Search

Head-to-head studies comparing COVID-19 mRNA vaccine effectiveness in immunocompromised individuals, who are vulnerable to severe disease are lacking, as large sample sizes are required to make meaningful inferences.This observational comparative effectiveness study was conducted in closed administrative claims data from the US HealthVerity database (December 11, 2020-January 10, 2022, before omicron). A 2-dose mRNA-1273 versus BNT162b2 regimen was assessed for preventing medically-attended breakthrough COVID-19 diagnosis and hospitalizations among immunocompromised adults. Inverse probability of treatment weighting was applied to balance baseline characteristics between vaccine groups. Incidence rates from patient-level data and hazard ratios (HRs) using weighted Cox proportional hazards models were calculated.Overall, 57,898 and 66,981 individuals received a 2-dose regimen of mRNA-1273 or BNT161b2, respectively. Among the weighted population, mean age was 51 years, 53 % were female, and baseline immunodeficiencies included prior blood transplant (8%-9%), prior organ transplant (7%), active cancer (12%-13%), primary immunodeficiency (5-6%), HIV (20%-21%), and immunosuppressive therapy use (60%-61%). Rates per 1,000 person-years (PYs; 95% confidence intervals [CI]s) of breakthrough medically-attended COVID-19 were 25.82 (23.83-27.97) with mRNA-1273 and 30.98 (28.93, 33.18) with BNT162b2 (HR, 0.83; 95% CI, 0.75-0.93). When requiring evidence of an antigen or polymerase chain reaction test before COVID-19 diagnosis, the HR for medically-attended COVID-19 was 0.78 (0.67-0.92). Breakthrough COVID-19 hospitalization rates per 1,000 PYs (95% CI) were 3.66 (2.96-4.51) for mRNA-1273 and 4.68 (3.91-5.59) for BNT162b2 (HR, 0.78; 0.59-1.03). Utilizing open and closed claims for outcome capture only, or both cohort entry/outcome capture, produced HRs (95% CIs) for COVID-19 hospitalization of 0.72 (0.57-0.92) and 0.66 (0.58-0.76), respectively.Among immunocompromised adults, a 2-dose mRNA-1273 regimen was more effective in preventing medically-attended COVID-19 in any setting (inpatient and outpatient) than 2-dose BNT162b2. Results were similar for COVID-19 hospitalization, although statistical power was limited when using closed claims only.NCT05366322.

Algorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data.Using Healthverity chargemaster and claims data, we selected patients hospitalized with COVID-19 between April 2020 and February 2021, and classified them by severity at admission using an algorithm we developed based on respiratory support requirements (supplemental oxygen or non-invasive ventilation, O2/NIV, invasive mechanical ventilation, IMV, or NEITHER). To evaluate the utility of the algorithm, patients were followed from admission until death, discharge, or a 28-day maximum to report mortality risks and rates overall and by stratified by severity. Trends for heterogeneity in mortality risk and rate across severity classifications were evaluated using Cochran-Armitage and Logrank trend tests, respectively.Among 118 117 patients, the algorithm categorized patients in increasing severity as NEITHER (36.7%), O2/NIV (54.3%), and IMV (9.0%). Associated mortality risk (and 95% CI) was 11.8% (11.6-12.0%) overall and increased with severity [3.4% (3.2-3.5%), 11.5% (11.3-11.8%), 47.3% (46.3-48.2%); p 0.001]. Mortality rate per 1000 person-days (and 95% CI) was 15.1 (14.9-15.4) overall and increased with severity [5.7 (5.4-6.0), 14.5 (14.2-14.9), 32.7 (31.8-33.6); p 0.001].As expected, we observed a positive association between the algorithm-defined severity on admission and 28-day mortality risk and rate. Although performance remains to be validated, this provides some assurance that this algorithm may be used for confounding control or stratification in treatment effect studies.

Transparency is increasingly promoted to instill trust in nonrandomized studies using real-world data. Graphics and data visualizations support transparency by aiding communication and understanding, and can inform study design and analysis decisions. However, other than graphical representation of a study design and flow diagrams (e.g., a Consolidated Standards of Reporting Trials [CONSORT] like diagram), specific standards on how to maximize validity and transparency with visualization are needed. This paper provides guidance on how to use visualizations throughout the life cycle of a pharmacoepidemiology study-from initial study design to final report-to facilitate rationalized and transparent decision-making about study design and implementation, and clear communication of study findings. Our intent is to help researchers align their practices with current consensus statements on transparency.

