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BACKGROUND: Comparing causal effect estimates obtained using observational data to those obtained from the gold standard (i.e., randomized controlled trials [RCTs]) helps assess the validity of these estimates. However, comparisons are challenging due to differences between observational data and RCT generated data. The unknown treatment assignment mechanism in the observational data and varying sampling mechanisms between the RCT and the observational data can lead to confounding and sampling bias, respectively. AIMS: The objective of this study is to propose a two-step framework to validate causal effect estimates obtained from observational data by adjusting for both mechanisms. MATERIALS AND METHODS: An estimator of causal effects related to the two mechanisms is constructed. A two-step framework for comparing causal effect estimates is derived from the estimator. An R package RCTrep is developed to implement the framework in practice. RESULTS: A simulation study is conducted to show that using our framework observational data can produce causal effect estimates similar to those of an RCT. A real-world application of the framework to validate treatment effects of adjuvant chemotherapy obtained from registry data is demonstrated. CONCLUSION: This  study constructs a framework for comparing causal effect estimates between observational data and RCT data, facilitating the assessment of the validity of causal effect estimates obtained from observational data.

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BACKGROUND: Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation. METHODS: We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically. RESULTS: Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis. CONCLUSION: Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data.

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Phase 3 randomized controlled trials (RCTs), while the gold standard for treatment efficacy and safety, are not always feasible, are expensive, can be prolonged and can be limited in generalizability. Other under-recognized sources of evidence can also help advance drug development. Basic science, proof-of-concept studies and early-phase RCTs can provide evidence regarding the potential for clinical benefit. Real-world evidence generated from registries or observational datasets can provide insights into the treatment of rare diseases that often pose a challenge for trial recruitment. Pragmatic trials embedded in healthcare systems can assess the treatment effects in clinical settings among patient populations sometimes excluded from trials. This Perspective discusses potential sources of evidence that may be used to complement explanatory phase 3 RCTs and to speed the development of new cardiovascular medications. Content is derived from the 19th Global Cardiovascular Clinical Trialists meeting (December 2022), involving clinical trialists, patients, clinicians, regulators, funders and industry representatives.

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BACKGROUND: According to long-term follow-up data of malignant tumor patients, assessing treatment effects requires careful consideration of competing risks. The commonly used cause-specific hazard ratio (CHR) and sub-distribution hazard ratio (SHR) are relative indicators and may present challenges in terms of proportional hazards assumption and clinical interpretation. Recently, the restricted mean time lost (RMTL) has been recommended as a supplementary measure for better clinical interpretation. Moreover, for observational study data in epidemiological and clinical settings, due to the influence of confounding factors, covariate adjustment is crucial for determining the causal effect of treatment. METHODS: We construct an RMTL estimator after adjusting for covariates based on the inverse probability weighting method, and derive the variance to construct interval estimates based on the large sample properties. We use simulation studies to study the statistical performance of this estimator in various scenarios. In addition, we further consider the changes in treatment effects over time, constructing a dynamic RMTL difference curve and corresponding confidence bands for the curve. RESULTS: The simulation results demonstrate that the adjusted RMTL estimator exhibits smaller biases compared with unadjusted RMTL and provides robust interval estimates in all scenarios. This method was applied to a real-world cervical cancer patient data, revealing improvements in the prognosis of patients with small cell carcinoma of the cervix. The results showed that the protective effect of surgery was significant only in the first 20 months, but the long-term effect was not obvious. Radiotherapy significantly improved patient outcomes during the follow-up period from 17 to 57 months, while radiotherapy combined with chemotherapy significantly improved patient outcomes throughout the entire period. CONCLUSIONS: We propose the approach that is easy to interpret and implement for assessing treatment effects in observational competing risk data.

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BACKGROUND: In recent years, there has been a growing trend in the utilization of observational studies that make use of routinely collected healthcare data (RCD). These studies rely on algorithms to identify specific health conditions (e.g. diabetes or sepsis) for statistical analyses. However, there has been substantial variation in the algorithm development and validation, leading to frequently suboptimal performance and posing a significant threat to the validity of study findings. Unfortunately, these issues are often overlooked. METHODS: We systematically developed guidance for the development, validation, and evaluation of algorithms designed to identify health status (DEVELOP-RCD). Our initial efforts involved conducting both a narrative review and a systematic review of published studies on the concepts and methodological issues related to algorithm development, validation, and evaluation. Subsequently, we conducted an empirical study on an algorithm for identifying sepsis. Based on these findings, we formulated specific workflow and recommendations for algorithm development, validation, and evaluation within the guidance. Finally, the guidance underwent independent review by a panel of 20 external experts who then convened a consensus meeting to finalize it. RESULTS: A standardized workflow for algorithm development, validation, and evaluation was established. Guided by specific health status considerations, the workflow comprises four integrated steps: assessing an existing algorithm's suitability for the target health status; developing a new algorithm using recommended methods; validating the algorithm using prescribed performance measures; and evaluating the impact of the algorithm on study results. Additionally, 13 good practice recommendations were formulated with detailed explanations. Furthermore, a practical study on sepsis identification was included to demonstrate the application of this guidance. CONCLUSIONS: The establishment of guidance is intended to aid researchers and clinicians in the appropriate and accurate development and application of algorithms for identifying health status from RCD. This guidance has the potential to enhance the credibility of findings from observational studies involving RCD.

