Journal articles: 'Clinical Trial Randomization'

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Harvin, John A., Ben L. Zarzaur, Raminder Nirula, Benjamin T. King, and Ajai K. Malhotra. "Alternative clinical trial designs." Trauma Surgery & Acute Care Open 5, no. 1 (February 2020): e000420. http://dx.doi.org/10.1136/tsaco-2019-000420.

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High-quality clinical trials are needed to advance the care of injured patients. Traditional randomized clinical trials in trauma have challenges in generating new knowledge due to many issues, including logistical difficulties performing individual randomization, unclear pretrial estimates of treatment effect leading to often unpowered studies, and difficulty assessing the generalizability of an intervention given the heterogeneity of both patients and trauma centers. In this review, we discuss alternative clinical trial designs that can address some of these difficulties. These include pragmatic trials, cluster randomization, cluster randomized stepped wedge designs, factorial trials, and adaptive designs. Additionally, we discuss how Bayesian methods of inference may provide more knowledge to trauma and acute care surgeons compared with traditional, frequentist methods.

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Baghbaninaghadehi, Fatemeh. "Fundamentals of Randomization in Clinical Trial." International Journal of Advanced Nutritional and Health Science 4, no. 1 (2016): 174–87. http://dx.doi.org/10.23953/cloud.ijanhs.143.

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Deserno, Thomas M., and András P. Keszei. "Mobile access to virtual randomization for investigator-initiated trials." Clinical Trials 14, no. 4 (April 28, 2017): 396–405. http://dx.doi.org/10.1177/1740774517706509.

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Background/aims Randomization is indispensable in clinical trials in order to provide unbiased treatment allocation and a valid statistical inference. Improper handling of allocation lists can be avoided using central systems, for example, human-based services. However, central systems are unaffordable for investigator-initiated trials and might be inaccessible from some places, where study subjects need allocations. We propose mobile access to virtual randomization, where the randomization lists are non-existent and the appropriate allocation is computed on demand. Methods The core of the system architecture is an electronic data capture system or a clinical trial management system, which is extended by an R interface connecting the R server using the Java R Interface. Mobile devices communicate via the representational state transfer web services. Furthermore, a simple web-based setup allows configuring the appropriate statistics by non-statisticians. Our comprehensive R script supports simple randomization, restricted randomization using a random allocation rule, block randomization, and stratified randomization for un-blinded, single-blinded, and double-blinded trials. For each trial, the electronic data capture system or the clinical trial management system stores the randomization parameters and the subject assignments. Results Apps are provided for iOS and Android and subjects are randomized using smartphones. After logging onto the system, the user selects the trial and the subject, and the allocation number and treatment arm are displayed instantaneously and stored in the core system. So far, 156 subjects have been allocated from mobile devices serving five investigator-initiated trials. Conclusion Transforming pre-printed allocation lists into virtual ones ensures the correct conduct of trials and guarantees a strictly sequential processing in all trial sites. Covering 88% of all randomization models that are used in recent trials, virtual randomization becomes available for investigator-initiated trials and potentially for large multi-center trials.

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Supawattan, Busaba, and Lily Ingsrisawa. "Bayesian Adaptive Randomization Designs for Clinical Trial." Journal of Applied Sciences 15, no. 2 (January 15, 2015): 374–76. http://dx.doi.org/10.3923/jas.2015.374.376.

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Michael, Nelson L. "Clinical trial design: The nobility of randomization." Science Translational Medicine 9, no. 419 (December 6, 2017): eaaq0810. http://dx.doi.org/10.1126/scitranslmed.aaq0810.

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Tiwari, Jawahar, and Cornelius J. Lynch. "81P Tightening the clinical trial by randomization." Controlled Clinical Trials 15, no. 3 (June 1994): 122–23. http://dx.doi.org/10.1016/0197-2456(94)90209-7.

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Grant, William C. "Run-Reversal Equilibrium for Clinical Trial Randomization." PLOS ONE 10, no. 6 (June 16, 2015): e0128812. http://dx.doi.org/10.1371/journal.pone.0128812.

