Actual Trials Research Articles

Influence of personal and socioeconomic factors on patient’s perception and experience of clinical trial participation at rural NCI designated comprehensive cancer center.

e13520 Background: Due to limited access to healthcare providers, lengthy travel times to clinics, and disadvantageous socioeconomic dynamics, patients in rural locations face significant challenges causing a low recruitment in clinical trials. However, there may be preconceived assumptions of a patient’s desire to participate in a trial leading to bias from healthcare organizations and thereby, decreased efforts in enrollment. Dartmouth Cancer Center is the smallest NCI designated comprehensive cancer center, serving rural New Hampshire and Vermont. In this study, we surveyed patients with cancer to assess their level of understanding of clinical trials, factors influencing patients’ willingness to enroll, and the difficulties faced after accrual in the trials. Methods: A 23 items anonymous questionnaire was distributed to the oncology patients at the outpatient clinic. The questions included demographics, socioeconomic status, time to travel, challenges faced in participating in the trials. Cox proportional regression analysis was done to identify factors influencing decision to participate in trials. Results: Among 93 respondents, 74% are within the age of 61 – 80 years, 64.45% are male and 74% are married, 51% are retired and 59.7% have household income of ≤$100,000. 32.2% of our respondents travelled over 1 hour to receive care. 73.3% of the respondents understood what it means to participate in a clinical trial. 91.8% are willing, yet only 40% were given an option to participate in a trial. Only 10.5% ever declined to participate. Additional cost, frequent travel, and possibility of receiving a placebo are the biggest factors in declining a participation. Up to 95% of the participants enrolled in a trial had positive experience and have the ultraistic motives. None of the socioeconomic or personal factors had any significant bearing on willingness to participate. Conclusions: Socioeconomic and regional factors have little bearing on a person's inclination to participate in clinical trials. There is a strong interest in understanding and enrolling in clinical studies. Cancer centers need to reduce the gap between patient interest in trials and actual trial participation.[Table: see text]

Underwater terrain positioning method based on Markov random field for unmanned underwater vehicles

Underwater terrain-matching navigation technologies have become a popular topic for the high-precision positioning and navigation of autonomous underwater vehicles. This paper proposes an underwater terrain-matching positioning method based on a Markov random field model, which is based on real-time terrain data obtained using a multi-beam echo sounder. It focuses on the strong correlation between adjacent terrain data, which can improve terrain adaptability and matching accuracy. Playback simulation tests were conducted based on actual sea trial data, and the results showed that the proposed method has good positioning performance, which can correct the cumulative errors of inertial navigation systems. The results demonstrated the usability of the proposed method for positioning correction in underwater engineering applications.

Perspectives of international multi-center clinical trials on traditional Chinese herbal medicine.

With the introduction of various subjects, such as clinical epidemiology and evidence-based medicine, the qualities and levels of Traditional Chinese Herbal Medicine (TCHM) in China improved substantially, and the processes of internationalization of Traditional Chinese Medicine (TCM) are further accelerated. Since, a variety of drug products in China have been approved for marketing in other countries, and approximately 10 products have submitted the IND application to FDA of United States, of which various Chinese herbal preparations such as compound Danshen dripping pills, Xingling granules, and HMPL-004 have been approved to be investigated in phase III clinical trials. In general, multi-center studies of TCHM are increasing with years, but most of the studies are performed in some certain country, and the actual international multi-center clinical trials are very rare. Number of SCI literatures on multi-center clinical trials of TCHM that published in the recent decade also showed increasing tendency with years, despite the evident reduction in the past 2years due to the influence of COVID-19 pandemic. Of the multi-center clinical trials of TCHM that performed by mainland China and other oversees regions, except for Taiwan, China, nearly 70% were focused on classic Chinese medicinal formulae and Chinese patent medicine, while the other 30% were on dietary supplements and plant extracts. Facing the future, the "human experience" has attracted close attentions from researchers throughout the world. Effectively utilizing the historic "human experience" is an important method to vitalize potential of original scientific and technological resources of TCHM. Performing multi-center clinical trials with high qualities is still an essential method for TCHM in accessing the mainstream medicine market. In addition, it is also required to further improve the evaluation techniques and methods that not only meet the international standards but also meet the characteristics of TCHM. Furthermore, we should also focus on the TCHM specific clinical values and scientific reports.