Decision-makers have become increasingly interested in incorporating real-world evidence (RWE) into their decision-making process. Due to concerns regarding the reliability and quality of RWE, stakeholders have issued numerous recommendation documents to assist in setting RWE standards. The fragmented nature of these documents poses a challenge to researchers and decision-makers looking for guidance on what is ‘high-quality’ RWE and how it can be used in decision-making. We offer researchers and decision-makers a structure to organize the landscape of RWE recommendations and identify consensus and gaps in the current recommendations. To provide researchers with a much needed pathway for generating RWE, we discuss how decision-makers can move from fragmented recommendations to comprehensive guidance.

IntroductionHead-to-head studies comparing COVID-19 mRNA vaccine effectiveness in immunocompromised individuals, who are vulnerable to severe disease are lacking, as large sample sizes are required to make meaningful inferences.MethodsThis observational comparative effectiveness study was conducted in closed administrative claims data from the US HealthVerity database (December 11, 2020-January 10, 2022, before omicron). A 2-dose mRNA-1273 versus BNT162b2 regimen was assessed for preventing medically-attended breakthrough COVID-19 diagnosis and hospitalizations among immunocompromised adults. Inverse probability of treatment weighting was applied to balance baseline characteristics between vaccine groups. Incidence rates from patient-level data and hazard ratios (HRs) using weighted Cox proportional hazards models were calculated.ResultsOverall, 57,898 and 66,981 individuals received a 2-dose regimen of mRNA-1273 or BNT161b2, respectively. Among the weighted population, mean age was 51 years, 53% were female, and baseline immunodeficiencies included prior blood transplant (8%-9%), prior organ transplant (7%), active cancer (12%-13%), primary immunodeficiency (25%-26%), HIV (20%-21%), and immunosuppressive therapy use (60%-61%). Rates per 1,000 person-years (PYs; 95% confidence intervals [CI]s) of breakthrough medically-attended COVID-19 were 25.82 (23.83-27.97) with mRNA-1273 and 30.98 (28.93, 33.18) with BNT162b2 (HR, 0.83; 95% CI, 0.75-0.93). When requiring evidence of an antigen or polymerase chain reaction test before COVID-19 diagnosis, the HR for medically-attended COVID-19 was 0.78 (0.67-0.92). Breakthrough COVID-19 hospitalization rates per 1,000 PYs (95% CI) were 3.66 (2.96-4.51) for mRNA-1273 and 4.68 (3.91-5.59) for BNT162b2 (HR, 0.78; 0.59-1.03). Utilizing open and closed claims for outcome capture only, or both cohort entry/outcome capture, produced HRs (95% CIs) for COVID-19 hospitalization of 0.72 (0.57-0.92) and 0.66 (0.58-0.76), respectively.ConclusionsAmong immunocompromised adults, a 2-dose mRNA-1273 regimen was more effective in preventing medically-attended COVID-19 in any setting (inpatient and outpatient) than 2-dose BNT162b2. Results were similar for COVID-19 hospitalization, although statistical power was limited when using closed claims only.Study RegistrationNCT05366322

Real-world evidence provides important information about the effects of medicines in routine clinical practice. To engender trust that evidence generated for regulatory purposes is sufficiently valid, transparency in the reasoning that underlies study design decisions is critical. Building on existing guidance and frameworks, we developed the Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence (SPACE) as a process for identifying design elements and minimal criteria for feasibility and validity concerns, and for documenting decisions. Starting with an articulated research question, we identify key components of the randomized controlled trial needed to maximize validity, and pragmatic choices are considered when required. A causal diagram is used to justify the variables identified for confounding control, and key decisions, assumptions, and evidence are captured in a structured way. In this way, SPACE may improve dialogue and build trust among healthcare providers, patients, regulators, and researchers.

To complement real-world evidence (RWE) guidelines, the 2019 Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real-world Evidence (SPACE) framework elucidated a process for designing valid and transparent real-world studies. As an extension to SPACE, here, we provide a structured framework for conducting feasibility assessments-a step-by-step guide to identify decision grade, fit-for-purpose data, which complements the United States Food and Drug Administration (FDA)'s framework for a RWE program. The process was informed by our collective experience conducting systematic feasibility assessments of existing data sources for pharmacoepidemiology studies to support regulatory decisions. Used with the SPACE framework, the Structured Process to Identify Fit-For-Purpose Data (SPIFD) provides a systematic process for conducting feasibility assessments to determine if a data source is fit for decision making, helping ensure justification and transparency throughout study development, from articulation of a specific and meaningful research question to identification of fit-for-purpose data and study design.

