Characteristics, Progression, and Output of Randomized Platform Trials: A Systematic Review.

Importance: Platform trials have become increasingly common, and evidence is needed to determine how this trial design is actually applied in current research practice. Objective: To determine the characteristics, progression, and output of randomized platform trials. Evidence Review: In this systematic review of randomized platform trials, Medline, Embase, Scopus, trial registries, gray literature, and preprint servers were searched, and citation tracking was performed in July 2022. Investigators were contacted in February 2023 to confirm data accuracy and to provide updated information on the status of platform trial arms. Randomized platform trials were eligible if they explicitly planned to add or drop arms. Data were extracted in duplicate from protocols, publications, websites, and registry entries. For each platform trial, design features such as the use of a common control arm, use of nonconcurrent control data, statistical framework, adjustment for multiplicity, and use of additional adaptive design features were collected. Progression and output of each platform trial were determined by the recruitment status of individual arms, the number of arms added or dropped, and the availability of results for each intervention arm. Findings: The search identified 127 randomized platform trials with a total of 823 arms; most trials were conducted in the field of oncology (57 [44.9%]) and COVID-19 (45 [35.4%]). After a more than twofold increase in the initiation of new platform trials at the beginning of the COVID-19 pandemic, the number of platform trials has since declined. Platform trial features were often not reported (not reported: nonconcurrent control, 61 of 127 [48.0%]; multiplicity adjustment for arms, 98 of 127 [77.2%]; statistical framework, 37 of 127 [29.1%]). Adaptive design features were only used by half the studies (63 of 127 [49.6%]). Results were available for 65.2% of closed arms (230 of 353). Premature closure of platform trial arms due to recruitment problems was infrequent (5 of 353 [1.4%]). Conclusions and Relevance: This systematic review found that platform trials were initiated most frequently during the COVID-19 pandemic and declined thereafter. The reporting of platform features and the availability of results were insufficient. Premature arm closure for poor recruitment was rare.

Effect of dulaglutide in promoting abstinence during smoking cessation: 12-month follow-up of a single-centre, randomised, double-blind, placebo-controlled, parallel group trial.

Background: Smoking cessation is challenging, despite making use of established smoking cessation therapies. Preclinical studies and one clinical pilot study suggest the antidiabetic drug glucagon-like peptide-1 (GLP-1) analogue to modulate addictive behaviours and nicotine craving. Previously, we reported the short-term results of a randomised, double-blind, placebo-controlled trial. Herein we report long-term abstinence rates and weight developments after 24 and 52 weeks. Methods: This single-centre, randomised, double-blind, placebo-controlled, parallel group trial was done at the University Hospital Basel in Switzerland. We randomly assigned (1:1) individuals with at least a moderate nicotine dependence willing to quit smoking to either a 12-week treatment with dulaglutide 1.5 mg or placebo subcutaneously once weekly in addition to standard of care smoking cessation therapy (varenicline 2 mg/day and behavioural counselling). After 12 weeks, dulaglutide or placebo injections were discontinued and the participants were followed up at week 24 and 52. The primary outcome of self-reported and biochemically confirmed point prevalence abstinence rate, and secondary outcome of secondary outcome of weight change were assessed at weeks 24 and 52. All participants who received one dose of the study drug were included in the intention to treat set and participants who received at least 10/12 doses of the study drug formed the per protocol set. The trial was registered at ClinicalTrials.gov, NCT03204396. Findings: Of the 255 participants who were randomly assigned between June 22, 2017 and December 3, 2020, 63% (80/127) (dulaglutide group) and 65% (83/128) (placebo group) were abstinent after 12 weeks. These abstinence rates declined to 43% (54/127) and 41% (52/128), respectively, after 24 weeks and to 32% (41/127) and 32% (41/128), respectively, after 52 weeks. Post-cessation weight gain was prevented in the dulaglutide group (-1.0 kg, standard deviation [SD] 2.7) as opposed to the placebo group (+1.9 kg, SD 2.4) after 12 weeks. However, at week 24, increases in weight from baseline were observed in both groups (median, interquartile range [IQR]: dulaglutide: +1.5 kg, [-0.4, 4.1], placebo: +3.0 kg, [0.6, 4.6], baseline-adjusted difference in weight change -1.0 kg (97.5% CI [-2.16, 0.16])), and at week 52 the groups showed similar weight gain (median, IQR: dulaglutide: +2.8 kg [-0.4, 4.7], placebo: +3.1 kg [-0.4, 6.0], baseline-adjusted difference in weight change: -0.35 kg (95% CI [-1.72, 1.01])). In the follow-up period (week 12 to week 52) 51 (51%) and 48 (48%) treatment-unrelated adverse events were recorded in the dulaglutide and the placebo group, respectively. No treatment-related serious adverse events or deaths occurred. Interpretation: Dulaglutide does not improve long-term smoking abstinence, but has potential to counteract weight gain after quitting. However, 3 months of treatment did not have a sustained beneficial effect on weight at 1 year. As post-cessation weight gain is highest in the first year after quitting smoking, future studies should consider a longer treatment duration with a GLP-1 analogue in abstinent individuals. Funding: Swiss National Science Foundation, the Gottfried and Julia Bangerter-Rhyner Foundation, the Goldschmidt-Jacobson Foundation, the Hemmi-Foundation, the University of Basel, the Swiss Academy of Medical Sciences.