Design Plastic Injection Mold with the Help of Modular Software Mold Wizard

The purpose of the article is to research and apply CAD/CAM theory and techniques to design a set of plastic injection molds for supporting industries, providing plastic products for the smartphone manufacturing process in Vietnam, that is, injection molds, plastic connector between the power cord and the phone's charger. This set of molds has a much lower cost when compared to imported equipment of the same type, has guaranteed quality and is commercially viable. Especially, the result of the project is a model for students to grasp about the technology of manufacturing plastic products, contributing to improving the training quality of the CAD/CAM-CNC module.
 CAE at the design stage can be performed on a computer to design modification options, conduct evaluation (Evaluate), evaluate (Verify) and optimize (Optimize), reduce testing time and cost, mold, actual mold fix, shorten actual trial and error cycle, shorten product development time and time-to-market, reduce waste, time and money in stages.

Design and analysis of factorial clinical trials: The impact of one treatment's effectiveness on the statistical power and required sample size of the other.

Factorial trials allow for the simultaneous evaluation of more than one treatment, by randomizing patients to their possible combinations, including control. However, the statistical power of one treatment can be influenced by the effectiveness of the other, a matter that has not been widely recognized. In this paper, we evaluate the relationship between the observed effectiveness of one treatment and the implied power for a second treatment in the same trial, under a range of conditions. We provide analytic and numerical solutions for a binary outcome, under the additive, multiplicative, and odds ratio scales for treatment interaction. We demonstrate how the minimum required sample size for a trial depends on the two treatment effects. Relevant factors include the event rate in the control group, sample size, treatment effect sizes, and Type-I error rate thresholds. We show that that power for one treatment decreases as a function of the observed effectiveness of the other treatment if there is no multiplicative interaction. A similar pattern is observed with the odds ratio scale at low control rates, but at high control rates, power may increase if the first treatment is moderately more effective than its planned value. When treatments do not interact additively, power may either increase or decrease, depending on the control event rate. We also determine where the maximum power occurs for the second treatment. We illustrate these ideas with data from two actual factorial trials. These results can benefit investigators in planning the analysis of factorial clinical trials, in particular, to alert them to the potential for losses in power when one observed treatment effect differs from its originally postulated value. Updating the power calculation and modifying the associated required sample size can then ensure sufficient power for both treatments.

Sequential learning navigation method and general correction model for Autonomous Underwater Vehicle

The errors introduced in the modeling and state estimation by conventional navigation methods will affect the AUV navigation accuracy. Therefore, this paper proposes a sequential learning method for AUV navigation. Firstly, a Temporal Convolutional Network (TCN) module is used to learn the temporal relationships from raw sensor data. After the feature representation, a Self-Attention (SA) mechanism is implemented to weigh the data at different time steps. Finally, the Fully Connected (FC) layers are adopted to regress the AUV displacements. Since the sequential learning navigation method does not require state estimation computation and can calculate the AUV position by displacement accumulation, it can avoid introducing related errors. Meanwhile, this paper proposes a general correction model based on the sequential learning method to correct the errors of various state estimation techniques. The experimental results based on actual sea trial data from Sailfish 210 AUV show that the average AUV navigation accuracy of the presented sequential learning method is improved by 56.78% compared to EKF, 57.64% compared to UKF, and 47.85% compared to CKF. Meanwhile, the navigation accuracy of the proposed general correction model is improved by 62.88% compared to EKF, 49.45% compared to UKF, and 42.86% compared to CKF.

Dynamic use of historical controls in clinical trials for rare disease research: A re-evaluation of the MILES trial.

