Evaluating response-adaptive randomization procedures for recurrent events and terminal event data using a composite endpoint.

Introduction: Recurrent event data are common in many longitudinal studies. Often, a terminating event such as death can be correlated with the recurrent event process. A shared frailty model applied to account for the association between recurrent and terminal events. In some situations, a fraction of subjects experience neither recurrent events nor death; these subjects are cured.
 Methods: In this paper, we discussed the Bayesian approach of a joint frailty model for recurrent and terminal events in the presence of cure fraction. We compared estimates of parameters in the Frequentist and Bayesian approaches via simulation studies in various sample sizes; we applied the joint frailty model in the presence of cure fraction with Frequentist and Bayesian approaches for breast cancer.
 Results: In small sample size Bayesian approach compared to Frequentist approach had a smaller standard error and mean square error, and the coverage probabilities close to nominal level of 95%. Also, in Bayesian approach, the sampling means of the estimated standard errors were close to the empirical standard error.
 Conclusion: The simulation results suggested that when sample size was small, the use of Bayesian joint frailty model in the presence of cure fraction led to more efficiency in parameter estimation and statistical inference.

Few methods are currently available for group sequential analysis of recurrent events data subject to a terminal event in the clinical trial setting. This research helps fill this gap by developing a completely nonparametric group sequential monitoring procedure for use with the two-sample Tayob and Murray statistic. Advantages of the Tayob and Murray statistic include high power to detect treatment differences when there is correlation between recurrent event times or between recurrent and terminal events in an individual. This statistic does not suffer bias from dependent censoring, regardless of the correlation between event times in an individual. This manuscript briefly reviews the Tayob and Murray statistic, develops and describes how to use methods for its group sequential analysis, and through simulation, compares its operating characteristics with those of Cook and Lawless, which is currently in use as the only available nonparametric method for group sequential analysis of recurrent event data. The merits of our proposed approach are most clearly demonstrated when gap times between recurrent events are correlated; when gap times between events are independent, the Cook and Lawless method is difficult to beat. Simulations demonstrate that as correlation between recurrent event times grows, the reduction in power using the Cook and Lawless approach is substantial when compared to our method. Finally, we use our method to analyze recurrent acute exacerbation outcomes from the azithromycin in chronic obstructive pulmonary disease trial.

assumption is often untenable, necessitating a conceptual framework with greater specificity.

The primary goal of clinical trials is to collect enough scientific evidence for a new intervention. Despite the widespread use of equal randomization in clinical trials, response-adaptive randomization has attracted considerable interest in terms of ethical concerns. In this thesis, delayed response problems and innovative designs for cytostatic agents in oncology clinical trials are studied.
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\nThere is typically a prerun of equal randomization before the implementation of response-adaptive randomization, while it is often not clear how many subjects are needed in this prephase, and in practice an arbitrary number of patients are allocated in this equal randomization stage. In addition, real-time response-adaptive randomization often requires patient response to be immediately available after the treatment, while clinical response, such as tumor shrinkage, may take a relatively long period of time to exhibit. In the first part of the thesis, a nonparametric fractional model and a parametric optimal allocation scheme are developed to tackle the common problem caused by delayed response. In addition, a two-stage procedure to achieve a balance between power and the number of responders is investigated, which is equipped with a likelihood ratio test before skewing the allocation probability toward a better treatment. The operating characteristics of the two-stage designs are evaluated through extensive simulation studies and an HIV clinical trial is used for illustration. Numerical results show that the proposed method satisfactorily resolves the issues involved in response-adaptive randomization and delayed response.
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\nIn phase I clinical trials with cytostatic agents, toxicity endpoints, as well as efficacy effects, should be taken into consideration for identifying the optimal biological dose (OBD). In the second part of the thesis, a two-stage Bayesian mixture modeling approach is developed, which first locates the maximum tolerated dose (MTD) through a mixture of parametric and nonparametric models, and then determines the most efficacious dose using Bayesian adaptive randomization among multiple candidate models. In the first stage searching for the MTD, a beta-binomial model in conjunction with a probit model as a mixture modeling approach is studied, and decisions are made based on the model that better fits the toxicity data. The model fitting adequacy is measured by the deviance information criterion and the posterior model probability. In the second stage searching for the OBD, the assumption that efficacy monotonically increases with the dose is abandoned and, instead, all the possibilities that each dose could have the highest efficacy effect are enumerated so that the dose-efficacy curve can be increasing, decreasing, or umbrella-shape. Simulation studies show the advantages of the proposed mixture modeling approach for pinpointing the MTD and OBD, and demonstrate its satisfactory performance with cytostatic agents.