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Hilgers, Ralf-Dieter, Martin Manolov, Nicole Heussen, and William F. Rosenberger. "Design and analysis of stratified clinical trials in the presence of bias." Statistical Methods in Medical Research 29, no. 6 (May 10, 2019): 1715–27. http://dx.doi.org/10.1177/0962280219846146.

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Background Among various design aspects, the choice of randomization procedure have to be agreed on, when planning a clinical trial stratified by center. The aim of the paper is to present a methodological approach to evaluate whether a randomization procedure mitigates the impact of bias on the test decision in clinical trial stratified by center. Methods We use the weighted t test to analyze the data from a clinical trial stratified by center with a two-arm parallel group design, an intended 1:1 allocation ratio, aiming to prove a superiority hypothesis with a continuous normal endpoint without interim analysis and no adaptation in the randomization process. The derivation is based on the weighted t test under misclassification, i.e. ignoring bias. An additive bias model combing selection bias and time-trend bias is linked to different stratified randomization procedures. Results Various aspects to formulate stratified versions of randomization procedures are discussed. A formula for sample size calculation of the weighted t test is derived and used to specify the tolerated imbalance allowed by some randomization procedures. The distribution of the weighted t test under misclassification is deduced, taking the sequence of patient allocation to treatment, i.e. the randomization sequence into account. An additive bias model combining selection bias and time-trend bias at strata level linked to the applied randomization sequence is proposed. With these before mentioned components, the potential impact of bias on the type one error probability depending on the selected randomization sequence and thus the randomization procedure is formally derived and exemplarily calculated within a numerical evaluation study. Conclusion The proposed biasing policy and test distribution are necessary to conduct an evaluation of the comparative performance of (stratified) randomization procedure in multi-center clinical trials with a two-arm parallel group design. It enables the choice of the best practice procedure. The evaluation stimulates the discussion about the level of evidence resulting in those kind of clinical trials.

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Sidani, Souraya, Mary Fox, and Laura Collins. "Towards Patient-Centered Clinical Trial Designs." European Journal for Person Centered Healthcare 5, no. 3 (September 26, 2017): 300. http://dx.doi.org/10.5750/ejpch.v5i3.1308.

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Rationale, aims and objectives: Evidence shows a trend towards low enrollment in randomized clinical trials (RCTs), which negatively affect validity of conclusions. Low enrollment is associated with different factors, but has recently been attributed to an increasing proportion of patients expressing concerns about randomization. In this paper, we summarize the evidence on reasons for non-enrollment, and we propose preference-based and shared decision-making as alternative methods for allocating patients to treatments in effectiveness and comparative effectiveness trials.Methods: This paper is a narrative review of available literature.Results: Converging findings of quantitative and qualitative studies revealed three interrelated and frequently mentioned reasons for declining enrollment in RCTs: 1) concerns about randomization related to the lack of understanding of equipoise, lack of appreciation of the scientific merits of randomization, and unfavorable perceptions of randomization as not reflecting methods of treatment selection used in practice; 2) preferences for treatments under evaluation, which contribute to unwillingness to be randomized; and 3) desires for involvement in treatment decision-making, which are not respected with randomization.Conclusions: Alternative methods for treatment allocation are needed to make effectiveness and comparative effectiveness trials attractive to patients. Preference-based and shared decision-making are viable methods that respectively represent the informed choice and the collaborative choice styles of treatment selection commonly used in practice. The extent to which these two methods of treatment allocation enhance enrollment should be further investigated.

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Cellamare, Matteo, Steffen Ventz, Elisabeth Baudin, Carole D. Mitnick, and Lorenzo Trippa. "A Bayesian response-adaptive trial in tuberculosis: The endTB trial." Clinical Trials 14, no. 1 (September 23, 2016): 17–28. http://dx.doi.org/10.1177/1740774516665090.

