Randomized Controlled Trials Data Research Articles

Patent Foramen Ovale Closure in Older Patients With Stroke: Patient Selection for Trial Feasibility.

Whether patent foramen ovale (PFO) closure benefits older patients with PFO and cryptogenic stroke is unknown because randomized controlled trials (RCTs) have predominantly enrolled patients younger than 60 years of age. Our objective was to estimate anticipated effects of PFO closure in older patients to predict the numbers needed to plan an RCT. Effectiveness estimates are derived from major observational studies (Risk of Paradoxical Embolism [RoPE] Study and Oxford Vascular Study, together referred to as the "RoPE-Ox" database) and all 6 major RCTs (Systematic, Collaborative, PFO Closure Evaluation [SCOPE] Consortium). To estimate stroke recurrence risk, observed outcomes were calculated for patients older than 60 years in the age-inclusive observational databases (n = 549). To estimate the reduction in the rate of recurrent stroke associated with PFO closure vs medical therapy based on the RoPE score and the presence of high-risk PFO features, a Cox proportional hazards regression model was developed on the RCT data in the SCOPE database (n = 3,740). These estimates were used to calculate sample sizes required for a future RCT. Five-year risk of stroke recurrence using Kaplan-Meier estimates was 13.7 (95% CI 10.5-17.9) overall, 14.9% (95% CI 10.2-21.6) in those with high-risk PFO features. Predicted relative reduction in the event rate with PFO closure was 12.9% overall, 48.8% in those with a high-risk PFO feature. Using these estimates, enrolling all older patients with cryptogenic stroke and PFO would require much larger samples than those used for prior PFO closure trials, but selectively enrolling patients with high-risk PFO features would require totals of 630 patients for 90% power and 471 patients for 80% power, with an average of 5 years of follow-up. Based on our projections, anticipated effect sizes in older patients with high-risk features make a trial in these subjects feasible. With lengthening life expectancy in almost all regions of the world, the utility of PFO closure in older adults is increasingly important to explore.

#2609 Comparison of baseline patient characteristics of the CONVINCE randomized controlled trial with existing trial and registry populations

Abstract Background and Aims The generalizability of randomized controlled trials (RCTs) results is based on representativeness of the population involved. Trial populations may have different baseline characteristics compared to the general population that could benefit from the intervention. We compare the baseline characteristics of patients involved in the CONVINCE trial with those involved in all previous RCTs and representative registries of patients receiving hemodialysis as renal replacement therapy. Method Based on world class methods for systematic reviews, we identified all RCTs comparing hemodiafiltration (HDF) with hemodialysis (HD) published up to January 2024 and extracted data on baseline patient characteristics. Where data were missing, authors have been contacted and data so far obtained were included in this analysis (Fig. 1). Additional contact attempts are being performed. In addition, a comprehensive search was conducted to identify renal registries from all geographic regions. Those without information in English, without information on HDF and registries focusing on special patient groups (e.g. transplantation only) were excluded. This registry data will not be analyzed in this abstract as we are in progress of extracting the patient data from the latest registry annual reports available up to January 2024 for further analysis. Results We have selected 11 RCTs for further analysis, with the selection criteria and outcomes presented in Fig. 1. Key patient characteristics from RCTs were compared with those of patients in the CONVINCE trial using descriptive statistics and forest plots (Fig. 1). Comparison of differences was analyzed with the Kruskal-Wallis-Test. Interpretation of the results are as follows: • Age: Mean age of 76.5 years noted in Morena et al. (2017) because study inclusion criterion was ≥65 years. Other RCTs ranged from 54.1 to 68 years, which is consistent with 62.4 years in the CONVINCE trial (χ2 = 10, df = 10, p = 0.44). • Sex: 16.7% and 50% female noted in Selby et al. (2006) and Meert et al. (2009) respectively probably due to limited sample size (12 patients and 14 patients respectively). Other RCTs ranges from 30.4% to 42.5%, which is consistent with 36.0% in the CONVINCE trial (χ2 = 11, df = 11, p = 0.44). • Diabetes comorbidities: Ranges from 17.8% to 45%. CONVINCE trial is 35.0% (χ2 = 6, df = 6, p = 0.42). • Cardiovascular diseases comorbidities: Range from 26.4% to 45%. CONVINCE trial is 45% (χ2 = 3, df = 3, p = 0.39). • AVF as vascular access: value of 100% in Pedrini et al. (2011) because it was an inclusion criterion. Other RCTs ranged from 79.4% to 90%, which is consistent with 82% in the CONVINCE trial (χ2 = 7, df = 7, p = 0.43). There were no significant differences between CONVINCE and other RCTs data regarding all 5 patient baseline characteristics. Conclusion Our analysis showed the CONVINCE trial has no significant differences in patient baseline characteristics compared to previous RCTs. For a more comprehensive evaluation of representativeness of the trial's population, we are currently retrieving missing data from the trial authors and analyzing data from hemodialysis registries.

