Placebo immune-related adverse events (irAEs): A neglected phenomenon in cancer immunotherapy trials.

PURPOSE: This study aims to investigate the underexplored prevalence of placebo-reported immune-related adverse events (irAEs) in immune checkpoint inhibitor (ICI) trials. METHODS: We searched public databases for randomized clinical trials (RCTs) involving ICI versus placebo treatments in patients with malignancies. Study characteristics and irAEs occurrences were extracted for meta-analyses using a random-effects model. MAIN OUTCOMES: Proportions of patients reported to experience any grade and grade 3 to 5 placebo irAEs; the risk ratio (RR) of reporting 'false' irAEs in the experiment arm (defined as 'false-irAE ratio', calculated by dividing the proportion of patients documented with irAEs in the placebo arm by that in the experimental arm). RESULTS: 47 RCTs with 30,119 patients were analyzed. The pooled proportion of patients reported to experience any grade and grade 3 to 5 irAEs among placebo participants was 22.85 % (17.33 %-29.50 %) and 3.40 % (2.35 %-4.63 %), respectively. The pooled proportion of placebo-treated patients who experienced serious irAEs was 0.67 % (0.03 %-1.91 %). Treatment discontinuation and death due to placebo irAEs occurred in 0.69 % (<0.01 %-1.30 %) and 0.12 % (<0.01 %-0.40 %) of patients, respectively. The false-irAE ratio for any grade and grade 3 to 5 irAEs were 0.49 and 0.28. The false-irAE ratio was significantly higher in RCTs with control arms of placebo plus non-immunotherapy than in those with placebo alone (any grade: 0.57 vs. 0.32, P < 0.001; grade 3 to 5: 0.36 vs. 0.12, P = 0.009). CONCLUSION: Our analyses of placebo-treated participants in ICI RCTs document the common occurrence of placebo irAEs. These findings are important for interpreting irAE profiles, avoiding inappropriate therapeutic interventions.

The Effectiveness of Dialectical Behavior Therapy Compared to Schema Therapy for Borderline Personality Disorder: A Randomized Clinical Trial.

INTRODUCTION: In the treatment of borderline personality disorder (BPD), there is empirical support for both dialectical behavior therapy (DBT) and schema therapy (ST); these treatments have never been compared directly. This study examines whether either of them is more effective than the other in treating patients with BPD. METHODS: In this randomized, parallel-group, rater-blind clinical trial, outpatients aged between 18 and 65 years with a primary diagnosis of BPD were recruited in a tertiary outpatient treatment center (Lübeck, Germany). Participants were randomized to DBT or ST with one individual and one group session per week over 1.5 years. The primary outcome was the BPD symptom severity assessed with the mean score of the Borderline Personality Disorder Severity Index at 1-year naturalistic follow-up. RESULTS: Between November 26, 2014, and December 14, 2018, we enrolled 164 patients (mean age = 33.7 [SD = 10.61] years). Of these, 81 (49.4%) were treated with ST and 83 (50.6%) with DBT, overall, 130 (79.3%) were female. Intention-to-treat analysis with generalized linear mixed models did not show a significant difference at 1-year naturalistic follow-up between DBT and ST for the BPDSI total score (mean difference 3.32 [95% CI: -0.58-7.22], p = 0.094, d = -24 [-0.69; 0.20]) with lower scores for DBT. Pre-to-follow-up effect sizes were large in both groups (DBT: d = 2.45 [1.88-3.02], ST: d = 1.78 [1.26-2.29]). CONCLUSION: Patients in both treatment groups showed substantial improvements indicating that even severely affected patients with BPD and various comorbid disorders can be treated successfully with DBT and ST. An additional non-inferiority trial is needed to show if both treatments are equally effective. The trial was retrospectively registered on the German Clinical Trials Register, DRKS00011534 without protocol changes.

Enhancing glycaemic control and promoting cardiovascular health: the therapeutic potential of Trigonella foenumgraecum in diabetic patients - a systematic review and meta-analysis.

