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Real world evidence is broadly reassuring

Covid-19 has exposed both the strengths and the limitations of our safety monitoring systems

In pharmacoepidemiology, routinely collected data from electronic health records (including primary care databases, registries, and administrative healthcare claims) are a resource for research evaluating the real world effectiveness and safety of medicines. Currently available guidelines for the reporting of research using non-randomised, routinely collected data—specifically the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) and the Strengthening the Reporting of OBservational studies in Epidemiology (STROBE) statements—do not capture the complexity of pharmacoepidemiological research. We have therefore extended the RECORD statement to include reporting guidelines specific to pharmacoepidemiological research (RECORD-PE). This article includes the RECORD-PE checklist (also available on www.record-statement.org) and explains each checklist item with examples of good reporting. We anticipate that increasing use of the RECORD-PE guidelines by researchers and endorsement and adherence by journal editors will improve the standards of reporting of pharmacoepidemiological research undertaken using routinely collected data. This improved transparency will benefit the research community, patient care, and ultimately improve public health.

Measures of interaction on an additive scale (relative excess risk due to interaction [RERI], attributable proportion [AP], synergy index [S]), were developed for risk factors rather than preventive factors. It has been suggested that preventive factors should be recoded to risk factors before calculating these measures. We aimed to show that these measures are problematic with preventive factors prior to recoding, and to clarify the recoding method to be used to circumvent these problems. Recoding of preventive factors should be done such that the stratum with the lowest risk becomes the reference category when both factors are considered jointly (rather than one at a time). We used data from a case-control study on the interaction between ACE inhibitors and the ACE gene on incident diabetes. Use of ACE inhibitors was a preventive factor and DD ACE genotype was a risk factor. Before recoding, the RERI, AP and S showed inconsistent results (RERI = 0.26 [95% CI: -0.30; 0.82], AP = 0.30 [95% CI: -0.28; 0.88], S = 0.35 [95% CI: 0.02; 7.38]), with the first two measures suggesting positive interaction and the third negative interaction. After recoding the use of ACE inhibitors, they showed consistent results (RERI = -0.37 [95% CI: -1.23; 0.49], AP = -0.29 [95% CI: -0.98; 0.40], S = 0.43 [95% CI: 0.07; 2.60]), all indicating negative interaction. Preventive factors should not be used to calculate measures of interaction on an additive scale without recoding.

To assess the current practice of propensity score (PS) analysis in the medical literature, particularly the assessment and reporting of balance on confounders. A PubMed search identified studies using PS methods from December 2011 through May 2012. For each article included in the review, information was extracted on important aspects of the PS such as the type of PS method used, variable selection for PS model, and assessment of balance. Among 296 articles that were included in the review, variable selection for PS model was explicitly reported in 102 studies (34.4%). Covariate balance was checked and reported in 177 studies (59.8%). P-values were the most commonly used statistical tools to report balance (125 of 177, 70.6%). The standardized difference and graphical displays were reported in 45 (25.4%) and 11 (6.2%) articles, respectively. Matching on the PS was the most commonly used approach to control for confounding (68.9%), followed by PS adjustment (20.9%), PS stratification (13.9%), and inverse probability of treatment weighting (IPTW, 7.1%). Balance was more often checked in articles using PS matching and IPTW, 70.6% and 71.4%, respectively. The execution and reporting of covariate selection and assessment of balance is far from optimal. Recommendations on reporting of PS analysis are provided to allow better appraisal of the validity of PS-based studies.

This study aims to assess how clinical outcomes of immunotherapy in real-world (effectiveness) correspond to outcomes in clinical trials (efficacy) and to look into factors that might explain an efficacy-effectiveness (EE) gap. All patients diagnosed with stage IV non-small cell lung cancer (NSCLC) in 2015–2018 in six Dutch large teaching hospitals (Santeon network) were identified and followed-up from date of diagnosis until death or end of data collection. Progression-free survival (PFS) and overall survival (OS) from first-line (1L) pembrolizumab and second-line (2L) nivolumab were compared with clinical trial data by calculating hazard ratios (HRs). From 1950 diagnosed patients, 1005 (52%) started with any 1L treatment, of which 83 received pembrolizumab. Nivolumab was started as 2L treatment in 141 patients. For both settings, PFS times were comparable between real-world and trials (HR 1.08 (95% CI 0.75–1.55), and HR 0.91 (95% CI 0.74–1.14), respectively). OS was significantly shorter in real-world for 1L pembrolizumab (HR 1.55; 95% CI 1.07–2.25). Receiving subsequent lines of treatment was less frequent in real-world compared to trials. There is no EE gap for PFS from immunotherapy in patients with stage IV NSCLC. However, there is a gap in OS for 1L pembrolizumab. Fewer patients proceeding to a subsequent line of treatment in real-world could partly explain this.