Progress in methodological research on bridging the efficacy-effectiveness gap of clinical interventions(2): to improve the extrapolation of efficacy

Objective: Randomized controlled trials (RCT) usually have strict implementation criteria. The included subjects' characteristics of the conditions for the intervention implementation are quite different from the actual clinical environment, resulting in discrepancies between the risk-benefit of interventions in actual clinical use and the risk-benefit shown in RCT. Therefore, some methods are needed to enhance the extrapolation of RCT results to evaluate the real effects of drugs in real people and clinical practice settings. Methods: Six databases (PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP) were searched up to 31st December 2022 with detailed search strategies. A scoping review method was used to integrate and qualitatively describe the included literature inductively. Results: A total of 12 articles were included. Three methods in the included literature focused on: ①improving the design of traditional RCT to increase population representation; ②combining RCT Data with real-world data (RWD) for analysis;③calibrating RCT results according to real-world patient characteristics. Conclusions: Improving the design of RCT to enhance the population representation can improve the extrapolation of the results of RCT. Combining RCT data with RWD can give full play to the advantages of data from different sources; the results of the RCT were calibrated against real-world population characteristics so that the effects of interventions in real-world patient populations can be predicted.

The use of real-world data for clinical investigation of effectiveness in drug development

ABSTRACT With the growing interest in leveraging real-world data (RWD) to support effectiveness evaluations for new indications, new target populations, and post-market performance, the United States Food and Drug Administration has published several guidance documents on RWD sources and real-world studies (RWS) to assist sponsors in generating credible real-world evidence (RWE). Meanwhile, the randomized controlled trial (RCT) remains the gold standard in drug evaluation. Along this line, we propose a hybrid two-stage adaptive design to evaluate effectiveness based on evidence from both RCT and RWS. At the first stage, a typical non-inferiority test is conducted using RCT data to test for not-ineffectiveness. Once not-ineffectiveness is established, the study proceeds to the second stage to conduct an RWS and test for effectiveness using integrated information from RCT and RWD. The composite likelihood approach is implemented as a down-weighing strategy to account for the impact of high variability in RWS population. An optimal sample size determination procedure for RCT and RWS is introduced, aiming to achieve the minimal expected sample size. Through extensive numerical study, the proposed design demonstrates the ability to control type I error inflation in most cases and consistently maintain statistical power above the desired level. In general, this RCT/RWS hybrid two-stage adaptive design is beneficial for effectiveness evaluations in drug development, especially for oncology and rare diseases.

Screening Implications for Distribution of Colorectal Cancer Subsite by Age and Role of Flexible Sigmoidoscopy.

Objectives: The effectiveness of colonoscopy to reduce colorectal cancer (CRC) mortality is extrapolated from cohort studies in the absence of randomized controlled trial (RCT) data, whereas flexible sigmoidoscopy is supported by RCT data and may be easier to implement in practice. We characterized the anatomic distribution of CRC to determine the proportion that is visible with sigmoidoscopy. Methods: Patients with a primary diagnosis of colorectal adenocarcinoma were identified in the Surveillance, Epidemiology, and End Results program (2000-2020). Tumors from the rectum to the descending colon were categorized as visible by sigmoidoscopy, whereas more proximal tumors required colonoscopy. Differential prognosis between tumor locations, stratified by age groups and stage, was assessed using the overall restricted mean survival time (RMST) at 2, 5, and 10 years. Results: Among 309,466 patients, 58% had tumors visible by sigmoidoscopy, including 73% of those under age 50 (OR 2.10, 95% CI 2.03-2.16 age < 45, OR 2.20, 95% CI 2.13-2.27 age 45-49 versus age ≥ 50). Male sex (OR 1.54, 95% CI 1.51-1.56) and Asian or Pacific Islander race (OR 1.60, 95% CI 1.56-1.64) were also positively associated with tumors visualizable by sigmoidoscopy. Across age groups, for local disease, RMST was comparable for tumors visible versus not visible on sigmoidoscopy. For regional and metastatic cancer, patients with tumors visible by sigmoidoscopy had improved RMST versus those with more proximal tumors. Conclusions: 58% of CRC arises in locations visible by flexible sigmoidoscopy. Flexible sigmoidoscopy should be considered as a viable option for CRC screening, particularly in younger patients unwilling or unable to undergo colonoscopy.

