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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.

The aim of this study was to evaluate how often the European Medicines Agency (EMA) has authorized drugs based on nonrandomized studies and whether there is an association between treatment effects and EMA preference for further testing in randomized clinical trials (RCTs). We reviewed all initial marketing authorizations in the EMA database on human medicines between 1995 and 2015 and included authorizations granted without randomized data. We extracted data on treatment effects and EMA preference for further testing in RCTs. Of 723 drugs, 51 were authorized based on nonrandomized data. These 51 drugs were licensed for 71 indications. In the 51 drug-indication pairs with no preference for further RCT testing, effect estimates were large [odds ratio (OR): 12.0 (95% confidence interval {CI}: 8.1–17.9)] compared to effect estimates in the 20 drug-indication pairs for which future RCTs were preferred [OR: 4.3 (95% CI 2.8–6.6)], with a significant difference between effects (P = 0.0005). Nonrandomized data were used for 7% of EMA drug approvals. Larger effect sizes were associated with greater likelihood of approval based on nonrandomized data alone. We did not find a clear treatment effect threshold for drug approval without RCT evidence.

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.

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.

Most previous trials enrolled only autistic adults or children without intellectual disability, primarily due to the necessity of maintaining a prolonged stationary position during rTMS sessions.3 Continuous theta-burst stimulation (cTBS), a novel form of rTMS, reduces the stimulation duration while achieving comparable or even superior neuromodulatory effects.7 Moreover, the accelerated design, characterised by spaced sessions with higher pulse doses, may enhance and sustain neuromodulatory effects while reducing the treatment course to just a few days, potentially improving both compliance and overall efficacy.8 9 We integrated these considerations and developed a novel accelerated cTBS (a-cTBS) protocol targeting the left M1 for therapeutic intervention and involvement of a broader autistic population. The control group was identified from the Shanghai Autism Early Development (SAED) cohort who had completed both an initial SRS assessment at diagnosis and a voluntary follow-up SRS evaluation 30–50 days thereafter (aligned with the mean 40-day interval in the a-cTBS trial).15 We excluded individuals exhibiting characteristics that were exclusive in only one group and then applied the inverse probability of treatment weighting (IPTW) approach to balance baseline characteristics, including sex, age, full-scale intelligence quotient and Childhood Autism Rating Scale (CARS), using the ipw package (V.1.2). [...]according to the median of the ΔSRS1mon, we divided the participants into high- and low-response groups. [...]we tested the changes in children’s language abilities (CCDI, PPVT and MAIN scores) from pretreatment to 1-month follow-up by comparing the differences to zero. [...]we applied the linear regression to investigate whether the baseline characteristics were predictive of ΔSRS1mon.

We aimed to review how ‘Risk of Bias In Non-randomized Studies–of Interventions’ (ROBINS-I), a Cochrane risk of bias assessment tool, has been used in recent systematic reviews. Database and citation searches were conducted in March 2020 to identify recently published reviews using ROBINS-I. Reported ROBINS-I assessments and data on how ROBINS-I was used were extracted from each review. Methodological quality of reviews was assessed using AMSTAR 2 (‘A MeaSurement Tool to Assess systematic Reviews’). Of 181 hits, 124 reviews were included. Risk of bias was serious/critical in 54% of assessments on average, most commonly due to confounding. Quality of reviews was mostly low, and modifications and incorrect use of ROBINS-I were common, with 20% reviews modifying the rating scale, 20% understating overall risk of bias, and 19% including critical-risk of bias studies in evidence synthesis. Poorly conducted reviews were more likely to report low/moderate risk of bias (predicted probability 57% [95% CI: 47–67] in critically low-quality reviews, 31% [19–46] in high/moderate-quality reviews). Low-quality reviews frequently apply ROBINS-I incorrectly, and may thus inappropriately include or give too much weight to uncertain evidence. Readers should be aware that such problems can lead to incorrect conclusions in reviews.

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.

Web-based interventions are promising tools for increasing the understanding of illness and treatment among patients with serious mental disorders. This study aimed to test the feasibility and acceptability of a Web-based patient education intervention using a quasi-experimental cluster design to report feedback on patient education sessions and the website used and to report preliminary evidence of the intervention's impact on patients with schizophrenia spectrum disorder. A single-blind, parallel, quasi-experimental cluster study over a 6-month period comparing Web-based education (n=33) with a nonequivalent control group (treatment as usual, n=24) for people with schizophrenia spectrum disorder was conducted. Participants (N=57) were recruited from one psychiatric hospital (6 wards). Feasibility was assessed by participants' commitment (refusal rate, dropout rate) to the study. Acceptability was assessed as participants' commitment to the intervention. Patient education sessions and website feedback were assessed by the patients and health care professionals. The preliminary impact of the sessions on patients' self-efficacy, self-esteem, illness cognition, and knowledge level was measured at baseline and follow-ups (8 weeks, 6 months) with self-rated questionnaires. The refusal rate among patients was high with no statistically significant difference (69% [74/107] in the intervention group, 76% [76/100] in the control group; P=.21). The same result was found for the dropout rates (48% [16/33] vs 58% [14/24]; P=.46). The acceptability of the intervention was good; 31 participants out of 33 (94%) completed all five sessions. Feedback on the intervention was mainly positive; three out of four subscales of session were rated above the midpoint of 4.0. Feedback on the website was also positive, with a grade of good for content (69%, 20/29 patients; 75%, 21/28 professionals), layout (62%, 18/29 patients; 61%, 17/28 professionals), and usability (62%, 18/29 patients; and 68%, 19/28 professionals). The patients using the intervention had significantly higher scores 6 months after the sessions in self-efficacy (baseline mean 26.12, SD 5.64 vs 6-month mean 29.24, SD 6.05; P=.003) and regarding knowledge level about schizophrenia (mean 11.39, SD 4.65 vs 6-month mean 15.06, SD 5.26; P=.002), and lower scores in the subscale of helplessness in illness cognition (mean 2.26, SD 0.96 vs 6-month mean 1.85, SD 0.59; P=.03). Differences from the control group were not significant. No differences were found in patients' self-esteem or other subscales in illness cognition. The patients were reluctant to participate in the study and tended to drop out before the follow-ups. Once they had participated, their acceptance of the intervention was high. A more effective recruitment strategy and monitoring method will be needed in future studies. To assess the impact of the intervention, a more rigorous study design with an adequately powered sample size will be used in cooperation with outpatient mental health services.

Although the number of quasi-experiments conducted by health researchers has increased in recent years, there clearly remains unrealized potential for using these methods for causal evaluation of health policies and programs globally. This article proposes five prescriptions for capturing the full value of quasi-experiments for health research. First, new funding opportunities targeting proposals that use quasi-experimental methods should be made available to a broad pool of health researchers. Second, administrative data from health programs, often amenable to quasi-experimental analysis, should be made more accessible to researchers. Third, training in quasi-experimental methods should be integrated into existing health science graduate programs to increase global capacity to use these methods. Fourth, clear guidelines for primary research and synthesis of evidence from quasi-experiments should be developed. Fifth, strategic investments should be made to continue to develop new innovations in quasi-experimental methodologies. Tremendous opportunities exist to expand the use of quasi-experimental methods to increase our understanding of which health programs and policies work and which do not. Health researchers should continue to expand their commitment to rigorous causal evaluation with quasi-experimental methods, and international institutions should increase their support for these efforts.