The emergence and global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an urgent need for evidence on medical interventions and outcomes of the resulting disease, coronavirus disease 2019 (COVID-19). Although many randomized controlled trials (RCTs) evaluating treatments and vaccines for COVID-19 are already in progress, the number of clinical questions of interest greatly outpaces the available resources to conduct RCTs. Therefore, there is growing interest in whether nonrandomized real-world evidence (RWE) can be used to supplement RCT evidence and aid in clinical decision making, but concerns about nonrandomized RWE have been highlighted by a proliferation of RWE studies on medications and COVID-19 outcomes with widely varying conclusions. The objective of this paper is to review some clinical questions of interest, potential data types, challenges, and merits of RWE in COVID-19, resulting in recommendations for nonrandomized RWE designs and analyses based on established RWE principles.

In this chapter we discuss existing methodology and reporting guidelines for pragmatic randomized clinical trials (pRCTs) identified from a literature review, consensus recommendations from these guidelines, and areas of need for future guidelines. Consensus from existing pRCT methodology and reporting guidance can assist in the design, conduct, and reporting of high-quality pRCTs. Further, lack of consensus indicates areas of need where methodology and reporting guidelines are limited or have not yet been developed. Consensus recommendations support the use of the Pragmatic-Explanatory Continuum Indicator Summary, version 2 (PRECIS-2) for both the design and reporting of pRCTs. Further, journals are more often requiring the Consolidated Standards of Reporting Trials (CONSORT) extension for pragmatic trials checklist with manuscript submissions. Existing guidance can be strengthened in the areas of patient stakeholder involvement, effective communication of study results, improved understanding of usual care, including concomitant medications and changes in therapy, and balance between validity and fit-for-purpose among study design choices (traditional RCTs vs. routine clinical practice).

Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

Importance Azithromycin is one of the most commonly prescribed antibiotics in the US. It has been associated with an increased risk of cardiovascular death in some observational studies. Objective To estimate the relative and absolute risks of cardiovascular and sudden cardiac death after an outpatient azithromycin prescription compared with amoxicillin, an antibiotic not known to increase cardiovascular events. Design, Setting, and Participants This retrospective cohort study included 2 large, diverse, community-based integrated care delivery systems with comprehensive capture of encounters and prescriptions from January 1, 1998, to December 31, 2014. The cohort included patients aged 30 to 74 years who had at least 12 months of health-plan enrollment prior to antibiotic exposure. The exclusion criteria were absence of prescription benefits, prescription for more than 1 type of study antibiotic within 10 days, hospitalization or nursing home residence, and serious medical conditions. Risk of cardiovascular death associated with azithromycin vs amoxicillin exposure was calculated after controlling for confounding factors using a propensity score. Data were analyzed from December 1, 2016, to March 30, 2020. Exposures Outpatient prescription of azithromycin or amoxicillin. Main Outcomes and Measures The primary outcomes were cardiovascular death and sudden cardiac death. An a priori subgroup analysis quantified the effects of azithromycin exposure among patients with increased baseline cardiovascular risk. The secondary outcomes were noncardiovascular death and all-cause mortality. Results The study included 7 824 681 antibiotic exposures, including 1 736 976 azithromycin exposures (22.2%) and 6 087 705 amoxicillin exposures (77.8%), among 2 929 008 unique individuals (mean [SD] age, 50.7 [12.3] years; 1 810 127 [61.8%] women). Azithromycin was associated with a significantly increased hazard of cardiovascular death (hazard ratio [HR], 1.82; 95% CI, 1.23-2.67) but not sudden cardiac death (HR, 1.59; 95% CI, 0.90-2.81) within 5 days of exposure. No increases in risk were found 6 to 10 days after exposure. Similar results were observed in patients within the top decile of cardiovascular risk (HR, 1.71; 95% CI, 1.06-2.76). Azithromycin was also associated with an increased risk of noncardiovascular death (HR, 2.17; 95% CI, 1.44-3.26) and all-cause mortality (HR, 2.00; 95% CI, 1.51-2.63) within 5 days of exposure. Conclusions and Relevance These findings suggest that outpatient azithromycin use was associated with an increased risk of cardiovascular death and noncardiovascular death. Causality cannot be established, particularly for noncardiovascular death, owing to the likelihood of residual confounding.