Randomized controlled trials offer the best design for eliminating bias in estimating treatment effects but can be slow and costly in rare disease research. Additionally, an equal randomization approach may not be optimal in studies in which prior evidence of superiority of one or more treatments exist. Supplementing prospectively enrolled, concurrent controls with historical controls can reduce recruitment requirements and provide patients a higher likelihood of enrolling in a new and possibly superior treatment arm. Appropriate methods need to be employed to ensure comparability of concurrent and historical controls to minimize bias and variability in the treatment effect estimates and reduce the chances of drawing incorrect conclusions regarding treatment benefit. MILES was a phase III placebo-controlled trial employing 1:1 randomization that led to US Food and Drug Administration approval of sirolimus for treating patients with lymphangioleiomyomatosis. We re-analyzed the MILES trial data to learn whether substituting concurrent controls with controls from a historical registry could have accelerated subject enrollment while leading to similar study conclusions. We used propensity score matching to identify exchangeable historical controls from a registry balancing the baseline characteristics of the two control groups. This allowed more new patients to be assigned to the sirolimus arm. We used trial data and simulations to estimate key outcomes under an array of alternative designs. Borrowing information from historical controls would have allowed the trial to enroll fewer concurrent controls while leading to the same conclusion reached in the trial. Simulations showed similar statistical performance for borrowing as for the actual trial design without producing type I error inflation and preserving power for the same study size when concurrent and historical controls are comparable. Substituting concurrent controls with propensity score-matched historical controls can allow more prospectively enrolled patients to be assigned to the active treatment and enable the trial to be conducted with smaller overall sample size, while maintaining covariate balance and study power and minimizing bias in response estimation. This approach does not fully eliminate the concern that introducing non-randomized historical controls in a trial may lead to bias in estimating treatment effects, and should be carefully considered on a case-by-case basis. Borrowing historical controls is best suited when conducting randomized controlled trials with conventional designs is challenging, as in rare disease research. High-quality data on covariates and outcomes must be available for candidate historical controls to ensure the validity of these designs. Additional precautions are needed to maintain blinding of the treatment assignment and to ensure comparability in the assessment of treatment safety.MILES ClinicalTrials.gov Number: NCT00414648.

Multiply Robust Weighted Generalized Estimating Equations for Incomplete Longitudinal Binary Data Using Empirical Likelihood

In clinical trials, missing data may lead to serious misinterpretation of trial results. To address this issue, it is important to collect post-randomization data (such as efficacy measurement data and adverse event onset data). Such post-randomization data are called auxiliary variables and they can be useful for constructing missingness and imputation models. A multiply robust estimator using an empirical likelihood method was previously proposed by Han and Wang and by Han. However, that estimator was developed for cross-sectional data and situations in which no auxiliary variables are missing. This is contrary to actual clinical trial settings, in which some auxiliary variables will invariably be missing. Consequently, to apply Han’s method to longitudinal data, missing auxiliary variables need to be imputed. This article proposes a new method that extends Han’s method to a longitudinal outcome model by applying weighted generalized estimating equations with new weights. Monte Carlo simulations of a repeated binary response with missing at random dropouts demonstrated that the proposed estimator is multiply robust and exhibits better performance than that of augmented inverse probability weighted complete-case estimating equations under several simulation scenarios. We also successfully applied the proposed method to plaque psoriasis study data.

Information criteria for detecting change-points in the Cox proportional hazards model.

The Cox proportional hazards model, commonly used in clinical trials, assumes proportional hazards. However, it does not hold when, for example, there is a delayed onset of the treatment effect. In such a situation, an acute change in the hazard ratio function is expected to exist. This paper considers the Cox model with change-points and derives Akaike information criterion (AIC)-type information criteria for detecting those change-points. The change-point model does not allow for conventional statistical asymptotics due to its irregularity, thus a formal AIC that penalizes twice the number of parameters would not be analytically derived, and using it would clearly give overfitting analysis results. Therefore, we will construct specific asymptotics using the partial likelihood estimation method in the Cox model with change-points, and propose information criteria based on the original derivation method for AIC. If the partial likelihood is used in the estimation, information criteria with penalties much larger than twice the number of parameters could be obtained in an explicit form. Numerical experiments confirm that the proposed criteria are clearly superior in terms of the original purpose of AIC, which are to provide an estimate that is close to the true structure. We also apply the proposed criterion to actual clinical trial data to indicate that it will easily lead to different results from the formalAIC.

Precision recruitment for high-risk participants in a COVID-19 cohort study

BackgroundStudies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals. MethodsWe conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials. ResultsWhen we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts. ConclusionThis risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.