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Purpose: To evaluate the use of Bayesian adaptive randomization for clinical trials of new treatments for multidrug-resistant tuberculosis. Methods: We built a response-adaptive randomization procedure, adapting on two preliminary outcomes for tuberculosis patients in a trial with five experimental regimens and a control arm. The primary study outcome is treatment success after 73 weeks from randomization; preliminary responses are culture conversion at 8 weeks and treatment success at 39 weeks. We compared the adaptive randomization design with balanced randomization using hypothetical scenarios. Results: When we compare the statistical power under adaptive randomization and non-adaptive designs, under several hypothetical scenarios we observe that adaptive randomization requires fewer patients than non-adaptive designs. Moreover, adaptive randomization consistently allocates more participants to effective arm(s). We also show that these advantages are limited to scenarios consistent with the assumptions used to develop the adaptive randomization algorithm. Conclusion: Given the objective of evaluating several new therapeutic regimens in a timely fashion, Bayesian response-adaptive designs are attractive for tuberculosis trials. This approach tends to increase allocation to the effective regimens.

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Wathen, J. Kyle, and Peter F. Thall. "A simulation study of outcome adaptive randomization in multi-arm clinical trials." Clinical Trials 14, no. 5 (February 1, 2017): 432–40. http://dx.doi.org/10.1177/1740774517692302.

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Randomizing patients among treatments with equal probabilities in clinical trials is the established method to obtain unbiased comparisons. In recent years, motivated by ethical considerations, many authors have proposed outcome adaptive randomization, wherein the randomization probabilities are unbalanced, based on interim data, to favor treatment arms having more favorable outcomes. While there has been substantial controversy regarding the merits and flaws of adaptive versus equal randomization, there has not yet been a systematic simulation study in the multi-arm setting. A simulation study was conducted to evaluate four different Bayesian adaptive randomization methods and compare them to equal randomization in five-arm clinical trials. All adaptive randomization methods included an initial burn-in with equal randomization and some combination of other modifications to avoid extreme randomization probabilities. Trials either with or without a control arm were evaluated, using designs that may terminate arms early for futility and select one or more experimental treatments at the end. The designs were evaluated under a range of scenarios and sample sizes. For trials with a control arm and maximum same size 250 or 500, several commonly used adaptive randomization methods have very low probabilities of correctly selecting a truly superior treatment. Of those studied, the only adaptive randomization method with desirable properties has a burn-in with equal randomization and thereafter randomization probabilities restricted to the interval 0.10–0.90. Compared to equal randomization, this method has a favorable sample size imbalance but lower probability of correctly selecting a superior treatment. In multi-arm trials, compared to equal randomization, several commonly used adaptive randomization methods give much lower probabilities of selecting superior treatments. Aside from randomization method, conducting a multi-arm trial without a control arm may lead to very low probabilities of selecting any superior treatments if differences between the treatment success probabilities are small.

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Antman, K., D. Amato, W. Wood, J. Carson, H. Suit, K. Proppe, R. Carey, J. Greenberger, R. Wilson, and E. Frei. "Selection bias in clinical trials." Journal of Clinical Oncology 3, no. 8 (August 1985): 1142–47. http://dx.doi.org/10.1200/jco.1985.3.8.1142.

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Of 90 patients with intermediate or high-grade sarcoma eligible for a randomized trial of adjuvant doxorubicin (Adriamycin, Adria Laboratories, Columbus, Ohio), 48 were not entered: 24 (27%) by physician's choice and 24 refused randomization. Sixty-five percent of lower stage patients were randomized compared with 37% of those with higher stage (P = .02). Patients with extremity lesions were more frequently offered participation in the study (P = .07). Patients with lower stage lesions accepted randomization more readily than those with higher stage lesions (P = .01). As predicted by the higher stage and percentage of central lesions, the disease-free survival of nonrandomized patients was inferior to that of randomized patients (P = .15). Thus, patients at high risk appeared to avoid randomization and adjuvant doxorubicin in this trial, resulting in an inferior disease-free survival for the nonrandomized control group. Important questions generally require randomized trials that reliably determine relative treatment differences. If, however, the patients in a clinical trial are not representative of the entire patient population because of patient and physician selection biases, the generalizability of the results to the entire patient population may be compromised. For example, the prognosis of the general population cannot necessarily be inferred from the selected group in the study. In this study, the randomized and nonrandomized series yielded differing conclusions regarding treatment efficacy, even when an adjustment was made for known prognostic facts.