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PURPOSE: Metadata for data dIscoverability aNd study rEplicability in obseRVAtional studies (MINERVA), a European Medicines Agency-funded project (EUPAS39322), defined a set of metadata to describe real-world data sources (RWDSs) and piloted metadata collection in a prototype catalogue to assist investigators from data source discoverability through study conduct. METHODS: A list of metadata was created from a review of existing metadata catalogues and recommendations, structured interviews, a stakeholder survey, and a technical workshop. The prototype was designed to comply with the FAIR principles (findable, accessible, interoperable, reusable), using MOLGENIS software. Metadata collection was piloted by 15 data access partners (DAPs) from across Europe. RESULTS: A total of 442 metadata variables were defined in six domains: institutions (organizations connected to a data source); data banks (data collections sustained by an organization); data sources (collections of linkable data banks covering a common underlying population); studies; networks (of institutions); and common data models (CDMs). A total of 26 institutions were recorded in the prototype. Each DAP populated the metadata of one data source and its selected data banks. The number of data banks varied by data source; the most common data banks were hospital administrative records and pharmacy dispensation records (10 data sources each). Quantitative metadata were successfully extracted from three data sources conforming to different CDMs and entered into the prototype. CONCLUSIONS: A metadata list was finalized, a prototype was successfully populated, and a good practice guide was developed. Setting up and maintaining a metadata catalogue on RWDSs will require substantial effort to support discoverability of data sources and reproducibility of studies in Europe.

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Critical care trials evaluate the effect of interventions in patients with diverse personal histories and causes of illness, often under the umbrella of heterogeneous clinical syndromes, such as sepsis or acute respiratory distress syndrome. Given this variation, it is reasonable to expect that the effect of treatment on outcomes may differ for individuals with variable characteristics. However, in randomized controlled trials, efficacy is typically assessed by the average treatment effect (ATE), which quantifies the average effect of the intervention on the outcome in the study population. Importantly, the ATE may hide variations of the treatment's effect on a clinical outcome across levels of patient characteristics, which may erroneously lead to the conclusion that an intervention does not work overall when it may in fact benefit certain patients. In this review, we describe methodological approaches for assessing heterogeneity of treatment effect (HTE), including expert-derived subgrouping, data-driven subgrouping, baseline risk modeling, treatment effect modeling, and individual treatment rule estimation. Next, we outline how insights from HTE analyses can be incorporated into the design of clinical trials. Finally, we propose a research agenda for advancing the field and bringing HTE approaches to the bedside.

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BACKGROUND: Observational studies are frequently used to estimate the comparative effectiveness of different colorectal cancer (CRC) screening methods due to the practical limitations and time needed to conduct large clinical trials. However, time-varying confounders, e.g. polyp detection in the last screening, can bias statistical results. Recently, generalized methods, or G-methods, have been used for the analysis of observational studies of CRC screening, given their ability to account for such time-varying confounders. Discretization, or the process of converting continuous functions into discrete counterparts, is required for G-methods when the treatment and outcomes are assessed at a continuous scale. DEVELOPMENT: This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency. APPLICATION: First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations. CONCLUSIONS: The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs.

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BACKGROUND: The favorable benefits of early rhythm control (ERC) therapy in newly diagnosed patients with atrial fibrillation (AF) have been demonstrated in the EAST-AFNET 4 trial. However, the generalizability and applicability of ERC in real-world clinical settings remain inconclusive. METHODS: We conducted a systematic search of the PubMed and Embase databases to identify observational studies published between January 2020 and February 2024 that focused on real-world evidence pertaining to ERC. The effectiveness and safety outcomes in our study were analogous to those evaluated in the EAST-AFNET 4 trial. RESULTS: A total of 4 observational studies that fulfilled the inclusion criteria of EAST-AFNET 4 were included, involving 130,970 patients with AF, 30.7% of whom received ERC therapy. In our pooled analysis using the fixed-effects model, compared with rate control, ERC significantly decreased the occurrence risk of the primary composite outcome (hazard ratio [HR] 0.86, 95% confidence interval[CI] 0.82-0.91), cardiovascular death (HR 0.87, 95% CI 0.78-0.98), stroke (HR 0.80, 95% CI 0.73-0.87), and hospitalization with worsening heart failure (HR 0.91, 95% CI 0.84-0.99) or acute coronary syndrome (HR 0.72, 95% CI 0.59-0.87). In terms of safety outcomes, there were no differences in the composite safety outcome (HR 1.00, 95% CI 0.95-1.05) and all-cause death (HR 0.93, 95% CI 0.82-1.06) between the two studied groups. CONCLUSIONS: ERC therapy showed favorable effectiveness outcomes compared with rate control, whereas the safety outcomes between the two therapeutic strategies did not differ significantly, supporting the benefits of ERC therapy over rate control in selected real-world patients with AF. REGISTRATION: The study protocol was registered to PROSPERO (CRD42023443569).

