Transparency and Reproducibility of Observational Cohort Studies Using Large Healthcare Databases

Empagliflozin and the Risk of Heart Failure Hospitalization in Routine Clinical Care

BACKGROUND

The EMPA-REG OUTCOME trial (Empagliflozin Cardiovascular Outcome Event Trial in Type 2 diabetes Mellitus Patients) showed that empagliflozin, a sodium-glucose cotransporter-2 inhibitor, reduces the risk of hospitalization for heart failure (HHF) by 35%, on top of standard of care in patients with type 2 diabetes mellitus (T2D) and established cardiovascular disease. The EMPRISE (Empagliflozin Comparative Effectiveness and Safety) study aims to assess empagliflozin's effectiveness, safety, and healthcare utilization in routine care from August 2014 through September 2019. In this first interim analysis, we investigated the risk of HHF among T2D patients initiating empagliflozin versus sitagliptin, a dipeptidyl peptidase-4 inhibitor.

METHODS

Within 2 commercial and 1 federal (Medicare) claims data sources in the United States, we identified a 1:1 propensity score-matched cohort of T2D patients ≥18 years old initiating empagliflozin or sitagliptin from August 2014 through September 2016. The HHF outcome was defined as a HF discharge diagnosis in the primary position (HHF-specific); a broader definition was based on a HF discharge diagnosis in any position (HHF-broad). Hazard ratios (HRs) and 95% CIs were estimated controlling for over 140 baseline characteristics in each data source and pooled by fixed-effects meta-analysis.

RESULTS

After propensity-score matching, we identified 16,443 patient pairs who initiated empagliflozin or sitagliptin. Average age was approximately 59 years, almost 54% of the participants were males, and approximately 25% had records of existing cardiovascular disease. Compared with sitagliptin, the initiation of empagliflozin decreased the risk of HHF-specific by 50% (HR, 0.50; 95% CI, 0.28-0.91), and the risk of HHF-broad by 49% (HR, 0.51;95% CI, 0.39-0.68), over a mean follow-up of 5.3 months. The results were consistent in patients with and without baseline cardiovascular disease, and for empagliflozin at both the 10- and 25-mg daily doses; analyses comparing empagliflozin versus the dipeptidyl peptidase-4 inhibitor class, and comparing sodium-glucose cotransporter-2 inhibitor versus dipeptidyl peptidase-4 inhibitor classes also produced consistent findings.

CONCLUSIONS

The first interim analysis from EMPRISE showed that compared with sitagliptin, the initiation of empagliflozin was associated with a decreased risk of HHF among patients with T2D as treated in routine care, with and without a history of cardiovascular disease.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov . Unique identifier: NCT03363464.

Psychosis with Methylphenidate or Amphetamine in Patients with ADHD

BACKGROUND

The prescription use of the stimulants methylphenidate and amphetamine for the treatment of attention deficit-hyperactivity disorder (ADHD) has been increasing. In 2007, the Food and Drug Administration mandated changes to drug labels for stimulants on the basis of findings of new-onset psychosis. Whether the risk of psychosis in adolescents and young adults with ADHD differs among various stimulants has not been extensively studied.

METHODS

We used data from two commercial insurance claims databases to assess patients 13 to 25 years of age who had received a diagnosis of ADHD and who started taking methylphenidate or amphetamine between January 1, 2004, and September 30, 2015. The outcome was a new diagnosis of psychosis for which an antipsychotic medication was prescribed during the first 60 days after the date of the onset of psychosis. To estimate hazard ratios for psychosis, we used propensity scores to match patients who received methylphenidate with patients who received amphetamine in each database, compared the incidence of psychosis between the two stimulant groups, and then pooled the results across the two databases.

RESULTS

We assessed 337,919 adolescents and young adults who received a prescription for a stimulant for ADHD. The study population consisted of 221,846 patients with 143,286 person-years of follow up; 110,923 patients taking methylphenidate were matched with 110,923 patients taking amphetamines. There were 343 episodes of psychosis (with an episode defined as a new diagnosis code for psychosis and a prescription for an antipsychotic medication) in the matched populations (2.4 per 1000 person-years): 106 episodes (0.10%) in the methylphenidate group and 237 episodes (0.21%) in the amphetamine group (hazard ratio with amphetamine use, 1.65; 95% confidence interval, 1.31 to 2.09).