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Yaffe, Martin J., Jean M. Seely, Paula B. Gordon, Shushiela Appavoo, and Daniel B. Kopans. "The randomized trial of mammography screening that was not—A cautionary tale." Journal of Medical Screening 29, no. 1 (November 23, 2021): 7–11. http://dx.doi.org/10.1177/09691413211059461.

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Two randomized trials were conducted in Canada in the 1980s to test the efficacy of breast cancer screening. Neither of the trials demonstrated benefit. Concerns were raised regarding serious errors in trial design and conduct. Here we describe the conditions that could allow subversion of randomization to occur and the inclusion of many symptomatic women in a screening trial. We examine anomalies in data where the balance would be expected between trial arms. “Open book” randomization and performance of clinical breast examination on all women before allocation to a trial arm allowed women with palpable findings to be mis-randomized into the mammography arm. Multiple indicators raising suspicion of subversion are present including a large excess in poor-prognosis cancers in the mammography trial arm at prevalence screen. Personnel described shifting of women from the control group into the mammography group. There is compelling evidence of subversion of randomization in Canadian National Breast Screening Study. Mis-randomization of even a few women with advanced breast cancer could markedly affect measured screening efficacy. The Canadian National Breast Screening Study trials should not influence breast screening policies.

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Petosa, R. L., and L. Smith. "Effective Recruitment of Schools for Randomized Clinical Trials: Role of School Nurses." Journal of School Nursing 34, no. 6 (July 4, 2017): 430–34. http://dx.doi.org/10.1177/1059840517717592.

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In school settings, nurses lead efforts to improve the student health and well-being to support academic success. Nurses are guided by evidenced-based practice and data to inform care decisions. The randomized controlled trial (RCT) is considered the gold standard of scientific rigor for clinical trials. RCTs are critical to the development of evidence-based health promotion programs in schools. The purpose of this article is to present practical solutions to implementing principles of randomization to RCT trials conducted in school settings. Randomization is a powerful sampling method used to build internal and external validity. The school’s daily organization and educational mission provide several barriers to randomization. Based on the authors’ experience in conducting school-based RCTs, they offer a host of practical solutions to working with schools to successfully implement randomization procedures. Nurses play a critical role in implementing RCTs in schools to promote rigorous science in support of evidence-based practice.

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Rosenberger, William F., Diane Uschner, and Yanying Wang. "Randomization: The forgotten component of the randomized clinical trial." Statistics in Medicine 38, no. 1 (July 25, 2018): 1–12. http://dx.doi.org/10.1002/sim.7901.

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Rovers, Maroeska M., Huub Straatman, Gerhard A. Zielhuis, Koen Ingels, and Gert-Jan van der Wilt. "USING A BALANCING PROCEDURE IN MULTICENTER CLINICAL TRIALS." International Journal of Technology Assessment in Health Care 16, no. 1 (January 2000): 276–81. http://dx.doi.org/10.1017/s0266462300161239.

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Objective: A basic issue in randomized controlled trials (RCTs) is whether we can safely assume comparability between groups at baseline with respect to all potentially important prognostic factors. In other words, did randomization work sufficiently well? In small trials balanced allocation procedures are employed, whereas in large-scale trials simple randomization will do. The question is: When should balancing be considered?Methods: We performed a simulation study in which we varied the number of categories in the prognostic factors and the number of patients.Results: Simulation showed that, in all instances, a balancing procedure almost always led to perfect or almost perfect balance, while the imbalance with simple randomization was larger. To study the effect of balanced and random allocation on subgroup analyses in our OME trial, we compared the quotient of the width of the confidence intervals (CI). The widest CI in random allocation over the 13 hospitals was on average 13% wider than in balanced allocation.Conclusion: Investigators should always consider balanced allocation, especially in categories with a low number of patients and when subgroup analysis over many categories is requested.

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Liu, Hao, Xiao Lin, and Xuelin Huang. "An oncology clinical trial design with randomization adaptive to both short- and long-term responses." Statistical Methods in Medical Research 28, no. 7 (December 12, 2017): 2015–31. http://dx.doi.org/10.1177/0962280217744816.