Bridging the gap: identifying factors impacting mRNA severe acute respiratory syndrome coronavirus 2 vaccine booster response in people with HIV-1.

OBJECTIVES: This study aimed to investigate the association of demographic and clinical characteristics, including HIV-specific parameters with the antibody response to a third dose of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccine in people with HIV-1 (PWH). DESIGN: Post hoc analysis of data collected during the observational extension of the COrona VaccinE tRiAL pLatform trial (COVERALL-2) nested into the Swiss HIV Cohort Study (SHCS). METHODS: Serological measurements were conducted on a total of 439 PWH who had received a third dose of either mRNA-1273 (Moderna) or BNT162b2 (Pfizer-BioNTech) SARS-CoV-2 vaccine. Antibody reactivity was assessed using the multifactorial ABCORA immunoassay that defines SARS-CoV-2 seroconversion and predicts neutralization activity. The association between log transformed antibody reactivity and various baseline factors, including vaccine type, demographics, immune and viral status, smoking status, comorbidities, infection history, and co-medication with chemotherapy and immunosuppressive drugs, was investigated using a multivariable linear regression model. RESULTS: Antibody response to third SARS-CoV-2 vaccination was significantly lower among PWH with CD4 + cell count less than 350 cells/µl [ratio of means 0.79; 95% confidence interval (CI) 0.65-0.95]. Having a detectable HIV-1 viral load at least 50 copies/ml and being on concurrent chemotherapy was associated with an overall lower humoral immune response (ratio of means 0.75; 95% CI 0.57-1.00 and 0.34; 95% CI 0.22-0.52, respectively). CONCLUSION: The study highlights the importance of optimal antiretroviral treatment for PWH, emphasizing the need for timely intervention to enhance the vaccine immunogenicity in this population. Moreover, it underscores the significance of sequential mRNA vaccination and provides important evidence for informing vaccine guidelines.

Glucagon-like peptide-1 analogues: a new way to quit smoking? (SKIP)-a structured summary of a study protocol for a randomized controlled study.

