-90 days (the negative control period) and nonvertebral fractures (the negative control outcome) within days 1-30. We estimated propensity-score-matched risk ratios (RRs) per season. During the 2014-2015 influenza season, oseltamivir use was associated with a reduction in the risk of pneumonia (RR = 0.72, 95% confidence interval (CI): 0.70, 0.75) and all-cause hospitalization (RR = 0.54, 95% CI: 0.53, 0.55) in days 1-30. During days 61-90, estimates were near-null for pneumonia (RR = 1.04, 95% CI: 0.95, 1.15) and hospitalization (RR = 0.94, 95% CI: 0.91, 0.98) but slightly increased for antibiotic dispensing (RR = 1.14, 95% CI: 1.08, 1.21). The RR for fractures was near-null (RR = 1.09, 95% CI: 0.99, 1.20). Estimates for the 2016-2017 influenza season were comparable, while the 2015-2016 season had conflicting results. Our study suggests minimal residual confounding for specific outcomes, but results differed by season.

Evolving channeling in prescribing SGLT-2 inhibitors as first-line treatment for type 2 diabetes

PURPOSE

Sodium-glucose cotransporter-2 inhibitors (SGLT-2i) are increasingly being considered as first-line treatment for type 2 diabetes (T2D). The benefits of SGLT-2i from cardiovascular outcome trials may lead to preferential prescribing of SGLT-2i to patients at high cardiovascular risk, possibly causing confounding in non-randomized studies of SGLT-2i as first-line treatment. We assessed evolving imbalances in characteristics of patients starting SGLT-2i versus metformin as first-line monotherapy.

METHODS

Using claims data from two US commercial health insurance and Medicare, we identified patients with T2D aged ≥18 years (>65 years in Medicare) initiating first-line SGLT-2i or metformin from 2013 through 2019. Standardized differences (SDs) for patient characteristics were assessed during four consecutive calendar time blocks (T1:4/2013-12/2014; T2:1/2015-6/2016; T3:7/2016-12/2017; and T4:1/2018-12/2019). We also estimated the propensity score of receiving SGLT-2i versus metformin within each time block and evaluated time trends in model discrimination with c-statistics.

RESULTS

We identified 9113 initiators of first-line SGLT-2i and 810 348 initiators of first-line metformin. During T1, SGLT-2i initiators were younger (SD = -0.24) and less likely to have seen cardiologists (-0.07) with a similar prevalence of CVD (0.04) compared with metformin. During T4, patients were more balanced for age (-0.01). Cardiologist visits (0.08) and CVD (0.25) became more prevalent among SGLT-2i initiators.

CONCLUSIONS

When comparing initiators of first-line SGLT-2i versus metformin, imbalances in patient characteristics evolved from 2013 through 2019, particularly channeling SGLT-2i to individuals at high cardiovascular risk. Evolving channeling in prescribing first-line SGLT-2i should be expected and accounted for in non-randomized comparative effectiveness research.

The impact of high-risk medications on mortality risk among older adults with polypharmacy: evidence from the English Longitudinal Study of Ageing

BACKGROUND

Polypharmacy is common among older people and is associated with an increased mortality risk. However, little is known about whether the mortality risk is related to specific medications among older adults with polypharmacy. This study therefore aimed to investigate associations between high-risk medications and all-cause and cause-specific mortality among older adults with polypharmacy.

METHODS

This study included 1356 older adults with polypharmacy (5+ long-term medications a day for conditions or symptoms) from Wave 6 (2012/2013) of the English Longitudinal Study of Ageing. First, using the agglomerative hierarchical clustering method, participants were grouped according to the use of 14 high-risk medication categories. Next, the relationship between the high-risk medication patterns and all-cause and cause-specific mortality (followed up to April 2018) was examined. All-cause mortality was assessed by Cox proportional hazards model and competing-risk regression was employed for cause-specific mortality.

RESULTS

Five high-risk medication patterns-a renin-angiotensin-aldosterone system (RAAS) inhibitors cluster, a mental health drugs cluster, a central nervous system (CNS) drugs cluster, a RAAS inhibitors and antithrombotics cluster, and an antithrombotics cluster-were identified. The mental health drugs cluster showed increased risks of all-cause (HR = 1.55, 95%CI = 1.05, 2.28) and cardiovascular disease (CVD) (SHR = 2.11, 95%CI = 1.10, 4.05) mortality compared with the CNS drug cluster over 6 years, while others showed no differences in mortality. Among these patterns, the mental health drugs cluster showed the highest prevalence of antidepressants (64.1%), benzodiazepines (10.4%), antipsychotics (2.4%), antimanic agents (0.7%), opioids (33.2%), and muscle relaxants (21.5%). The findings suggested that older adults with polypharmacy who took mental health drugs (primarily antidepressants), opioids, and muscle relaxants were at higher risk of all-cause and CVD mortality, compared with those who did not take these types of medications.

CONCLUSIONS

This study supports the inclusion of opioids in the current guidance on structured medication reviews, but it also suggests that older adults with polypharmacy who take psychotropic medications and muscle relaxants are prone to adverse outcomes and therefore may need more attention. The reinforcement of structured medication reviews would contribute to early intervention in medication use which may consequently reduce medication-related problems and bring clinical benefits to older adults with polypharmacy.

Antidepressant use and stroke or mortality risk in the elderly

BACKGROUND AND PURPOSE

Current evidence on antidepressant-related stroke or mortality risk is inconsistent. Because the elderly have the highest exposure to antidepressants, the aim was to quantify their association with stroke and mortality risks in this vulnerable population.

METHODS

Persons over 65 years old and registered in the Information System for Research in Primary Care of Catalonia during 2010-2015 comprised the study population. Antidepressant exposure was categorized into current-users, recent-users, past-users and antidepressant non-users (controls). The effect of antidepressant exposure on stroke or death, whichever came first, was analyzed by Cox regression adjusted for established risk factors.

RESULTS

Of the 1,068,117 participants included, 20% had antidepressant reimbursements during follow-up, 17% had a stroke and 3% died. The risk of experiencing stroke or death was higher in antidepressant current-users (hazard ratio [HR] 1.04; 95% confidence interval [CI] 1.02-1.06), recent-users (HR 3.34; 95% CI 3.27-3.41) and past-users (HR 2.06; 95% CI 2.02-2.10) compared to antidepressant non-users. Antidepressant current-use was associated with increased stroke (HR 1.56; 95% CI 1.50-1.61) but decreased mortality risk (HR 0.93; 95% CI 0.91-0.94). During antidepressant recent-use and past-use, both stroke and mortality risks were significantly increased compared to no antidepressant use.

CONCLUSIONS

Antidepressant use may be associated with increased stroke risk in the elderly. When using antidepressants in this population, the potential risks should be considered.

Association of glaucoma with 10-year mortality in a population-based longitudinal study in urban Southern China: the Liwan Eye Study

OBJECTIVES

To investigate the association between glaucoma and 10-year mortality rate in an adult population in China.

DESIGN

Population-based cohort study.

SETTING

The Liwan Eye Study, China.

PARTICIPANTS

1405 baseline participants aged 50 years and older were invited to attend a 10-year follow-up examination.

PRIMARY AND SECONDARY OUTCOME MEASURES

The International Society of Geographic and Epidemiologic Ophthalmology criteria was used to define glaucoma. Detailed information of mortality was confirmed using the Chinese Centre for Disease Control and Prevention. Presenting visual impairment (PVI) was defined as a presenting visual acuity of less than 20/40 in the better-seeing eye. The 10-year mortality rates were compared using the log-rank test. Cox proportional hazards regression models were used to investigate the association between glaucoma and mortality.