Recurrent event data analyses are usually conducted under the assumption that the censoring time is independent of the recurrent event process. In many applications the censoring time can be informative about the underlying recurrent event process, especially in situations where a correlated failure event could potentially terminate the observation of recurrent events. In this article, we consider a semiparametric model of recurrent event data that allows correlations between censoring times and recurrent event process via frailty. This flexible framework incorporates both time-dependent and time-independent covariates in the formulation, while leaving the distributions of frailty and censoring times unspecified. We propose a novel semiparametric inference procedure that depends on neither the frailty nor the censoring time distribution. Large sample properties of the regression parameter estimates and the estimated baseline cumulative intensity functions are studied. Numerical studies demonstrate that the proposed methodology performs well for realistic sample sizes. An analysis of hospitalization data for patients in an AIDS cohort study is presented to illustrate the proposed method.

Advanced heart failure (HF) is a progressive disease characterized by recurrent hospitalizations and high risk of mortality. Indeed, outcomes in late stages of HF approximate those seen in patients with various aggressive malignancies. Clinical trials assessing beneficial outcomes of new treatments in patients with cancer have used innovative approaches to measure impact on total disease burden or surrogates to assess treatment efficacy. Although most cardiovascular outcomes trials continue to use time-to-first event analyses to assess the primary efficacy end point, such analyses do not adequately reflect the impact of new treatments on the totality of the chronic disease burden. Consequently, patient enrichment and other strategies for ongoing clinical trial design, as well as new statistical methodologies, are important considerations, particularly when studying a population with advanced chronic HF. The DREAM-HF trial (Double-Blind Randomized Assessment of Clinical Events With Allogeneic Mesenchymal Precursor Cells in Advanced Heart Failure) is an ongoing, randomized, sham-controlled phase 3 study of the efficacy and safety of mesenchymal precursor cells as immunotherapy in patients with advanced chronic HF with reduced ejection fraction. Mesenchymal precursor cells have a unique multimodal mechanism of action that is believed to result in polarization of proinflammatory type 1 macrophages in the heart to an anti-inflammatory type 2 macrophage state, inhibition of maladaptive adverse left ventricular remodeling, reversal of cardiac and peripheral endothelial dysfunction, and recovery of deranged vasculature. The objective of DREAM-HF is to confirm earlier phase 2 results and evaluate whether mesenchymal precursor cells will reduce the rate of nonfatal recurrent HF-related major adverse cardiac events while delaying or preventing progression of HF to terminal cardiac events.DREAM-HF is an example of an ongoing contemporary events-driven cardiovascular cell–based immunotherapy study that has utilized the concepts of baseline disease enrichment, prognostic enrichment, and predictive enrichment to improve its efficiency by using accumulating data from within as well as external to the trial. Adaptive enrichment designs and strategies are important components of a rational approach to achieve clinical research objectives in shorter clinical trial timelines and with increased cost-effectiveness without compromising ethical standards or the overall statistical integrity of the study. The DREAM-HF trial also presents an alternative approach to traditional composite time-to-first event primary efficacy end points. Statistical methodologies such as the joint frailty model provide opportunities to expand the scope of events-driven HF with reduced ejection fraction clinical trials to utilize time to recurrent nonfatal HF-related major adverse cardiac events as the primary efficacy end point without compromising the integrity of the statistical analyses for terminal cardiac events. In advanced chronic HF with reduced ejection fraction studies, the joint frailty model is utilized to reflect characteristics of the high-risk patient population with important unmet therapeutic needs. In some cases, use of the joint frailty model may substantially reduce sample size requirements. In addition, using an end point that is acceptable to the Food and Drug Administration and the European Medicines Agency, such as recurrent nonfatal HF-related major adverse cardiac events, enables generation of clinically relevant pharmacoeconomic data while providing comprehensive views of the patient’s overall cardiovascular disease burden. The major goal of this review is to provide lessons learned from the ongoing DREAM-HF trial that relate to biologic plausibility and flexible clinical trial design and are potentially applicable to other development programs of innovative therapies for patients with advanced cardiovascular disease.Clinical Trial Registration:URL: https://www.clinicaltrials.gov. Unique identifier: NCT02032004.