BACKGROUND: Cigarette smoking is the leading preventable cause of premature death. Despite dedicated programmes, quit rates remain low due to barriers such as nicotine withdrawal syndrome or post-cessation weight gain. Glucagon-like peptide-1 (GLP-1) analogues reduce energy intake and body weight and seem to modulate addictive behaviour. These GLP-1 properties are of major interest in the context of smoking cessation. The aim of this study is to evaluate the GLP-1 analogue dulaglutide as a new therapy for smoking cessation. METHODS: This is a placebo-controlled, double-blind, parallel group, superiority, single-centre randomized study including 255 patients. The intervention consists of a 12-week dulaglutide treatment phase with 1.5 mg once weekly or placebo subcutaneously, in addition to standard of care (behavioural counselling and pharmacotherapy with varenicline). A 40-week non-treatment phase follows. The primary outcome is the point prevalence abstinence rate at week 12. Smoking status is self-reported and biochemically confirmed by end-expiratory exhaled carbon monoxide measurement. Further endpoints include post-cessational weight gain, nicotine craving analysis, glucose homeostasis and long-term nicotine abstinence. Two separate substudies assess behavioural, functional and structural changes by functional magnetic resonance imaging and measures of energy metabolism (i.e. resting energy expenditure, body composition). DISCUSSION: Combining behavioural counselling and medical therapy, e.g. with varenicline, improves abstinence rates and is considered the standard of care. We expect a further increase in quit rates by adding a second component of medical therapy and assume a dual effect of dulaglutide treatment (blunting nicotine withdrawal symptoms and reducing post-cessational weight gain). This project is of high relevance as it explores novel treatment options aimed at preventing the disastrous consequences of nicotine consumption and obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT03204396 . Registered on June 26, 2017.

Effect of dulaglutide in promoting abstinence during smoking cessation: a single-centre, randomized, double-blind, placebo-controlled, parallel group trial.

Background: Quitting smoking is difficult due to barriers such as craving for cigarettes and post-cessation weight gain. Recent experimental data suggest a role of glucagon-like peptide-1 (GLP-1) in the pathophysiology of addiction in addition to appetite regulation and weight control. We hypothesized that a pharmacological intervention with the GLP-1 analogue dulaglutide during smoking cessation may improve abstinence rates and reduce post-cessation weight gain. Methods: This is a single-centre, randomized, double-blind, placebo-controlled, parallel group, superiority study conducted in the University Hospital Basel in Switzerland. We included adult smokers with at least moderate cigarette dependence who wanted to quit. Participants were randomly assigned to a 12-week treatment with dulaglutide 1.5 mg once weekly or placebo subcutaneously in addition to standard of care including behavioural counselling and oral varenicline pharmacotherapy of 2 mg/day. The primary outcome was self-reported and biochemically confirmed point prevalence abstinence rate at week 12. Secondary outcomes included post-cessation weight, glucose metabolism, and craving for smoking. All participants who received one dose of study drug were included in the primary and safety analyses. The trial was registered on ClinicalTrials.gov (NCT03204396). Findings: Between June 22, 2017, and December 3, 2020, 255 participants were enrolled and randomly assigned to each group (127 in the dulaglutide group and 128 in the placebo group). After 12 weeks, 63% (80/127) participants on dulaglutide and 65% (83/128) on placebo treatment were abstinent (difference in proportions -1.9% [95% Confidence interval (CI) -10.7, 14.4], p-value (p) = 0.859). Dulaglutide decreased post-cessation weight (-1 kg [standard deviation (SD) 2.7]), while weight increased on placebo (+1.9 kg [SD 2.4]). The baseline-adjusted difference in weight change between groups was -2.9 kg (95% CI -3.59, -2.3, p < 0.001). Haemoglobin A1c (HbA1c) level declined on dulaglutide treatment (baseline-adjusted median difference in HbA1c between groups -0.25% [interquartile range (IQR) -0.36, -0.14], p < 0.001). Craving for smoking declined during treatment without any difference between the groups. Treatment-emergent gastrointestinal symptoms were very common in both groups: 90% (114/127) of participants on dulaglutide and 81% (81/128) on placebo). Interpretation: Dulaglutide had no effect on abstinence rates but prevented post-cessation weight gain and decreased HbA1c levels. GLP-1 analogues may play a role in future cessation therapy targeting metabolic parameters such as weight and glucose metabolism. Funding: Swiss National Science Foundation, the Gottfried Julia Bangerter-Rhyner Foundation, the Goldschmidt-Jacobson Foundation, the Hemmi-Foundation, the University of Basel, the Swiss Academy of Medical Sciences.

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.

OBJECTIVES: In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. STUDY DESIGN AND SETTING: We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. RESULTS: We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. CONCLUSION: The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology.