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In oncology clinical trials, both short-term response and long-term survival are important. We propose an urn-based adaptive randomization design to incorporate both of these two outcomes. While short-term response can update the randomization probability quickly to benefit the trial participants, long-term survival outcome can also change the randomization to favor the treatment arm with definitive therapeutic benefit. Using generalized Friedman’s urn, we derive an explicit formula for the limiting distribution of the number of subjects assigned to each arm. With prior or hypothetical knowledge on treatment effects, this formula can be used to guide the selection of parameters for the proposed design to achieve desirable patient number ratios between different treatment arms, and thus optimize the operating characteristics of the trial design. Simulation studies show that the proposed design successfully assign more patients to the treatment arms with either better short-term tumor response or long-term survival outcome or both.

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Myles, Paul S., Helen E. Fletcher, Sesto Cairo, Hilary Madder, Roderick McRae, James Cooper, Debra Devonshire, et al. "Randomized Trial of Informed Consent and Recruitment for Clinical Trials in the Immediate Preoperative Period." Anesthesiology 91, no. 4 (October 1, 1999): 969. http://dx.doi.org/10.1097/00000542-199910000-00016.

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Background The standard process of obtaining informed consent sometimes prevents physicians or patients from participating in clinical trials, partly because they are concerned about eventual treatment allocation or the physician is concerned the patient might harbor some uncertainty about the best treatment. Alternative randomization methods have been advocated that may address these and other concerns. Methods After institutional ethics committee gave its approval, the authors interviewed 770 patients before operation and asked them to consider enrolling in a mock anesthesia trial. Patients were allocated randomly to one of five methods of randomization and consent: one-sided informed consent (the most common approach), prerandomized consent to experimental treatment, prerandomized consent to standard treatment, one-sided physician-modified informed consent, or one-sided patient-modified informed consent. Recruitment rates were compared and sociodemographic and perioperative predictors of recruitment were identified. Results The randomization method did not result in any significant difference in recruitment rates: one-sided informed consent, 55.6%; prerandomized consent to experimental treatment, 53.3%; prerandomized consent to standard treatment, 53%; one-sided physician-modified informed consent, 60.7%; and one-sided patient-modified informed consent, 56.7% (P = 0.66). Multivariate predictors of recruitment were patient age >45 yr (odds ratio, 1.44; 95% confidence interval [CI], 1.08 to 1.93), English-speaking at home (1.49; 1.0 to 2.21), and male researcher-male patient interaction (1.37; 1.20 to 1.57). Conclusions No evidence emerged that alternative randomization and consent designs resulted in increased recruitment rates compared with simple one-sided informed consent for a sham anesthesia trial in patients awaiting elective surgery. Older, male patients were more likely to provide consent.

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Kay, R. "Statistical Principles for Clinical Trials." Journal of International Medical Research 26, no. 2 (March 1998): 57–65. http://dx.doi.org/10.1177/030006059802600201.

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If a trial is to be well designed, and the conclusions drawn from it valid, a thorough understanding of the benefits and pitfalls of basic statistical principles is required. When setting up a trial, appropriate sample-size calculation is vital. If initial calculations are inaccurate, trial results will be unreliable. The principle of intent-to-treat in comparative trials is examined. Randomization as a method of selecting patients to treatment is essential to ensure that the treatment groups are equalized in terms of avoiding biased allocation in the mix of patients within groups. Once trial results are available the correct calculation and interpretation of the P-value is important. Its limitations are examined, and the use of the confidence interval to help draw valid conclusions regarding the clinical value of treatments is explored.

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Proschan, Michael A. "Re-randomization tests for unplanned changes in clinical trials." Clinical Trials 14, no. 5 (June 16, 2017): 425–31. http://dx.doi.org/10.1177/1740774517710657.