In clinical trials, comparisons of treatments with ordinal responses are frequently conducted using the proportional odds model. However, the use of this model necessitates the adoption of the proportional odds assumption, which may not be appropriate. In particular, when responses are skewed, the use of the proportional odds model may result in a markedly inflated type I error rate. The latent Weibull distribution has recently been proposed to remedy this problem, and it has been demonstrated to be superior to the proportional odds model, especially when response-adaptive randomization is incorporated. However, there are several drawbacks associated with the latent Weibull model and the previously suggested response-adaptive treatment randomization scheme. In this paper, we propose the modified latent Weibull model to address these issues. Based on the modified latent Weibull model, the original response-adaptive design was also revised. In addition, the group sequential monitoring mechanism was included to enable interim analyses to be performed to determine, during a trial, whether a specific treatment is significantly more effective than another. If so, this will enable the trial to be terminated at a much earlier stage than a trial based on a fixed sample size. We performed a simulation study that clearly demonstrated the merits of our proposed framework. Furthermore, we redesigned a clinical study to further illustrate the advantages of our response-adaptive approach.

T he cardiovascular disease (CVD) risk associated with diabetes mellitus (DM) has become increasingly evident, accounting for Ϸ80% of deaths among patients with this disease 1 and making CVD risk modification a key therapeutic objective in diabetic patients.2][3][4] The rapidly increasing global burden of DM, 5 coupled with the associated CVD risk, underscores the imperative for continued generation and application of evidence-based therapies to reduce CVD risk in this high-risk cohort.In this 2-part series, we first review the CVD effects of the thiazolidinedione medications, the most broadly investigated class of antihyperglycemic drugs evaluated in the context of CVD risk.In the second part, we review the modulators of the incretin axis that have most recently achieved US Food and Drug Administration approval, with a focus on CVD considerations; introduce selected drugs in advanced development; summarize glucose control strategies for cardiovascular patients; and discuss the regulatory review of glucose-lowering medications.