Whole-family programmes for families living with parental mental illness: a systematic review and meta-analysis.

Several interventions have been developed to support families living with parental mental illness (PMI). Recent evidence suggests that programmes with whole-family components may have greater positive effects for families, thereby also reducing costs to health and social care systems. This review aimed to identify whole-family interventions, their common characteristics, effectiveness and acceptability. A systematic review was conducted according to PRISMA 2020 guidelines. A literature search was conducted in ASSIA, CINAHL, Embase, Medline, and PsycINFO in January 2021 and updated in August 2022. We double screened 3914 abstracts and 212 papers according to pre-set inclusion and exclusion criteria. The Mixed Methods Appraisal Tool was used for quality assessment. Quantitative and qualitative data were extracted and synthesised. Randomised-control trial data on child and parent mental health outcomes were analysed separately in random-effects meta-analyses. The protocol, extracted data, and meta-data are accessible via the Open Science Framework ( https://osf.io/9uxgp/ ). Data from 66 reports-based on 41 independent studies and referring to 30 different interventions-were included. Findings indicated small intervention effects for all outcomes including children's and parents' mental health (dc = -0.017, -027; dp = -0.14, -0.16) and family outcomes. Qualitative evidence suggested that most families experienced whole-family interventions as positive, highlighting specific components as helpful, including whole-family components, speaking about mental illness, and the benefits of group settings. Our findings highlight the lack of high-quality studies. The present review fills an important gap in the literature by summarising the evidence for whole-family interventions. There is a lack of robust evidence coupled with a great need in families affected by PMI which could be addressed by whole-family interventions. We recommend the involvement of families in the further development of these interventions and their evaluation.

Treatment effect estimation using the propensity score in clinical trials with historical control

BackgroundClinical trials assessing new treatment effects require a control group to compare the pure treatment effects. However, in clinical trials on regenerative medicine, rare diseases, and intractable diseases, it may be ethically difficult to assign participants to the control group. In recent years, the use of historical control data has attracted attention as a method for supplementing the number of participants in the control group. When combining historical control data with new randomized controlled trial (RCT) data, the assessment of heterogeneity using outcome data is not sufficient. Therefore, several statistical methods that consider participant outcomes and baseline characteristics, including the propensity score (PS) method have been proposed.MethodsWe propose a new method considering “information on whether the data are RCT data or not” in the PS model when combining the RCT and historical control data. The performance of the proposed method in estimating the treatment effect is evaluated using simulation data.ResultsWhen the distribution of covariates is similar between the RCT and historical control data, not much difference in performance is found between the proposed and conventional methods to estimate the treatment effect. On the other hand, when the distribution of covariates is not similar between the two kinds of data, the proposed method shows higher performance.ConclusionsEven when it is not known whether RCT and historical control data are similar, the proposed PS model is useful to estimate the treatment effect appropriately in RCTs using historical control data.

Comparison Between Drug-Coated Balloon and Stents in Large De Novo Coronary Artery Disease: A Systematic Review and Meta-Analysis of RCT Data.

Although a number of studies involving small-vessel de novo coronary disease showed clinical benefits of drug-coated balloons (DCB), the role of DCB in large vessel lesions is still unclear. We searched main electronic databases for randomized controlled trials (RCTs) comparing DCB with stents for large vessel de novo coronary artery disease. The primary endpoint was major cardiovascular adverse events (MACE), composite cardiovascular death (CD), myocardial infarction (MI), or target lesion revascularization (TLR). This study included 7 RCTs with 770 participants.DCB were associated with a marked risk reduction in MACE [Risk Ratio (RR): 0.48; 95% confidence interval [CI]: 0.24 to 0.97; P = 0.04], TLR (RR: 0.53; 95% CI: 0.25 to 1.14; P = 0.10), and late lumen loss [standard mean difference (SMD): -0.57; 95% CI: -1.09 to -0.05; P = 0.03] as compared with stents. There is no significant difference in MI (RR: 0.58; 95% CI: 0.21 to 1.54; P = 0.27), CD (RR: 0.33; 95% CI: 0.06 to 1.78; P = 0.19), and minimal lumen diameter (SMD: -0.34; 95% CI: -0.72 to 0.05; P = 0.08) between groups. In subgroup analyses, the risk reduction of MACE persisted in patients with chronic coronary syndrome (RR: 0.25; 95% CI: 0.07 to 0.89; P = 0.03), and patients receiving DCB vs. bare metal stent (RR: 0.19; 95% CI: 0.05 to 0.73; P = 0.01). In addition, there was no significant difference between the DCB group and the drug eluting stent group for MACE (RR: 0.69; 95% CI: 0.30 to 1.60; P = 0.38). DCB may be an effective therapeutic option in patients with large vessel de novo coronary artery disease.

