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Coronavirus Disease 2019 (COVID-19) is a rapidly evolving global pandemic for which more than a thousand clinical trials have been registered to secure therapeutic effectiveness, expeditiously. Most of these are single-center non-randomized studies rather than multi-center, randomized controlled trials. Single-arm trials have several limitations and may be conducted when spontaneous improvement is not anticipated, small placebo effect exists, and randomization to a placebo is not ethical. In an emergency where saving lives takes precedence, it is ethical to conduct trials with any scientifically proven design, however, safety must not be compromised. A phase II or III trial can be conducted directly in a pandemic with appropriate checkpoints and stopping rules. COVID-19 has two management paradigms- antivirals, or treatment of its complications. Simultaneous assessment of two different treatments can be done using 2 × 2 factorial schema. World Health Organization’s SOLIDARITY trial is a classic example of the global research protocol which can evaluate the preferred treatment to combat COVID-19 pandemic. Short of that, a trial design must incorporate the practicality of the intervention used, and an appropriate primary endpoint which should ideally be a clinical outcome. Collaboration between institutions is needed more than ever to successfully execute and accrue in randomized trials.

Quasi-experimental designs are gaining popularity in epidemiology and health systems research—in particular for the evaluation of health care practice, programs, and policy—because they allow strong causal inferences without randomized controlled experiments. We describe the concepts underlying five important quasi-experimental designs: Instrumental Variables, Regression Discontinuity, Interrupted Time Series, Fixed Effects, and Difference-in-Differences designs. We illustrate each of the designs with an example from health research. We then describe the assumptions required for each of the designs to ensure valid causal inference and discuss the tests available to examine the assumptions.

There is a growing trend to include nonrandomized studies of interventions (NRSIs) in meta-analyses of randomized controlled trials (RCTs) for health decision-making. The study aimed to quantify the impact of integrating NRSI on the evidence derived from RCTs within the same systematic review. We searched PubMed for systematic reviews published between December 9, 2017, and December 9, 2022, that included both RCTs and NRSIs under the same outcome. Using the DerSimonian–Laird random-effects model, we reanalyzed the pooled estimates to compare those derived from RCTs with those from combined RCTs and NRSIs. We examined changes in point estimates, subgroup differences, statistical heterogeneity, and the weight of RCTs in pooled estimates. Results were defined as being in qualitative agreement if both estimates demonstrated statistical significance in the same direction or if neither achieved statistical significance. A total of 220 eligible systematic reviews were identified and 217 meta-analyses were reanalyzed. Qualitative disagreement between RCTs only and pooled estimates combining RCTs and NRSIs was observed in 78 meta-analyses (35.9%), of which 69 (88.5%) gained statistical significance after the inclusion of NRSIs. Point estimates in 58 meta-analyses (26.7%) failed to meet predefined agreement criteria, and statistically significant subgroup differences between RCTs and NRSIs were identified in 32 meta-analyses (14.8%). The incorporation of NRSIs raised the heterogeneity from 21.8% to 36.9%, whereas RCTs accounted for a median weight of 33.9% in the pooled estimates. These findings highlight the need for caution in conducting and interpreting meta-analyses combining RCTs and NRSIs, particularly in scenarios where RCTs yield nonsignificant results whereas the inclusion of NRSIs achieves statistical significance. Although randomized controlled trials (RCTs) remain the gold standard for clinical evidence, they are often insufficient to address complex clinical questions. Nonrandomized studies of interventions (NRSIs), leveraging real-world clinical data, are increasingly used to supplement RCT findings. Despite growing interest in integrating NRSIs into meta-analyses with RCTs, the clinical and statistical implications of this approach remain uncertain. To address this gap, we conducted a systematic evaluation of how NRSI inclusion impacts meta-analytic results by analyzing 220 systematic reviews that combined RCTs and NRSIs under the same outcome. Our analysis revealed that incorporating NRSIs altered effect estimates in over one-third of cases, with 88.5% of meta-analyses achieving statistical significance only after NRSI inclusion–a finding with critical implications for decision-making. In addition, NRSI integration elevated statistical heterogeneity, although RCTs accounted for less than one-third of the weight in pooled estimates. These findings collectively underscore the necessity for robust evaluation and cautious interpretation when merging NRSI data with RCTs in meta-analyses. [Display omitted] •Including NRSIs in meta-analyses of RCTs altered the estimates in more than one-third of the studies.•The inclusion of NRSIs increased statistical heterogeneity of the pooled estimates. •The median weight of the RCTs in the pooled estimates was approximately one-third.