BACKGROUND: Patient registries are organized systems that use observational methods to collect uniform data on specified outcomes in a population defined by a particular disease, condition, or exposure. Data collected in registries often coincide with data that could support clinical trials. Integrating clinical trials within registries to create registry-embedded clinical trials offers opportunities to reduce duplicative data collection, identify and recruit patients more efficiently, decrease time to database lock, accelerate time to regulatory decision-making, and reduce clinical trial costs. This article describes a project of the Clinical Trials Transformation Initiative (CTTI) intended to help clinical trials researchers determine when a registry could potentially serve as the platform for the conduct of a clinical trial. METHODS: Through a review of registry-embedded clinical trials and commentaries, semi-structured interviews with experts, and a multi-stakeholder expert meeting, the project team addressed how to identify and describe essential registry characteristics, practices, and processes required to for conducting embedded clinical trials intended for regulatory submissions in the United States. RESULTS: Recommendations, suggested practices, and decision trees that facilitate the assessment of whether a registry is suitable for embedding clinical trials were developed, as well as considerations for the design of new registries. Essential registry characteristics include relevancy, robustness, reliability, and assurance of patient protections. CONCLUSIONS: The project identifies a clear role for registries in creating a sustainable and reusable infrastructure to conduct clinical trials. Adoption of these recommendations will facilitate the ability to perform high-quality and efficient prospective registry-based clinical trials. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s43441-020-00185-5) contains supplementary material, which is available to authorized users.

Purpose Because of an increasing demand for quality comparative effectiveness research (CER), methods guidance documents have been published, such as those from the Agency for Healthcare Research and Quality (AHRQ) and the Patient-Centered Outcomes Research Institute (PCORI). Our objective was to identify CER methods guidance documents and compare them to produce a summary of important recommendations which could serve as a consensus of CER method recommendations. Methods We conducted a systematic literature review to identify CER methods guidance documents published through 2014. Identified documents were analyzed for methods guidance recommendations. Individual recommendations were categorized to determine the degree of overlap. Results We identified nine methods guidance documents, which contained a total of 312 recommendations, 97% of which were present in two or more documents. All nine documents recommended transparency and adaptation for relevant stakeholders in the interpretation and dissemination of results. Other frequently shared CER methods recommendations included: study design and operational definitions should be developed a priori and allow for replication (n = 8 documents); focus on areas with gaps in current clinical knowledge that are relevant to decision-makers (n = 7); validity of measures, instruments, and data should be assessed and discussed (n = 7); outcomes, including benefits and harms, should be clinically meaningful, and objectively measured (n = 7). Assessment for and strategies to minimize bias (n = 6 documents), confounding (n = 6), and heterogeneity (n = 4) were also commonly shared recommendations between documents. Conclusions We offer a field-consensus guide based on nine CER methods guidance documents that will aid researchers in designing CER studies and applying CER methods. Copyright © 2016 John Wiley & Sons, Ltd.

BACKGROUND: Observational studies including time-varying treatments are prone to confounding. We compared time-varying Cox regression analysis, propensity score (PS) methods, and marginal structural models (MSMs) in a study of antidepressant [selective serotonin reuptake inhibitors (SSRIs)] use and the risk of hip fracture. METHODS: A cohort of patients with a first prescription for antidepressants (SSRI or tricyclic antidepressants) was extracted from the Dutch Mondriaan and Spanish Base de datos para la Investigacion Farmacoepidemiologica en Atencion Primaria (BIFAP) general practice databases for the period 2001-2009. The net (total) effect of SSRI versus no SSRI on the risk of hip fracture was estimated using time-varying Cox regression, stratification and covariate adjustment using the PS, and MSM. In MSM, censoring was accounted for by inverse probability of censoring weights. RESULTS: The crude hazard ratio (HR) of SSRI use versus no SSRI use on hip fracture was 1.75 (95%CI: 1.12, 2.72) in Mondriaan and 2.09 (1.89, 2.32) in BIFAP. After confounding adjustment using time-varying Cox regression, stratification, and covariate adjustment using the PS, HRs increased in Mondriaan [2.59 (1.63, 4.12), 2.64 (1.63, 4.25), and 2.82 (1.63, 4.25), respectively] and decreased in BIFAP [1.56 (1.40, 1.73), 1.54 (1.39, 1.71), and 1.61 (1.45, 1.78), respectively]. MSMs with stabilized weights yielded HR 2.15 (1.30, 3.55) in Mondriaan and 1.63 (1.28, 2.07) in BIFAP when accounting for censoring and 2.13 (1.32, 3.45) in Mondriaan and 1.66 (1.30, 2.12) in BIFAP without accounting for censoring. CONCLUSIONS: In this empirical study, differences between the different methods to control for time-dependent confounding were small. The observed differences in treatment effect estimates between the databases are likely attributable to different confounding information in the datasets, illustrating that adequate information on (time-varying) confounding is crucial to prevent bias. Copyright © 2016 John Wiley & Sons, Ltd.