Background: The antidiabetic potential of fenugreek has been highlighted in past literature, and various in-vitro and in-vivo studies have validated its glucose-lowering effects; however, very limited data are available on its effects on diabetic patients. Objective: An updated systematic review and meta-analysis of randomized control trials that assessed patients who were administered fenugreek. Methods: The PRISMA guidelines (Supplemental Digital Content 1, http://links.lww.com/MS9/A361) were followed when conducting this meta-analysis. PubMed, Scopus, Google Scholar and MEDLINE were searched from inception until June 2023, for randomized control trials that compared fenugreek with control in patients with type 2 diabetes mellitus (DM) and reported the following outcomes of interest: fasting blood glucose, glycated haemoglobin A1c (HbA1c) and postprandial glucose levels. The findings were presented as mean difference (MD) with 95% confidence intervals (CIs) and were pooled using a random effects model. Results: Fenugreek significantly (P<0.001) reduced the fasting blood sugar (FBS), HbA1c levels and postprandial glucose levels in diabetic patients when compared to the control. Conclusion: Among patients with type 2 DM, our comparisons demonstrated a reduction in FBS, HbA1c levels and postprandial glucose levels with the administration of fenugreek seed at 2-5 mg dose in powder form.

The Polypill: A New Alternative in the Prevention and Treatment of Cardiovascular Disease.

Cardiovascular disease (CVD) is the primary cause of death and disability worldwide. Although age-standardized CVD mortality rates decreased globally by 14.5% between 2006 and 2016, the burden of CVD remains disproportionately higher in low- and middle-income countries compared to high-income countries. Even though proven, effective approaches based on multiple-drug intake aimed at the prevention and treatment of CVD are currently available, poor adherence, early discontinuation of treatment, and suboptimal daily execution of the prescribed therapeutic regimes give rise to shortfalls in drug exposure, leading to high variability in the responses to the prescribed medications. Wald and Law, in their landmark paper published in BMJ 2003, hypothesized that the use of a fixed-dose combination of statins, ß-blockers, angiotensin receptor blockers, angiotensin-converting enzyme inhibitors, and aspirin (classic Polypill composition) may increase adherence and decrease CVD by up to 80% when prescribed as primary prevention or in substitution of traditional protocols. Since then, many clinical trials have tested this hypothesis, with comparable results. This review aims to describe the available clinical trials performed to assess the impact of fixed-dose combinations on adherence, cost-effectiveness, and the risk factors critical to the onset of CVD.

The Efficacy of Multivitamin, Vitamin A, Vitamin B, Vitamin C, and Vitamin D Supplements in the Prevention and Management of COVID-19 and Long-COVID: An Updated Systematic Review and Meta-Analysis of Randomized Clinical Trials.

This review aims to evaluate the efficacy of any vitamin administration(s) in preventing and managing COVID-19 and/or long-COVID. Databases were searched up to May 2023 to identify randomized clinical trials comparing data on the effects of vitamin supplementation(s) versus placebo or standard of care on the two conditions of interest. Inverse-variance random-effects meta-analyses were conducted to estimate pooled risk ratios (RRs) and 95% confidence intervals (CIs) for all-cause mortality between supplemented and non-supplemented individuals. Overall, 37 articles were included: two regarded COVID-19 and long-COVID prevention and 35 records the COVID-19 management. The effects of vitamin D in preventing COVID-19 and long-COVID were contrasting. Similarly, no conclusion could be drawn on the efficacy of multivitamins, vitamin A, and vitamin B in COVID-19 management. A few positive findings were reported in some vitamin C trials but results were inconsistent in most outcomes, excluding all-cause mortality (RR = 0.84; 95% CI: 0.72-0.97). Vitamin D results were mixed in most aspects, including mortality, in which benefits were observed in regular administrations only (RR = 0.67; 95% CI: 0.49-0.91). Despite some benefits, results were mostly contradictory. Variety in recruitment and treatment protocols might explain this heterogeneity. Better-designed studies are needed to clarify these vitamins' potential effects against SARS-CoV-2.

Ethical Problems of Observational Studies and Big Data Compared to Randomized Trials.