The Continuous Fragility Index of Statistically Significant Findings in Randomized Controlled Trials That Compare Interventions for Anterior Shoulder Instability.

Evidence-based care relies on robust research. The fragility index (FI) is used to assess the robustness of statistically significant findings in randomized controlled trials (RCTs). While the traditional FI is limited to dichotomous outcomes, a novel tool, the continuous fragility index (CFI), allows for the assessment of the robustness of continuous outcomes. To calculate the CFI of statistically significant continuous outcomes in RCTs evaluating interventions for managing anterior shoulder instability (ASI). Meta-analysis; Level of evidence, 2. A search was conducted across the MEDLINE, Embase, and CENTRAL databases for RCTs assessing management strategies for ASI from inception to October 6, 2022. Studies that reported a statistically significant difference between study groups in ≥1 continuous outcome were included. The CFI was calculated and applied to all available RCTs reporting interventions for ASI. Multivariable linear regression was performed between the CFI and various study characteristics as predictors. There were 27 RCTs, with a total of 1846 shoulders, included. The median sample size was 61 shoulders (IQR, 43). The median CFI across 27 RCTs was 8.2 (IQR, 17.2; 95% CI, 3.6-15.4). The median CFI was 7.9 (IQR, 21; 95% CI, 1-22) for 11 studies comparing surgical methods, 22.6 (IQR, 16; 95% CI, 8.2-30.4) for 6 studies comparing nonsurgical reduction interventions, 2.8 for 3 studies comparing immobilization methods, and 2.4 for 3 studies comparing surgical versus nonsurgical interventions. Significantly, 22 of 57 included outcomes (38.6%) from studies with completed follow-up data had a loss to follow-up exceeding their CFI. Multivariable regression demonstrated that there was a statistically significant positive correlation between a trial's sample size and the CFI of its outcomes (r = 0.23 [95% CI, 0.13-0.33]; P < .001). More than a third of continuous outcomes in ASI trials had a CFI less than the reported loss to follow-up. This carries the significant risk of reversing trial findings and should be considered when evaluating available RCT data. We recommend including the FI, CFI, and loss to follow-up in the abstracts of future RCTs.

Electronic cigarettes for smoking cessation.

Electronic cigarettes for smoking cessation.

Mixed-methods randomised study exploring the feasibility and acceptability of eye-movement desensitisation and reprocessing for improving the mental health of traumatised survivors of intensive care following hospital discharge: protocol

IntroductionPost-traumatic symptoms are common among patients discharged from intensive care units (ICUs), adversely affecting well-being, increasing healthcare utilisation and delaying return to work. Non-pharmacological approaches (eg, music, therapeutic touch and...

Flattening the Truth Pyramid: Reconsidering the Evidence Hierarchy

For decades, the evidence hierarchy - which places randomized controlled trials (RCTs) and systematic reviews at its apex – has contributed positively to decision-to-treat considerations. Nonetheless, RCTs have often failed to reveal efficacy and safety concerns relating to the studied treatments. Moreover, many of the questions posed in clinical practice are best answered by means other than RCTs. This discussion examines the flaws in RCT statistical methodology that contribute to their limitations. It also presents novel methodologies, that combine RCT data with observational data, and thereby enable clinicians to make personalized treatment decisions for individual patients – something RCTs alone cannot do. Finally, this discussion explores what constitutes the best evidence to answer the many questions clinicians confront on a daily basis. The upshot is a flattened evidence hierarchy wherein RCTs, observational studies and novel methodologies are placed in their proper context, so that their relevance to clinical medicine is neither exaggerated nor ignored.