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Despite the best efforts of investigators, problems forcing design changes can occur in clinical trials. Changes are usually relatively minor, but sometimes not. The primary endpoint or analysis may need to be revised, for example. It is common to regard any conclusion from such a tarnished trial as hypothesis-generating rather than definitive. This article reviews a very useful technique, re-randomization tests, for dealing with such anomalies. Re-randomization tests remain valid for testing a strong null hypothesis that treatment has no effect on the data that led to design changes. Another way of expressing this is that the data used to inform a design change must give no information about the treatment labels. This restriction has implications for limiting the amount of information examined by a committee deciding whether to make design alterations. While nothing can eliminate the pall cast by breaches of protocol, re-randomization tests following blinded and limited data examination go a long way toward amelioration.

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Verberk, W. J., A. A. Kroon, A. G. H. Kessels, P. J. Nelemans, J. W. Van Ree, J. W. M. Lenders, T. Thien, et al. "Comparison of randomization techniques for clinical trials with data from the HOMERUS‐trial." Blood Pressure 14, no. 5 (January 2005): 306–14. http://dx.doi.org/10.1080/08037050500331538.

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RUDY, ELLEN B., PATRICIA L. VASKA, BARBARA J. DALY, MARY BETH HAPP, and PAMELA SHIAO. "Permuted Block Design For Randomization In a Nursing Clinical Trial." Nursing Research 42, no. 5 (September 1993): 287???289. http://dx.doi.org/10.1097/00006199-199309000-00008.

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Shortreed, Susan M., Carolyn M. Rutter, Andrea J. Cook, and Gregory E. Simon. "Improving pragmatic clinical trial design using real-world data." Clinical Trials 16, no. 3 (March 13, 2019): 273–82. http://dx.doi.org/10.1177/1740774519833679.

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Background Pragmatic clinical trials often use automated data sources such as electronic health records, claims, or registries to identify eligible individuals and collect outcome information. A specific advantage that this automated data collection often yields is having data on potential participants when design decisions are being made. We outline how this data can be used to inform trial design. Methods Our work is motivated by a pragmatic clinical trial evaluating the impact of suicide-prevention outreach interventions on fatal and non-fatal suicide attempts in the 18 months after randomization. We illustrate our recommended approaches for designing pragmatic clinical trials using historical data from the health systems participating in this study. Specifically, we illustrate how electronic health record data can be used to inform the selection of trial eligibility requirements, to estimate the distribution of participant characteristics over the course of the trial, and to conduct power and sample size calculations. Results Data from 122,873 people with patient health questionnaire (PHQ) responses, recorded in their electronic health records between 1 July 2010 and 31 March 2012, were used to show that the suicide attempt rate in the 18 months following completion of the questionnaire varies by response to item nine of the PHQ. We estimated that the proportion of individuals with a prior recorded elevated PHQ (i.e. history of suicidal ideation) would decrease from approximately 50% at the beginning of a trial to about 5%, 50 weeks later. Using electronic health record data, we conducted simulations to estimate the power to detect a 25% reduction in suicide attempts. Simulation-based power calculations estimated that randomizing 8000 participants per randomization arm would allow 90% power to detect a 25% reduction in the suicide attempt rate in the intervention arm compared to usual care at an alpha rate of 0.05. Conclusions Historical data can be used to inform the design of pragmatic clinical trials, a strength of trials that use automated data collection for randomizing participants and assessing outcomes. In particular, realistic sample size calculations can be conducted using real-world data from the health systems in which the trial will be conducted. Data-informed trial design should yield more realistic estimates of statistical power and maximize efficiency of trial recruitment.

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White, Ian R., Sarah Walker, and Abdel Babiker. "strbee: Randomization-based Efficacy Estimator." Stata Journal: Promoting communications on statistics and Stata 2, no. 2 (June 2002): 140–50. http://dx.doi.org/10.1177/1536867x0200200203.

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strbee analyzes a two-group clinical trial with a survival outcome, in which some subjects may “crossover” to receive the treatment of the other arm. Adjustment for treatment crossover is done by a randomization-respecting method that preserves the intention-to-treat p-value.