OBJECTIVEThe Follow-Up Study of patients previously enrolled in Exubera controlled clinical trials (FUSE) was designed to evaluate whether patients previously treated with Exubera (EXU; insulin human [rDNA origin], inhaled powder) in controlled clinical trials died because of incident primary lung cancer at a substantially higher rate than patients treated with a comparator.RESEARCH DESIGN AND METHODSFUSE is a hybrid, randomized, controlled trial/cohort study including participants of 17 prior EXU clinical trials. Pooled patient data from these trials were used, and the subset of patients enrolled in the follow-up cohort study was followed prospectively for 2 years in order to evaluate the incidence of fatal and nonfatal primary lung cancers and all-cause mortality.RESULTSThere were 24,409 person-years (PY) of observation among 7,439 trial patients, with 4,017 PY (16.5%) from the period after the trials but before the prospective follow-up and 5,299 PY (21.7%) from the prospective follow-up. Just over half of the 2,631 patients (51.6%) in the prospective follow-up were randomized to EXU in the original trial. The incidence density ratio was 2.8 (95% CI 0.5, 28.5) for lung cancer–related mortality and 3.7 (95% CI 1.0, 20.7) for incident primary lung cancer. The hazard ratio for all-cause mortality was 0.81 (95% CI 0.60, 1.10).CONCLUSIONSThese data cannot exclude an increased risk of lung cancer–related mortality associated with EXU use. If real, the absolute increased risk of lung cancer–related mortality was small (0.48 cases per 1,000 PY). For all-cause mortality—the most reliably measured end point with the clearest interpretation—EXU users did not experience an excess all-cause death rate (relative or absolute) compared with users of other diabetes treatments over the study period.

The 47th annual meeting of the Society for Epidemiologic Research hosted 17 invited speakers charged by the Executive Committee with presenting some of the many ways that epidemiologists have improved the health of the general population. There were 9 “Then and Now” sessions that were structured to focus on how early epidemiologists overcame research hurdles and advanced health through innovative strategies. For most topics, a longstanding expert was paired with an excellent contemporary epidemiologist working in the area, and both were given the freedom to deliver an integrated story about epidemiology's temporal role in protecting and promoting public health. Epidemiologic discoveries in cardiovascular, cancer, and perinatal epidemiology were discussed on day 1, followed by discussions of accomplishments in reducing exposures that adversely impact health (nutrition, environment/occupation, and tobacco use) on day 2. Topics with relevancy for many aspects of epidemiology were presented on day 3, including infectious diseases, social forces, and causal thinking in epidemiologic research. Given the large number of outstanding senior and junior epidemiologists that attended the meeting, choosing speakers was a unique challenge. What became evident from all sessions was the passion that epidemiologists have for population health, tempered with concerns for remaining true to epidemiologic principles, the timely adoption of innovative methods, and the responsible interpretation of research findings.

Epidemiology textbooks typically divide biases into 3 general categories-confounding, selection bias, and information bias. Despite the ubiquity of this categorization, authors often use these terms to mean different things. This hinders communication among epidemiologists and confuses students who are just learning about the field. To understand the sources of this problem, we reviewed current general epidemiology textbooks to examine how the authors defined and categorized biases. We found that much of the confusion arises from different definitions of "validity" and from a mixing of 3 overlapping organizational features in defining and differentiating among confounding, selection bias, and information bias: consequence, the result of the problem; cause, the processes that give rise to the problem; and cure, how these biases can be addressed once they occur. By contrast, a consistent taxonomy would provide (1) a clear and consistent definition of what unites confounding, selection bias, and information bias and (2) a clear articulation and consistent application of the feature that distinguishes these categories. Based on a distillation of these textbook discussions, we provide an example of a taxonomy that we think meets these criteria.