Clinical trials often use composite events as a primary endpoint to increase power, and are often analysed using time-to-first event. For example, TOPCAT evaluated the effect of spironolactone in patients with heart failure and a preserved left ventricular ejection fraction using a composite of death from cardiovascular causes, aborted cardiac arrest, and heart failure hospitalization.1 This TOPCAT analysis demonstrated that spironolactone did not significantly reduce the time-to-first composite events. However, over the course of the 3.3 years of mean follow-up, patients may have potentially met the primary endpoint several times due to recurrent episodes of aborted cardiac arrest or heart failure hospitalization. In fact, an often overlooked sub-analysis of TOPCAT showed a numeric decrease in cumulative heart failure hospitalizations in the spironolactone arm (394 vs. 475 for placebo). The pre-specified choice of a clinical trial's endpoint is key to its success or failure. Several recent heart failure trials have chosen a cumulative events analysis plan to increase power with decreased sample size and avoid leaving out clinically important events during the study period.2-4 As there is significant interest in the research community for lower cost clinical trials with smaller sample sizes, we sought to investigate the results of a cumulative events analysis in TOPCAT. The TOPCAT database was obtained and queried for cumulative events for the primary outcome of heart failure hospitalization, aborted cardiac arrest, and death from cardiovascular causes. To be enrolled in the trial, patients needed to have symptoms of heart failure, a left ventricular ejection fraction ≥45%, and either a hospitalization for heart failure within the previous 12 months or an elevated natriuretic peptide level. We used a conditional risk set model to assess for the composite primary outcome by counting all events over the study duration. We analysed the entire dataset, as well as the subset randomized from the Americas, as this higher-risk subgroup has previously been shown to potentially derive benefit from spironolactone use.5 In the entire cohort of 3445 TOPCAT patients, 19.5% met the study's original composite primary endpoint. There were 320 first events in the spironolactone arm and 351 first events in the placebo arm. When accounting for cumulative events, there were 737 events in the spironolactone arm and 831 in the placebo arm. Spironolactone was not associated with the endpoint of cumulative adverse events (P = 0.085). In the subgroup of patients from the Americas, spironolactone did significantly lower the risk of cumulative adverse events (P = 0.010). Table 1 summarizes findings from the original time-to-first event and cumulative events analyses. Because patients experience the cumulative effect of all clinical events, and not just the first one, there is a compelling logic to analyse cumulative events for clinical trials to better quantify the benefits of therapy. However, despite the theoretical advantage of examining the efficacy of recurrent events, this approach did not alter the original conclusion of the TOPCAT study. The overall trial results remained negative for the primary endpoint, while the analysis confined to the Americas demonstrated a significant benefit of spironolactone. Hazard ratios, 95% confidence intervals, and P-values were similar, regardless of the statistical method used. These findings are consistent with prior re-analyses of clinical trials in medical therapy for heart failure with preserved ejection fraction, which have demonstrated minimal changes to the primary results when cumulative events were analysed.2, 3 Given over twice as many events in the cumulative events analysis, a reduced sample size and follow-up duration may be used while maintaining statistical power. With the increasing costs and complexity of clinical trials, these data add support to the practice of utilising an analysis plan including cumulative first and recurrent events without significantly changing study results. Conflict of interest: none declared.