CONCLUSIONS

Among adolescents and young adults with ADHD who were receiving prescription stimulants, new-onset psychosis occurred in approximately 1 in 660 patients. Amphetamine use was associated with a greater risk of psychosis than methylphenidate. (Funded by the National Institute of Mental Health and others.).

Opioid dispensing patterns after oocyte retrieval

OBJECTIVE

To study opioid dispensing patterns following oocyte retrieval.

DESIGN

Retrospective cohort.

SETTING

Not applicable.

PATIENT(S)

Women undergoing oocyte retrieval with a maximum of 1 opioid prescription in the 12 weeks prior to the procedure, without an opioid use or other substance use disorder.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

We measured the frequency of opioids dispensed within 3 days of oocyte retrieval, most common opioids dispensed; and quantity dispensed, in median (interquartile range [IQR] and 10th-90th percentile ranges) oral morphine milligram equivalents (MME). Multivariate regression analyses were used to calculate odds ratios and 95% confidence intervals (CI) to examine the association between patient characteristics and the occurrence of an opioid dispensing.

RESULT(S)

In total, 61,463 women with an oocyte retrieval met the criteria for analysis. After oocyte retrieval, 11.9% were dispensed an opioid, most commonly hydrocodone (48.5%), codeine (23.0%), and oxycodone (17.7%). The median (IQR; 10th-90th percentile) oral MME dose dispensed after retrieval was 90 (50-125; 50-207). Women with mood disorders (adjusted odds ratio [aOR] 1.17, 95% CI 1.00-1.36), tobacco use (aOR 1.67, 95% CI 1.18-2.37), or anti-depressant use (aOR 1.62, 95% CI 1.47-1.80) were more likely to fill an opioid prescription, compared to those without these diagnoses.

CONCLUSION(S)

Although only a small proportion of women fill a prescription for opioids after oocyte retrieval, there is substantial variation in the amount dispensed. Patients with a concurrent mood disorder or those taking anti-depressants were more likely to fill an opioid prescription.

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.

Fracture Risk After Initiation of Use of Canagliflozin: A Cohort Study

BACKGROUND

Sodium-glucose cotransporter-2 inhibitors promote glycosuria, resulting in possible effects on calcium, phosphate, and vitamin D homeostasis. Canagliflozin is associated with decreased bone mineral density and a potential increased risk for fracture.

OBJECTIVE

To estimate risk for nonvertebral fracture among new users of canagliflozin compared with a glucagon-like peptide-1 (GLP-1) agonist.

DESIGN

Population-based new-user cohort study.

SETTING

Two U.S. commercial health care databases providing data on more than 70 million patients from March 2013 to October 2015.

PATIENTS

Persons with type 2 diabetes who initiated use of canagliflozin were propensity score-matched in a 1:1 ratio to those initiating use of a GLP-1 agonist.

MEASUREMENTS

The primary outcome was a composite end point of humerus, forearm, pelvis, or hip fracture requiring intervention. Secondary outcomes included fractures at other sites. A fixed-effects meta-analysis that pooled results from the 2 databases provided an overall hazard ratio (HR).

RESULTS

79 964 patients initiating use of canagliflozin were identified and matched to 79 964 patients initiating use of a GLP-1 agonist. Mean age was 55 years, 48% were female, average baseline hemoglobin A1c level was 8.7%, and 27% were prescribed insulin. The rate of the primary outcome was similar for canagliflozin (2.2 events per 1000 person-years) and GLP-1 agonists (2.3 events per 1000 person-years), with an overall HR of 0.98 (95% CI, 0.75 to 1.26). Risk for pelvic, hip, humerus, radius, ulna, carpal, metacarpal, metatarsal, or ankle fracture was also similar for canagliflozin (14.5 events per 1000 person-years) and GLP-1 agonists (16.1 events per 1000 person-years) (overall HR, 0.92 [CI, 0.83 to 1.02]).

LIMITATION

Unmeasured confounding, measurement error, and low fracture rate.

CONCLUSION

In this study of middle-aged patients with type 2 diabetes and relatively low fracture risk, canagliflozin was not associated with increased risk for fracture compared with GLP-1 agonists.