BACKGROUND: Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain. METHODS: We conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published between 01/01/2019 and 05/09/2019. The primary outcome was risk of bias, judged using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We described risk of bias overall and for each domain, by development and validation analyses separately. RESULTS: We included 62 publications (48 development-only; 14 development with validation). 152 models were developed across all publications and 37 models were validated. 84% (95% CI: 77 to 89) of developed models and 51% (95% CI: 35 to 67) of validated models were at overall high risk of bias. Bias introduced in the analysis was the largest contributor to the overall risk of bias judgement for model development and validation. 123 (81%, 95% CI: 73.8 to 86.4) developed models and 19 (51%, 95% CI: 35.1 to 67.3) validated models were at high risk of bias due to their analysis, mostly due to shortcomings in the analysis including insufficient sample size and split-sample internal validation. CONCLUSIONS: The quality of machine learning based prognostic models in the oncology domain is poor and most models have a high risk of bias, contraindicating their use in clinical practice. Adherence to better standards is urgently needed, with a focus on sample size estimation and analysis methods, to improve the quality of these models.

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.

BACKGROUND: Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. METHODS: We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. RESULTS: Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. CONCLUSIONS: The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

Antibody Response in Immunocompromised Patients After the Administration of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Vaccine BNT162b2 or mRNA-1273: A Randomized Controlled Trial.

BACKGROUND: BNT162b2 by Pfizer-BioNTech and mRNA-1273 by Moderna are the most commonly used vaccines to prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Head-to-head comparison of the efficacy of these vaccines in immunocompromised patients is lacking. METHODS: Parallel, 2-arm (allocation 1:1), open-label, noninferiority randomized clinical trial nested into the Swiss HIV Cohort Study and the Swiss Transplant Cohort Study. People living with human immunodeficiency virus (PLWH) or solid organ transplant recipients (SOTR; ie, lung and kidney) from these cohorts were randomized to mRNA-1273 or BNT162b2. The primary endpoint was antibody response to SARS-CoV-2 spike (S1) protein receptor binding domain (Elecsys Anti-SARS-CoV-2 immunoassay, Roche; cutoff ≥0.8 units/mL) 12 weeks after first vaccination (ie, 8 weeks after second vaccination). In addition, antibody response was measured with the Antibody Coronavirus Assay 2 (ABCORA 2). RESULTS: A total of 430 patients were randomized and 412 were included in the intention-to-treat analysis (341 PLWH and 71 SOTR). The percentage of patients showing an immune response was 92.1% (95% confidence interval [CI]: 88.4-95.8; 186/202) for mRNA-1273 and 94.3% (95% CI: 91.2-97.4; 198/210) for BNT162b2 (difference: -2.2%; 95% CI: -7.1 to 2.7), fulfilling noninferiority of mRNA-1273. With the ABCORA 2 test, 89.1% had an immune response to mRNA-1273 (95% CI: 84.8-93.4; 180/202) and 89.5% to BNT162b2 (95% CI: 85.4-93.7; 188/210). Based on the Elecsys test, all PLWH had an antibody response (100.0%; 341/341), whereas for SOTR, only 60.6% (95% CI: 49.2-71.9; 43/71) had titers above the cutoff level. CONCLUSIONS: In immunocompromised patients, the antibody response of mRNA-1273 was noninferior to BNT162b2. PLWH had in general an antibody response, whereas a high proportion of SOTR had no antibody response.

Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved.

OBJECTIVE: Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING: We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD. RESULTS: Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n=12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]). CONCLUSION: Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste.

Clinical Trial Evidence Supporting US Food and Drug Administration Approval of Novel Cancer Therapies Between 2000 and 2016.