Backgrounds Syntheses of non-randomized studies of interventions (NRSIs) and randomized controlled trials (RCTs) are increasingly used in decision-making. This study aimed to summarize when NRSIs are included in evidence syntheses of RCTs, with a particular focus on the methodological issues associated with combining NRSIs and RCTs. Methods We searched PubMed to identify clinical systematic reviews published between 9 December 2017 and 9 December 2022, randomly sampling reviews in a 1:1 ratio of Core and non-Core clinical journals. We included systematic reviews with RCTs and NRSIs for the same clinical question. Clinical scenarios for considering the inclusion of NRSIs in eligible studies were classified. We extracted the methodological characteristics of the included studies, assessed the concordance of estimates between RCTs and NRSIs, calculated the ratio of the relative effect estimate from NRSIs to that from RCTs, and evaluated the impact on the estimates of pooled estimates when NRSIs are included. Results Two hundred twenty systematic reviews were included in the analysis. The clinical scenarios for including NRSIs were grouped into four main justifications: adverse outcomes ( n  = 140, 63.6%), long-term outcomes ( n  = 36, 16.4%), the applicability of RCT results to broader populations ( n  = 11, 5.0%), and other ( n  = 33, 15.0%). When conducting a meta-analysis, none of these reviews assessed the compatibility of the different types of evidence prior, 203 (92.3%) combined estimates from RCTs and NRSIs in the same meta-analysis. Of the 203 studies, 169 (76.8%) used crude estimates of NRSIs, and 28 (13.8%) combined RCTs and multiple types of NRSIs. Seventy-seven studies (35.5%) showed “qualitative disagree” between estimates from RCTs and NRSIs, and 101 studies (46.5%) found “important difference”. The integration of NRSIs changed the qualitative direction of estimates from RCTs in 72 out of 200 studies (36.0%). Conclusions Systematic reviews typically include NRSIs in the context of assessing adverse or long-term outcomes. The inclusion of NRSIs in a meta-analysis of RCTs has a substantial impact on effect estimates, but discrepancies between RCTs and NRSIs are often ignored. Our proposed recommendations will help researchers to consider carefully when and how to synthesis evidence from RCTs and NRSIs.

“Non-randomized studies of interventions” (NRSI) can provide valuable insights into the real-world performance of interventions, especially when randomized controlled trials (RCTs) are impractical, unethical, or lack generalizability. We investigated how Cochrane authors have incorporated evidence from NRSI in their reviews and whether this has changed over time. We conducted a scoping review and identified Cochrane reviews, including NRSI which were published in the Cochrane Library in 2019 or 2023. We extracted data including how NRSI had been analyzed and assessed for risk of bias, and to what extent the authors had followed guidance in the Cochrane Handbook. This allowed us to identify the areas where review authors may need further guidance and support. We identified 87 Cochrane reviews, 60 published in 2019 and 27 in 2023. In general, adherence to the guidance was low. Our key findings were that less than half of the reviews justified the inclusion of NRSI (36 reviews, 41%), less than a third stated prioritizing adjusted effect measures (25 reviews, 29%), and six analyzed RCTs and NRSI in the same meta-analysis, with no justification of this approach. Despite being the recommended tool for use in Cochrane reviews, only 25 reviews (29%) used the Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I). We did find that adherence to the guidance improved between 2019 and 2023 but remained low. Cochrane should consider how to increase the use of NRSI guidance, especially with the launch of Cochrane's Scientific Strategy, which may lead to an increase in the demand for reviews including NRSI evidence. •87 Cochrane reviews included nonrandomized studies of interventions (NRSI).•Less than half justified, including NRSI.•Less than a third stated prioritizing adjusted effect measures.•Less than a third used the ROBINS-I to assess risk of bias.•Therefore, there is low adherence to guidance on including NRSI.