RESULTS

A total of 1372 (97.7%) participants with available gonioscopic data were included in the analysis. Of these, 136 (9.9%), 33 (2.4%) and 21 (1.5%) participants had primary angle closure (PAC) suspect (PACS), PAC and PAC glaucoma (PACG), and 29 (2.1%) had primary open angle glaucoma (POAG). After 10 years, 306 (22.3%) participants were deceased. The 10-year mortality was significantly associated with PACG (HR, 2.15, 95% CI 1.14 to 4.04, p=0.018) but not associated with PAC (HR, 1.27, 95% CI 0.67 to 2.39, p=0.463), PACS (HR, 1.32, 95% CI 0.95 to 1.83, p=0.099) and POAG (HR, 0.74, 95% CI 0.36 to 1.49, p=0.395) when age and gender were adjusted for. This association was no longer statistically significant (HR, 1.60, 95% CI 0.70 to 3.61, p=0.263) when covariables, such as income, education, body mass index, PVI, history of diabetes and hypertension, were adjusted for. Larger vertical cup-to-disc ratio (VCDR >0.30) was only a significant risk factor in multivariable analysis (HR, 1.60, 95% CI 1.11 to 2.33, p=0.011).

CONCLUSIONS

PACG was significantly associated with higher long-term mortality, but this association was likely to be confounded by other systemic risk factors. VCDR >0.3 was the only independent predictor, implying that it may be a marker of ageing and frailty.

Systematic Review and Meta-Analysis on the Associations of Polypharmacy and Potentially Inappropriate Medication With Adverse Outcomes in Older Cancer Patients

BACKGROUND

Both polypharmacy and potentially inappropriate medication (PIM) intake are highly prevailing in older cancer patients. However, only studies on the association of polypharmacy and postoperative complications have been meta-analyzed previously.

METHODS

A systematic review and a meta-analysis of prospective/retrospective observational studies reporting associations of polypharmacy or PIM with at least one out of five predefined adverse health outcomes in a population of older cancer patients (≥60 years) were carried out. PubMed and Web of Science were used to search for relevant studies published between January 1991 and March 2020. Data were pooled by adopting a random-effects model.

RESULTS

Overall, 42 publications were included in the systematic review. Meta-analyses could be performed on 39 studies about polypharmacy and 13 studies about PIM. Polypharmacy was found to be statistically significantly associated with all-cause mortality (risk ratio [95% confidence interval]: 1.37 [1.25-1.50]), hospitalization (1.53 [1.37-1.71]), treatment-related toxicity (1.22 [1.01-1.47]), and postoperative complications (1.73 [1.36-2.20]). The association of polypharmacy with prolongation of hospitalization was not statistically significant at the p < .05 significance level (1.62 [0.98-2.66]). With respect to PIM, a statistically significant association with all-cause mortality (1.43 [1.08-1.88]) was observed but not with other adverse outcomes.

CONCLUSIONS

Polypharmacy was found to be associated with several adverse outcomes and PIM use with all-cause mortality in older cancer patients. However, these results should be interpreted with caution because about three-quarters of the studies identified did not adjust for comorbidity and are prone to confounding by indication.

Hydroxychloroquine and Mortality Among Patients With Systemic Lupus Erythematosus in the General Population

OBJECTIVE

Hydroxychloroquine (HCQ) has been associated with improved survival among patients with systemic lupus erythematosus (SLE) from tertiary referral centers. We aimed to determine the potential impact of HCQ use on the risk of mortality among SLE patients in the general population.

METHODS

We conducted a nested case-control study within an incident SLE cohort from the entire population of British Columbia, Canada. Deceased patients were matched with up to 3 living controls by age, sex, and SLE disease duration. HCQ exposure was categorized by the time between the last HCQ prescription date covered (i.e., end of supply) and the index date (i.e., death date) as current (<30 days), recent (30-365 days), remote (>365 days), or never used. We used conditional logistic regression to assess the risk of all-cause mortality associated with current or recent HCQ exposure compared with remote HCQ users.

RESULTS

Among 6,241 patients with incident SLE, we identified 290 deceased patients and 502 matched SLE controls. Adjusted odd ratios for all-cause mortality were 0.50 (95% confidence interval [95% CI] 0.30-0.82) for current users and 2.47 (95% CI 1.21-5.05) for recent users compared with remote users. Associations were similar in subgroups according to SLE duration (≤5 years versus >5 years).

CONCLUSION

Our general population data support a substantial survival benefit associated with current HCQ use. Increased mortality among patients who had discontinued HCQ recently could be due to a sick stopper effect or the loss of actual HCQ benefits.

An Aggregated Comorbidity Measure Based on History of Filled Drug Prescriptions: Development and Evaluation in Two Separate Cohorts

BACKGROUND

The ability to account for comorbidity when estimating survival in a population diagnosed with cancer could be improved by using a drug comorbidity index based on filled drug prescriptions.

METHODS

We created a drug comorbidity index from age-stratified univariable associations between filled drug prescriptions and time to death in 326,450 control males randomly selected from the general population to men with prostate cancer. We also evaluated the index in 272,214 control females randomly selected from the general population to women with breast cancer.

RESULTS

The new drug comorbidity index predicted survival better than the Charlson Comorbidity Index (CCI) and a previously published prescription index during 11 years of follow-up. The concordance (C)-index for the new index was 0.73 in male and 0.76 in the female population, as compared with a C-index of 0.67 in men and 0.69 in women for the CCI. In men of age 75-84 years with CCI = 0, the median survival time was 7.1 years (95% confidence interval [CI] = 7.0, 7.3) in the highest index quartile. Comparing the highest to the lowest drug comorbidity index quartile resulted in a hazard ratio (HR) of 2.2 among men (95% CI = 2.1, 2.3) and 2.4 among women (95% CI = 2.3, 2.6).

CONCLUSIONS

A new drug comorbidity index based on filled drug prescriptions improved prediction of survival beyond age and the CCI alone. The index will allow a more accurate baseline estimation of expected survival for comparing treatment outcomes and evaluating treatment guidelines in populations of people with cancer.

Risks associated with continuation of potentially inappropriate antihypertensive medications in older adults receiving hemodialysis

Background and objectives

After dialysis initiation, older adults may experience orthostatic or post-dialysis hypotension. Some orthostasis-causing antihypertensives (i.e., central alpha agonists and alpha blockers), are considered potentially inappropriate medications (PIMs) for older adults because they carry more risk than benefit. We sought to (1) describe antihypertensive PIM prescribing patterns before and after dialysis initiation and (2) ascertain the potential risk of adverse outcomes when these medications are continued after dialysis initiation.

Design, setting, participants, and measurements

Using United States Renal Data System data, we evaluated monthly prevalence of antihypertensive PIM claims in the period before and after dialysis initiation among older adults aged ≥66 years initiating in-center hemodialysis in the US between 2013 and 2014. Patients with an antihypertensive PIM prescription at hemodialysis initiation and who survived for 120 days were classified as ‘continuers’ or ‘discontinuers’ based on presence or absence of a refill within the 120 days after initiation. We compared rates of hospitalization and risk of death across these groups from day 121 through 24 months after dialysis initiation.