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Rahman, Rifaquat, Lorenzo Trippa, Eudocia Quant Lee, Isabel Arrillaga-Romany, Mehdi Touat, Geffrey Fell, Christine McCluskey, et al. "CTNI-40. EVALUATING FEASIBILITY AND EFFICIENCY OF PHASE II ADAPTIVE PLATFORM TRIAL DESIGNS BASED ON THE INDIVIDUALIZED SCREENING TRIAL OF INNOVATIVE GLIOBLASTOMA THERAPY (INSIGhT) EXPERIENCE." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi68—vi69. http://dx.doi.org/10.1093/neuonc/noab196.265.

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Abstract BACKGROUND The Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) is a phase II platform trial with Bayesian adaptive randomization and deep genomic profiling to more efficiently test experimental agents in newly diagnosed glioblastoma and to prioritize therapies for late-stage testing. METHODS In the ongoing INSIGhT trial, patients with newly diagnosed MGMT-unmethylated glioblastoma are randomized to the control arm or one of three experimental therapy arms (CC-115, abemaciclib, and neratinib). The control arm therapy is radiotherapy with concomitant and adjuvant temozolomide, and primary endpoint is overall survival. Randomization has been adapted based on Bayesian estimation of biomarker-specific probability of treatment impact on progression-free survival (PFS). All tumors undergo detailed molecular sequencing, and this is facilitated with the companion ALLELE protocol. To evaluate feasibility of this approach, we assessed the status of this ongoing trial. RESULTS Since INSIGhT was activated 4.3 years ago, it has expanded to include 12 sites across the United States. A total of 247 patients have been enrolled. Randomization probabilities have been repeatedly adjusted over time based upon early PFS results to alter the randomization ratio from standard 1:1:1:1 randomization. All three arms have completed accrual and efficacy estimates are available based upon comparison to the common control arm in context of relevant biomarkers. There are 87 patients alive and in follow-up, and there are ongoing plans to add additional arms to evaluate further treatments in the future. CONCLUSION The INSIGhT trial demonstrates that a multi-center Bayesian adaptive platform trial is a feasible and effective approach to help prioritize therapies and biomarkers for newly diagnosed GBM. The trial has maintained robust accrual, and the simultaneous testing of multiple agents, sharing a common control arm and adaptive randomization serve as features to increase trial efficiency relative to traditional clinical trial designs.

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Kundt, G. "Comparative Evaluation of Balancing Properties of Stratified Randomization Procedures." Methods of Information in Medicine 48, no. 02 (2009): 129–34. http://dx.doi.org/10.3414/me0538.

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Summary Objectives: If in a clinical trial prognostic factors are known in advance to be associated with the outcome of a patient it is often recommended that the randomization for a clinical trial should be stratified on these factors, particularly in a multicenter trial. Unfortunately, stratified or covariate-adaptive randomization does not always promote greater balance between the numbers of treatment assignments to A and B within each stratum and thus overall. Because such designs have numerous parameters that must be specified, simulation is a good tool to investigate the impact of these parameters on balance. Methods: We investigate and discuss in more detail the difference in balancing performance of three stratified randomization procedures. The permuted-block randomization within strata, the “minimization” method and “self-adjusting” design are assessed overall, within levels of prognostic factors, and within strata. Results: We show the superior performance of “self-adjusting” design and the extent of balancing losses occurring with permuted-block randomization within levels of factors and with “minimization” within strata. A summary of principal conclusions regarding the balancing properties of stratified randomization procedures is presented and general recommendations are offered. Conclusions: The relative merits of each procedure should be weighted carefully in relation to the characteristics of the trial. Considering the likelihood of imbalances, the sample size and values of parameters of stratified randomization procedures (number of prognostic factors, number of factor levels, block size) are important when choosing a randomization procedure.

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Detre, Katherine M. "Design of Clinical Trials." Prehospital and Disaster Medicine 1, S1 (1985): 33–35. http://dx.doi.org/10.1017/s1049023x00043697.

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A Controlled Clinical Trial is the formal experimental approach to treatment evaluation. In such a trial, the investigator controls the assignment of treatments—ideally by randomization— to experimental groups. When well executed, such a trial provides the strongest evidence for the relative effectiveness of the different therapeutic interventions. Some people, myself among them, believe that the clinical trial approach should be used early when a new therapeutic modality is first introduced.The purpose of a controlled clinical trial is to answer the question of whether the new therapy is preferable to standard therapies. If no standard therapy exists, then new therapy has to compete either with no treatment or placebo.