In contrast to other observational study designs, quasi-experimental approaches (eg, difference-in-differences, interrupted time series, regression discontinuity, instrumental variable, synthetic control) account for some sources of unmeasured confounding and can estimate causal effects under weaker assumptions. Studies which apply quasi-experimental approaches have increased in popularity in recent decades, therefore investigators conducting systematic reviews of observational studies, particularly in biomedical, public health, or epidemiologic content areas, must be prepared to encounter and appropriately assess these approaches. Our objective is to describe key methodological challenges and considerations for systematic reviews including quasi-experimental studies, with attention to current recommendations and approaches which have been applied in previous reviews. Recommendations for authors of systematic reviews: We recommend that individuals conducting systematic reviews including quasi-experimental studies: (1) search a broad range of bibliographic databases and gray literature, including preprint repositories; (2) do not use search strategies which require specific terms for study design for identification, given inconsistent nomenclature and poor database indexing for quasi-experimental studies; (3) ensure that their review team includes several individuals with expertise in quasi-experimental designs for screening and risk of bias assessment in duplicate; (4) use an approach to risk of bias assessment which is sufficiently granular to identify studies most likely to report unbiased estimates of causal effects (eg, modified Risk Of Bias In Nonrandomized Studies - of Interventions); and (5) consider the implications of varied estimands when interpreting estimates from different quasi-experimental designs. Researchers may also consider restricting systematic review inclusion to quasi-experimental studies for feasibility when addressing research questions with large bodies of literature. However, a more inclusive approach is preferred, as well-designed studies using a variety of methodological approaches may be more credible than a quasi-experiment which violates causal assumptions. Recommendations for the research community: Many of the challenges faced in conducting systematic reviews of quasi-experimental studies would be ameliorated by improved consistency in nomenclature, as well as greater transparency from authors in describing their research designs. The broader community (eg, research networks, journals) should consider the creation and implementation of reporting standards and protocol registration for quasi-experimental studies to improve study identification in systematic reviews. •Guidance for conducting systematic reviews of quasi-experimental studies is limited.•Search strategies should not condition on quasi-experimental design labels.•Risk of bias assessment requires relevant content and methodological expertise.•Data synthesis involves a range of estimands across quasi-experimental approaches.•Defining, reporting, and registration of quasi-experimental studies could be improved.

The objective of this study was to contrast the historical development of experiments and quasi-experiments and provide the motivation for a journal series on quasi-experimental designs in health research. A short historical narrative, with concrete examples, and arguments based on an understanding of the practice of health research and evidence synthesis. Health research has played a key role in developing today's gold standard for causal inference—the randomized controlled multiply blinded trial. Historically, allocation approaches developed from convenience and purposive allocation to alternate and, finally, to random allocation. This development was motivated both by concerns for manipulation in allocation as well as statistical and theoretical developments demonstrating the power of randomization in creating counterfactuals for causal inference. In contrast to the sequential development of experiments, quasi-experiments originated at very different points in time, from very different scientific perspectives, and with frequent and long interruptions in their methodological development. Health researchers have only recently started to recognize the value of quasi-experiments for generating novel insights on causal relationships. While quasi-experiments are unlikely to replace experiments in generating the efficacy and safety evidence required for clinical guidelines and regulatory approval of medical technologies, quasi-experiments can play an important role in establishing the effectiveness of health care practice, programs, and policies. The papers in this series describe and discuss a range of important issues in utilizing quasi-experimental designs for primary research and quasi-experimental results for evidence synthesis.

Background Children of parents with a mental illness or substance use disorder (COPMI) have an increased risk of developing social-emotional problems themselves. Fear of stigmatisation or unawareness of problems prevents children and parents from understanding each other. Little is known about COPMI with mild intellectual disabilities (ID), except that they have a high risk of developing social-emotional problems and require additional support. In this study, we introduce a program for this group, the effectiveness of which we will study using a quasi-experimental design based on matching. The program ‘You are okay’ consists of a support group for children and an online educational program for parents. The goal of the program is to increase children and parents’ perceived competence with an aim to prevent social-emotional problems in children. Methods/Design Children between ten and twenty years old with mild ID (IQ between 50 and 85) and at least one of their parents with a mental illness will be included in the study. The children will receive part time treatment or residential care from an institute for children with mild ID and behavioural problems. Participants will be assigned to the intervention or the control group. The study has a quasi-experimental design. The children in the intervention group will join a support group, and their parents will be offered an online educational program. Children in the control group will receive care as usual, and their parents will have no extra offer. Assessments will be conducted at baseline, post-test, and follow up (6 months). Children, parents, and social workers will fill out the questionnaires. Discussion The ‘You are okay’ program is expected to increase children and parents’ perceived competence, which can prevent (further) social-emotional problem development. Because the mental illness of parents can be related to the behavioural problems of their children, it is important that children and parents understand each other. When talking about the mental illness of parents becomes standard in children’s treatment, stigmatisation and the fear for stigmatisation can decrease. Trial registration Dutch Trial Register NTR4845 . Registered 9 October 2014.