Importance: Clinical trial evidence used to support drug approval is typically the only information on benefits and harms that patients and clinicians can use for decision-making when novel cancer therapies become available. Various evaluations have raised concern about the uncertainty surrounding these data, and a systematic investigation of the available information on treatment outcomes for cancer drugs approved by the US Food and Drug Administration (FDA) is warranted. Objective: To describe the clinical trial data available on treatment outcomes at the time of FDA approval of all novel cancer drugs approved for the first time between 2000 and 2016. Design, Setting, and Participants: This comparative effectiveness study analyzed randomized clinical trials and single-arm clinical trials of novel drugs approved for the first time to treat any type of cancer. Approval packages were obtained from drugs@FDA, a publicly available database containing information on drug and biologic products approved for human use in the US. Data from January 2000 to December 2016 were included in this study. Main Outcomes and Measures: Regulatory and clinical trial characteristics were described. For randomized clinical trials, summary treatment outcomes for overall survival, progression-free survival, and tumor response across all therapies were calculated, and median absolute survival increases were estimated. Tumor types and regulatory characteristics were assessed separately. Results: Between 2000 and 2016, 92 novel cancer drugs were approved by the FDA for 100 indications based on data from 127 clinical trials. The 127 clinical trials included a median of 191 participants (interquartile range [IQR], 106-448 participants). Overall, 65 clinical trials (51.2%) were randomized, and 95 clinical trials (74.8%) were open label. Of 100 indications, 44 indications underwent accelerated approval, 42 indications were for hematological cancers, and 58 indications were for solid tumors. Novel drugs had mean hazard ratios of 0.77 (95% CI, 0.73-0.81; I2 = 46%) for overall survival and 0.52 (95% CI, 0.47-0.57; I2 = 88%) for progression-free survival. The median tumor response, expressed as relative risk, was 2.37 (95% CI, 2.00-2.80; I2 = 91%). The median absolute survival benefit was 2.40 months (IQR, 1.25-3.89 months). Conclusions and Relevance: In this study, data available at the time of FDA drug approval indicated that novel cancer therapies were associated with substantial tumor responses but with prolonging median overall survival by only 2.40 months. Approval data from 17 years of clinical trials suggested that patients and clinicians typically had limited information available regarding the benefits of novel cancer treatments at market entry.

UMBRELLA protocol: systematic reviews of multivariable biomarker prognostic models developed to predict clinical outcomes in patients with heart failure.

BACKGROUND: Heart failure (HF) is a chronic and common condition with a rising prevalence, especially in the elderly. Morbidity and mortality rates in people with HF are similar to those with common forms of cancer. Clinical guidelines highlight the need for more detailed prognostic information to optimise treatment and care planning for people with HF. Besides proven prognostic biomarkers and numerous newly developed prognostic models for HF clinical outcomes, no risk stratification models have been adequately established. Through a number of linked systematic reviews, we aim to assess the quality of the existing models with biomarkers in HF and summarise the evidence they present. METHODS: We will search MEDLINE, EMBASE, Web of Science Core Collection, and the prognostic studies database maintained by the Cochrane Prognosis Methods Group combining sensitive published search filters, with no language restriction, from 1990 onwards. Independent pairs of reviewers will screen and extract data. Eligible studies will be those developing, validating, or updating any prognostic model with biomarkers for clinical outcomes in adults with any type of HF. Data will be extracted using a piloted form that combines published good practice guidelines for critical appraisal, data extraction, and risk of bias assessment of prediction modelling studies. Missing information on predictive performance measures will be sought by contacting authors or estimated from available information when possible. If sufficient high quality and homogeneous data are available, we will meta-analyse the predictive performance of identified models. Sources of between-study heterogeneity will be explored through meta-regression using pre-defined study-level covariates. Results will be reported narratively if study quality is deemed to be low or if the between-study heterogeneity is high. Sensitivity analyses for risk of bias impact will be performed. DISCUSSION: This project aims to appraise and summarise the methodological conduct and predictive performance of existing clinically homogeneous HF prognostic models in separate systematic reviews.Registration: PROSPERO registration number CRD42019086990.

Adequate reporting of the sample size calculation in surgical randomized controlled trials.