Introduction: Many clinical trials terminate early due to safety and efficacy concerns, and less often due to unexpected “positive” findings. However, early termination of post-approval (Phase IV) pragmatic randomized trials for commercial reasons is less frequent, may be more complex, and may require added flexibility in closure methods, including short term follow-up. VOLUME was a randomized, open-label, post-approval pragmatic clinical trial (PCT) or large simple trial that terminated early due to product withdrawal. The aim of this paper is to describe circumstances unique to post-approval PCTs that may require a closure amendment rather than immediate study termination, and our recommendations for operational study closure in these circumstances. We use the VOLUME case study throughout to provide a practical example. Methods: Study closeout considerations at the study level include: notifying external governance bodies, e.g., data monitoring committees (DMC), and scientific steering committees (SSC); executing a study closure amendment; notifying and training of study physicians; and institutional review board (IRB)/ethics committee (EC) approvals. Study closure considerations at the patient level focus on patient safety and include: patient notification, efficient transition to alternative treatments, the need for re-consenting; and drug supply shortages. Conclusions: Early study closeout logistics require careful analysis, detailed planning, and close coordination, and are ideally considered at the study planning phase. Lessons learned from the VOLUME closeout should help other researchers devise contingencies when terminating post approval pragmatic trials that utilize a marketed product.ClinicalTrials.gov: NCT00359801. Keywords: Large simple trial, Closure, Product withdrawal, Commercial withdrawal, Pragmatic

Because of an increasing demand for quality comparative effectiveness research (CER), methods guidance documents have been published, such as those from the Agency for Healthcare Research and Quality (AHRQ) and the Patient-Centered Outcomes Research Institute (PCORI). Our objective was to identify CER methods guidance documents and compare them to produce a summary of important recommendations which could serve as a consensus of CER method recommendations.We conducted a systematic literature review to identify CER methods guidance documents published through 2014. Identified documents were analyzed for methods guidance recommendations. Individual recommendations were categorized to determine the degree of overlap.We identified nine methods guidance documents, which contained a total of 312 recommendations, 97% of which were present in two or more documents. All nine documents recommended transparency and adaptation for relevant stakeholders in the interpretation and dissemination of results. Other frequently shared CER methods recommendations included: study design and operational definitions should be developed a priori and allow for replication (n = 8 documents); focus on areas with gaps in current clinical knowledge that are relevant to decision-makers (n = 7); validity of measures, instruments, and data should be assessed and discussed (n = 7); outcomes, including benefits and harms, should be clinically meaningful, and objectively measured (n = 7). Assessment for and strategies to minimize bias (n = 6 documents), confounding (n = 6), and heterogeneity (n = 4) were also commonly shared recommendations between documents.We offer a field-consensus guide based on nine CER methods guidance documents that will aid researchers in designing CER studies and applying CER methods. Copyright © 2016 John WileySons, Ltd.

The requirement for framing all causal questions as well-defined interventions is being promoted in the causal inference literature within epidemiology. One can consider this perspective as an intervention on the field which requires a refocusing of epidemiologic questions and retooling of epidemiologic methods. Although this intervention has produced many positive results, we think that its underlying assumptions and the possibilities of unintended consequences warrant examination. In so doing, we argue that this approach can lead to the neglect of causal identification as a useful link between associations and the estimation of intervention effects.

Background The case-only study for investigating gene‐environment interactions provides increased statistical efficiency over case-control analyses. This design has been criticized for being susceptible to bias arising from non-independence between the genetic and environmental factors in the population. Given that independence is critical to the validity of case-only estimates of interaction, researchers frequently use controls to evaluate whether the independence assumption is tenable, as advised in the literature. Our work investigates to what extent this approach is appropriate and how non-independence can be accounted for in case-only analyses. Methods We provide a formula in epidemiological terms that illustrates the relationship between the gene‐environment association measured among controls and the gene‐environment association in the source population. Using this formula, we conducted sensitivity analyses to describe the circumstances in which controls can be used as proxy for the source population when evaluating gene‐ environment independence. Lastly, we generated hypothetical cohort data to examine whether multivariable modelling approaches can be used to control for non-independence. Results Our sensitivity analyses show that controls should not be used to evaluate gene‐ environment independence in the population, even when the baseline risk of disease is low (i.e. 1%), and the interaction and independent effects are moderate (i.e. risk ratio = 2). When the factors are associated, it is possible to remove bias arising from non-independence using standard statistical multivariable techniques in case-only analyses. Conclusions Even when the disease risk is low, evaluation of gene‐environment independence in controls does not provide a consistent test for bias in the caseonly study. Given that control for non-independence is possible when the source of the non-independence can be conceptualized, the case-only design may still be a useful epidemiological tool for examining gene‐environment interactions.