In the contemporary era of evidence-based medicine (EBM), the question arises as to what would constitute the best research evidence. Editors of high-quality journals have a variety of manuscripts flung at them, and for them, selecting a good manuscript is like identifying 'a cygnet from a paddling of ducklings'. Study design, which is often not correctly formulated, plays a dominant role not only in interpretation but also in the acceptance and publication of research. It has been mentioned time and again, that faulty study design can be a cause for manuscript rejection.[1] There exists a general hierarchy that ranks the type of research based on the validity of the findings and effectiveness of the intervention. There are several types of study designs, and randomised controlled trials (RCTs) were initially ranked as the highest level of evidence which has been currently replaced by meta-analysis and systematic reviews.[2] [Figure 1] In another recent modification, Murad et al. removed them from the pyramid and suggested them to be a lens through which evidence is reviewed.[3]Figure 1: Hierarchy of Level of Evidence (RCT:Randomised controlled trial; SR: Systematic review; MA: Meta-analysis)WHY ARE RCTS RATED SO HIGH? Clinical trials generally aim to ascertain whether any intervention, program, or drug treatment brings about a positive outcome for the recipients. An RCT is a prospective, comparative, interventional study of quantitative nature performed in controlled conditions in randomly selected subjects.[4] It provides the most dependable evidence of the efficacy of preventive, diagnostic, and therapeutic interventions because of the robust and rigorous scientific techniques used to ascertain a cause-effect relationship between intervention and outcome.[2] RCTs are ubiquitously regarded as the 'gold standard' of biomedical research. However, when the population being studied for research is divided into groups, there is a chanceof imbalance between them with respect to known or unknown confounding factors even before a comparison is started. Randomness and blinding in the selection process are important aspects to eliminate biases, ensure the veracity of the results in clinical research and maintenance of scientific reliability.[5] The utility of a control group makes these randomised trials the most powerful research tools in the current scientific era. A 'double-blind' study, in which the concealment of allocation is maintained till analysis, removes our all-too-human misery, optimism, and aspiration from the comparison. However, these aspects have often been found to be deficient in the studies submitted for peer review; nevertheless, several workshops and sessions on 'Research Methodology and Publication' are being conducted in various national and state conferences and Continuing Medical Education (CME) activities which provide a basic understanding of biomedical research and statistics. DEFICIENCIES OF RCTS A rose, however, beautiful has thorns. Similarly, though RCTs constitute the best quality evidence, it is possible only when their methodology is rigorous, and they are conducted in a scientific manner. Hence, not all RCTs qualify as a high level of evidence and while grading them, the presence of selection bias, inadequate sample size, improper randomisation, lack of blinding, inconsistency, imprecision, not performing intention to treat analysis or not following Consolidated Standards of Reporting Trials (CONSORT) statement can lead to a serious deterioration in their quality and downgrading of the evidence generated by them. A meticulously designed clinical trial is not always practical, and it has been documented in the past that the quality of the randomised trial and randomisation process itself is not always properly maintained. There is ample evidence that the quality of randomisation is often suboptimal in RCTs, and these are not truly randomised clinical trials.[6] It may not be practical to perform RCTs in some scenarios such as trials where results are dependent on patient cooperation or conditions for which there is already an established gold standard for treatment. It may be difficult to attain and conceal a true placebo in an RCT. In fact, it may be unethical to give a placebo in certain situations such as in a trial on a life-limiting illness. A placebo may not be justified if it would mean refuting the participant's usual management. Further, animal research can be inadequate for making recommendations for or against the treatment in humans as the response may not be identical in many circumstances. In fact, authorisation of medications based on animal research has led to substantial harm to the human population in the past. The animal studies may be conducted in conditions that would never be used while conducting anaesthesia on human infants and children. Also, biologically, postnatal growth and development in animals and human infants do not match even in similar age-groups. Huge costs are often incurred in conducting RCTs. Rarely, the results come out to be so beneficial that it would be considered unethical to deprive the control group of its benefits. In some medication trials, the investigators must terminate the trial prematurely because of harmful or negative results in the interim analysis. Sometimes, the results may not mimic real-life treatment, and the subjects will not reciprocate the highly controlled environment involved in the selection and exclusion of patients As it is not always feasible to conduct an RCT, researchers have to, at times, resort to other trial designs. It has also been contended that it is not always possible to guide every clinical decision on RCTs.[7] WHAT ARE THE ALTERNATIVES TO RCTS? There are other forms of interventional studies which are not categorised as RCTs but are ranked higher than their observational counterparts.[8] When RCTs cannot be used, the interrupted time series design (ITS) is a possible alternative. Many a times, intervention can be done, and the pre- and post-effects can be compared without a control group. This issue of the Indian Journal of Anaesthesia (IJA) has one such study wherein the pre- and post-effects of an intervention have been compared.[9] The main objective of ITS is to examine whether the data pattern observed post-intervention is different from that of pre-intervention.[10] Sometimes, a control group can be an active group receiving another form of treatment instead of a placebo to avoid ethical problems and to provide additional benefit for the participants in the control group. Few studies need not require a control group to validate the results. Such studies follow a single group interventional design as has been followed in the study on paediatric thoracotomy cases published in this issue.[11] ROLE OF SYSTEMATIC REVIEWS AND META-ANALYSES Many of the scientific articles that are published have contradictory results, lack certainty, and many results have been refuted over time. In such circumstances, clinical decision-making can be guided only by the reconciliation of studies and reviewing them scientifically. A systematic review uses a transparent process to identify and compile explicit results of multiple studies for a specific focused question after critical appraisal of these studies. These data can be pooled in a graphic form usually as a forest plot also known as the blobbogram, where the point estimate and 95% confidence intervals are presented on an individual line.[12] An interesting observation is that the number of systematic reviews has increased drastically over time with initiatives such as the Cochrane collaboration providing assistance and free software which helps in the process of compiling the data and providing meaningful results. Bias unique to this type of research is publication bias. The chance of publication is more for studies with positive results and in the English language. This forms the major difference between systematic reviews and narrative reviews. A well-conducted systematic review attempts to reduce such bias. Whenever there is a difficulty in doing multicentre trials which include a large number of patients, a meta-analysis can be conducted. The word ' meta' stands for 'beyond' in Greek. This tool, which has a systematic approach, analyses the results in a transparent manner by combining many studies, thus, increasing the sample size. It helps us to draw meaningful conclusions, resolve conflicting results and clear uncertainties. Meta-analyses provide results with increased precision and true estimation of the effect size.