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The test-negative design (TND) is an observational study design to evaluate vaccine effectiveness (VE) that enrolls individuals receiving diagnostic testing for a target disease as part of routine care. VE is estimated as one minus the adjusted odds ratio of testing positive versus negative comparing vaccinated and unvaccinated patients. Although the TND is related to case-control studies, it is distinct in that the ratio of test-positive cases to test-negative controls is not typically pre-specified. For both types of studies, sparse cells are common when vaccines are highly effective. We consider the implications of these features on power for the TND. We use simulation studies to explore three hypothesis-testing procedures and associated sample size calculations for case-control and TND studies. These tests, all based on a simple logistic regression model, are a standard Wald test, a continuity-corrected Wald test, and a score test. The Wald test performs poorly in both case-control and TND when VE is high because the number of vaccinated test-positive cases can be low or zero. Continuity corrections help to stabilize the variance but induce bias. We observe superior performance with the score test as the variance is pooled under the null hypothesis of no group differences. We recommend using a score-based approach to design and analyze both case-control and TND. We propose a modification to the TND score sample size to account for additional variability in the ratio of controls over cases. This work enhances our understanding of the data generating mechanism in a test-negative design (TND) and how it is distinct from that of a case-control study due to its passive recruitment of controls.

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BACKGROUND: Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it's an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation. METHODS: In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing. RESULTS: Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene. CONCLUSIONS: The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

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Propensity score methods are popular to control for confounding in observational biomedical studies of risk factors or medical treatments. This paper focused on aspects of propensity score methods that often remain undiscussed, including unmeasured confounding, missing data, variable selection, statistical efficiency, estimands, the positivity assumption, and predictive performance of the propensity score model.

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OBJECTIVES: To evaluate the impact of acute kidney injury on transition to chronic kidney disease (CKD) after cardiac surgery and to determine frequency of incident CKD in these patients. DESIGN: A systematic review and meta-analysis of observational studies. SETTING: Electronic databases Medline and Embase were systematically searched from 1974 to February 6, 2023. PARTICIPANTS: Eligible studies were original observational studies on adult cardiac surgery patients, written in the English language, and with clear kidney disease definitions. Exclusion criteria were studies with previously transplanted populations, populations with preoperative kidney impairment, ventricular assist device procedures, endovascular procedures, a kidney follow-up period of <90 days, and studies not presenting necessary data for effect size calculations. INTERVENTIONS: Patients developing postoperative acute kidney injury after cardiac surgery were compared with patients who did not develop acute kidney injury. MEASUREMENTS AND MAIN RESULTS: The search identified 4,329 unique studies, 87 underwent full-text review, and 12 were included for analysis. Mean acute kidney injury occurrence across studies was 16% (minimum-maximum: 8-50), while mean occurrence of CKD was 24% (minimum-maximum: 3-35), with high variability depending on definitions and follow-up time. Acute kidney injury was associated with increased odds of CKD in all individual studies. The pooled odds ratio across studies was 5.67 (95% confidence interval, 3.34-9.64; p < 0.0001). CONCLUSIONS: Acute kidney injury after cardiac surgery was associated with a more than 5-fold increased odds of developing CKD. New-onset CKD occurred in almost 1 in 4 patients in the years after surgery.