BACKGROUND: The sample size calculation for a randomized controlled trial should assure that a statistically significant and clinically relevant difference can be detected with a high probability. To be certain that readers of a randomized controlled trial can critically judge whether the assumptions for the sample size calculation were realistic, a number of specific subitems should be reported. METHODS: In this minireview, the frequency of adequate reporting of specific sub-items of the sample size was assessed from surgical randomized controlled trials that were published in the 5 leading general surgical journals in 2017. Subitems from the Consolidated Standards for Reporting Trials and from the Standard Protocol Items: Recommendations for Interventional Trials Statement were considered. RESULTS: A total of 85 relevant randomized controlled trials were identified over PubMed. The overall adequate reporting of the sample size according to the Consolidated Standards for Reporting Trials was 53% (45 of 85). When considering additional subitems from the Standard Protocol Items: Recommendations for Interventional Trials Statement which are not explicitly requested by the Consolidated Standards for Reporting Trials Statement, the adherence decreased to 12% (10 of 85). CONCLUSION: The adherence in reporting potentially important subitems of the sample size from the Consolidated Standards for Reporting Trials Statement was moderate among surgical randomized controlled trials. It is crucial that surgeons who plan and conduct randomized controlled trials be aware of these subitems.

Reporting Quality of Journal Abstracts for Surgical Randomized Controlled Trials Before and After the Implementation of the CONSORT Extension for Abstracts.

BACKGROUND: Adequate reporting is crucial in full-text publications but even more so in abstracts because they are the most frequently read part of a publication. In 2008, an extension for abstracts of the Consolidated Standards of Reporting Trials (CONSORT-A) statement was published, defining which items should be reported in abstracts of randomized controlled trials (RCTs). Therefore, we compared the adherence of RCT abstracts to CONSORT-A before and after the publication of CONSORT-A. METHODS: RCTs published in the five surgical journals with the highest impact factor were identified through PubMed for 2005-2007 and 2014-2016. Adherence to 15 CONSORT-A items and two additional items for abstracts of non-pharmacological trials was assessed in duplicate. We compared the overall adherence to CONSORT-A between the two time periods using an unpaired t test and explored adherence to specific items. RESULTS: A total of 192 and 164 surgical RCT abstracts were assessed (2005-2007 and 2014-2016, respectively). In the pre-CONSORT-A phase, the mean score of adequately reported items was 6.14 (95% confidence interval [CI] 5.90-6.38) and 8.11 in the post-CONSORT-A phase (95% CI 7.83-8.39; mean difference 1.97, 95% CI 1.60-2.34; p < 0.0001). The comparison of individual items indicated a significant improvement in 9 of the 15 items. The three least reported items in the post-CONSORT-A phase were randomization (2.4%), blinding (13.4%), and funding (0.0%). Specific items for non-pharmacological trials were rarely reported (approximately 10%). CONCLUSION: The reporting in abstracts of surgical RCTs has improved after the implementation of CONSORT-A. More importantly, there is still ample room for improvement.

Single pivotal trials with few corroborating characteristics were used for FDA approval of cancer therapies.

BACKGROUND AND OBJECTIVE: Novel cancer therapies are often approved with evidence from a single pivotal trial alone. There are concerns about the credibility of this evidence. Higher validity may be indicated by five methodological and statistical characteristics of pivotal trial evidence that were described by the U.S. Food and Drug Administration (FDA), which may corroborate the reliance on a single trial alone for approval decisions. STUDY DESIGN: We did a metaepidemiologic evaluation of all single pivotal trials supporting FDA approval of novel drugs and therapeutic biologicals for cancers between 2000 and 2016. For each trial, we determined the presence of these five characteristics, which we operationalized as (1) large and multicenter trial (≥200 patients; more than one center); consistent treatment benefits across (2) multiple patient subgroups (in view of FDA reviewers), (3) multiple endpoints (including overall survival, progression-free survival, response rate, health related quality of life), and (4) multiple treatment comparisons (e.g., multi-arm studies); and (5) "statistically very persuasive" results (P-values <0.00125). RESULTS: Thirty-five of 100 approvals were based on evidence from a single pivotal trial without any further supporting evidence on beneficial effects (20 randomized controlled trials and 15 single-arm trials). The number increased substantially from one approval before 2006 to 23 after 2011. Sixty-six percent (23/35) of the trials were large multicenter trials (median 301 patients and 63 centers). Consistent effects were demonstrated across subgroups in 66% (23/35), across endpoints in 43% (15/35), and across multiple comparisons in 3% (1/35). Very low P-values for the primary endpoint were seen in 34% (12/35). At least one of the corroborating characteristics was present in 94% (33/35) of all approvals, two or more were present in 54% (19/35), and none had all characteristics. CONCLUSIONS: Single pivotal trials typically have some of the corroborating characteristics, but often only one or two. These characteristics need to be better operationalized, defined, and reported and whether single trials with such characteristics provide similar evidence about benefits and harms of novel treatments as multiple trials would do needs to be shown.