The temptation to use prospective observational studies (POS) instead of conducting difficult trials (RCTs) has always existed, but with the advent of powerful computers and large databases, it can become almost irresistible. We examine the potential consequences, were this to occur, by comparing two hypothetical studies of a new treatment: one RCT, and one POS. The POS inevitably submits more patients to inferior research methodology. In RCTs, patients are clearly informed of the research context, and 1:1 randomized allocation between experimental and validated treatment balances risks for each patient. In POS, for each patient, the risks of receiving inferior treatment are impossible to estimate. The research context and the uncertainty are down-played, and patients and clinicians are at risk of becoming passive research subjects in studies performed from an outsider's view, which potentially has extraneous objectives, and is conducted without their explicit, autonomous, and voluntary involvement and consent.

Quantifying proportion of treatment effect by surrogate endpoint under heterogeneity.

When the primary endpoints in randomized clinical trials require long term follow-up or are costly to measure, it is often desirable to assess treatment effects on surrogate instead of clinical endpoints. Prior to adopting a surrogate endpoint for such purposes, the extent of its surrogacy on the primary endpoint must be assessed. There is a rich statistical literature on assessing surrogacy in the overall population, much of which is based on quantifying the proportion of treatment effect on the primary endpoint that is explained by the treatment effect on the surrogate endpoint. However, the surrogacy of an endpoint may vary across different patient subgroups according to baseline demographic characteristics, and limited methods are currently available to assess overall surrogacy in the presence of potential surrogacy heterogeneity. In this paper, we propose methods that incorporate covariates for baseline information, such as age, to improve overall surrogacy assessment. We use flexible semi-non-parametric modeling strategies to adjust for covariate effects and derive a robust estimate for the proportion of treatment effect of the covariate-adjusted surrogate endpoint. Simulation results suggest that the adjusted surrogate endpoint has greater proportion of treatment effect compared to the unadjusted surrogate endpoint. We apply the proposed method to data from a clinical trial of infliximab and assess the adequacy of the surrogate endpoint in the presence of age heterogeneity.

Design of randomized clinical trials with a binary endpoint: Conditional versus unconditional analyses of a two-by-two table.

When designing a randomized clinical trial to compare two treatments, the sample size required to have desired power with a specified type 1 error depends on the hypothesis testing procedure. With a binary endpoint (e.g., response), the trial results can be displayed in a 2 × 2 table. If one does the analysis conditional on the number of positive responses, then using Fisher's exact test has an actual type 1 error less than or equal to the specified nominal type 1 error. Alternatively, one can use one of many unconditional "exact" tests that also preserve the type 1 error and are less conservative than Fisher's exact test. In particular, the unconditional test of Boschloo is always at least as powerful as Fisher's exact test, leading to smaller required sample sizes for clinical trials. However, many statisticians have argued over the years that the conditional analysis with Fisher's exact test is the only appropriate procedure. Since having smaller clinical trials is an extremely important consideration, we review the general arguments given for the conditional analysis of a 2 × 2 table in the context of a randomized clinical trial. We find the arguments not relevant in this context, or, if relevant, not completely convincing, suggesting the sample-size advantage of the unconditional tests should lead to their recommended use. We also briefly suggest that since designers of clinical trials practically always have target null and alternative response rates, there is the possibility of using this information to improve the power of the unconditional tests.

genRCT: a statistical analysis framework for generalizing RCT findings to real-world population.

When evaluating the real-world treatment effect, the analysis based on randomized clinical trials (RCTs) often introduces generalizability bias due to the difference in risk factors between the trial participants and the real-world patient population. This problem of lack of generalizability associated with the RCT-only analysis can be addressed by leveraging observational studies with large sample sizes that are representative of the real-world population. A set of novel statistical methods, termed "genRCT", for improving the generalizability of the trial has been developed using calibration weighting, which enforces the covariates balance between the RCT and observational study. This paper aims to review statistical methods for generalizing the RCT findings by harnessing information from large observational studies that represent real-world patients. Specifically, we discuss the choices of data sources and variables to meet key theoretical assumptions and principles. We introduce and compare estimation methods for continuous, binary, and survival endpoints. We showcase the use of the R package genRCT through a case study that estimates the average treatment effect of adjuvant chemotherapy for the stage 1B non-small cell lung patients represented by a large cancer registry.

Efficacy and Safety of Long-Term Dual Antiplatelet Therapy: A Systematic Review and Meta-Analysis.