Results

Our study included 30,760 total patients, of whom 5981 (19%) patients had an antihypertensive PIM claim at dialysis initiation and survived ≥120 days. Most [65% (n = 3920)] were continuers. Those who continued (versus discontinued) were more likely to be black race (26% versus 21%), have dual Medicare-Medicaid coverage (31% versus 27%), have more medications on average (12 versus 9) and have no functional limitations (84% versus 80%). Continuers experienced fewer all-cause hospitalizations and deaths, but neither were statistically significant after adjustment (Hospitalization: RR 0.93, 95% CI 0.86, 1.00; Death: HR 0.89, 95% CI: 0.78–1.02).

Conclusions

Nearly one in five older adults had an antihypertensive PIM at dialysis initiation. Among those who survived ≥120 days, continuation of an antihypertensive PIM was not associated with increased risk of all-cause hospitalization or mortality.

When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?

The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real-world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design-related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high-quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions.

Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis

BACKGROUND

Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension.

METHODS

In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296.

FINDINGS

Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84-1·14) or combination use exposure (1·01, 0·90-1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68-1·21; with heterogeneity of >40%) or combination use (0·95, 0·83-1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79-0·99) and non-significant for monotherapy (0·85, 0·69-1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons.

INTERPRETATION

No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19.

FUNDING

Wellcome Trust, UK National Institute for Health Research, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, IQVIA, South Korean Ministry of Health and Welfare Republic, Australian National Health and Medical Research Council, and European Health Data and Evidence Network.

Measuring Frailty in Administrative Claims Data: Comparative Performance of Four Claims-Based Frailty Measures in the U.S. Medicare Data

BACKGROUND

There has been increasing effort to measure frailty in the U.S. Medicare data. The performance of claims-based frailty measures has not been compared.

METHODS

This cross-sectional study included 3,097 community-dwelling fee-for-service Medicare beneficiaries (mean age 75.6 years) who participated in the 2008 Health and Retirement Study examination. Four claims-based frailty measures developed by Davidoff, Faurot, Segal, and Kim were compared against frailty phenotype, a deficit-accumulation frailty index (FI), and activities of daily living (ADL) dependence using Spearman correlation coefficients and C-statistics.

RESULTS

Claims-based frailty measures were positively associated with frailty phenotype (prevalence in ≤10th vs >90th percentile: 8.0% vs 41.3% for Davidoff; 5.9% vs 53.1% for Faurot; 3.3% vs 48.0% for Segal; 2.9% vs 51.0% for Kim) and FI (mean in ≤10th vs >90th percentile: 0.17 vs 0.33 for Davidoff; 0.13 vs 0.37 for Faurot; 0.12 vs 0.31 for Segal; 0.10 vs 0.37 for Kim). The age and sex-adjusted C-statistics for frailty phenotype for Davidoff, Faurot, Segal, and Kim indices were 0.73, 0.74, 0.73, and 0.78, respectively, and partial correlation coefficients with FI were 0.18, 0.32, 0.26, and 0.55, respectively. The results for ADL dependence were similar (prevalence in ≤10th vs >90th percentile: 3.7% vs 50.5% for Davidoff; 2.3% vs 55.0% for Faurot; 3.0% vs 38.3% for Segal; 2.3% vs 50.8% for Kim). The age and sex-adjusted C-statistics for the indices were 0.79, 0.80, 0.74, and 0.81, respectively.

CONCLUSIONS

The choice of a claims-based frailty measure can influence the identification of older adults with frailty and disability in Medicare data.

Using propensity scores to estimate effects of treatment initiation decisions: State of the science

Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effects. Propensity score methods allow researchers to reduce bias from measured confounding by summarizing the distributions of many measured confounders in a single score based on the probability of receiving treatment. This score can then be used to mitigate imbalances in the distributions of these measured confounders between those who received the treatment of interest and those in the comparator population, resulting in less biased treatment effect estimates. This methodology was formalized by Rosenbaum and Rubin in 1983 and, since then, has been used increasingly often across a wide variety of scientific disciplines. In this review article, we provide an overview of propensity scores in the context of real-world evidence generation with a focus on their use in the setting of single treatment decisions, that is, choosing between two therapeutic options. We describe five aspects of propensity score analysis: alignment with the potential outcomes framework, implications for study design, estimation procedures, implementation options, and reporting. We add context to these concepts by highlighting how the types of comparator used, the implementation method, and balance assessment techniques have changed over time. Finally, we discuss evolving applications of propensity scores.

Emulating Randomized Clinical Trials With Nonrandomized Real-World Evidence Studies: First Results From the RCT DUPLICATE Initiative

BACKGROUND

Regulators are evaluating the use of noninterventional real-world evidence (RWE) studies to assess the effectiveness of medical products. The RCT DUPLICATE initiative (Randomized, Controlled Trials Duplicated Using Prospective Longitudinal Insurance Claims: Applying Techniques of Epidemiology) uses a structured process to design RWE studies emulating randomized, controlled trials (RCTs) and compare results. We report findings of the first 10 trial emulations, evaluating cardiovascular outcomes of antidiabetic or antiplatelet medications.

METHODS

We selected 3 active-controlled and 7 placebo-controlled RCTs for replication. Using patient-level claims data from US commercial and Medicare payers, we implemented inclusion and exclusion criteria, selected primary end points, and comparator populations to emulate those of each corresponding RCT. Within the trial-mimicking populations, we conducted propensity score matching to control for >120 preexposure confounders. All study measures were prospectively defined and protocols registered before hazard ratios and 95% CIs were computed. Success criteria for the primary analysis were prespecified for each replication.

RESULTS

Despite attempts to emulate RCT design as closely as possible, differences between the RCT and corresponding RWE study populations remained. The regulatory conclusions were equivalent in 6 of 10. The RWE emulations achieved a hazard ratio estimate that was within the 95% CI from the corresponding RCT in 8 of 10 studies. In 9 of 10, either the regulatory or estimate agreement success criteria were fulfilled. The largest differences in effect estimates were found for RCTs where second-generation sulfonylureas were used as a proxy for placebo regarding cardiovascular effects. Nine of 10 replications had a standardized difference between effect estimates of <2, which suggests differences within expected random variation.

CONCLUSIONS

Agreement between RCT and RWE findings varies depending on which agreement metric is used. Interim findings indicate that selection of active comparator therapies with similar indications and use patterns enhances the validity of RWE. Even in the context of active comparators, concordance between RCT and RWE findings is not guaranteed, partially because trials are not emulated exactly. More trial emulations are needed to understand how often and in what contexts RWE findings match RCTs. Registration: URL: https://www.clinicaltrials.gov; Unique identifiers: NCT03936049, NCT04215523, NCT04215536, NCT03936010, NCT03936036, NCT03936062, NCT03936023, NCT03648424, NCT04237935, NCT04237922.

New methodological approaches were able to effectively reduce immeasurable time bias in case-only designs

OBJECTIVES

The objective of this study was to assess approaches to reduce immeasurable time bias in case-crossover (CCO), case-time-control (CTC), and case-case-time-control (CCTC) designs.

STUDY DESIGN AND SETTING

We used Korea's health care database that has inpatient and outpatient prescriptions and an empirical example of benzodiazepines and mortality among the elderly. We defined our unbiased exposure setting using all prescriptions and a pseudo-outpatient setting using outpatient records only. In the pseudo-outpatient setting, we assessed 10 approaches of restricting, adjusting, stratifying, or weighting on hospitalization-related factors. We conducted conditional logistic regression to estimate odds ratio (OR) with 95% confidence intervals (CI), where an approach was considered effective when its OR was within the unbiased exposure setting OR's 95% CI.

