Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD).

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.

Metadata for Data dIscoverability aNd Study rEplicability in obseRVAtional Studies (MINERVA): Development and Pilot of a Metadata List and Catalogue in Europe.

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.

Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies.

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.

Hypothesis testing and sample size considerations for the test-negative design.

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.

Bias due to coarsening of time intervals in the inference for the effectiveness of colorectal cancer screening.

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.

Real-world evidence of early rhythm control in patients with atrial fibrillation: A systematic review and meta-analysis.

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).

Frequency of use and characterization of frailty assessments in observational studies on older women with breast cancer: a systematic review.

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.

The Impact of Acute Kidney Injury on Chronic Kidney Disease After Cardiac Surgery: A Systematic Review and Meta-analysis.

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.

High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study.

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.

Ten things to remember about propensity scores.

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.

Diabetes Mellitus and Intervertebral Disc Degeneration: A Meta-Analysis.

BACKGROUND: Diabetes mellitus (DM) has been related to a higher risk of intervertebral disc degeneration (IVDD). However, the previous studies showed inconsistent results. We performed a systematic review and meta-analysis to comprehensively investigate the association between DM and IVDD in adult population. METHODS: Observational studies relevant to the aim of the meta-analysis were retrieved by search of electronic databases including PubMed, Web of Science, and Embase. A random-effects model was used to combine the data by incorporating the influence of between-study heterogeneity. RESULTS: Eleven observational studies involving 2,881,170 adults were included. Among them, 1,211,880 had DM. Compared to those with normoglycemia, patients with DM were associated with a higher odds ratio of IVDD (OR: 1.68, 95% confidence interval: 1.24 to 2.29, P<0.001; I2=98%). Further sensitivity analysis excluding database studies with IVDD diagnosed via International Classification of Diseases codes showed consistent results (odds ratio: 1.47, 95% confidence interval: 1.06 to 2.02, P=0.02) with no statistical heterogeneity (I2=0%). Subgroup analyses showed a stronger association between DM and IVDD in cohort studies than that in cross-sectional studies, in studies evaluating overall IVDD than that evaluating lumbar disc degeneration, and in studies that adjusted age and body mass index than that did not (P for subgroup differences all <0.05). Subgroup analyses according to study country and quality score did not significantly affect the association. CONCLUSIONS: DM may be associated with IVDD in adult population, which seems to be independent of age and body mass index.

The burden of skull base chordomas: insights from a meta-analysis of observational studies.

OBJECTIVE: The aim of this study was to provide a quantitative synthesis of the survival outcomes for patients with skull base chordomas, focusing on the role of 1) the extent of resection (gross-total [GTR] vs non-GTR), 2) the type of surgery (primary vs revision), 3) tumor histology, and 4) the different use of adjuvant therapies (proton beam radiotherapy [PBRT], photon radiotherapy [RT], or none). METHODS: A systematic review with a meta-analysis was conducted following the 2020 PRISMA guidelines. Observational studies describing adult and pediatric patient cohorts harboring skull base chordomas were included. The primary outcome measures were represented by the 5-year overall survival (OS) and progression-free survival (PFS) rates. The main intervention effects were represented by the extent of resection (GTR vs non-GTR), type of surgical excision (primary vs revision surgeries), tumor histology, and the different use of adjuvant therapies (PBRT, RT, or none). The pooled estimates were calculated using random forest models. The risk of bias was evaluated using the Joanna Briggs Institute checklist for case series. RESULTS: Six hundred forty-four studies were identified through a database and register search. After study selection, 51 studies and 3871 patients were included in the meta-analysis. The overall 5-year OS rate was 73%, which increased to 84% among patients undergoing GTR. The overall 5-year PFS rate was 52%, increasing to 74% for patients receiving GTR. The 5-year OS and PFS rates for patients undergoing PBRT were 86% and 71%, compared with 71% and 54% for patients receiving RT, and 55% and 25% when no adjuvant treatments were used. Patients undergoing their first surgery had 2.13-fold greater chances of being disease-free and 1.4-fold greater chances of being alive at 5 years follow-up compared with patients who received a revision surgery. Patients harboring chondroid chordomas had 1.13- and 1.9-fold greater chances of being alive at 5 years compared with patients with conventional and de-differentiated chordomas, respectively. The overall risk of bias was low in the included studies. CONCLUSIONS: The results of this comprehensive meta-analysis highlight the tremendous impact of GTR and adjuvant PBRT on improving OS and PFS of patients harboring skull base chordomas, with better survival rates demonstrated for patients with chondroid tumors. Even in experienced hands, the rate of surgical morbidity remains high. Proper management in high-volume centers is mandatory to reach the expected resection goal at the first surgical attempt and to reduce surgical morbidity. The introduction of the endoscopic endonasal approach was related to improved surgical and functional outcomes.