PRIMARY FUNDING SOURCE

Brigham and Women's Hospital, Division of Pharmacoepidemiology and Pharmacoeconomics.

Assessment of Use of Combined Dextromethorphan and Quinidine in Patients With Dementia or Parkinson Disease After US Food and Drug Administration Approval for Pseudobulbar Affect

IMPORTANCE

In 2010, the US Food and Drug Administration (FDA) approved a combination of dextromethorphan hydrobromide and quinidine sulfate for the treatment of pseudobulbar affect after studies in patients with amyotrophic lateral sclerosis (ALS) or multiple sclerosis (MS). This medication, however, may be commonly prescribed in patients with dementia and/or Parkinson disease (PD).

OBJECTIVE

To investigate the prescribing patterns of dextromethorphan-quinidine, including trends in associated costs.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study of patients prescribed dextromethorphan-quinidine used data from 2 commercial insurance databases, Optum Clinformatics Data Mart and Truven Health MarketScan. The Medicare Part D Prescription Drug Program data set was used to evaluate numbers of prescriptions and total reported spending by the Centers for Medicare & Medicaid Services. Patients were included if they were prescribed dextromethorphan-quinidine from October 29, 2010, when the drug was approved, through March 1, 2017, for Optum and December 31, 2015, for Truven. Data were analyzed from December 1, 2017, through August 1, 2018.

MAIN OUTCOMES AND MEASURES

The proportion of patients prescribed dextromethorphan-quinidine with a diagnosis of MS, ALS, or dementia and/or PD, as well as the number of patients with a history of heart failure (a contraindication for the drug).

RESULTS

In the commercial health care databases, 12 858 patients filled a prescription for dextromethorphan-quinidine during the study period. Mean (SD) age was 66.0 (18.5) years, 66.7% were women, and 13.3% had a history of heart failure. Combining results from both databases, few patients had a diagnosis of MS (8.4%) or ALS (6.8%); most (57.0%) had a diagnosis of dementia and/or PD. In the Medicare Part D database, the number of patients prescribed dextromethorphan-quinidine increased 15.3-fold, from 3296 in 2011 to 50 402 in 2016. Reported spending by Centers for Medicare & Medicaid Services on this medication increased from $3.9 million in 2011 to $200.4 million in 2016.

CONCLUSIONS AND RELEVANCE

Despite approval by the FDA for pseudobulbar affect based on studies of patients with ALS or MS, dextromethorphan-quinidine appears to be primarily prescribed for patients with dementia and/or PD.

Comparative risk of genital infections associated with sodium-glucose co-transporter-2 inhibitors

The extent to which sodium-glucose co-transporter-2 (SGLT2) inhibitors increase the risk of genital infections in routine clinical care, compared with other antidiabetic medications, is not clear, or whether the increased risk is consistent across gender or age subgroups, within individual SGLT2 agents, or if it is more pronounced at a particular time after treatment initiation. We conducted a retrospective cohort study using two US commercial claims databases (2013-2017). In the primary analysis, 1:1 propensity score-matched cohorts of female and male subjects with type 2 diabetes mellitus initiating SGLT2 versus dipeptidyl peptidase-4 inhibitors were created. The outcome was a composite of genital candidal infections, vaginitis or vulvovaginitis in women, and genital candidal infections, balanitis, balanoposthitis, phimosis or paraphimosis in men. Among propensity score-matched cohorts of 129 994 women and 156 074 men, the adjusted hazard ratio (HR) and excess risk per 1000 person-years for SGLT2 versus DPP-4 inhibitors was 2.81 (95% confidence interval [CI], 2.64, 2.99) and 87.4 (95% CI, 79.1, 96.2) respectively for women, and was 2.68 (95% CI, 2.31, 3.11) and 11.9 (95% CI, 9.3-15.0) for men. Findings were similar in the SGLT2 inhibitor versus GLP-1 agonist comparison, more pronounced in the subgroup of patients aged ≥60 (HR, 4.45 [95% CI, 3.83-5.17] in women and 3.30 [95% CI, 2.56-4.25] in men), and no meaningful difference across individual SGLT2 inhibitors was identified. This increase in risk was evident in the first month of treatment initiation and remained elevated throughout the course of therapy. SGLT2 inhibitors were associated with an approximately 3-fold increase in risk of genital infections.