Robustness of Longitudinal Safety and Efficacy After Paclitaxel-Based Endovascular Therapy for Treatment of Femoro-Popliteal Artery Occlusive Disease: An Updated Systematic Review and Meta-Analysis of Randomized Controlled Trials

The aims of this study were: i) to assess fragility indices (FIs) of individual randomized controlled trials (RCTs) that compared paclitaxel-based drug-coated balloons (DCBs) or drug-eluting stents (DESs) versus standard endovascular devices, and ii) to meta-analyze mid-term and long-term safety and efficacy outcomes from available RCT data while also estimating the FI of pooled results. This systematic review has been registered in the PROSPERO public database (CRD42022304326 http://www.crd.york.ac.uk/PROSPERO). A query of PubMed (Medline), EMBASE (Excerpta Medical Database), Scopus, and CENTRAL (Cochrane Central Register of Controlled Trials) databases was performed to identify eligible RCTs. Rates of primary patency (PP) and target lesion revascularization (TLR) were assessed as efficacy outcomes, while lower limb amputation (LLA) consisting of major amputation that is. below or above the knee and all-cause mortality were estimated as safety outcomes. All outcomes were pooled with a random effects model to account for any clinical and study design heterogeneity. The analyses were performed by dividing the RCTs according to their maximal follow-up length (mid-term was defined as results up to 2-3years, while long-term was defined as results up to 4-5years). For each individual outcome, the FI and reverse fragility index (RFI) were calculated according to whether the outcome results were statistically significant or not, respectively. The fragility quotient (FQ) and reverse fragility quotient (RFQ), which are the FI or RFI divided by the sample size, were also calculated. A total of 2,337 patients were included in the systematic review and meta-analysis. There were 2 RCTs examining DES devices and 14 RCTs evaluating different DCBs. For efficacy outcomes, there was evidence that paclitaxel-based endovascular therapy increased the PP rate and reduced the TLR rate at mid-term, with a calculated pooled risk ratio (RR) of 1.66 for patency (95% CI, 1.55-1.86; P<0.001), with a corresponding number needed-to-treat (NNT) of 3 patients (95% CI, 2.9-3.8) and RR of 0.44 for TLR (95% CI, 0.35-0.54; P=0.027), respectively. Similarly, there was evidence that paclitaxel-based endovascular therapy both increased PP and decreased TLR rates at long-term, with calculated pooled RR values of 1.73 (95% CI, 1.12-2.61; P=0.004) and 0.53 (95% CI, 0.45-0.62; P=0.82), respectively. For safety outcomes, there was evidence that paclitaxel-based endovascular therapy increased all-cause mortality at mid-term, with a calculated pooled RR of 2.05 (95% CI, 1.21-3.24). However, there was no difference between treatment arms in LLA at mid-term (95% CI, 0.1-2.7; P=0.68). Similarly, neither all-cause mortality nor LLA at long-term differed between treatment arms, with a calculated pooled RR of 0.66, 1.02 (95% CI, 0.31-3.42) and 1.02 (95% CI, 0.30-5.21; P=0.22), respectively. The pooled estimates of PP at mid-term were robust (FI=28 and FQ=1.9%) as were pooled rates of TLR (FI=18 and FQ=0.9%). However, when safety outcomes were analyzed, the robustness of the meta-analysis decreased significantly. In fact, the relationship between the use of paclitaxel-coated devices and all-cause mortality at mid-term showed very low robustness (FI=4 and FQ=0.2%). At 5years, only the benefit of paclitaxel-based devices to reduce TLR remained robust, with an FI of 32 and an FQ of 3.1%. The data supporting clinical efficacy endpoints of RCTs that examined paclitaxel-based devices in the treatment of femoral-popliteal arterial occlusive disease were robust; however, the pooled safety endpoints were highly fragile and prone to bias due to loss of patient follow-up in the original studies. These findings should be considered in the ongoing debate concerning the safety of paclitaxel-based devices.