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BACKGROUND: Breast cancer and frailty frequently co-occur in older women, and frailty status has been shown to predict negative health outcomes. However, the extent to which frailty assessments are utilized in observational research for the older breast cancer population is uncertain. Therefore, the aim of this review was to determine the frequency of use of frailty assessments in studies investigating survival or mortality, and characterize them, concentrating on literature from the past 5 years (2017-2022). METHODS: MEDLINE, EMBASE and Cochrane Library were systematically queried to identify observational studies (case-control, cohort, cross-sectional) published from 2017-2022 that focus on older females (≥ 65 years) diagnosed with breast cancer, and which evaluate survival or mortality outcomes. Independent reviewers assessed the studies for eligibility using Covidence software. Extracted data included characteristics of each study as well as information on study design, study population, frailty assessments, and related health status assessments. Risk of bias was evaluated using the appropriate JBI tool. Information was cleaned, classified, and tabulated into review level summaries. RESULTS: In total, 9823 studies were screened for inclusion. One-hundred and thirty studies were included in the final synthesis. Only 11 (8.5%) of these studies made use of a frailty assessment, of which 4 (3.1%) quantified frailty levels in their study population, at baseline. Characterization of frailty assessments demonstrated that there is a large variation in terms of frailty definitions and resulting patient classification (i.e., fit, pre-frail, frail). In the four studies that quantified frailty, the percentage of individuals classified as pre-frail and frail ranged from 18% to 29% and 0.7% to 21%, respectively. Identified frailty assessments included the Balducci score, the Geriatric 8 tool, the Adapted Searle Deficits Accumulation Frailty index, the Faurot Frailty index, and the Mian Deficits of Accumulation Frailty Index, among others. The Charlson Comorbidity Index was the most used alternative health status assessment, employed in 56.9% of all 130 studies. Surprisingly, 31.5% of all studies did not make use of any health status assessments. CONCLUSION: Few observational studies examining mortality or survival outcomes in older women with breast cancer incorporate frailty assessments. Additionally, there is significant variation in definitions of frailty and classification of patients. While comorbidity assessments were more frequently included, the pivotal role of frailty for patient-centered decision-making in clinical practice, especially regarding treatment effectiveness and tolerance, necessitates more deliberate attention. Addressing this oversight more explicitly could enhance our ability to interpret observational research in older cancer patients.

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The unique jobs, exposures, and deployments in the military generate questions regarding cancer risks; however, incidence rates alone from retrospective observational studies provide limited information. Incorporating screening rates, staging, and mortality rates allows a more comprehensive perspective regarding cancer risk in the military.

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IMPORTANCE: Large-scale estimates of bronchopulmonary dysplasia (BPD) are warranted for adequate prevention and treatment. However, systematic approaches to ascertain rates of BPD are lacking. OBJECTIVE: To conduct a systematic review and meta-analysis to assess the prevalence of BPD in very low birth weight (≤ 1,500 g) or very low gestational age (< 32 weeks) neonates. DATA SOURCES: A search of MEDLINE from January 1990 until September 2019 using search terms related to BPD and prevalence was performed. STUDY SELECTION: Randomized controlled trials and observational studies evaluating rates of BPD in very low birth weight or very low gestational age infants were eligible. Included studies defined BPD as positive pressure ventilation or oxygen requirement at 28 days (BPD28) or at 36 weeks postmenstrual age (BPD36). DATA EXTRACTION AND SYNTHESIS: Two reviewers independently conducted all stages of the review. Random-effects meta-analysis was used to calculate the pooled prevalence. Subgroup analyses included gestational age group, birth weight group, setting, study period, continent, and gross domestic product. Sensitivity analyses were performed to reduce study heterogeneity. MAIN OUTCOMES AND MEASURES: Prevalence of BPD defined as BPD28, BPD36, and by subgroups. RESULTS: A total of 105 articles or databases and 780,936 patients were included in this review. The pooled prevalence was 35% (95% CI, 28-42%) for BPD28 (n = 26 datasets, 132,247 neonates), and 21% (95% CI, 19-24%) for BPD36 (n = 70 studies, 672,769 neonates). In subgroup meta-analyses, birth weight category, gestational age category, and continent were strong drivers of the pooled prevalence of BPD. CONCLUSIONS AND RELEVANCE: This study provides a global estimation of BPD prevalence in very low birth weight/low gestation neonates.

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BACKGROUND: Randomized controlled trials are considered the gold standard in regulatory decision making, as observational studies are known to have important methodological limitations. However, real-world evidence may be helpful in specific situations. This review investigates how the effect estimates obtained from randomized controlled trials compare to those obtained from observational studies, using drug therapy for relapsing-remitting multiple sclerosis as an example. STUDY DESIGN AND SETTING: A systematic review of randomized controlled trials and observational studies was conducted. The primary outcome was the annualized relapse rate. Using (network) meta-analysis together with posterior predictive distributions, the drug-specific rate ratios from the network of randomized controlled trials were compared with those from the network of observational studies. RESULTS: Effect estimates from 26 observational studies showed greater magnitudes and were less precise compared to estimates obtained from 21 randomized controlled trials. Twenty of the 28 treatment comparisons between designs had similar rate ratios. Seven inconsistencies in observed rate ratios could be attributed to two specific disease-modifying therapies. CONCLUSION: In this case study, estimates from observational studies predominantly agreed with estimates from randomized controlled trials given their posterior predictive distributions. Multiple observational studies together may therefore supplement additional pivotal randomized controlled trials in relapsing-remitting multiple sclerosis, for instance facilitating the extrapolation of trial results to the broader patient population.

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