An Integrated Navigation Method for Small-Sized AUV in Shallow-Sea Applications

The navigation and localization of Autonomous Underwater Vehicle (AUV) face many challenges. In surface missions, AUV typically relies only on Global Positioning System (GPS) to perform navigation. However, the GPS data output frequency is low, usually only 1 Hz. Meanwhile, the navigation system is susceptible to interference from GPS measurement outliers. For underwater missions, without the assistance of additional underwater absolute positioning measurements, the unknown errors introduced by the navigation system modeling process and state estimation process will affect the navigation accuracy. Therefore, in this paper, we propose an integrated navigation method for small-sized AUV in shallow-sea applications. Firstly, a robust GPS/Attitude and Heading Reference System (AHRS)/Doppler Velocity Log (DVL) navigation method for surface missions is proposed to maintain high-frequency output and robustness. Secondly, a Multiple Model (MM) integrated navigation method is proposed for AUV underwater missions. An AHRS/DVL/NavNet sub-model is proposed to perform measurement correction to correct the unknown errors. At the same time, an adaptive AHRS/DVL sub-model based on Variational Bayesian (VB) is presented to estimate the time-varying measurement noise covariance to improve the state estimation accuracy without the extra external observations. Two sub-models run parallel based on the Interacting Multiple Model (IMM). The effectiveness of the proposed integrated navigation method is verified by actual sea trial data from Sailfish 210 AUV.

Impact of correlations between prioritized outcomes onthenet benefit and its estimate by generalized pairwise comparisons.

Benefit-risk balance is gaining interest in clinical trials. For the comprehensive assessment of benefits and risks, generalized pairwise comparisons are increasingly used to estimate the net benefit based on multiple prioritized outcomes. Although previous research has demonstrated that the correlations between the outcomes impact the net benefit and its estimate, the direction and magnitude of this impact remain unclear. In this study, we investigated the impact of correlations between two binary or Gaussian variables on the true net benefit values via theoretical and numerical analyses. We also explored the impact of correlations between survival and categorical variables on the net benefit estimates based on four existing methods (Gehan, Péron, Gehan with correction, and Péron with correction) in the presence of right censoring via simulation and application to actual oncology clinical trial data. Our theoretical and numerical analyses revealed that the true net benefit values were impacted by the correlations in various directions depending on the outcome distributions. With binary endpoints, this direction was governed by a simple rule with a threshold of 50% for a favorable outcome. Our simulation showed that the net benefit estimates based on Gehan's or Péron's scoring rule could be substantially biased in the presence of right censoring, and that the direction and magnitude of this bias were associated with the outcome correlations. The recently proposed correction method greatly reduced this bias, even in the presence of strong outcome correlations. The impact of correlations should be carefully considered when interpreting the net benefit and its estimate.

Construction cost prediction based on adaptive boosting and artificial neural networks

The artificial bee colony algorithm and multilayer error back-propagation neural networks commonly used in construction project cost forecasting suffer from slow training speeds and high costs. A combination of the beetle antennae search, support vector machines, adaptive boosting and genetic algorithms was proposed to solve these problems. Support vector machine optimisation was accomplished using the beetle antennae search algorithm. The enhanced genetic algorithm was then used directly to swap out the fit solutions for the unfit ones. One hundred projects completed during the last 3 years were chosen from a network integration database to serve as the training data set after developing the prediction model. Using actual cost information and trial and error, appropriate parameters were chosen, and combinations of algorithms were selected for comparison. The maximum relative error of the improved method was 9.01%, which was 34.68% lower than that of the baseline method, while the smallest relative error was 0.59%, which was 1.58% lower than that of the baseline method. The ’innovation of the study lies in the addition of the beetle antennae search algorithm and enhancement of the genetic algorithm. The former significantly increased the search efficiency of the network, while the latter generally increased the population fitness and mitigated the drawback of the genetic algorithm which was prone to local convergence.

Motor Imagery Training Is Beneficial for Motor Memory of Upper and Lower Limb Tasks in Very Old Adults.

