Objectives: To determine the effectiveness of ranitidine and sucralfate in the prevention of stress ulcer in critical patients and to assess if these treatments affect the risk of nosocomial pneumonia. Design: Published studies retrieved through Medline and other databases. Five meta-analyses evaluated effectiveness in terms of bleeding rates (A: ranitidine v placebo; B: sucralfate v placebo) and infectious complications in terms of incidence of nosocomial pneumonia (C: ranitidine v placebo; D: sucralfate v placebo; E: ranitidine v sucralfate). Trial quality was determined with an empirical ad hoc procedure. Main outcome measures: Rates of clinically important gastrointestinal bleeding and nosocomial pneumonia (compared between the two study arms and expressed with odds ratios specific for individual studies and meta-analytic summary odds ratios). Results: Meta-analysis A (five studies) comprised 398 patients; meta-analysis C (three studies) comprised 311 patients; meta-analysis D (two studies) comprised 226 patients: and meta-analysis E (eight studies) comprised 1825 patients. Meta-analysis B was not carried out as the literature search selected only one clinical trial. In meta-analysis A ranitidine was found to have the same effectiveness as placebo (odds ratio of bleeding 0.72, 95% confidence interval 0.30 to 1.70, P=0.46). In placebo controlled studies (meta-analyses C and D) ranitidine and sucralfate had no influence on the incidence of nosocomial pneumonia. In comparison with sucralfate, ranitidine significantly increased the incidence of nosocomial pneumonia (meta-analysis E: 1.35, 1.07 to 1.70, P=0.012). The mean quality score in the four analyses (on a 0 to 10 scale) ranged from 5.6 in meta-analysis E to 6.6 in meta-analysis A. Conclusions: Ranitidine is ineffective in the prevention of gastrointestinal bleeding in patients in intensive care and might increase the risk of pneumonia. Studies on sucralfate do not provide conclusive results. These findings are based on small numbers of patients, and firm conclusions cannot presently be proposed.

Some anti-cancer treatments (e. g., immunotherapies) determine, on the long term, a durable survival in a small percentage of treated patients; in graphical terms, long-term survivors typically give rise to a plateau in the right tail of the survival curve. In analysing these datasets, medians are unable to recognize the presence of this plateau. To account for long-term survivors, both value-frameworks of ASCO and ESMO have incorporated post-hoc corrections that upgrade the framework scores when a survival plateau is present. However, the empiric nature of these post-hoc corrections is self-evident. To capture the presence of a survival plateau by quantitative methods, two approaches have thus far been proposed: the milestone method and the area-under-the-curve (AUC) method. The first approach identifies a long-term time-point in the follow-up (“milestone”) at which survival percentages are extracted. The second approach, which is based on the measurement of AUC of survival curves, essentially is the rearrangement of previous methods determining mean lifetime survival; similarly to the milestone method, the application of AUC can be “restricted” to a pre-specified time-point of the follow-up. This Mini-Review examines the literature published on this topic. The main characteristics of these two methods are highlighted along with their advantages and disadvantages. The conclusion is that both the milestone method and the AUC method are able to capture the presence of a survival plateau.

In conducting a survival meta-analysis, the typical methodological approach analyses the hazard ratios (HRs) of individual trials and then combines them into a pooled meta-analytical estimate. The length of follow-up of individual trials is not generally accounted for. Recent techniques aimed at individual patient-data reconstruction from Kaplan-Meier graphs represent an important methodological innovation. These techniques permit the combination of the survival curves published in a single clinical trial but are also applicable to more than one trial. In the case of multiple trials, a meta-analysis can be conducted without using any statistical model of meta-analysis. As an example of this new approach, we applied a technique of individual patient data reconstruction to the Kaplan-Meier graphs of overall survival reported in two phase-III trials, which were conducted on patients with locally advanced/advanced non-small cell lung cancer selected according to their PD-L1 expression status, not previously treated for their metastatic disease. Only subjects with PD-L1 ≥50% were considered for our analysis. The experimental arms received pembrolizumab monotherapy while the control arms were given platinum-based chemotherapy. The survival graphs were obtained for both trials. For each Kaplan-Meier curve, the graph was firstly digitalized. Then, the Shiny package was used to reconstruct patient-level data. Finally, the pooled survival curves were generated from the reconstructed patient-level data along with the relevant Cox statistics; for this purpose, we used three packages (“coxph”, “survfit”, and “ggsurvplot”) under the R-platform. In our pooled analysis based on this procedure, we compared 453 patients given pembrolizumab vs. 451 controls given chemotherapy. The HR estimated from reconstructed patient-level data was 0.670 (95% confidence interval [CI], 0.566 to 0.793). The analysis described herein demonstrates the easy applicability of the Shiny technique. This technique was successful in generating a pooled survival graph for the experimental treatment groups vs. controls and efficiently estimated the pooled HR in which the results of the two trials were combined.