RESULTS

Immeasurable time bias negatively biased the unbiased exposure setting's OR in all three case-only designs, overestimating the protective effect of benzodiazepines on mortality. Of the 10 approaches examined, stratifying the proportion of hospitalized time in 0.01 intervals most effectively repaired the bias in the CCO (OR 1.25, 95% CI 1.10-1.43) and CTC analyses (1.11, 0.95-1.30); no approach was effective in the CCTC analysis.

CONCLUSION

Stratifying the proportion of hospitalized time in 0.01 intervals best approximated the unbiased exposure setting estimate by overcoming the significant impact of immeasurable time bias in CCO and CTC designs.

Adverse outcomes and mortality in users of non-steroidal anti-inflammatory drugs who tested positive for SARS-CoV-2: A Danish nationwide cohort study

BACKGROUND

Concerns over the safety of non-steroidal anti-inflammatory drug (NSAID) use during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection have been raised. We studied whether use of NSAIDs was associated with adverse outcomes and mortality during SARS-CoV-2 infection.

METHODS AND FINDINGS

We conducted a population-based cohort study using Danish administrative and health registries. We included individuals who tested positive for SARS-CoV-2 during the period 27 February 2020 to 29 April 2020. NSAID users (defined as individuals having filled a prescription for NSAIDs up to 30 days before the SARS-CoV-2 test) were matched to up to 4 non-users on calendar week of the test date and propensity scores based on age, sex, relevant comorbidities, and use of selected prescription drugs. The main outcome was 30-day mortality, and NSAID users were compared to non-users using risk ratios (RRs) and risk differences (RDs). Secondary outcomes included hospitalization, intensive care unit (ICU) admission, mechanical ventilation, and acute renal replacement therapy. A total of 9,236 SARS-CoV-2 PCR-positive individuals were eligible for inclusion. The median age in the study cohort was 50 years, and 58% were female. Of these, 248 (2.7%) had filled a prescription for NSAIDs, and 535 (5.8%) died within 30 days. In the matched analyses, treatment with NSAIDs was not associated with 30-day mortality (RR 1.02, 95% CI 0.57 to 1.82, p = 0.95; RD 0.1%, 95% CI -3.5% to 3.7%, p = 0.95), risk of hospitalization (RR 1.16, 95% CI 0.87 to 1.53, p = 0.31; RD 3.3%, 95% CI -3.4% to 10%, p = 0.33), ICU admission (RR 1.04, 95% CI 0.54 to 2.02, p = 0.90; RD 0.2%, 95% CI -3.0% to 3.4%, p = 0.90), mechanical ventilation (RR 1.14, 95% CI 0.56 to 2.30, p = 0.72; RD 0.5%, 95% CI -2.5% to 3.6%, p = 0.73), or renal replacement therapy (RR 0.86, 95% CI 0.24 to 3.09, p = 0.81; RD -0.2%, 95% CI -2.0% to 1.6%, p = 0.81). The main limitations of the study are possible exposure misclassification, as not all individuals who fill an NSAID prescription use the drug continuously, and possible residual confounding by indication, as NSAIDs may generally be prescribed to healthier individuals due to their side effects, but on the other hand may also be prescribed for early symptoms of severe COVID-19.

CONCLUSIONS

Use of NSAIDs was not associated with 30-day mortality, hospitalization, ICU admission, mechanical ventilation, or renal replacement therapy in Danish individuals who tested positive for SARS-CoV-2.

TRIAL REGISTRATION

The European Union electronic Register of Post-Authorisation Studies EUPAS34734.

Real-world evidence: the devil is in the detail

Much has been written about real-world evidence (RWE), a concept that offers an understanding of the effects of healthcare interventions using routine clinical data. The reflection of diverse real-world practices is a double-edged sword that makes RWE attractive but also opens doors to several biases that need to be minimised both in the design and analytical phases of non-experimental studies. Additionally, it is critical to ensure that researchers who conduct these studies possess adequate methodological expertise and ability to accurately implement these methods. Critical design elements to be considered should include a clearly defined research question using a causal inference framework, choice of a fit-for-purpose data source, inclusion of new users of a treatment with comparators that are as similar as possible to that group, accurately classifying person-time and deciding censoring approaches. Having taken measures to minimise bias 'by design', the next step is to implement appropriate analytical techniques (for example propensity scores) to minimise the remnant potential biases. A clear protocol should be provided at the beginning of the study and a report of the results after, including caveats to consider. We also point the readers to readings on some novel analytical methods as well as newer areas of application of RWE. While there is no one-size-fits-all solution to evaluating RWE studies, we have focused our discussion on key methods and issues commonly encountered in comparative observational cohort studies with the hope that readers are better equipped to evaluate non-experimental studies that they encounter in the future. Graphical abstract.

Association of Nonsteroidal Anti-inflammatory Drug Use and Adverse Outcomes Among Patients Hospitalized With Influenza

IMPORTANCE

During the ongoing coronavirus disease 2019 pandemic, case reports have suggested that the use of nonsteroidal anti-inflammatory drugs (NSAIDs) may lead to adverse outcomes.

OBJECTIVE

To study the association of NSAID use with adverse outcomes in patients hospitalized with influenza or influenza pneumonia.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used propensity score matching among 7747 individuals aged 40 years or older who were hospitalized with influenza, confirmed by polymerase chain reaction or antigen testing, between 2010 and 2018. Data were collected using Danish nationwide registers. All analyses reported were performed on May 29, 2020.

EXPOSURES

Prescription fill of an NSAID within 60 days before admission.

MAIN OUTCOMES AND MEASURES

Risk ratio (RR) and risk difference (RD) with 95% CIs for intensive care unit admission and death within 30 days of admission.

RESULTS

A total of 7747 patients (median [interquartile range] age, 71 [59-80] years, 3980 [51.4%] men) with confirmed influenza were identified. Of these, 520 (6.7%) were exposed to NSAIDs. In the unmatched cohorts, 104 of 520 patients (20.0%) who used NSAIDs and 958 of 7227 patients (13.3%) who did not use NSAIDs were admitted to the intensive care unit. For death within 30 days of admission, we observed 37 events (7.1%) among those who used NSAIDs compared with 563 events (7.8%) among those who did not. Current NSAID use was associated with intensive care unit admission (RR, 1.51; 95% CI, 1.26 to 1.81; RD, 6.7%; 95% CI, 3.2% to 10.3%), while NSAID use was not associated with death (RR, 0.91; 95% CI, 0.66 to 1.26; RD, -0.7%; 95% CI, -3.0% to 1.6%). In the matched cohorts, risks were unchanged for patients who used NSAIDs, while 83 ICU admissions (16.0%) and 36 deaths (6.9%) were observed among matched individuals who did not use NSAIDs. Matched (ie, adjusted) analyses yielded attenuated risk estimates for intensive care unit admission (RR, 1.25; 95% CI, 0.95 to 1.63; RD, 4.0%; 95% CI, -0.6% to 8.7%) and death (RR, 1.03; 95% CI, 0.66 to 1.60; RD, 0.2%; 95% CI, -2.9% to 3.3%). Associations were more pronounced among patients who used NSAIDs for a longer period (eg, for intensive care unit admission: RR, 1.90; 95% CI, 1.19 to 3.06; RD, 13.4%; 95% CI, 4.0% to 22.8%).

CONCLUSIONS AND RELEVANCE

In this cohort study of adult patients hospitalized with influenza, the use of NSAIDs was not associated with 30-day intensive care unit admission or death in adjusted analyses. There was an association between long-term use of NSAIDs and intensive care unit admission.