Human aging is associated with a decline in the capacity to memorize recently acquired motor skills. Motor imagery training is a beneficial method to compensate for this deterioration in old adults. It is not yet known whether these beneficial effects are maintained in very old adults (>80 years), who are more affected by the degeneration processes. The aim of this study was to evaluate the effectiveness of a mental training session of motor imagery on the memorization of new motor skills acquired through physical practice in very old adults. Thus, 30 very old adults performed 3 actual trials of a manual dexterity task (session 1) or a sequential footstep task (session 2) as fast as they could before and after a 20 min motor imagery training (mental-training group) or watching a documentary for 20 min (control group). Performance was improved after three actual trials for both tasks and both groups. For the control group, performance decreased in the manual dexterity task after the 20 min break and remained stable in the sequential footstep task. For the mental-training group, performance was maintained in the manual dexterity task after the 20 min motor imagery training and increased in the sequential footstep task. These results extended the benefits of motor imagery training to the very old population, showing that even a short motor imagery training session improved their performance and favored the motor memory process. These results confirmed that motor imagery training is an effective method to complement traditional rehabilitation protocols.

An autonomous cooperative system of multi-AUV for underwater targets detection and localization

This paper proposes a cooperative online target detection methodology by multiple autonomous underwater vehicles (Multi-AUV) equipped with the side-scan sonar (SSS) sensor for real-time, accurate, and efficient underwater target detection and positioning in unknown environments. Due to the existence of unfavorable factors such as severe noises and geometric deformation of SSS images, this study incorporates the prior-based threshold segmentation with multi-scale cascaded networks (MSCNet) to reduce the high false alarm rate significantly. Specifically, to the real-time requirements of the AUVs computational platform, this study proposes the sequentially dual-branch lightweight block (LWBlock) as a baseline to obtain dense feature maps, which provide a good trade-off between accuracy and speed. Meanwhile, this study establishes the comprehensive correction model, which obtains the accurate target positioning information fusing with the predicted results. Furthermore, according to the target information provided by the automatic target recognition (ATR) system, the data-driven behavior-based (DDBB) path re-planning algorithm is performed that endows each AUV to scan above the interest target autonomously and in detail by designed maneuver behavior. Simulation and actual sea trial experimental results show that the proposed method outperforms other state-of-the-art algorithms, and achieves the recognition accuracy of 92.16%, inference speed of 2.45 s, and obtained the best FPR indicator in three SSS targets of 2.54% (metal ball),1.96% (seabed rock) and 1.03% (metal rod), respectively. At the same time, the proposed algorithm can improve detection efficiency by at least 40% compared with a single AUV, which can be widely used in marine mission exploration and resource deployment.

Recruitment across two decades of NIH-funded Alzheimer’s disease clinical trials

BackgroundTimely accrual of a representative sample is a key factor in whether Alzheimer’s disease (AD) clinical trials successfully answer the scientific questions under study. Studies in other fields have observed that, over time, recruitment to trials has become increasingly reliant on larger numbers of sites, with declines in the average per-site recruitment rate. Here, we examined the trends in recruitment over a 20-year period of NIH-funded AD clinical trials conducted by the Alzheimer’s Disease Cooperative Study (ADCS), a temporally consistent network of sites devoted to interventional research.MethodsWe performed retrospective analyses of eleven ADCS randomized clinical trials. To examine the recruitment planning, we calculated the expected number of participants to be enrolled per site for each trial. To examine the actual trial recruitment rates, we quantified the number of participants enrolled per site per month.ResultsNo effects of time were observed on recruitment planning or overall recruitment rates across trials. No trial achieved an overall recruitment rate greater than one subject per site per month. We observed the fastest recruitment rates in trials with no competition and the slowest in trials that overlapped in time. The highest recruitment rates were consistently seen early within trials and declined over the course of studies.ConclusionsTrial recruitment projections should plan for fewer than one participant randomized per site per month and consider the number of other AD trials being conducted concurrently.

Automatic Docking Trajectory Design-Based Time-Varying-Radius Dubins for Unmanned Surface Vessel

Facing the demand of efficient automatic docking for USV, this paper proposes a new kind of iterative varying-radius Dubins curve design. Due to the special mobility of dockyards, one novel trajectory optimization problem considering time optimum is modeled first. Then, we use the ant colony optimization (ACO) algorithm to solve such a complex problem. In addition to the global exhaustive search algorithm and ACO-based solution, a novel distributed local path planning algorithm named ADTA-TRD is proposed to obtain the solution of the above optimization problem restricted by the local perception of the USV. In essence, this paper draws lessons from the idea of the circular arc segment and straight line segment in the traditional Dubins curve, and consequently, the smoothness of the derived automatic docking trajectory can be guaranteed. Finally, the performance of proposed automatic docking path planning method is verified through simulation results and an actual sea trial.

