Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome

BACKGROUND: Designs and analyses of clinical trials with a time-to-event outcome almost invariably rely on the hazard ratio to estimate the treatment effect and implicitly, therefore, on the proportional hazards assumption. However, the results of some recent trials indicate that there is no guarantee that the assumption will hold. Here, we describe the use of the restricted mean survival time as a possible alternative tool in the design and analysis of these trials. METHODS: The restricted mean is a measure of average survival from time 0 to a specified time point, and may be estimated as the area under the survival curve up to that point. We consider the design of such trials according to a wide range of possible survival distributions in the control and research arm(s). The distributions are conveniently defined as piecewise exponential distributions and can be specified through piecewise constant hazards and time-fixed or time-dependent hazard ratios. Such designs can embody proportional or non-proportional hazards of the treatment effect. RESULTS: We demonstrate the use of restricted mean survival time and a test of the difference in restricted means as an alternative measure of treatment effect. We support the approach through the results of simulation studies and in real examples from several cancer trials. We illustrate the required sample size under proportional and non-proportional hazards, also the significance level and power of the proposed test. Values are compared with those from the standard approach which utilizes the logrank test. CONCLUSIONS: We conclude that the hazard ratio cannot be recommended as a general measure of the treatment effect in a randomized controlled trial, nor is it always appropriate when designing a trial. Restricted mean survival time may provide a practical way forward and deserves greater attention

A Comparative Analysis for Filter-Based Feature Selection Techniques with Tree-based Classification

The selection of features is crucial as an essential pre-processing method, used in the area of research as Data Mining, Text mining, and Image Processing. Raw datasets for machine learning, comprise a combination of multidimensional attributes which have a huge amount of size. They are used for making predictions. If these datasets are used for classification, due to the majority of the presence of features that are inconsistent and redundant, it occupies more resources according to time and produces incorrect results and effects on the classification. With the intention of improving the efficiency and performance of the classification, these features have to be eliminated. A variety of feature subset selection methods had been presented to find and eliminate as many redundant and useless features as feasible. A comparative analysis for filter-based feature selection techniques with tree-based classification is done in this research work. Several feature selection techniques and classifiers are applied to different datasets using the Weka Tool. In this comparative analysis, we evaluated the performance of six different feature selection techniques and their effects on decision tree classifiers using 10-fold cross-validation on three datasets. After the analysis of the result, It has been found that the feature selection method ChiSquaredAttributeEval + Ranker search with Random Forest classifier beats other methods for effective and efficient evaluation and it is applicable to numerous real datasets in several application domain

Comparative study of perioperative morbidities of the conventional and ultrasound-guided suprapubic catheterization in the patients of urinary retention during emergency

Background: Urinary retention is one of the common urological emergencies and conventional ‘blind’ SPC frequently used comfortable as well superior procedure for patients. During conventional SPC, the distended bladder is identified by palpation or percussion without proper attention to intervening bowel segment and other structures. However, the recently published data suggests that if, ultrasound is used during SPC, and it identifies not only bladder but also intervening bowel segment which complications. Therefore, the objective of this study was to assess and compare the perioperative complications of both methods.Methods: This prospective study was conducted between years November’2017 to June’2019. Sixty patients (n=60) of urinary retention were randomized to undergo ultrasound guided or conventional SPC procedures. Patients were divided into two equal groups of 30 patients in US-SPC (Group-A) and C-SPC (Group-B). After either SPC, the patients were closely observed for development of complications.Results: Overall, the patients had mean age of 53.87+21.418 and 53.87+21.418 years in C-SPC and US-SPC group, respectively. Mean operative time and subsequent initial urine drainage were almost equal in both groups. However, in C-SPC group, 5(16.7%) patients developed complications in the form of 03 misplaced catheters outside bladder, 01 into retro pubic space and another 01 into rectum. All patients in Group-A required ultrasound guided revision of SPC compared to none in Group-B.Conclusion: Overall, the ultrasound-guided SPC (US-SPC) is safer procedure compared to conventional ‘blind’ C-SPC in relieving urinary retention in emergency, thus it should be recommended procedure whenever need arise for SPC procedure

How do you design randomised trials for smaller populations? A framework.

How should we approach trial design when we can get some, but not all, of the way to the numbers required for a randomised phase III trial?We present an ordered framework for designing randomised trials to address the problem when the ideal sample size is considered larger than the number of participants that can be recruited in a reasonable time frame. Staying with the frequentist approach that is well accepted and understood in large trials, we propose a framework that includes small alterations to the design parameters. These aim to increase the numbers achievable and also potentially reduce the sample size target. The first step should always be to attempt to extend collaborations, consider broadening eligibility criteria and increase the accrual time or follow-up time. The second set of ordered considerations are the choice of research arm, outcome measures, power and target effect. If the revised design is still not feasible, in the third step we propose moving from two- to one-sided significance tests, changing the type I error rate, using covariate information at the design stage, re-randomising patients and borrowing external information.We discuss the benefits of some of these possible changes and warn against others. We illustrate, with a worked example based on the Euramos-1 trial, the application of this framework in designing a trial that is feasible, while still providing a good evidence base to evaluate a research treatment.This framework would allow appropriate evaluation of treatments when large-scale phase III trials are not possible, but where the need for high-quality randomised data is as pressing as it is for common diseases

Facilities for optimizing and designing multiarm multistage (MAMS) randomized controlled trials with binary outcomes

We introduce two commands, nstagebin and nstagebinopt, that can be used to facilitate the design of multiarm multistage (MAMS) trials with binary outcomes. MAMS designs are a class of efficient and adaptive randomized clinical trials that have successfully been used in many disease areas, including cancer, tuberculosis, maternal health, COVID-19, and surgery. The nstagebinopt command finds a class of efficient “admissible” designs based on an optimality criterion using a systematic search procedure. The nstagebin command calculates the stagewise sample sizes, trial timelines, and overall operating characteristics of MAMS designs with binary outcomes. Both commands allow the use of Dunnett’s correction to account for multiple testing. We also use the ROSSINI 2 MAMS design, an ongoing MAMS trial in surgical wound infection, to illustrate the capabilities of both commands. The new commands facilitate the design of MAMS trials with binary outcomes where more than one research question can be addressed under one protocol