There is ongoing debate about whether education or socioeconomic status (SES) should be inputs into cardiovascular disease (CVD) prediction algorithms and clinical risk adjustment models. It is also unclear whether intervening on education will affect CVD, in part because there is controversy regarding whether education is a determinant of CVD or merely correlated due to confounding or reverse causation. We took advantage of a natural experiment to estimate the population-level effects of educational attainment on CVD and related risk factors. We took advantage of variation in United States state-level compulsory schooling laws (CSLs), a natural experiment that was associated with geographic and temporal differences in the minimum number of years that children were required to attend school. We linked census data on educational attainment (N = approximately 5.4 million) during childhood with outcomes in adulthood, using cohort data from the 1992-2012 waves of the Health and Retirement Study (HRS; N = 30,853) and serial cross-sectional data from 1971-2012 waves of the National Health and Nutrition Examination Survey (NHANES; N = 44,732). We examined self-reported CVD outcomes and related risk factors, as well as relevant serum biomarkers. Using instrumental variables (IV) analysis, we found that increased educational attainment was associated with reduced smoking (HRS β -0.036, 95%CI: -0.06, -0.02, p < 0.01; NHANES β -0.032, 95%CI: -0.05, -0.02, p < 0.01), depression (HRS β -0.049, 95%CI: -0.07, -0.03, p < 0.01), triglycerides (NHANES β -0.039, 95%CI: -0.06, -0.01, p < 0.01), and heart disease (HRS β -0.025, 95%CI: -0.04, -0.002, p = 0.01), and improvements in high-density lipoprotein (HDL) cholesterol (HRS β 1.50, 95%CI: 0.34, 2.49, p < 0.01; NHANES β 0.86, 95%CI: 0.32, 1.48, p < 0.01), but increased BMI (HRS β 0.20, 95%CI: 0.002, 0.40, p = 0.05; NHANES β 0.13, 95%CI: 0.01, 0.32, p = 0.05) and total cholesterol (HRS β 2.73, 95%CI: 0.09, 4.97, p = 0.03). While most findings were cross-validated across both data sets, they were not robust to the inclusion of state fixed effects. Limitations included residual confounding, use of self-reported outcomes for some analyses, and possibly limited generalizability to more recent cohorts. This study provides rigorous population-level estimates of the association of educational attainment with CVD. These findings may guide future implementation of interventions to address the social determinants of CVD and strengthen the argument for including educational attainment in prediction algorithms and primary prevention guidelines for CVD.

Purpose In the literature, the propriety of the meta-analytic treatment-effect produced by combining randomized controlled trials (RCT) and non-randomized studies (NRS) is questioned, given the inherent confounding in NRS that may bias the meta-analysis. The current study compared an implicitly principled pooled Bayesian meta-analytic treatment-effect with that of frequentist pooling of RCT and NRS to determine how well each approach handled the NRS bias. Materials & methods Binary outcome Critical-Care meta-analyses, reflecting the importance of such outcomes in Critical-Care practice, combining RCT and NRS were identified electronically. Bayesian pooled treatment-effect and 95% credible-intervals (BCrI), posterior model probabilities indicating model plausibility and Bayes-factors (BF) were estimated using an informative heavy-tailed heterogeneity prior (half-Cauchy). Preference for pooling of RCT and NRS was indicated for Bayes-factors > 3 or < 0.333 for the converse. All pooled frequentist treatment-effects and 95% confidence intervals (FCI) were re-estimated using the popular DerSimonian-Laird (DSL) random effects model. Results Fifty meta-analyses were identified (2009–2021), reporting pooled estimates in 44; 29 were pharmaceutical-therapeutic and 21 were non-pharmaceutical therapeutic. Re-computed pooled DSL FCI excluded the null (OR or RR = 1) in 86% (43/50). In 18 meta-analyses there was an agreement between FCI and BCrI in excluding the null. In 23 meta-analyses where FCI excluded the null, BCrI embraced the null. BF supported a pooled model in 27 meta-analyses and separate models in 4. The highest density of the posterior model probabilities for 0.333 < Bayes factor < 1 was 0.8. Conclusions In the current meta-analytic cohort, an integrated and multifaceted Bayesian approach gave support to including NRS in a pooled-estimate model. Conversely, caution should attend the reporting of naïve frequentist pooled, RCT and NRS, meta-analytic treatment effects.