Implementation of a Health Plan Program for Switching From Analogue to Human Insulin and Glycemic Control Among Medicare Beneficiaries With Type 2 Diabetes

IMPORTANCE

Prices for newer analogue insulin products have increased. Lower-cost human insulin may be effective for many patients with type 2 diabetes.

OBJECTIVE

To evaluate the association between implementation of a health plan-based intervention of switching patients from analogue to human insulin and glycemic control.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective cohort study using population-level interrupted times series analysis of members participating in a Medicare Advantage and prescription drug plan operating in 4 US states. Participants were prescribed insulin between January 1, 2014, and December 31, 2016 (median follow-up, 729 days). The intervention began in February 2015 and was expanded to the entire health plan system by June 2015.

EXPOSURES

Implementation of a health plan program to switch patients from analogue to human insulin.

MAIN OUTCOMES AND MEASURES

The primary outcome was the change in mean hemoglobin A1c (HbA1c) levels estimated over three 12-month periods: preintervention (baseline) in 2014, intervention in 2015, and postintervention in 2016. Secondary outcomes included rates of serious hypoglycemia or hyperglycemia using ICD-9-CM and ICD-10-CM diagnostic codes.

RESULTS

Over 3 years, 14 635 members (mean [SD] age: 72.5 [9.8] years; 51% women; 93% with type 2 diabetes) filled 221 866 insulin prescriptions. The mean HbA1c was 8.46% (95% CI, 8.40%-8.52%) at baseline and decreased at a rate of -0.02% (95% CI, -0.03% to -0.01%; P <.001) per month before the intervention. There was an association between the start of the intervention and an overall HbA1c level increase of 0.14% (95% CI, 0.05%-0.23%; P = .003) and slope change of 0.02% (95% CI, 0.01%-0.03%; P < .001). After the completion of the intervention, there were no significant differences in changes in the level (0.08% [95% CI, -0.01% to 0.17%]) or slope (<0.001% [95% CI, -0.008% to 0.010%]) of mean HbA1c compared with the intervention period (P = .09 and P = 0.81, respectively). For serious hypoglycemic events, there was no significant association between the start of the intervention and a level (2.66/1000 person-years [95% CI, -3.82 to 9.13]; P = .41) or slope change (-0.66/1000 person-years [95% CI, -1.59 to 0.27]; P = .16). The level (1.64/1000 person-years [95% CI, -4.83 to 8.11]; P = .61) and slope (-0.23/1000 person-years [95% CI, -1.17 to 0.70]; P = .61) changes in the postintervention period were not significantly different compared with the intervention period. The baseline rate of serious hyperglycemia was 22.33 per 1000 person-years (95% CI, 12.70-31.97). For the rate of serious hyperglycemic events, there was no significant association between the start of the intervention and a level (4.23/1000 person-years [95% CI, -8.62 to 17.08]; P = .51) or slope (-0.51/1000 person-years [95% CI, -2.37 to 1.34]; P = .58) change.

CONCLUSIONS AND RELEVANCE

Among Medicare beneficiaries with type 2 diabetes, implementation of a health plan program that involved switching patients from analogue to human insulin was associated with a small increase in population-level HbA1c.

Risk of serious infections in tocilizumab versus other biologic drugs in patients with rheumatoid arthritis: a multidatabase cohort study

OBJECTIVE

To investigate the rate of serious bacterial, viral or opportunistic infection in patients with rheumatoid arthritis (RA) starting tocilizumab (TCZ) versus tumour necrosis factor inhibitors (TNFi) or abatacept.

METHODS

Using claims data from US Medicare from 2010 to 2015, and IMS and MarketScan from 2011 to 2015, we identified adults with RA who initiated TCZ or TNFi (primary comparator)/abatacept (secondary comparator) with prior use of ≥1 different biologic drug or tofacitinib. The primary outcome was hospitalised serious infection (SI), including bacterial, viral or opportunistic infection. To control for >70 confounders, TCZ initiators were propensity score (PS)-matched to TNFi or abatacept initiators. Database-specific HRs were combined by a meta-analysis.