OP51 Use Of Real-World Data In Cost-effectiveness Analysis Of Sequential Biologic Treatment For Rheumatoid Arthritis

IntroductionIn health technology assessment (HTA), economic evaluations assessing biologic drugs for rheumatoid arthritis (RA) involve modeling patients’ responses to multiple treatments given sequentially over a lifetime horizon. When data from randomized controlled trials (RCTs) are scarce, data from non-randomized studies (e.g., single-arm trials [SATs] and disease registries) can be used to supplement the evidence base. This research aimed to demonstrate meta-analytic methods for combining effectiveness data from randomized and non-randomized studies and their corresponding impact on cost-effectiveness estimates.MethodsData comparing patients receiving second-line rituximab with continued background non-biologic treatment were extracted from one RCT and six SATs identified in an HTA assessing second-line rituximab for RA, and from the British Society for Rheumatology Biologics Register-Rheumatoid Arthritis, by applying a target trial emulation approach. A binomial meta-analysis model was used to estimate the probabilities of achieving the European League against Rheumatism (EULAR) response criteria by pooling data from the RCT, SATs, and the registry. The probabilities were entered into a decision model from a previous HTA to derive incremental cost-effectiveness ratio (ICER) estimates for treatment strategies with and without biologic drugs.ResultsCompared with the original analysis, the estimated probability of at least a moderate EULAR response on rituximab from combined sources was substantially lower. For example, the probability obtained from an RCT was 0.68 (95% credible interval [CrI]: 0.345, 0.907), but only 0.29 (95% [CrI]: 0.242, 0.333) when using RCT plus registry data and 0.29 (95% CrI: 0.244, 0.336) for combined RCT, registry, and SAT data. In the cost-effectiveness analysis, the median ICERs were higher when including real-world data.ConclusionsSynthesis of all relevant data, including RWD, provides additional information regarding the variability in cost-effectiveness estimates and can be considered in sensitivity analyses for HTA decision-making.

Evaluating Physician-AI Interaction for Multiple Myeloma Management: Paving the Path Towards Precision Oncology

Introduction The gap between clinical trials and real-world care continues to grow in the field of multiple myeloma (MM). Randomized controlled trials (RCTs) cannot account for every patient scenario. Modern clinical decision support systems (CDSSs) that leverage machine learning (ML) models offer an alternate path to precision oncology. However, little work has been done to understand how clinicians reconcile RCT with ML data, particularly when results are discordant. Aim We designed a CDSS that displays simulated survival and adverse event data from an RCT and ML model and conducted a survey study to evaluate how clinicians incorporate the available data to make treatment decisions for 12 patients with MM. Methods Participants were presented with varying combinations of RCT and ML results in increasing “tiers” of information for 12 patients (A-L) with MM (Table 1). In tier 1, participants were provided RCT data only. In tier 2, participants were given outcomes of an ML model and in tier 3, they were provided with information about how the model was trained and validated. At each tier, participants were asked to select a treatment (“red pill” or “blue pill”), rate their confidence in treatment on a Likert scale from 1-10, and when ML data was available, rate their perceived reliability of the model on a Likert scale from 1-10. Participants were recruited from internal medicine and hematology/oncology departments via email between January and April 2023, and were offered a $50 Amazon gift card as incentive. We used descriptive statistics to analyze respondent characteristics. For each scenario, we ran two-sample paired t-tests to compare the change in confidence and reliability between tier 2 and tier 1 (ML versus RCT data) and tier 3 versus tier 2 (ML data with information about training and validation versus without). We utilized a Bonferroni correction to adjust the alpha level for significance. We also ran McNemar's tests with a Bonferroni correction to assess the difference in proportions of blue pill selection at different tiers to characterize the extent of treatment switching. Results A total of 284 physicians were invited to participate in the study and 32 participated, for a response rate of 11.3%. Half were internal medicine residents and half were hematology/oncology fellows and attendings. A majority were male (72.0%), white (69.0%) and all were less than 40 years of age. For scenarios A-D, the patient met inclusion criteria for the RCT and was well represented in training data for the ML model. Confidence was highest for scenario A where survival results between the RCT and ML model were concordant (Figure 1). Across scenarios B, C, and D, confidence dropped after seeing the ML results (B: p=0.05, C: p=0.05, D: p=0.36) but increased after participants learned the ML model was trained on patients like theirs (B: p=.002, C: p=0.17, D: p=0.06). In scenarios C and D, a majority of participants switched treatment after learning the ML model showed no benefit with the red pill (C: p=5.1x10 -4, D: p=1.5x10 -5). In scenarios E-H, the patient did not meet inclusion criteria for the RCT but was well represented in the training data for the ML model. When the ML model showed worse adverse events (F) or no benefit with the red pill (G, H) the majority of participants switched treatment choice (F: p=0.27, G: p=0.002, H: p=1.9x10 -4). There was an increase in confidence when participants learned the ML model had been trained on representative patients (E: p=0.008, F: p=0.01, G: p=0.18, H: p=2.0x10 -3). In scenarios I-L, the patient did not meet inclusion criteria for the RCT and was not well represented in the training data for the ML model. There was a decrease in confidence when participants learned this in tier 3 (I: p=0.18, J: p=3.0x10 -4, K: p=0.05, L: p=0.009). However, the majority of participants still switched to the blue pill when the ML model showed no benefit with the red pill (K: p=1.9x10 -4). Conclusions Confidence in treatment was highest when RCT and ML findings were concordant. Participants chose treatments based on ML model estimates even before assessing how the ML model was trained or validated. Participants preferred the treatment that demonstrated a survival benefit, regardless of whether it was supported by RCT data or an ML model.