Vomiting is associated with lower levels of ticagrelor concentration and higher platelet reactivity in the early hours of ST-elevation myocardial infarction. These results support reloading with a ticagrelor loading dose and/or treatment with intravenous platelet inhibitors when patients vomit.

An agile internationally harmonised surveillance system is essential to maintain safety and trust in vaccines, argue Vincent Lo Re and colleagues

A non-inferiority (NI) trial is intended to show that the effect of a new treatment is not worse than the comparator. We conducted a review to identify how NI trials were conducted and reported, and whether the standard requirements from the guidelines were followed. From 300 randomly selected articles on NI trials registered in PubMed at 5 February 2009, we included 227 NI articles that referred to 232 trials. We excluded studies on bioequivalence, trials on healthy volunteers, non-drug trials, and articles of which the full-text version could not be retrieved. A large proportion of trials (34.0%) did not use blinding. The NI margin was reported in 97.8% of the trials, but only 45.7% of the trials reported the method to determine the margin. Most of the trials used either intention to treat (ITT) (34.9%) or per-protocol (PP) analysis (19.4%), while 41.8% of the trials used both methods. Less than 10% of the trials included a placebo arm to confirm the efficacy of the new drug and active comparator against placebo, and less than 5.0% were reporting the similarity of the current trial with the previous comparator's trials. In general, no difference was seen in the quality of reporting before and after the release of the CONSORT statement extension 2006 or between the high-impact and low-impact journals. The conduct and reporting of NI trials can be improved, particularly in terms of maximizing the use of blinding, the use of both ITT and PP analysis, reporting the similarity with the previous comparator's trials to guarantee a valid constancy assumption, and most importantly reporting the method to determine the NI margin.

Background There is no consensus on the preferred method for defining the non-inferiority margin in non-inferiority trials, and previous studies showed that the rationale for its choice is often not reported. This study investigated how the non-inferiority margin is defined in the published literature, and whether its reporting has changed over time. Methods A systematic PubMed search was conducted for all published randomized, double-blind, non-inferiority trials from January 1, 1966, to February 6, 2015. The primary outcome was the number of margins that were defined by methods other than the historical evidence of the active comparator. This was evaluated for a time trend. We also assessed the under-reporting of the methods of defining the margin as a secondary outcome, and whether this changed over time. Both outcomes were analyzed using a Poisson log-linear model. Predictors for better reporting of the methods, and the use of the fixed-margin method (one of the historical evidence methods) were also analyzed using logistic regression. Results Two hundred seventy-three articles were included, which account for 273 non-inferiority margins. There was no statistically significant difference in the number of margins that were defined by other methods compared to those defined based on the historical evidence (ratio 2.17, 95% CI 0.86 to 5.82, p  = 0.11), and this did not change over time. The number of margins for which methods were unreported was similar to those with reported methods (ratio 1.35, 95% CI 0.76 to 2.43, p  = 0.31), with no change over time. The method of defining the margin was less often reported in journals with low-impact factors compared to journals with high-impact factors (OR 0.20; 95% CI 0.10 to 0.37, p  < 0.0001). The publication of the FDA draft guidance in 2010 was associated with increased reporting of the fixed-margin method (after versus before 2010) (OR 3.54; 95% CI 1.12 to 13.35, p  = 0.04). Conclusions Non-inferiority margins are not commonly defined based on the historical evidence of the active comparator, and they are poorly reported. Authors, reviewers, and editors need to take notice of reporting this critical information to allow for better judgment of non-inferiority trials.