RESULTS

The primary cohort included 16 074 TCZ PS-matched to 33 109 TNFi initiators. The risk of composite SI was not different between TCZ and TNFi initiators (combined HR 1.05, 95% CI 0.95 to 1.16). However, TCZ was associated with an increased risk of serious bacterial infection (HR 1.19, 95% CI 1.07 to 1.33), skin and soft tissue infections (HR 2.38, 95% CI 1.47 to 3.86), and diverticulitis (HR 2.34, 95% CI 1.64 to 3.34) versus TNFi. An increased risk of composite SI, serious bacterial infection, diverticulitis, pneumonia/upper respiratory tract infection and septicaemia/bacteraemia was observed in TCZ versus abatacept users.

CONCLUSIONS

This large multidatabase cohort study found no difference in composite SI risk in patients with RA initiating TCZ versus TNFi after failing ≥1 biologic drug or tofacitinib. However, the risk of serious bacterial infection, skin and soft tissue infections, and diverticulitis was higher in TCZ versus TNFi initiators. The risk of composite SI was higher in TCZ initiators versus abatacept.

How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias

Several pharmacoepidemiology networks have been developed over the past decade that use a distributed approach, implementing the same analysis at multiple data sites, to preserve privacy and minimize data sharing. Distributed networks are efficient, by interrogating data on very large populations. The structure of these networks can also be leveraged to improve replicability, increase transparency, and reduce bias. We describe some features of distributed networks using, as examples, the Canadian Network for Observational Drug Effect Studies, the Sentinel System in the USA, and the European Research Network of Pharmacovigilance and Pharmacoepidemiology. Common protocols, analysis plans, and data models, with policies on amendments and protocol violations, are key features. These tools ensure that studies can be audited and repeated as necessary. Blinding and strict conflict of interest policies reduce the potential for bias in analyses and interpretation. These developments should improve the timeliness and accuracy of information used to support both clinical and regulatory decisions.

Network-based approach to prediction and population-based validation of in silico drug repurposing

Here we identify hundreds of new drug-disease associations for over 900 FDA-approved drugs by quantifying the network proximity of disease genes and drug targets in the human (protein–protein) interactome. We select four network-predicted associations to test their causal relationship using large healthcare databases with over 220 million patients and state-of-the-art pharmacoepidemiologic analyses. Using propensity score matching, two of four network-based predictions are validated in patient-level data: carbamazepine is associated with an increased risk of coronary artery disease (CAD) [hazard ratio (HR) 1.56, 95% confidence interval (CI) 1.12–2.18], and hydroxychloroquine is associated with a decreased risk of CAD (HR 0.76, 95% CI 0.59–0.97). In vitro experiments show that hydroxychloroquine attenuates pro-inflammatory cytokine-mediated activation in human aortic endothelial cells, supporting mechanistically its potential beneficial effect in CAD. In summary, we demonstrate that a unique integration of protein-protein interaction network proximity and large-scale patient-level longitudinal data complemented by mechanistic in vitro studies can facilitate drug repurposing.

Repurposing approved drugs could accelerate treatment options for various diseases. Here, the authors use network proximity of disease gene products and drug targets in the human protein interactome to identify drug-disease associations for cardiovascular disease, and validate these using longitudinal healthcare data.

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

Purpose

Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.

Methods

We reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.

Conclusion

Evidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision‐makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram.

A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.

Real-World Data Analytics Fit for Regulatory Decision-Making

Healthcare database analyses (claims, electronic health records) have been identified by various regulatory initiatives, including the 21^st Century Cures Act and Prescription Drug User Fee Act ("PDUFA"), as useful supplements to randomized clinical trials to generate evidence on the effectiveness, harm, and value of medical products in routine care. Specific applications include accelerated drug approval pathways and secondary indications for approved medical products. Such real-world data ("RWD") analyses reflect how medical products impact health outside a highly controlled research environment. A constant stream of data from the routine operation of modern healthcare systems that can be analyzed in rapid cycles enables incremental evidence development for regulatory decision-making. Key evidentiary needs by regulators include 1) monitoring of medication performance in routine care, including the effectiveness, safety and value; 2) identifying new patient strata in which a drug may have added value or unacceptable harms; and 3) monitoring targeted utilization. Four broad requirements have been proposed to enable successful regulatory decision-making based on healthcare database analyses (collectively, "MVET"): Meaningful evidence that provides relevant and context-informed evidence sufficient for interpretation, drawing conclusions, and making decisions; valid evidence that meets scientific and technical quality standards to allow causal interpretations; expedited evidence that provides incremental evidence that is synchronized with the decision-making process; and transparent evidence that is audible, reproducible, robust, and ultimately trusted by decision-makers. Evidence generation systems that satisfy MVET requirements to a high degree will contribute to effective regulatory decision-making. Rapid-cycle analytics of healthcare databases is maturing at a time when regulatory overhaul increasingly demands such evidence. Governance, regulations, and data quality are catching up as the utility of this resource is demonstrated in multiple contexts.