Feedback-aided PD-type iterative learning control for time-varying systems with non-uniform trial lengths

In most implementations of iterative learning control (ILC) for trajectory tracking, it is usually required that the trial lengths of different iterations are uniform. However, this requirement may not always be ensured in practical applications. In this paper, a feedback-aided PD-type ILC design for time-varying systems with non-uniform trial lengths is proposed. Although the actual trial lengths are non-uniform, the designed update sequences provide uniform full-length signals for the update process. Meanwhile, information from the most recent valid iterations can be better used than the mechanisms that compensate with hypothesized data, such as zero. Their recursive generation also reduces the storage burden compared to search strategies. The feedback error signal can be additionally used as part of the correction term to improve the system performance compared to the traditional open-loop approaches. Under a deterministic model, the main convergence results are obtained by combining the [Formula: see text]-norm technique with the inductive analysis approach. At last, a linear numerical simulation and a nonlinear single-joint robot simulation are performed, respectively, to show that the proposed design can achieve the asymptotic tracking of the desired trajectories for time-varying systems with non-uniform trial lengths.

Analysis of Rates of Completion, Delays, and Participant Recruitment in Randomized Clinical Trials in Surgery

Discontinuation and nonpublication are established sources of avoidable waste among surgical trials, but rates of delayed completion and recruiting shortfalls remain unclear. To examine the rate of delayed completion, incomplete enrollment, and discontinuation among randomized clinical trials in surgical populations and the duration of delays and extent of recruiting shortfalls among these trials. This cross-sectional study examined randomized clinical trials in surgical populations registered on ClinicalTrials.gov between January 1, 2010, and December 31, 2014. Analysis was conducted between October 27, 2021, and June 30, 2022. The main outcomes were the percentages of trials completed on time or with full enrollment. Delays and recruiting shortfalls were identified by comparing projected enrollment and study timeframes prespecified at the time of registration with the actual study duration and enrollment reported on completion or discontinuation. Absolute and relative differences between planned and actual trial conduct were presented for discontinued trials and those completed with delays or recruiting shortfalls. In total, 2542 randomized clinical trials in surgical populations were included in the study sample, of which 370 (14.6%; 95% CI, 13.2%-15.9%) were completed both on time and with full enrollment. Approximately 1 in 5 trials (20.4%; 95% CI, 18.9%-22.0%) were completed within their planned timeframe, and 1166 trials (45.9%; 95% CI, 43.9%-47.8%) met their prespecified enrollment target. The median delay among completed trials was 12.2 months (IQR, 5.1-24.3 months) or 66.7% (IQR, 30.1%-135.8%) longer than planned. Among completed trials that did not meet their prespecified enrollment target, the median recruiting shortfall was equivalent to 31.0% (IQR, 12.7%-55.5%) of the planned study sample. A total of 546 trials (21.5%; 95% CI, 19.9%-23.1%) were discontinued. The median time to discontinuation was 26.4 months (IQR, 15.2-45.7 months), and the median recruiting shortfall among discontinued trials was equivalent to 92.7% (IQR, 65.0%-100.0%) of the trial's prespecified enrollment target. This cross-sectional study found that delayed completion, recruiting shortfalls, and untimely discontinuation were common among surgical trials. These findings highlight the importance of ensuring that investigators and funders do not overestimate the feasibility of planned trials.

Introduction: Historical Development and Progression of Clinical Research on Ageing.

Research on ageing has developed since Greek times. It had a very slow advance during the Middle Ages and a big increase in the Renaissance. Darwin contributed somehow to the understanding of the ageing process and initiated a cumulus of ageing explications under the name of Evolutionary Theories. Subsequently, science discovered a great number of genes, molecules, and cell processes that intervened in ageing. This led to the beginning of trials in animals to retard or avoid the ageing process. Alongside this, improvements, geriatric clinical investigations (with the evidence-based medicine tools) started to consolidate as a discipline and commenced to show the challenges and deficiencies of actual clinical trials in ageing; the COVID-19 outbreak revealed some of them. The history of clinical research in ageing has already begun and is essential to affront the challenges that the world will face with the increasing ageing population.