Correction to: Prophylactic Intraperitoneal Onlay Mesh Following Midline Laparotomy-Long-Term Results of a Randomized Controlled Trial.

In the original version of the article, Philippe M. Glauser's, Philippe Brosi's, Benjamin Speich's, Samuel A. Käser's, Andres Heigl's, and Christoph A. Maurer's first and last names were interchanged. The names are correct as reflected here. The original article has been corrected.

Prophylactic Intraperitoneal Onlay Mesh Following Midline Laparotomy-Long-Term Results of a Randomized Controlled Trial.

OBJECTIVES: Incisional hernia, a serious complication after laparotomy, is associated with high morbidity and costs. This trial examines the value of prophylactic intraperitoneal onlay mesh to reduce the risk of incisional hernia after a median follow-up time of 5.3 years. METHODS: We conducted a parallel group, open-label, single center, randomized controlled trial (NCT01003067). After midline incision, the participants were either allocated to abdominal wall closure according to Everett with a PDS-loop running suture reinforced by an intraperitoneal composite mesh strip (Group A) or the same procedure without the additional mesh strip (Group B). RESULTS: A total of 276 patients were randomized (Group A = 131; Group B = 136). Follow-up data after a median of 5.3 years after surgery were available from 183 patients (Group A = 95; Group B = 88). Incisional hernia was diagnosed in 25/95 (26%) patients in Group A and in 46/88 (52%) patients in Group B (risk ratio 0.52; 95% CI 0.36-0.77; p < 0.001). Eighteen patients with asymptomatic incisional hernia went for watchful waiting instead of hernia repair and remained free of symptoms after of a median follow-up of 5.1 years. Between the second- and fifth-year follow-up period, no complication associated with the mesh could be detected. CONCLUSION: The use of a composite mesh in intraperitoneal onlay position significantly reduces the risk of incisional hernia during a 5-year follow-up period. TRIAL REGISTRATION NUMBER: Ref. NCT01003067 (clinicaltrials.gov).

Single-dose versus 3-day cotrimoxazole prophylaxis in transurethral resection or greenlight laser vaporisation of the prostate: study protocol for a multicentre randomised placebo controlled non-inferiority trial (CITrUS trial).

BACKGROUND: Transurethral resection of the prostate (TURP) and Greenlight laser vaporisation (GL) of the prostate are frequently performed urological procedures. For TURP, a single-dose antimicrobial prophylaxis (AP) is recommended to reduce postoperative urinary tract infections. So far, no international recommendations for AP have been established for GL. In a survey-based study in Switzerland, Germany and Austria, urologists reported routinely extending AP primarily for 3 days after both interventions. We therefore aim to determine whether single-dose AP with cotrimoxazole is non-inferior to 3-day AP with cotrimoxazole in patients undergoing TURP or GL of the prostate. METHODS/DESIGN: We will conduct an investigator-initiated, multicentre, randomised controlled trial. We plan to assess the non-inferiority of single-dose AP compared to 3-day AP. The primary outcome is the occurrence of clinically diagnosed symptomatic urinary tract infections which are treated with antimicrobial agents within 30 days after randomisation. The vast majority of collected outcomes will be assessed from routinely collected data. The sample size was estimated to be able to show the non-inferiority of single-dose AP compared to 3-day AP with at least 80% power (1 - ß = 0.8) at a significance level of α = 5%, applying a 1:1 randomisation scheme. The non-inferiority margin was determined in order to preserve 70% of the effect of usual care on the primary outcome. For an assumed event rate of 9% in both treatment arms, this resulted in a non-inferiority margin of 4.4% (i.e. 13.4% to 9%). To prove non-inferiority, a total of 1574 patients should be recruited, in order to have 1416 evaluable patients. The study is supported by the Swiss National Science Foundation. DISCUSSION: For AP in TURP and GL, there is a large gap between usual clinical practice and evidence-based guidelines. If single-dose AP proves non-inferior to prolonged AP, our study findings may help to reduce the duration of AP in daily routine-potentially reducing the risk of emerging resistance and complications related to AP. TRIAL REGISTRATION: Clinicaltrials.gov, NCT03633643 . Registered 16 August 2018.