Use of Health Care Databases to Support Supplemental Indications of Approved Medications

IMPORTANCE

Manufacturers of US Food and Drug Administration-approved prescription drugs often apply for additional indications based on randomized clinical trials. Real-world database analyses on a medication's use and outcomes in routine settings of care might help to inform decision making regarding such supplemental indications.

OBJECTIVE

To examine whether longitudinal data from a health care database can support the results of a randomized clinical trial that led to a supplemental indication for telmisartan.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study of patients newly prescribed telmisartan or ramipril used insurance claims data from a nationwide health care database from January 1, 2003, through September 30, 2009, to compare patient outcomes. This study replicated the inclusion and exclusion criteria used in the Ongoing Telmisartan Alone and in Combination with Ramipril Global End-point Trial (ONTARGET) and used propensity score matching to balance 74 patient characteristics. Data analysis was performed from February 15, 2017, to May 24, 2017.

EXPOSURES

Telmisartan use vs ramipril use.

MAIN OUTCOMES AND MEASURES

The primary outcome was a composite of myocardial infarction, stroke, or hospitalization for congestive heart failure.

RESULTS

Of the 640 951 patients included in the study, 48 053 were newly prescribed ramipril (mean [SD] age, 68.29 [9.52] years; 31 940 male [66.5%]) and 4665 were newly prescribed telmisartan (mean [SD] age, 69.43 [9.60] years; 2413 male [51.7%]). After propensity score matching, a total of 4665 patients were newly prescribed telmisartan (mean [SD] age, 69.43 [9.60] years; 2413 [51.7%]), and 4665 patients were newly prescribed ramipril (mean [SD] age, 69.36 [9.67] years; 2343 male [50.2%]). As seen in ONTARGET, the composite risk of stroke, myocardial infarction, and hospitalization for congestive heart failure was similar for the 2 medications (hazard ratio, 1.0; 95% CI, 0.9-1.1). In addition, the study found that telmisartan was associated with a substantially decreased risk of angioedema (hazard ratio, 0.1; 95% CI, 0.03-0.56) compared with ramipril.

CONCLUSIONS AND RELEVANCE

Real-world data analyses of patients receiving routine care provided findings similar to those found in the randomized clinical trial that established telmisartan's supplemental indication. In certain situations, database studies may support supplemental applications for effectiveness for already approved medications.

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.

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

PURPOSE

Defining a study population and creating an analytic dataset from longitudinal healthcare databases involves many decisions. Our objective was to catalogue scientific decisions underpinning study execution that should be reported to facilitate replication and enable assessment of validity of studies conducted in large healthcare databases.

METHODS

We reviewed key investigator decisions required to operate a sample of macros and software tools designed to create and analyze analytic cohorts from longitudinal streams of healthcare data. A panel of academic, regulatory, and industry experts in healthcare database analytics discussed and added to this list.

CONCLUSION

Evidence generated from large healthcare encounter and reimbursement databases is increasingly being sought by decision-makers. Varied terminology is used around the world for the same concepts. Agreeing on terminology and which parameters from a large catalogue are the most essential to report for replicable research would improve transparency and facilitate assessment of validity. At a minimum, reporting for a database study should provide clarity regarding operational definitions for key temporal anchors and their relation to each other when creating the analytic dataset, accompanied by an attrition table and a design diagram. A substantial improvement in reproducibility, rigor and confidence in real world evidence generated from healthcare databases could be achieved with greater transparency about operational study parameters used to create analytic datasets from longitudinal healthcare databases.

Clinical Research Informatics: Supporting the Research Study Lifecycle

Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.