BACKGROUND: Dual antiplatelet therapy (DAPT) with aspirin and a P2Y12 inhibitor is a standard therapy in patients with ischemic vascular diseases (IVD) including coronary artery, cerebrovascular and peripheral arterial diseases, although the optimal duration of this treatment is still debated. Previous meta-analyses reported conflicting results about the effects of long-term and short-term as well as non-DAPT use in various clinical settings. Herein, we conducted a comprehensive meta-analysis to assess the efficacy and safety of different durations of DAPT. METHODS: We reviewed relevant articles and references from database, which were published prior to April 2023. Data from prospective studies were processed using RevMan5.0 software, provided by Cochrane Collaboration and transformed using relevant formulas. The inclusion criteria involved randomization to long-term versus short-term or no DAPT; the endpoints included at least one of total or cardiovascular (CV) mortalities, IVD recurrence, and bleeding. RESULTS: A total of 34 randomized studies involving 141 455 patients were finally included. In comparison with no or short-term DAPT, long-term DAPT reduced MI and stroke, but did not reduce the total and CV mortalities. Meanwhile, bleeding events were increased, even though intracranial and fatal bleedings were not affected. Besides, the reduction of MI and stroke recurrence showed no statistical significance between long-term and short-term DAPT groups. CONCLUSION: Long-term DAPT may not reduce the mortality of IVD besides increasing bleeding events, although reduced the incidences of MI and stroke early recurrence to a certain extent and did not increase the risk of fatal intracranial bleeding.

Reporting Quality and Risk of Bias Analysis of Published RCTs Assessing Anti-CGRP Monoclonal Antibodies in Migraine Prophylaxis: A Systematic Review.

Objective: Phase II/III randomized clinical trials (RCTs) are vulnerable to many types of bias beyond randomization. Insights into the reporting quality of RCTs involving migraine patients treated with monoclonal antibodies targeting the calcitonin gene-related peptide system (anti-CGRP MAbs) are currently lacking. Our aim was to analyze the reporting quality of phase II/III RCTs involving migraine patients treated with anti-CGRP MAbs. Methods: A systematic search was performed on the PubMed and EMBASE databases, according to PRISMA guidelines, for relevant RCTs in either episodic or chronic migraine prevention. Additionally, an adapted version of the 2010 CONSORT statement checklist was utilized. The ROBvis online tool was used to document the risk of bias. Results: From the initially identified 179 articles, we finally found 31 RCTs that were eligible for evaluation. The average CONSORT compliance was 88.7% (69.7-100%), while 93.5% (N = 29) of the articles had a compliance greater than 75%. Twenty-eight CONSORT items were reported in more than 75% of the articles. The average compliance of the analyzed RCTs was 93.9% for Galcanezumab, 91.3% for Fremanezumab, followed by 85.4% for Erenumab and Eptinezumab studies. Implementation of the ROB2 tool showed some concerning "missing information" arising from the inadequate reporting. Specifically, 50% of the studies (N = 16) were categorized as having inadequate information regarding the randomization process. Conclusions: Adequate reporting quality was disclosed in the evaluated RCTs with anti-CGRP MAbs in migraine prevention. However, some methodological issues need to be highlighted to be addressed in future studies assessing the efficacy of new molecules targeting CGRP or other candidate pathways implicated in migraine pathophysiology.

Leveraging meta-regression to test if medication effects on cue-induced craving are associated with clinical efficacy.