[13] As has been mentioned time and again, in the IJA, systematic reviews and meta-analyses get top priority in the publication process. SO WHERE DO OBSERVATIONAL STUDIES OR SURVEYS STAND? The great agnostic Robert G Ingersoll had stated 'reason, observation and experience'as the holy trinity of science. One has to always observe to identify and analyse which is the first step in the practice of any research. It is the process where a question leads to many more questions and many more possibilities. Such observations can imply associations and lead to concepts. One has to evaluate to establish such concepts and intervene to solve the problem. Understanding the natural history of the disease and its complications is an area of research exclusive to observational studies. The Mayo Anesthesia Safety in Kids (MASK) and Pediatric Anesthesia Neurodevelopment Assessment (PANDA) are some of the historical observational studies which have helped to provide additional information which cannot always be evaluated using a randomised study design.[1415] Though the General Anesthesia or Awake-regional Anesthesia in Infancy (GAS) study was performed as a muliticentre randomised controlled equivalence trial assessing the effect of an hour of sevoflurane anaesthesia exposure and suggested that it is safe in infants, the effect of multiple exposures could only be determined by observational studies due to ethical constraints.[16] Observational studies (cohort, cross-sectional studies or case-control studies) have the potential for a higher external validity than RCTs which means that their results can be extrapolated to a broad population to a higher degree.[17] However, they have a lesser internal validity because of the possibility of bias and confounding variables. Nonetheless, the limitations in observational studies can be overcome to a certain extent by few statistical methods. Identification of potential confounders can be done using logistic regression analysis, and the effect of these potential confounders can be offset by propensity analysis scoring and, thus, reducing the selection bias.[18] Feinstein and Horwitz had in their article stressed that RCTs may be an 'elusive ideal' and stated that the high-quality prospective cohort or retrospective epidemiologic studies offer a sustainable alternative for the (many-a-times) unachievable scientific gold standard of an RCT.[7] They emphasised the need to conduct other clinical epidemiological research designs in a very rigorous manner. Another alternative is to set up international registries in which the registration of various data would be necessary. Though not free of biases, they have various advantages viz., no need for informed consent, data collected in an efficient manner, the feasibility of including large sample sizes, less costly or time-consuming, rare diseases or manifestations can be studied, and easier analysis of long-term effects.[19] CASE SERIES AND CASE REPORTS: WHAT IS THEIR WORTH? The least ranked form of research, that is, case series and reports are descriptive studies that describe clinical circumstances of individual patients and focus on the contextual analysis of events or conditions and their relationships. They are often not given preference by major reputed journals. Recently, numerous open access journals which exclusively publish anecdotal cases and series have come up.[20] These rekindle the interest and evoke curiosity among the readers. However, the utility of such knowledge in routine clinical practice remains questionable. In a recent amendment, the National Medical Commission has added 'cases series' in addition to the pre-existing criteria of 'original research articles' including systematic reviews and meta-analyses for academic promotions in medical colleges. From a reader's point of view, a letter to the editor is a much more popular, effective, precise and authentic means of conveying the relevant message as compared to a case report and that too in minimal words. QUESTIONNAIRES AND SURVEYS: CURRENTLY POPULAR RESEARCH TRENDS? Questionnaires and surveys are inexpensive and quick research tools applicable for the assessment of the current knowledge among clinicians as well as an assessment of the impact and reach of certain treatment methodologies or guidelines. Surveys also help to identify deficiencies and focus on areas of improvement.[21] A survey study in this issue of the IJA focuses on the improvement in postgraduate medical education in the country.[22] Electronic surveys are common and are easy to conduct. The coronavirus disease (COVID)-19 pandemic saw a multitude of e-surveys conducted conveniently during the lockdown period. Two more surveys published in this issue: a survey describing the practice of oropharyngeal pack insertion and removal practices amongst the anaesthesiologists of our country and a survey by showcasing extubation practices among anaesthesiologists are an attempt to improve safety and quality in health care.[2324] Questionnaires have their own set of limitations and biases such as self-reporting bias and social desirability bias. There is an article in this issue of the IJA that deals with the development and validation of a questionnaire.[25] This issue also has two interesting studies related to assessment and learning in postgraduate medical education, and they have questionnaires with a variety of questions in their study methodology for the study participants.[2627] REGISTRATION OF RESEARCH STUDIES: A VOLLEY OF DOUBTS? Registration of clinical trials is an important process to ensure transparency and credibility of the study and, thereby, improving the quality of evidence. Awareness about the registration of clinical studies is minimal among young researchers, and the purpose of registration is not completely understood by many. There are certain misconceptions involving the registration of clinical research. The IJA has been flooded with manuscripts in the past year with authors pouring in queries and doubts regarding the registration of their studies. Do observational studies, retrospective studies and surveys need to be registered? Can Clinical trial registry of India (CTRI) non-registration be a reason for manuscript rejection? CTRI is a domain thatprovides registration of clinical research performed in the country. This was launched in the year 2007, and the registration process which was initially voluntary has been made mandatory since 2009. Subsequently, editors of major journals in India, including the IJA have declared that only registered trials would be considered for publication. Any trial involving human participants, of any intervention such as drugs, devices, surgical procedures, preventive measures, lifestyle modifications, educational or behavioral treatment, and rehabilitation strategies is expected to be registered in the CTRI before enrolment of the first participant. The registration process is easy if an ethics committee approval is obtained. The committee approving the ethical perspectives of the study should be registered with the Drug Controller General of India (DCGI). Registration of certain phase 1 trials requires direct approval from the DCGI. At this point, it is important for all researchers (past, present and future) to note that registration is not limited to interventional clinical trials but also extends to observational clinical studies, post-marketing surveillance trials, bioavailability and bio- equivalence studies. International multicentre trials can be registered with international registries like the Clinical Trials Register hosted by the United States National Library of Medicine. These studies should also be registered additionally in the National Register. (ctri.nic.in) [28] WHAT NEXT? THE KIND OF RESEARCH TO BE TAKEN UP…. (RCTS?OBSERVATIONAL STUDIES? SURVEYS?) The year 2020 stands as evidence of various complexities and adversaries of clinical research. The withdrawal of published articles on hydroxychloroquine from The Lancet stands proof of bias, errors and lack of transparency in research. We were guided throughout the course of the coronavirus pandemic by the RCTs pouring in from all over the world. In the race of evidence, the supremacy of the quintessential RCTs over cross-sectional surveys and observational studies is undeniable, but the scientific evidence has its own value depending upon the research question and the methodological rigor. In 1998, René Favaloro, a revered cardiac surgeon, while signaling on the long debate about the need for RCTs for bypass grafting, had aptly mentioned, "RCTs have developed such high scientific stature and acceptance that they are accorded almost religious sanctification. If relied on exclusively, they may be dangerous."[29] This sets us thinking. Is it time for us to adopt and accept a diversity of approaches in research study and design? To conclude, one thing is certain. RCTs or meta-analyses……Surveys or observational studies…the clinical research journey has to continue and move on.