Open innovation: Towards sharing of data, models and workflows

Sharing of resources across organisations to support open innovation is an old idea, but which is being taken up by the scientific community at increasing speed, concerning public sharing in particular. The ability to address new questions or provide more precise answers to old questions through merged information is among the attractive features of sharing. Increased efficiency through reuse, and increased reliability of scientific findings through enhanced transparency, are expected outcomes from sharing. In the field of pharmacometrics, efforts to publicly share data, models and workflow have recently started. Sharing of individual-level longitudinal data for modelling requires solving legal, ethical and proprietary issues similar to many other fields, but there are also pharmacometric-specific aspects regarding data formats, exchange standards, and database properties. Several organisations (CDISC, C-Path, IMI, ISoP) are working to solve these issues and propose standards. There are also a number of initiatives aimed at collecting disease-specific databases - Alzheimer's Disease (ADNI, CAMD), malaria (WWARN), oncology (PDS), Parkinson's Disease (PPMI), tuberculosis (CPTR, TB-PACTS, ReSeqTB) - suitable for drug-disease modelling. Organized sharing of pharmacometric executable model code and associated information has in the past been sparse, but a model repository (DDMoRe Model Repository) intended for the purpose has recently been launched. In addition several other services can facilitate model sharing more generally. Pharmacometric workflows have matured over the last decades and initiatives to more fully capture those applied to analyses are ongoing. In order to maximize both the impact of pharmacometrics and the knowledge extracted from clinical data, the scientific community needs to take ownership of and create opportunities for open innovation.

Defining asthma and assessing asthma outcomes using electronic health record data: a systematic scoping review

There is currently no consensus on approaches to defining asthma or assessing asthma outcomes using electronic health record-derived data. We explored these approaches in the recent literature and examined the clarity of reporting.

We systematically searched for asthma-related articles published between January 1, 2014 and December 31, 2015, extracted the algorithms used to identify asthma patients and assess severity, control and exacerbations, and examined how the validity of these outcomes was justified.

From 113 eligible articles, we found significant heterogeneity in the algorithms used to define asthma (n=66 different algorithms), severity (n=18), control (n=9) and exacerbations (n=24). For the majority of algorithms (n=106), validity was not justified. In the remaining cases, approaches ranged from using algorithms validated in the same databases to using nonvalidated algorithms that were based on clinical judgement or clinical guidelines. The implementation of these algorithms was suboptimally described overall.

Although electronic health record-derived data are now widely used to study asthma, the approaches being used are significantly varied and are often underdescribed, rendering it difficult to assess the validity of studies and compare their findings. Given the substantial growth in this body of literature, it is crucial that scientific consensus is reached on the underlying definitions and algorithms.

Effects of expanding the look-back period to all available data in the assessment of covariates

BACKGROUND

A fixed baseline period has been a common covariate assessment approach in pharmacoepidemiological studies from claims but may lead to high levels of covariate misclassification. Simulation studies have recommended expanding the look-back approach to all available data (AAD) for binary indicators of diagnoses, procedures, and medications, but there have been few real data analyses using this approach.

OBJECTIVE

The objective of the study is to explore the impact on treatment effect estimates and covariate prevalence of expanding the look-back period within five validated studies in the Aetion system, a rapid cycle analytics platform.

METHODS

We reran the five studies and assessed covariates using (i) a fixed window approach (usually 180 days before treatment initiation), (ii) AAD prior to treatment initiation, and (iii) AAD with a categorized by recency approach, where the most recent occurrence of a covariate was labeled as recent (occurring within the fixed window) or past (before the start of the fixed window). For each covariate assessment approach, we adjusted for covariates via propensity score matching.

RESULTS

All studies had at least one covariate that had an increase in prevalence of 15% or higher from the fixed window to the AAD approach. However, there was little change in treatment effect estimates resulting from differing covariate assessment approaches. For example, in a study of acute coronary syndrome in high-intensity versus low-intensity statin users, the estimated hazard ratio from the fixed window approach was 1.11 (95% confidence interval 0.98, 1.25) versus 1.21 (1.07, 1.37) when using AAD and 1.19 (1.05, 1.35) using categorized by recency.

CONCLUSION

Expanding the baseline period to AAD improved covariate sensitivity by capturing data that would otherwise be missed yet did not meaningfully change the overall treatment effect estimates compared with the fixed window approach. Copyright © 2017 John Wiley & Sons, Ltd.