Compression therapies for the treatment of venous leg ulcers: study protocol for a process evaluation in a randomised controlled trial, VenUS 6

BackgroundThe VenUS 6 parallel-group randomised controlled trial (RCT) will compare the clinical and cost-effectiveness of compression wraps, two-layer compression bandage and evidence-based compression therapy, comprising of two-layers of hosiery or four-layer bandages, for healing time of venous leg ulcers. We will conduct an embedded process evaluation to evaluate the implementation of the trial and the various compression therapies and to gain a more in-depth understanding of trial participant and nursing staff views and experiences of these therapies.MethodsThis process evaluation will be a mixed-method study, embedded into a wider RCT. Qualitative data will be collected through semi-structured individual in-depth interviews with trial participants and staff members. Quantitative data will be collected using patient questionnaires and case report forms that are part of the main trial data collection process. Interview transcripts will be analysed using the Framework Analysis and interview data will be integrated with quantitative RCT data using the RE-AIM framework and the Pillar Integration Process.DiscussionWe describe the protocol for a process evaluation, designed to assess the implementation of the various venous leg ulcer compression therapies as evaluated in VenUS6, and the experiences of trial participants and nursing staff using these. This protocol provides one example of how an embedded mixed-method process evaluation can be conducted.Trial registrationISRCTN 67321719 (https://doi.org/10.1186/ISRCTN67321719). Prospectively registered on 14 September 2020.Recruitment Infographic SWAT—MRC Hub for Trials Methodology Research SWAT repository #116. Registered on 13 April 2020.Retention Thank You Card SWAT—MRC Hub for Trials Methodology Research SWAT repository #119. Registered on 13 April 2020.Retention Newsletter SWAT—MRC Hub for Trials Methodology Research SWAT repository #28. Registered on 01 July 2007.Retention Pen SWAT—MRC Hub for Trials Methodology Research SWAT repository #92. Registered on 01 April 2019.Protocol version: V1.5, 26 May 2022.

Assessing the potential for precision medicine of glucose-lowering drugs in type 2 diabetes mellitus: a meta-regression analysis considering body weight variability from 120 randomized trials

Abstract Background Thus far, randomized controlled trials (RCTs) of glucose-lowering drugs reporting on treatment effects on body weight (BW) as cardiovascular risk factor in type 2 diabetes mellitus have not included precision medicine approaches. However, taking into account treatment response heterogeneity, their potential may be analyzed from published data of RCTs using meta-regression analyses. Both treatment response variation and predictors are required conditions for precision medicine to work. Purpose To assess the potential benefits of precision medicine by quantifying the variation of body weight as patient response to treatment, and to identify predictors which could explain this variation. Methods We used four recent systematic reviews on RCTs comparing pharmacological treatment of type 2 diabetes (included but not limited to sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists and thiazolidinediones) to placebo. RCTs reporting on BW at baseline and after follow-up to allow for calculation of its logarithmic standard deviation (Log(SD)) in treatment and placebo groups were included. Meta-regression models accounting for treatment means, differing sample sizes, and correlation across treatment from the same trial were used. A separate meta-regression model to evaluate interactions of treatment and individual predictor variables (age, male sex, pre-randomization BW, duration of disease, duration of treatment, year of study and drug class) was applied. Results 120 randomized trials with a total of 43,663 participants were included. The median BW after treatment was 85.2 kg in the placebo groups and 86.2 kg in the therapy groups. A slightly greater treatment response heterogeneity was shown in the therapy groups with a median Log(SD)=2.83 compared to 2.79 from the placebo. After full adjustment in the meta-regression model the difference in BW Log(SD)s was -0.020 (95% CI: -0.063; 0.022) (Figure 1). Scatter plots adjusted for different sample sizes did not show any interaction (i.e. slope divergence) between predictors and the respective treatment (therapy or placebo). Figure 2 demonstrates this using pre-randomization BW as an example. Conclusions We found no major treatment response heterogeneity in RCTs of glucose-lowering drugs reporting on body weight in type 2 diabetes. In addition, none of the considered predictors were able to explain a potential variation in treatment response. The precision medicine approach may thus be of limited value in this setting.