Comparison of randomized controlled trials discontinued or revised for poor recruitment and completed trials with the same research question: a matched qualitative study.

BACKGROUND: More than a quarter of randomized controlled trials (RCTs) are prematurely discontinued, mostly due to poor recruitment of patients. In this study, we systematically compared RCTs discontinued or revised for poor recruitment and completed RCTs with the same underlying research question to better understand the causes of poor recruitment, particularly related to methodological aspects and context-specific study settings. METHODS: We compared RCTs that were discontinued or revised for poor recruitment to RCTs that were completed as planned, matching in terms of population and intervention. Based on an existing sample of RCTs discontinued or revised due to poor recruitment, we identified matching RCTs through a literature search for systematic reviews that cited the discontinued or revised RCT and matching completed RCTs without poor recruitment. Based on extracted data, we explored differences in the design, conduct, and study settings between RCTs with and without poor recruitment, separately for each research question using semi-structured discussions. RESULTS: We identified 15 separate research questions with a total of 29 RCTs discontinued or revised for poor recruitment and 48 RCTs completed as planned. Prominent research areas in the sample were cancer and acute care. The mean number of RCTs with poor recruitment per research question was 1.9 ranging from 1 to 4 suggesting clusters of research questions or settings prone to recruitment problems. The reporting quality of the recruitment process in RCT publications was generally low. We found that RCTs with poor recruitment often had narrower eligibility criteria, were investigator- rather than industry-sponsored, were associated with a higher burden for patients and recruiters, sometimes used outdated control interventions, and were often launched later in time than RCTs without poor recruitment compromising uncertainty about tested interventions through emerging evidence. Whether a multi- or single-center setting was advantageous for patient recruitment seemed to depend on the research context. CONCLUSIONS: Our study confirmed previously identified causes for poor recruitment, i.e., narrow eligibility criteria, investigator sponsorship, and a reduced motivation of patients and recruiters. Newly identified aspects were that researchers need to be aware of all other RCTs on a research question so that compromising effects on the recruitment can be minimized and that a larger number of centers is not always advantageous.

The Comparative Effectiveness of Innovative Treatments for Cancer (CEIT-Cancer) project: Rationale and design of the database and the collection of evidence available at approval of novel drugs.

BACKGROUND: The available evidence on the benefits and harms of novel drugs and therapeutic biologics at the time of approval is reported in publicly available documents provided by the US Food and Drug Administration (FDA). We aimed to create a comprehensive database providing the relevant information required to systematically analyze and assess this early evidence in meta-epidemiological research. METHODS: We designed a modular and flexible database of systematically collected data. We identified all novel cancer drugs and therapeutic biologics approved by the FDA between 2000 and 2016, recorded regulatory characteristics, acquired the corresponding FDA approval documents, identified all clinical trials reported therein, and extracted trial design characteristics and treatment effects. Herein, we describe the rationale and design of the data collection process, particularly the organization of the data capture, the identification and eligibility assessment of clinical trials, and the data extraction activities. DISCUSSION: We established a comprehensive database on the comparative effects of drugs and therapeutic biologics approved by the FDA over a time period of 17 years for the treatment of cancer (solid tumors and hematological malignancies). The database provides information on the clinical trial evidence available at the time of approval of novel cancer treatments. The modular nature and structure of the database and the data collection processes allow updates, expansions, and adaption for a continuous meta-epidemiological analysis of novel drugs. The database allows us to systematically evaluate benefits and harms of novel drugs and therapeutic biologics. It provides a useful basis for meta-epidemiological research on the comparative effects of innovative cancer treatments and continuous evaluations of regulatory developments.