Renin-angiotensin system blockers and susceptibility to COVID-19: a multinational open science cohort study

INTRODUCTION

Angiotensin converting enzyme inhibitors (ACEs) and angiotensin receptor blockers (ARBs) could influence infection risk of coronavirus disease (COVID-19). Observational studies to date lack pre-specification, transparency, rigorous ascertainment adjustment and international generalizability, with contradictory results.

METHODS

Using electronic health records from Spain (SIDIAP) and the United States (Columbia University Irving Medical Center and Department of Veterans Affairs), we conducted a systematic cohort study with prevalent ACE, ARB, calcium channel blocker (CCB) and thiazide diuretic (THZ) use to determine relative risk of COVID-19 diagnosis and related hospitalization outcomes. The study addressed confounding through large-scale propensity score adjustment and negative control experiments.

RESULTS

Following over 1.1 million antihypertensive users identified between November 2019 and January 2020, we observed no significant difference in relative COVID-19 diagnosis risk comparing ACE/ARB vs CCB/THZ monotherapy (hazard ratio: 0.98; 95% CI 0.84 - 1.14), nor any difference for mono/combination use (1.01; 0.90 - 1.15). ACE alone and ARB alone similarly showed no relative risk difference when compared to CCB/THZ monotherapy or mono/combination use. Directly comparing ACE vs. ARB demonstrated a moderately lower risk with ACE, non-significant for monotherapy (0.85; 0.69 - 1.05) and marginally significant for mono/combination users (0.88; 0.79 - 0.99). We observed, however, no significant difference between drug- classes for COVID-19 hospitalization or pneumonia risk across all comparisons.

CONCLUSION

There is no clinically significant increased risk of COVID-19 diagnosis or hospitalization with ACE or ARB use. Users should not discontinue or change their treatment to avoid COVID-19.

Validation of a Claims-Based Frailty Index Against Physical Performance and Adverse Health Outcomes in the Health and Retirement Study

BACKGROUND

A claims-based frailty index (CFI) was developed based on a deficit-accumulation approach using self-reported health information. This study aimed to independently validate the CFI against physical performance and adverse health outcomes.

METHODS

This retrospective cohort study included 3,642 community-dwelling older adults who had at least 1 health care encounter in the year prior to assessments of physical performance in the 2008 Health and Retirement Study wave. A CFI was estimated from Medicare claims data in the past year. Gait speed, grip strength, and the 2-year risk of death, institutionalization, disability, hospitalization, and prolonged (>30 days) skilled nursing facility (SNF) stay were evaluated for CFI categories (robust: <0.15, prefrail: 0.15-0.24, mildly frail: 0.25-0.34, moderate-to-severely frail: ≥0.35).

RESULTS

The prevalence of robust, prefrail, mildly frail, and moderate-to-severely frail state was 52.7%, 38.0%, 7.1%, and 2.2%, respectively. Individuals with higher CFI had lower mean gait speed (moderate-to-severely frail vs robust: 0.39 vs 0.78 m/s) and weaker grip strength (19.8 vs 28.5 kg). Higher CFI was associated with death (moderate-to-severely frail vs robust: 46% vs 7%), institutionalization (21% vs 5%), activity of daily living disability (33% vs 9%), instrumental activity of daily living disability (100% vs 22%), hospitalization (79% vs 23%), and prolonged SNF stay (17% vs 2%). The odds ratios per 1-SD (=0.07) difference in CFI were 1.46-2.06 for these outcomes, which remained statistically significant after adjustment for age, sex, and a comorbidity index.

CONCLUSION

The CFI is useful to identify individuals with poor physical function and at greater risks of adverse health outcomes in Medicare data.

Methodological considerations when analysing and interpreting real-world data

In the absence of relevant data from randomized trials, nonexperimental studies are needed to estimate treatment effects on clinically meaningful outcomes. State-of-the-art study design is imperative for minimizing the potential for bias when using large healthcare databases (e.g. claims data, electronic health records, and product/disease registries). Critical design elements include new-users (begin follow-up at treatment initiation) reflecting hypothetical interventions and clear timelines, active-comparators (comparing treatment alternatives for the same indication), and consideration of induction and latent periods. Propensity scores can be used to balance measured covariates between treatment regimens and thus control for measured confounding. Immortal-time bias can be avoided by defining initiation of therapy and follow-up consistently between treatment groups. The aim of this manuscript is to provide a non-technical overview of study design issues and solutions and to highlight the importance of study design to minimize bias in nonexperimental studies using real-world data.

Temporal Trends and Factors Associated with Bisphosphonate Discontinuation and Restart

Adverse events related to long-term use of bisphosphonates have raised interest in temporary drug discontinuation. Trends in bisphosphonate discontinuation and restart, as well factors associated with these decisions, are not fully understood at a population level. We investigated temporal trends of bisphosphonate discontinuation from 2010 to 2015 and identified factors associated with discontinuation and restart of osteoporosis therapy. Our cohort consisted of long-term bisphosphonate users identified from 2010 to 2015 Medicare data. We defined discontinuation as ≥12 months without bisphosphonate prescription claims. We used conditional logistic regression to compare factors associated with alendronate discontinuation or osteoporosis therapy restart in the 120-day period preceding discontinuation or restart referent to the 120-day preceding control periods. Among 73,800 long-term bisphosphonate users, 59,251 (80.3%) used alendronate, 6806 (9.2%) risedronate, and 7743 (10.5%) zoledronic acid, exclusively. Overall, 26,281 (35.6%) discontinued bisphosphonates for at least 12 months. Discontinuation of bisphosphonates increased from 1.7% in 2010, reaching a peak of 14% in 2012 with levels plateauing through 2015. The factors most strongly associated with discontinuation of alendronate were: benzodiazepine prescription (adjusted odds ratio [aOR] = 2.5; 95% confidence interval [CI] 2.1, 3.0), having a dual-energy X-ray absorptiometry (DXA) scan (aOR = 1.8; 95% CI 1.7, 2.0), and skilled nursing facility care utilization (aOR = 1.8; 95% CI 1.6, 2.1). The factors most strongly associated with restart of osteoporosis therapy were: having a DXA scan (aOR = 9.9; 95% CI 7.7, 12.6), sustaining a fragility fracture (aOR = 2.8; 95% CI 1.8, 4.5), and an osteoporosis or osteopenia diagnosis (aOR = 2.5; 95% CI 2.0, 3.1). Our national evaluation of bisphosphonate discontinuation showed that an increasing proportion of patients on long-term bisphosphonate therapy discontinue medications. The factors associated with discontinuation of alendronate were primarily related to worsening of overall health status, whereas traditional factors associated with worsening bone health were associated with restarting osteoporosis medication. © 2019 American Society for Bone and Mineral Research.

Short-term response in new users of anti-TNF predicts long-term productivity and non-disability: analysis of Czech ATTRA ankylosing spondylitis biologic registry

Objectives: To assess the role of short-term response to first anti-TNF in long-term prediction of disability.Methods: In nationwide registry ATTRA, we identified ankylosing spondylitis patients starting anti-TNF between 01/2003 and 12/2016. Full disability and work impairment (WI; WPAI questionnaire) were predicted via the Cox- and lagged-parameter mixed-effect regression.Results: 2,274 biologicals-naïve patients newly indicated to anti-TNF were prospectively followed (6,333 patient-years; median follow-up 1.9 years). Reaching BASDAI < 4 (77.4%) and ASDAS-CRP < 2.1 (61.1%) after 3 months of anti-TNF both decreased the risk of future disability by ≈2.5-fold. ASDAS-CRP < 2.1 predicted non-disability better than BASDAI < 4 & CRP < 5 mg/L (p = 0.032). BASDAI < 4 & CRP < 5 mg/L was comparable to BASDAI < 4 (p = 0.941) and to BASDAI change by >50% or by >2 points (p = 0.902). ASDAS-CRP change >1.1 and >2.0 both failed to predict non-disability. Once on anti-TNF therapy, the strongest predictor of WI was Pain (SF36). Yearly increase in indirect costs remains below €3,000 in those reaching ASDAS-CRP < 2.1.Conclusions: Low disease activity measured by ASDAS-CRP ≤ 2.1 should be used to measure the outcome of new anti-TNF therapy. Continuous WI could be decreased through pain management.