ABSTRACTSeamless phase II/III clinical trials have attracted increasing attention recently. They mainly use Bayesian response adaptive randomization (RAR) designs. There has been little research into seamless clinical trials using frequentist RAR designs because of the difficulty in performing valid statistical inference following this procedure. The well-designed frequentist RAR designs can target theoretically optimal allocation proportions, and they have explicit asymptotic results. In this paper, we study the asymptotic properties of frequentist RAR designs with adjusted target allocation proportions, and investigate statistical inference for this procedure. The properties of the proposed design provide an important theoretical foundation for advanced seamless clinical trials. Our numerical studies demonstrate that the design is ethical and efficient.

Background: Recurrent event data are often encountered in biomedical research, for example, recurrent infections or recurrent hospitalizations for patients after renal transplant. In many studies, there are more than one type of events of interest. We aimed to identify the association between two types of events using multivariate joint modeling and then apply this statistical method in the clinical data set.Study design: A retrospective cohort studyMethods: Overall, 342 subjects with breast cancer whose records were registered for follow-up in a Cancer Research Center at Shohadaye Tajrish Hospital, Tehran, Iran from 2006 to 2015 were investigated. These patients were monitored for at least 6 months after diagnosis and their latest status were recorded. Joint frailty model was used for modeling the relationship between two types of recurrences with Frailty package in R software.Results: When the terminal event was considered as death, three-year and five-year survival rates for the patients were 0.79 and 0.68, respectively. Given the results obtained from a fitted joint frailty model, the risk of multiple recurrences (local and metastases) increased for the patients with tumor grades greater than I.Conclusion: With regard to the significant variance of the frailty component of the metastases event, it can be inferred that patients with the same predictive variables are prone to different levels of metastases risk and, on the other hand, given the low frequency of types of recurrences, caution should be exercised when considering the obtained results.