Background: Although the risk of bowel perforation is often cited as a major factor in the choice between colonoscopy and sigmoidoscopy for colorectal screening, good estimates of the absolute and relative risks of perforation are lacking. Methods: We used a large population-based cohort that consisted of a random sample of 5% of Medicare beneficiaries living in regions of the United States covered by the Surveillance, Epidemiology, and End Results (SEER) Program registries to determine rates of perforation in people aged 65 years and older. We identified individuals who were cancerfree and had undergone colonoscopy or sigmoidoscopy between 1991 and 1998, calculated both the incidence and risk of perforation within 7 days of the procedure, and explored the impact on incidence and risk of perforation of age, race/ ethnicity, sex, comorbidities, and indication for the procedure. We also estimated the risk of death after perforation. Risks were calculated with odds ratios (ORs) and 95% confidence intervals (CIs). All statistical tests were two-sided. Results: There were 77 perforations after 39 286 colonoscopies (incidence = 1.96/1000 procedures) and 31 perforations after 35 298 sigmoidoscopies (incidence = 0.88/1000 procedures). After adjustment, the OR for perforation from colonoscopy relative to perforation from sigmoidoscopy was 1.8 (95% CI = 1.2 to 2.8). Risk of perforation from either procedure increased in association with increasing age (P trend

Observational studies including time-varying treatments are prone to confounding. We compared time-varying Cox regression analysis, propensity score (PS) methods, and marginal structural models (MSMs) in a study of antidepressant [selective serotonin reuptake inhibitors (SSRIs)] use and the risk of hip fracture.A cohort of patients with a first prescription for antidepressants (SSRI or tricyclic antidepressants) was extracted from the Dutch Mondriaan and Spanish Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria (BIFAP) general practice databases for the period 2001-2009. The net (total) effect of SSRI versus no SSRI on the risk of hip fracture was estimated using time-varying Cox regression, stratification and covariate adjustment using the PS, and MSM. In MSM, censoring was accounted for by inverse probability of censoring weights.The crude hazard ratio (HR) of SSRI use versus no SSRI use on hip fracture was 1.75 (95%CI: 1.12, 2.72) in Mondriaan and 2.09 (1.89, 2.32) in BIFAP. After confounding adjustment using time-varying Cox regression, stratification, and covariate adjustment using the PS, HRs increased in Mondriaan [2.59 (1.63, 4.12), 2.64 (1.63, 4.25), and 2.82 (1.63, 4.25), respectively] and decreased in BIFAP [1.56 (1.40, 1.73), 1.54 (1.39, 1.71), and 1.61 (1.45, 1.78), respectively]. MSMs with stabilized weights yielded HR 2.15 (1.30, 3.55) in Mondriaan and 1.63 (1.28, 2.07) in BIFAP when accounting for censoring and 2.13 (1.32, 3.45) in Mondriaan and 1.66 (1.30, 2.12) in BIFAP without accounting for censoring.In this empirical study, differences between the different methods to control for time-dependent confounding were small. The observed differences in treatment effect estimates between the databases are likely attributable to different confounding information in the datasets, illustrating that adequate information on (time-varying) confounding is crucial to prevent bias.

Causal inference requires an understanding of the conditions under which association equals causation. The exchangeability or no confounding assumption is well known and well understood as central to this task. More recently the epidemiologic literature has described additional assumptions related to the stability of causal effects. In this paper we extend the Sufficient Component Cause Model to represent one expression of this stability assumption--the Stable Unit Treatment Value Assumption. Approaching SUTVA from an SCC model helps clarify what SUTVA is and reinforces the connections between interaction and SUTVA.

Post-approval, observational drug safety studies face well known difficulties in controlling for confounding, particularly confounding by indication for drug use. A study design that addresses confounding by indication is the large simple trial (LST). LSTs are characterized by large sample sizes, often in the thousands; broad entry criteria consistent with the approved medication label; randomization based on equipoise, i.e. neither physician nor patient believes that one treatment option is superior; minimal, streamlined data collection requirements; objectively-measured endpoints (e.g. death, hospitalization); and follow-up that minimizes interventions or interference with normal clinical practice. In theory then, the LST is a preferred study design for drug and vaccine safety research because it controls for biases inherent to observational research while still providing results that are generalizable to 'real-world' use. To evaluate whether LSTs are used for comparative safety evaluation and if the design is, in fact, advantageous compared with other designs, we conducted a review of the published literature (1949 through 31 December 2010) and the ClinicalTrials.gov registry (2000 through 31 December 2010). Thirteen ongoing or completed safety LSTs were identified. The design has rarely been used in comparative drug safety research, which is due to the operational, financial and scientific hurdles of implementing the design. The studies that have been completed addressed important clinical questions and, in some cases, led to re-evaluation of medical practice. We conclude the design has demonstrated utility for comparative safety research of medicines and vaccines if the necessary scientific and operational conditions for its use are met.