Nonrandomized Real-World Evidence to Support Regulatory Decision Making: Process for a Randomized Trial Replication Project

Recent legislation mandates that the US Food and Drug Administration issue guidance regarding when real-world evidence (RWE) could be used to support regulatory decision making. Although RWE could come from randomized or nonrandomized designs, there are significant concerns about the validity of RWE assessing medication effectiveness based on nonrandomized designs. We propose an initiative using healthcare claims data to assess the ability of nonrandomized RWE to provide results that are comparable with those from randomized controlled trials (RCTs). We selected 40 RCTs, and we estimate that approximately 30 attempted replications will be completed after feasibility analyses. We designed an implementation process to ensure that each attempted replication is consistent, transparent, and reproducible. This initiative is the first to systematically evaluate the ability of nonrandomized RWE to replicate multiple RCTs using a structured process. Results from this study should provide insight on the strengths and limitations of using nonrandomized RWE from claims for regulatory decision making.

Effect of Initiating Cardiac Rehabilitation After Myocardial Infarction on Subsequent Hospitalization in Older Adults

PURPOSE

Outpatient cardiac rehabilitation (CR) participation after myocardial infarction (MI) reduces all-cause mortality; however, less is known about effects of CR on post-MI hospitalization. The study objective was to investigate effects of CR on hospitalization following acute MI among older adults.

METHODS

Medicare beneficiaries aged 65 to 88 yr hospitalized in 2008 with acute MI, who survived at least 60 d post-discharge, had a revascularization procedure during index hospitalization, and did not have an MI in previous year were eligible for this study. CR initiation was assessed in the 60 d post-discharge. Competing risk survival analysis was used to estimate the proportion of discharged beneficiaries hospitalized between the end of 60-d exposure window and December 31, 2009, treating death as a competing event.

RESULTS

The mean ± SD age of 32 851 Medicare beneficiaries meeting study criteria was 75 ± 6.0 yr, approximately half were male (52%), and the majority were white (88%). In this study, 21% of beneficiaries initiated CR within the exposure window. At 1 yr post-discharge, CR initiators had a lower risk of recurrent MI (4.2% [95% CI, 3.5-5.1]), cardiovascular (15.7% [95% CI, 14.3-17.2]), and all-cause (30.4% [95% CI, 28.8-32.1]) hospitalization than noninitiators (5.2% [95% CI, 5.0-5.5]; 18.0% [95% CI, 17.6-18.4]; and 33.2% [95% CI, 32.5-33.8], respectively). There was no difference in fracture risk (negative control outcome).

CONCLUSIONS

This study provides evidence that CR can reduce the 1-yr risk of cardiovascular and all-cause hospital admissions in Medicare aged MI survivors.

Measuring Frailty in Medicare Data: Development and Validation of a Claims-Based Frailty Index

Background

Frailty is a key determinant of health status and outcomes of health care interventions in older adults that is not readily measured in Medicare data. This study aimed to develop and validate a claims-based frailty index (CFI).

Methods

We used data from Medicare Current Beneficiary Survey 2006 (development sample: n = 5,593) and 2011 (validation sample: n = 4,424). A CFI was developed using the 2006 claims data to approximate a survey-based frailty index (SFI) calculated from the 2006 survey data as a reference standard. We compared CFI to combined comorbidity index (CCI) in the ability to predict death, disability, recurrent falls, and health care utilization in 2007. As validation, we calculated a CFI using the 2011 claims data to predict these outcomes in 2012.

Results

The CFI was correlated with SFI (correlation coefficient: 0.60). In the development sample, CFI was similar to CCI in predicting mortality (C statistic: 0.77 vs. 0.78), but better than CCI for disability, mobility impairment, and recurrent falls (C statistic: 0.62-0.66 vs. 0.56-0.60). Although both indices similarly explained the variation in hospital days, CFI outperformed CCI in explaining the variation in skilled nursing facility days. Adding CFI to age, sex, and CCI improved prediction. In the validation sample, CFI and CCI performed similarly for mortality (C statistic: 0.71 vs. 0.72). Other results were comparable to those from the development sample.

Conclusion

A novel frailty index can measure the risk for adverse health outcomes that is not otherwise quantified using demographic characteristics and traditional comorbidity measures in Medicare data.

Glucose-lowering medications and the risk of cancer: A methodological review of studies based on real-world data

AIM

To review the methodology of observational studies examining the association between glucose-lowering medications and cancer to identify the most common methodological challenges and sources of bias.

METHODS

We searched PubMed systematically to identify observational studies on glucose-lowering medications and cancer published between January 2000 and January 2016. We assessed the design and analytical methods used in each study, with a focus on their ability to achieve study validity, and further evaluated the prevalence of major methodological choices over time.

RESULTS

Of 155 studies evaluated, only 26% implemented a new-user design, 41% used an active comparator, 33% implemented a lag or latency period, and 51% adjusted for diabetes duration. Potential for immortal person-time bias was identified in 63% of the studies; 55% of the studies adjusted for variables measured during the follow-up without appropriate statistical methods. Aside from a decreasing trend in adjusting for variables measured during the follow-up, we observed no trends in methodological choices over time.

CONCLUSIONS

The prevalence of well-known design and analysis flaws that may lead to biased results remains high among observational studies on glucose-lowering medications and cancer, limiting the conclusions that can be drawn from these studies. Avoiding known pitfalls could substantially improve the quality and validity of real-world evidence in this field.

Restriction of Pharmacoepidemiologic Cohorts to Initiators of Medications in Unrelated Preventive Drug Classes to Reduce Confounding by Frailty in Older Adults

Nonexperimental studies of the effectiveness of seasonal influenza vaccine in older adults have found 40%-60% reductions in all-cause mortality associated with vaccination, potentially due to confounding by frailty. We restricted our cohort to initiators of medications in preventive drug classes (statins, antiglaucoma drugs, and β blockers) as an approach to reducing confounding by frailty by excluding frail older adults who would not initiate use of these drugs. Using a random 20% sample of US Medicare beneficiaries, we framed our study as a series of nonrandomized "trials" comparing vaccinated beneficiaries with unvaccinated beneficiaries who had an outpatient health-care visit during the 5 influenza seasons occurring in 2010-2015. We pooled data across trials and used standardized-mortality-ratio-weighted Cox proportional hazards models to estimate the association between influenza vaccination and all-cause mortality before influenza season, expecting a null association. Weighted hazard ratios among preventive drug initiators were generally closer to the null than those in the nonrestricted cohort. Restriction of the study population to statin initiators with an uncensored approach resulted in a weighted hazard ratio of 1.00 (95% confidence interval: 0.84, 1.19), and several other hazard ratios were above 0.95. Restricting the cohort to initiators of medications in preventive drug classes can reduce confounding by frailty in this setting, but further work is required to determine the most appropriate criteria to use.