This article discusses some important issues that may arise in the current usage of composite endpoints as primary endpoints for demonstrating the efficacy of new drugs in clinical trials. The discussion focuses on time-to-event composite endpoints. Issues discussed include validity of a composite endpoint, the often lack of follow-up of patients beyond first event, the analysis of a composite endpoint, its sub-composite and individual component endpoints and their interpretation. Actual published examples in the literature are used to illustrate some of these problems. It is recommended that a clinical trial using a composite endpoint as the primary endpoint should be designed to include patient follow-up beyond the first event if possible. For data collected from such trials, basic formats for tabular presentation of trial data and for results of analysis of the composite endpoint, its sub-composite and individual component endpoints are proposed for transparency and ease of interpretation.

Background: There is emerging evidence of the increasing uptake of response-adaptive randomization (RAR) in clinical trials. However, a systematic review of RAR trials, their context of use, characteristics, and stakeholder acceptance has been lacking. Methods: We performed a systematic review of clinical trials that utilized elements of RAR, identified via the Cortellis Regulatory Intelligence database following a pre-specified selection process. We report a summary of relevant characteristics of the identified trials. Results: Out of 170 records, 39 RAR trials were identified (22 completed, 17 ongoing as of October 2024). The majority were Phase 2-focused studies (phases 1/2, 2, 2b, and 2/3), academically sponsored, and concentrated in oncology, neurology, and infectious diseases. Small molecules and biologics were the most common investigational products. Among the 22 completed trials, seven reported positive outcomes. Notably, two of these trials provided pivotal data that informed the further development and subsequent regulatory approval of the investigational compounds. Conclusion: Over the past two decades, RAR has been increasingly utilized in complex adaptive trials across diverse therapeutic areas and clinical research phases. This systematic review provides a critical "baseline" for tracing the dynamics of RAR applications and should help the clinical research community recognize RAR as a valuable methodology for optimizing future trial designs.

To investigate pairwise interactions arising from recurrent event processes in a longitudinal network, the framework of the stochastic block model is followed, where every node belongs to a latent group and interactions between node pairs from two specified groups follow a conditional nonhomogeneous Poisson process. Our focus lies on discrete observation times, which are commonly encountered in reality for cost-saving purposes. The variational EM algorithm and variational maximum likelihood estimation are applied for statistical inference. A specific method based on the defined distribution function F and self-consistency algorithm for recurrent events is used when estimating the intensity functions of edges. Numerical simulations illustrate the performance of our proposed estimation procedure in uncovering the underlying structure in the longitudinal networks with recurrent event processes. The dataset of interactions between French schoolchildren for influenza monitoring is analyzed.