Real-world experience with cenobamate: A systematic review and meta-analysis.

Despite many new ASM, the rate of patients with drug-resistant epilepsy (DRE) has not changed. Cenobamate (CNB) is a novel ASM for the treatment of focal-onset seizures in adults with high seizure freedom rates in randomized controlled trials (RCT). Although CNB appears to be effective, it is not commonly prescribed to patients with DRE, resulting in a lack of "real-world data". To evaluate the real-world effect of CNB and to assess the generalizability of RCT data, a systematic review and meta-analysis was conducted. Pooled proportions were calculated using a random intercept logistic regression model. The analysis included seven studies with a total of 229 patients with DRE, 77.3% of whom were adults and 91.5% had focal-onset seizures. Seizure reduction >50% was achieved in 68% of patients [54.54; 79.07], with seizure freedom in 16.2% [8.38; 28.97]. There was no difference between pediatric and adult patients. CNB was discontinued in 10% [6.74; 14.6] of patients, mostly due to lack of efficacy (39%) or adverse effects (AE, 43%). AE, observed in 57.3% [39.7; 73.2] of patients, included fatigue and vertigo. A comparison of the rates calculated in this meta-analysis to the active arm of equivalent RCTs revealed no significant difference. CNB achieves a good treatment response in patients with DRE in real-world settings, like the effect reported in RCTs. The high heterogeneity between studies calls for studies focusing on specific DRE subpopulations.

"Project YES! has given me a task to reach undetectable": Qualitative findings from a peer mentoring program for youth living with HIV in Zambia.

The Project YES! clinic-based peer mentoring program was a randomized controlled trial (RCT) conducted among 276 youth from four HIV clinics to test the impact of the program on promoting HIV self-management and reducing internalized stigma among youth living with HIV (ages 15-24 years) in Ndola, Zambia. We conducted a qualitative sub-study involving in-depth interviews with 40 intervention youth participants (21 female, 19 male) to explore their experiences with Project YES! which included: an orientation meeting led by a healthcare provider, monthly individual and group counseling sessions over six months, and three optional caregiver group sessions. Using baseline RCT data, we used maximum variation sampling to purposively select youth by sex, age, change in virologic results between baseline and midline, and study clinic. A four-person team conducted thematic coding. Youth described their increased motivation to take their HIV care seriously due to Project YES!, citing examples of improvements in ART adherence and for some, virologic results. Many cited changes in behavior in the context of greater feelings of self-worth and acceptance of their HIV status, resulting in less shame and fear associated with living with HIV. Youth also attributed Project YES! with reducing their sense of isolation and described Project YES! youth peer mentors and peers as their community and "family." Findings highlight that self-worth and personal connections play a critical role in improving youths' HIV outcomes. Peer-led programs can help foster these gains through a combination of individual and group counseling sessions. Greater attention to the context in which youth manage their HIV, beyond medication intake, is needed to reach global HIV targets.

Estimation of treatment effects in early-phase randomized clinical trials involving external control data

ABSTRACT There are good reasons to perform a randomized controlled trial (RCT) even in early phases of clinical development. However, the low sample sizes in those settings lead to high variability of the treatment effect estimate. The variability could be reduced by adding external control data if available. For the common setting of suitable subject-level control group data only available from one external (clinical trial or real-world) data source, we evaluate different analysis options for estimating the treatment effect via hazard ratios. The impact of the external control data is usually guided by the level of similarity with the current RCT data. Such level of similarity can be determined via outcome and/or baseline covariate data comparisons. We provide an overview over existing methods, propose a novel option for a combined assessment of outcome and baseline data, and compare a selected set of approaches in a simulation study under varying assumptions regarding observable and unobservable confounder distributions using a time-to-event model. Our various simulation scenarios also reflect the differences between external clinical trial and real-world data. Data combinations via simple outcome-based borrowing or simple propensity score weighting with baseline covariate data are not recommended. Analysis options which conflate outcome and baseline covariate data perform best in our simulation study.