Counterpoint: Keeping the Demons at Bay When Handling Time-Varying Exposures-Beyond Avoiding Immortal Person-Time

The potential for immortal time bias is pervasive in epidemiologic studies with left truncation or time-varying exposures. Unlike other biases in epidemiologic research (e.g., measurement bias, confounding due to unmeasured factors, and selection based on unmeasured predictors of the outcome), immortal time bias can and should be avoided by the correct assignment of person-time during follow up. However, even when handing person-time correctly, allowing late entry into a study or into an exposure group can open the door to more insidious sources of bias, some of which we explore here. Clear articulation of the study question, including the treatment plans of interest, can provide navigation around these sources of bias and elucidate the assumptions needed for inference given the available data. Here, we use simulated data to illustrate the assumptions required under various approaches to estimate the effect of a time-varying treatment and describe how these assumptions relate to the assumptions necessary to estimate single sample rates and risks in settings with censoring and truncation.

Prostate cancer incidence and mortality among men using statins and non-statin lipid-lowering medications

BACKGROUND

Statins have demonstrated protection against aggressive/late-stage and/or lethal prostate cancer (PC), but prior studies are limited by small populations, short follow-up and unequal health-care access. Research has not demonstrated that non-statin lipid-lowering medications (NSLLMs) provide a similar benefit, which would support a cholesterol-based mechanism. We sought to rigorously test the hypothesis that cholesterol-lowering drugs affect PC incidence and severity.

METHODS

A retrospective cohort study was conducted by abstracting prescription and health service records for 249,986 Saskatchewan men aged ≥40 years between January 1, 1990 and December 31, 2014 and comparing first-time statin and NSLLM users with age-matched non-users and glaucoma medication (GM) users for PC incidence, metastases at diagnosis and PC mortality using Cox proportional hazards regression.

RESULTS

In comparing statin users to non-users, a weak association was detected with increased PC incidence (hazard ratio [HR] 1.07, 95% confidence interval [CI]: 1.02-1.12) that disappeared when compared with GM users. Substantial protective associations were observed between statin use and metastatic PC and PC mortality (HRs 0.69, 95% CI: 0.61-0.79 and 0.73, 95% CI: 0.66-0.81, respectively), which were stronger when compared with GM use (HRs 0.52, 95% CI: 0.40-0.68 and 0.51, 95% CI: 0.41-0.63, respectively). Similar associations were found for NSLLM versus GM for metastatic PC (HR 0.57, 95% CI: 0.41-0.79) and PC mortality (HR 0.66, 95% CI: 0.51-0.85).

CONCLUSIONS

Our analyses provide one of the more comprehensive findings to date that statins may reduce risk of metastatic PC and PC mortality, and the first to demonstrate that NSLLM have similar effects, supporting a cholesterol-based mechanism.

Potentially Inappropriate Prescribing Among Older Persons: A Meta-Analysis of Observational Studies

PURPOSE

Potentially inappropriate prescribing (PIP) is a common yet preventable medical error among older persons in primary care. It is uncertain whether PIP produces adverse outcomes in this population, however. We conducted a systematic review with meta-analysis to pool the adverse outcomes of PIP specific to primary care.

METHOD

We searched PubMed, Embase, CINAHL, Web of Science, Scopus, PsycINFO, and previous review articles for studies related to "older persons," "primary care," and "inappropriate prescribing." Two reviewers selected eligible articles, extracted data, and evaluated the risk of bias. Meta-analysis was conducted to pool studies with similar PIP criteria and outcome measures.

RESULTS

Of the 2,804 articles identified, we included 8 articles with a total of 77,624 participants. All included studies had cohort design and low risk of bias. Although PIP did not affect mortality (risk ratio [RR] 0.98; 95% CI, 0.93-1.05), it was significantly associated with the other available outcomes, including emergency room visits (RR 1.63; 95% CI, 1.32-2.00), adverse drug events (RR 1.34; 95% CI, 1.09-1.66), functional decline (RR 1.53; 95% CI, 1.08-2.18), health-related quality of life (standardized mean difference -0.26; 95% CI, -0.36 to -0.16), and hospitalizations (RR 1.25; 95% CI, 1.09-1.44). A majority of the pooled estimates had negligible heterogeneity.

CONCLUSIONS

This meta-analysis provides consolidated evidence on the wide-ranging impact of PIP among older persons in primary care. It highlights the need to identify PIP in primary care, calls for further research on PIP interventions in primary care, and points to the need to consider potential implications when deciding on the operational criteria of PIP.

Bias in pharmacoepidemiologic studies using secondary health care databases: a scoping review

BACKGROUND

The availability of clinical and therapeutic data drawn from medical records and administrative databases has entailed new opportunities for clinical and epidemiologic research. However, these databases present inherent limitations which may render them prone to new biases. We aimed to conduct a structured review of biases specific to observational clinical studies based on secondary databases, and to propose strategies for the mitigation of those biases.

METHODS

Scoping review of the scientific literature published during the period 2000-2018 through an automated search of MEDLINE, EMBASE and Web of Science, supplemented with manually cross-checking of reference lists. We included opinion essays, methodological reviews, analyses or simulation studies, as well as letters to the editor or retractions, the principal objective of which was to highlight the existence of some type of bias in pharmacoepidemiologic studies using secondary databases.

RESULTS

A total of 117 articles were included. An increasing trend in the number of publications concerning the potential limitations of secondary databases was observed over time and across medical research disciplines. Confounding was the most reported category of bias (63.2% of articles), followed by selection and measurement biases (47.0% and 46.2% respectively). Confounding by indication (32.5%), unmeasured/residual confounding (28.2%), outcome misclassification (28.2%) and "immortal time" bias (25.6%) were the subcategories most frequently mentioned.

CONCLUSIONS

Suboptimal use of secondary databases in pharmacoepidemiologic studies has introduced biases in the studies, which may have led to erroneous conclusions. Methods to mitigate biases are available and must be considered in the design, analysis and interpretation phases of studies using these data sources.

Evaluating the Use of Nonrandomized Real-World Data Analyses for Regulatory Decision Making

The analysis of longitudinal healthcare data outside of highly controlled parallel-group randomized trials, termed real-world evidence (RWE), has received increasing attention in the medical literature. In this paper, we discuss the potential role of RWE in drug regulation with a focus on the analysis of healthcare databases. We present several cases in which RWE is already used and cases in which RWE could potentially support regulatory decision making. We summarize key issues that investigators and regulators should consider when designing or evaluating such studies, and we propose a structured process for implementing analyses that facilitates regulatory review. We evaluate the empirical evidence base supporting the validity, transparency, and reproducibility of RWE from analysis of healthcare databases and discuss the work that still needs to be done to ensure that such analyses can provide decision-ready evidence on the effectiveness and safety of treatments.

Controlling for Frailty in Pharmacoepidemiologic Studies of Older Adults: Validation of an Existing Medicare Claims-based Algorithm

BACKGROUND

Frailty is a geriatric syndrome characterized by weakness and weight loss and is associated with adverse health outcomes. It is often an unmeasured confounder in pharmacoepidemiologic and comparative effectiveness studies using administrative claims data.