Sufficient causes of disease are redundant when an individual acquires the components of two or more sufficient causes. In this circumstance, the individual still would have become diseased even if one of the sufficient causes had not been acquired. In the context of a study, when any individuals acquire components of more than one sufficient cause over the observation period, the etiologic effect of the exposure (defined as the absolute or relative difference between the proportion of the exposed who develop the disease by the end of the study period and the proportion of those individuals who would have developed the disease at the moment they did even in the absence of the exposure) may be underestimated. Even in the absence of confounding and bias, the observed effect estimate represents only a subset of the etiologic effect. This underestimation occurs regardless of the measure of effect used.To some extent, redundancy of sufficient causes is always present, and under some circumstances, it may make a true cause of disease appear to be not causal. This problem is particularly relevant when the researcher's goal is to characterize the universe of sufficient causes of the disease, identify risk factors for targeted interventions, or construct causal diagrams. In this paper, we use the sufficient component cause model and the disease response type framework to show how redundant causation arises and the factors that determine the extent of its impact on epidemiologic effect measures.

It is well known that the incidence odds ratio approximates the risk ratio when the disease of interest is rare, but increasingly overestimates the risk ratio as the disease becomes more common. However when assessing interaction, incidence odds ratios may not approximate risk ratios even when the disease is rare. We use the term "distributional interaction" to refer to interaction that appears when using incidence odds ratios that does not appear, or appears to a lesser degree, when using risk ratios. The interpretational problems that arise from this discrepancy can have important implications in epidemiologic research. Therefore, quantification of the relationship between the interaction odds ratio and the interaction risk ratio is warranted. In this paper, we provide a formula to quantify the differences between incidence odds ratios and risk ratios when they are used to estimate effect modification on a multiplicative scale. Using this formula, we examine the conditions under which these two estimates diverge. Furthermore, we expand this discussion to the implications of using incidence odds ratios to assess effect modification on an additive scale. Finally, we illustrate how distributional interaction arises and the problems that it causes using an example from the literature. Whenever the risk of the outcome variable is non-negligible, distributional interaction is possible. This is true even when the disease is rare (e.g., disease risk is less than 5%). Therefore, when assessing interaction on either an additive or multiplicative scale, caution should be taken in interpreting interaction estimates based on incidence odds ratios.

Ill-defined causal questions present serious problems for observational studies—problems that are largely unappreciated. This paper extends the usual counterfactual framework to consider causal questions about compound treatments for which there are many possible implementations (for example, “prevention of obesity”). We describe the causal effect of compound treatments and their identifiability conditions, with a special emphasis on the consistency condition. We then discuss the challenges of using the estimated effect of a compound treatment in one study population to inform decisions in the same population and in other populations. These challenges arise because the causal effect of compound treatments depends on the distribution of the versions of treatment in the population. Such causal effects can be unpredictable when the versions of treatment are unknown. We discuss how such issues of “transportability” are related to the consistency condition in causal inference. With more carefully framed questions, the results of epidemiologic studies can be of greater value to decision-makers.

Purpose The propensity score, a data-based approach used to control for confounding, has been increasingly used in large-scale datasets. Recent developments in epidemiologic methods, however, suggest that data-based approaches to confounding that lack conceptual grounding may lead to less valid estimates. We examined whether the underlying causal scenario influences the extent to which the propensity score improves the validity of effect estimates. Methods Hypothetical cohorts were simulated for two alternative causal scenarios of the relationship between prescription ibuprofen and death. To investigate the effect of the causal model on the validity of the propensity score-adjusted effect estimate, in these models, we specify the relationships among several hypothetical causes of death and vary the prevalence of each cause. Each cohort was analyzed using logistic and Poisson regression to derive odds ratios and risk ratios (RRs), respectively, to calculate the effect estimates and propensity scores. Results Statistical analyses resulting from these simulations demonstrate that propensity scores derived from logistic regression may be problematic when the drug treatment of interest is common. In addition, mediators of the drug-disease process are often disguised as confounders and thus may be inadvertently included in the propensity score, leading to less valid estimates. Finally, the propensity scores may provide invalid estimates when single variables are included in the composite score without consideration of the joint effects among the variables. Under the most extreme scenario, the propensity score-adjusted estimate (RR prop ) reversed the direction of the true RR (RR true ) (e.g., RR true = 1.72 vs. RR prop = 0.65). Conclusion Under realistic causal models, propensity scores may lead to less valid effect estimates because they rely solely on data to identify confounding. Hypothesizing the causal model prior to generating propensity scores for an outcome of interest may lead to more valid estimates.