RATIONALE: The alcohol cue exposure paradigm is a common method for evaluating new treatments for alcohol use disorder (AUD); however, it is unclear if medication-related reductions in cue-induced craving in the human laboratory can predict the clinical success of those medications in reducing alcohol consumption during clinical trials. OBJECTIVES: To use a novel meta-analytic approach to test whether medication effect sizes on cue-induced alcohol craving are associated with clinical efficacy in clinical trials. METHOD: We searched the literature for medications tested for AUD treatment using both the alcohol cue-reactivity paradigm and randomized clinical trials (RCTs). For alcohol cue-reactivity studies, we computed medication effect sizes for cue-induced alcohol craving (k = 36 studies, 15 medications). For RCTs, we calculated medication effect sizes for heavy drinking and abstinence (k = 139 studies, 19 medications). Using medication as the unit of analysis, we applied the Williamson-York bivariate weighted least squares estimation to account for errors in both independent and dependent variables. We also conducted leave-one-out cross validation simulations to examine the predictive utility of cue-craving medication effect sizes on RCT heavy drinking and abstinence endpoints. RESULTS: There was no significant relationship between medication effects on cue-induced alcohol craving in the human laboratory and medication effects on heavy drinking ( ß ^ = 0.253, SE = 0.189, p = 0.090) and abstinence ( ß ^ = 0.829, SE = 0.747, p = 0.133) in RCTs. CONCLUSIONS: The preliminary results of the current study challenge the assumption that alcohol cue-reactivity alone can be used as an early efficacy indicator for AUD pharmacotherapy development. These findings suggest that a wider range of early efficacy indicators and experimental paradigms be considered for Phase II testing of novel compounds.

Inductive reasoning with large language models: a simulated randomized controlled trial for epilepsy.

Importance: The analysis of electronic medical records at scale to learn from clinical experience is currently very challenging. The integration of artificial intelligence (AI), specifically foundational large language models (LLMs), into an analysis pipeline may overcome some of the current limitations of modest input sizes, inaccuracies, biases, and incomplete knowledge bases. Objective: To explore the effectiveness of using an LLM for generating realistic clinical data and other LLMs for summarizing and synthesizing information in a model system, simulating a randomized clinical trial (RCT) in epilepsy to demonstrate the potential of inductive reasoning via medical chart review. Design: An LLM-generated simulated RCT based on a RCT for treatment with an antiseizure medication, cenobamate, including a placebo arm and a full-strength drug arm, evaluated by an LLM-based pipeline versus a human reader. Setting: Simulation based on realistic seizure diaries, treatment effects, reported symptoms and clinical notes generated by LLMs with multiple different neurologist writing styles. Participants: Simulated cohort of 240 patients, divided 1:1 into placebo and drug arms. Intervention: Utilization of LLMs for the generation of clinical notes and for the synthesis of data from these notes, aiming to evaluate the efficacy and safety of cenobamate in seizure control either with a human evaluator or AI-pipeline. Measures: The AI and human analysis focused on identifying the number of seizures, symptom reports, and treatment efficacy, with statistical analysis comparing the 50%-responder rate and median percentage change between the placebo and drug arms, as well as side effect rates in each arm. Results: AI closely mirrored human analysis, demonstrating the drug's efficacy with marginal differences (<3%) in identifying both drug efficacy and reported symptoms. Conclusions and Relevance: This study showcases the potential of LLMs accurately simulate and analyze clinical trials. Significantly, it highlights the ability of LLMs to reconstruct essential trial elements, identify treatment effects, and recognize reported symptoms, within a realistic clinical framework. The findings underscore the relevance of LLMs in future clinical research, offering a scalable, efficient alternative to traditional data mining methods without the need for specialized medical language training.

Cardiovascular outcomes and molecular targets for the cardiac effects of Sodium-Glucose Cotransporter 2 Inhibitors: A systematic review.

Sodium-glucose cotransporter 2 inhibitors (SGLT2i), a new class of glucose-lowering drugs traditionally used to control blood glucose levels in patients with type 2 diabetes mellitus, have been proven to reduce major adverse cardiovascular events, including cardiovascular death, in patients with heart failure irrespective of ejection fraction and independently of the hypoglycemic effect. Because of their favorable effects on the kidney and cardiovascular outcomes, their use has been expanded in all patients with any combination of diabetes mellitus type 2, chronic kidney disease and heart failure. Although mechanisms explaining the effects of these drugs on the cardiovascular system are not well understood, their effectiveness in all these conditions suggests that they act at the intersection of the metabolic, renal and cardiac axes, thus disrupting maladaptive vicious cycles while contrasting direct organ damage. In this systematic review we provide a state of the art of the randomized controlled trials investigating the effect of SGLT2i on cardiovascular outcomes in patients with chronic kidney disease and/or heart failure irrespective of ejection fraction and diabetes. We also discuss the molecular targets and signaling pathways potentially explaining the cardiac effects of these pharmacological agents, from a clinical and experimental perspective.