ABO and Rhesus blood groups and multiple health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies.

BACKGROUND: Numerous studies have been conducted to investigate the relationship between ABO and Rhesus (Rh) blood groups and various health outcomes. However, a comprehensive evaluation of the robustness of these associations is still lacking. METHODS: We searched PubMed, Web of Science, Embase, Scopus, Cochrane, and several regional databases from their inception until Feb 16, 2024, with the aim of identifying systematic reviews with meta-analyses of observational studies exploring associations between ABO and Rh blood groups and diverse health outcomes. For each association, we calculated the summary effect sizes, corresponding 95% confidence intervals, 95% prediction interval, heterogeneity, small-study effect, and evaluation of excess significance bias. The evidence was evaluated on a grading scale that ranged from convincing (Class I) to weak (Class IV). We assessed the certainty of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation criteria (GRADE). We also evaluated the methodological quality of included studies using the A Measurement Tool to Assess Systematic Reviews (AMSTAR). AMSTAR contains 11 items, which were scored as high (8-11), moderate (4-7), and low (0-3) quality. We have gotten the registration for protocol on the PROSPERO database (CRD42023409547). RESULTS: The current umbrella review included 51 systematic reviews with meta-analysis articles with 270 associations. We re-calculated each association and found only one convincing evidence (Class I) for an association between blood group B and type 2 diabetes mellitus risk compared with the non-B blood group. It had a summary odds ratio of 1.28 (95% confidence interval: 1.17, 1.40), was supported by 6870 cases with small heterogeneity (I2 = 13%) and 95% prediction intervals excluding the null value, and without hints of small-study effects (P for Egger's test > 0.10, but the largest study effect was not more conservative than the summary effect size) or excess of significance (P < 0.10, but the value of observed less than expected). And the article was demonstrated with high methodological quality using AMSTAR (score = 9). According to AMSTAR, 18, 32, and 11 studies were categorized as high, moderate, and low quality, respectively. Nine statistically significant associations reached moderate quality based on GRADE. CONCLUSIONS: Our findings suggest a potential relationship between ABO and Rh blood groups and adverse health outcomes. Particularly the association between blood group B and type 2 diabetes mellitus risk.

Rates of bronchopulmonary dysplasia in very low birth weight neonates: a systematic review and meta-analysis.

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.

A fast bootstrap algorithm for causal inference with large data.

Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful technique used to construct standard errors and confidence intervals of estimators. Its application however can be prohibitively demanding in settings involving large data. In addition, modern causal inference estimators based on machine learning and optimization techniques exacerbate the computational burden of the bootstrap. The bag of little bootstraps has been proposed in non-causal settings for large data but has not yet been applied to evaluate the properties of estimators of causal effects. In this article, we introduce a new bootstrap algorithm called causal bag of little bootstraps for causal inference with large data. The new algorithm significantly improves the computational efficiency of the traditional bootstrap while providing consistent estimates and desirable confidence interval coverage. We describe its properties, provide practical considerations, and evaluate the performance of the proposed algorithm in terms of bias, coverage of the true 95% confidence intervals, and computational time in a simulation study. We apply it in the evaluation of the effect of hormone therapy on the average time to coronary heart disease using a large observational data set from the Women's Health Initiative.

Do efficacy results obtained from randomized controlled trials translate to effectiveness data from observational studies for relapsing-remitting multiple sclerosis?

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.

A practical guide to estimating treatment effects in patients with rheumatic diseases using real-world data.

OBJECTIVE: Randomized controlled trials are considered the gold standard in study methodology. However, due to their study design and inclusion criteria, these studies may not capture the heterogeneity of real-world patient populations. In contrast, the lack of randomization and the presence of both measured and unmeasured confounding factors could bias the estimated treatment effect when using observational data. While causal inference methods allow for the estimation of treatment effects, their mathematical complexity may hinder their application in clinical research. METHODS: We present a practical, nontechnical guide using a common statistical package (Stata) and a motivational simulated dataset that mirrors real-world observational data from patients with rheumatic diseases. We demonstrate regression analysis, regression adjustment, inverse-probability weighting, propensity score (PS) matching and two robust estimation methods. RESULTS: Although the methods applied to control for confounding factors produced similar results, the commonly used one-to-one PS matching method could yield biased results if not thoroughly assessed. CONCLUSION: The guide we propose aims to facilitate the use of readily available methods in a common statistical package. It may contribute to robust and transparent epidemiological and statistical methods, thereby enhancing effectiveness research using observational data in rheumatology.