METHODS

Among the Atherosclerosis Risk in Communities (ARIC) Study Visit 5 participants (2011-2013; n = 3,146), we conducted a validation study to compare a Medicare claims-based algorithm of dependency in activities of daily living (or dependency) developed as a proxy for frailty with a reference standard measure of phenotypic frailty. We applied the algorithm to the ARIC participants' claims data to generate a predicted probability of dependency. Using the claims-based algorithm, we estimated the C-statistic for predicting phenotypic frailty. We further categorized participants by their predicted probability of dependency (<5%, 5% to <20%, and ≥20%) and estimated associations with difficulties in physical abilities, falls, and mortality.

RESULTS

The claims-based algorithm showed good discrimination of phenotypic frailty (C-statistic = 0.71; 95% confidence interval [CI] = 0.67, 0.74). Participants classified with a high predicted probability of dependency (≥20%) had higher prevalence of falls and difficulty in physical ability, and a greater risk of 1-year all-cause mortality (hazard ratio = 5.7 [95% CI = 2.5, 13]) than participants classified with a low predicted probability (<5%). Sensitivity and specificity varied across predicted probability of dependency thresholds.

CONCLUSIONS

The Medicare claims-based algorithm showed good discrimination of phenotypic frailty and high predictive ability with adverse health outcomes. This algorithm can be used in future Medicare claims analyses to reduce confounding by frailty and improve study validity.

Use of bone-modifying agents among breast cancer patients with bone metastasis: evidence from oncology practices in the US

Purpose

Bone-modifying agents (BMAs) are recommended for women with bone metastasis from breast cancer to prevent skeletal-related events. We examined the usage patterns and identified the factors associated with the use of BMAs (denosumab and intravenous bisphosphonates) among women in the US.

Patients and methods

Electronic health records from oncology clinics were used to identify women diagnosed with bone metastasis from breast cancer between 2013 and 2014. Patients were excluded if they had recently used a BMA or had concurrent cancer at an additional primary site. The incidence of BMA initiation, interruption, and reinitiation were estimated using competing risk regression models. A generalized linear model was used to estimate risk factors for treatment initiation and interruption.

Results

There were 589 women diagnosed with bone metastasis from breast cancer. By 1 year, 68% of these patients (95% CI: 64%, 71%) had initiated treatment with a BMA. Denosumab and zoledronic acid were the most commonly used agents, whereas pamidronate was used infrequently. Young women were more likely to initiate a BMA than older women (adjusted risk difference: 6.4 [95% CI: 1.5, 10.9]). Of the 412 patients who initiated a BMA, 46% (95% CI: 41%, 51%) experienced an interruption within 1 year. Seventy-four percent (95% CI: 68%, 79%) of patients who interrupted their treatment had reinitiated therapy within 1 year of interruption.

Conclusion

The majority of women diagnosed with bone metastasis from breast cancer initiate a BMA within 1 year of diagnosis, but a large proportion, particularly among the elderly, do not use these therapies.

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.

Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects

BACKGROUND

Decision makers in health care increasingly rely on nonrandomized database analyses to assess the effectiveness, safety, and value of medical products. Health care data scientists use data-adaptive approaches that automatically optimize confounding control to study causal treatment effects. This article summarizes relevant experiences and extensions.

METHODS

The literature was reviewed on the uses of high-dimensional propensity score (HDPS) and related approaches for health care database analyses, including methodological articles on their performance and improvement. Articles were grouped into applications, comparative performance studies, and statistical simulation experiments.

RESULTS

The HDPS algorithm has been referenced frequently with a variety of clinical applications and data sources from around the world. The appeal of HDPS for database research rests in 1) its superior performance in situations of unobserved confounding through proxy adjustment, 2) its predictable efficiency in extracting confounding information from a given data source, 3) its ability to automate estimation of causal treatment effects to the extent achievable in a given data source, and 4) its independence of data source and coding system. Extensions of the HDPS approach have focused on improving variable selection when exposure is sparse, using free text information and time-varying confounding adjustment.

CONCLUSION

Semiautomated and optimized confounding adjustment in health care database analyses has proven successful across a wide range of settings. Machine-learning extensions further automate its use in estimating causal treatment effects across a range of data scenarios.

P-values and decision-making: discussion of 'Limitations of empirical calibration of p-values using observational data'

Gruber and Tchetgen Tchetgen bring up many important limitations of p-value calibration as applied to the assessment of the safety and effectiveness of medications in secondary administrative data. In this discussion, I further examine the role of study design and selection of comparators in interpreting p-value calibration results, and we consider the advantages and disadvantages of automation in the epidemiology of medications, including p-value calibration. Copyright © 2016 John Wiley & Sons, Ltd.

Initiation of antihypertensive monotherapy and incident fractures among Medicare beneficiaries

BACKGROUND

Research suggests antihypertensive medications are associated with fractures in older adults, however results are inconsistent and few have examined how the association varies over time. We sought to examine the association between antihypertensive class and incident non-vertebral fractures among older adults initiating monotherapy according to time since initiation.

METHODS

We used a new-user cohort design to identify Medicare beneficiaries (≥ 65 years of age) initiating antihypertensive monotherapy during 2008-2011 using a 20% random sample of Fee-For-Service Medicare beneficiaries enrolled in parts A (inpatient services), B (outpatient services), and D (prescription medication) coverage. Starting the day after the initial antihypertensive prescription, we followed beneficiaries for incident non-vertebral fractures. We used multinomial logistic regression models to estimate propensity scores for initiating each antihypertensive drug class. Using these propensity scores, we weighted beneficiaries to achieve the same baseline covariate distribution as beneficiaries initiating with angiotensin-converting enzyme inhibitors. Lastly, we used weighted Cox proportional hazard models to estimate hazard ratios (HRs) of having an incident fractures according to antihypertensive class and time since initiation.

RESULTS

During 2008-2011, 122,629 Medicare beneficiaries initiated antihypertensive monotherapy (mean age 75, 61% women, 86% White). Fracture rates varied according to days since initiation and antihypertensive class. Beneficiaries initiating with thiazides had the highest fracture rate in the first 14 days following initiation (438 per 10,000 person-years, 95% confidence interval (CI): 294-628; HR: 1.40, 0.78-2.52). However, beneficiaries initiating with calcium channel blockers had the highest fracture rate during the 15-365 days after initiation (435 per 10,000 person-years, 95% CI: 404-468; HR: 1.11, 1.00-1.24). Beneficiaries initiating with angiotensin-receptor blockers had the lowest fracture rates during the initial 14 days (333 per 10,000 person-years, 190-546, HR: 0.92, 0.49-1.75) and during 15-365 days after initiation (321 per 10,000 person-years, 287-358, HR: 0.96, 0.84-1.09).

CONCLUSION

The association between antihypertensives and fractures varied according to class and time since initiation. Results suggest that when deciding upon antihypertensive therapy, clinicians may want to consider possible fracture risks when choosing between antihypertensive drug classes.

When and How Can Real World Data Analyses Substitute for Randomized Controlled Trials?

Regulators consider randomized controlled trials (RCTs) as the gold standard for evaluating the safety and effectiveness of medications, but their costs, duration, and limited generalizability have caused some to look for alternatives. Real world evidence based on data collected outside of RCTs, such as registries and longitudinal healthcare databases, can sometimes substitute for RCTs, but concerns about validity have limited their impact. Greater reliance on such real world data (RWD) in regulatory decision making requires understanding why some studies fail while others succeed in producing results similar to RCTs. Key questions when considering whether RWD analyses can substitute for RCTs for regulatory decision making are WHEN one can study drug effects without randomization and HOW to implement a valid RWD analysis if one has decided to pursue that option. The WHEN is primarily driven by externalities not controlled by investigators, whereas the HOW is focused on avoiding known mistakes in RWD analyses.