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Taljaard, Monica, Cory E. Goldstein, Bruno Giraudeau, Stuart G. Nicholls, Kelly Carroll, Spencer Phillips Hey, Jamie C. Brehaut, et al. "Cluster over individual randomization: are study design choices appropriately justified? Review of a random sample of trials." Clinical Trials 17, no. 3 (May 5, 2020): 253–63. http://dx.doi.org/10.1177/1740774519896799.

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Background: Novel rationales for randomizing clusters rather than individuals appear to be emerging from the push for more pragmatic trials, for example, to facilitate trial recruitment, reduce the costs of research, and improve external validity. Such rationales may be driven by a mistaken perception that choosing cluster randomization lessens the need for informed consent. We reviewed a random sample of published cluster randomized trials involving only individual-level health care interventions to determine (a) the prevalence of reporting a rationale for the choice of cluster randomization; (b) the types of explicit, or if absent, apparent rationales for the use of cluster randomization; (c) the prevalence of reporting patient informed consent for study interventions; and (d) the types of justifications provided for waivers of consent. We considered cluster randomized trials for evaluating exclusively the individual-level health care interventions to focus on clinical trials where individual randomization is only theoretically possible and where there is a general expectation of informed consent. Methods: A random sample of 40 cluster randomized trials were identified by implementing a validated electronic search filter in two electronic databases (Ovid MEDLINE and Embase), with two reviewers independently extracting information from each trial. Inclusion criteria were the following: primary report of a cluster randomized trial, evaluating exclusively an individual-level health care intervention, published between 2007 and 2016, and conducted in Canada, the United States, European Union, Australia, or low- and middle-income country settings. Results: Twenty-five trials (62.5%, 95% confidence interval = 47.5%–77.5%) reported an explicit rationale for the use of cluster randomization. The most commonly reported rationales were those with logistical or administrative convenience (15 trials, 60%) and those that need to avoid contamination (13 trials, 52%); five trials (20%) were cited rationales related to the push for more pragmatic trials. Twenty-one trials (52.5%, 95% confidence interval = 37%–68%) reported written informed consent for the intervention, two (5%) reported verbal consent, and eight (20%) reported waivers of consent, while in nine trials (22.5%) consent was unclear or not mentioned. Reported justifications for waivers of consent included that study interventions were already used in clinical practice, patients were not randomized individually, and the need to facilitate the pragmatic nature of the trial. Only one trial reported an explicit and appropriate justification for waiver of consent based on minimum criteria in international research ethics guidelines, namely, infeasibility and minimal risk. Conclusion: Rationales for adopting cluster over individual randomization and for adopting consent waivers are emerging, related to the need to facilitate pragmatic trials. Greater attention to clear reporting of study design rationales, informed consent procedures, as well as justification for waivers is needed to ensure that such trials meet appropriate ethical standards.

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Baldassari, Ivan, Andreina Oliverio, Vittorio Krogh, Eleonora Bruno, Giuliana Gargano, Mauro Cortellini, Alice Casagrande, et al. "Recruitment in randomized clinical trials: The MeMeMe experience." PLOS ONE 17, no. 3 (March 25, 2022): e0265495. http://dx.doi.org/10.1371/journal.pone.0265495.

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Introduction Recruitment is essential for the success of clinical trials. We are conducting a randomized clinical trial to test the effect of a Mediterranean dietary intervention with or without 1700 mg/day of metformin for the prevention of age-related chronic diseases, the MeMeMe trial (Trial registration number: EudraCT number: 2012-005427-32 ClinicalTrials.gov ID: NCT02960711). MeMeMe recruiting experience, highlighting strengths, limitations encountered and results is reported. Patients and methods Statistical analysis focused on the reasons for withdrawal according to the recruitment method (“active” versus “passive” criterion) and the time of withdrawal. Logistic regression models were used to explore the associations between the risk of withdrawal and sex, recruitment method, randomization arm, and with markers of compliance to the intervention, such as one-year change in body weight. Results Out of 2035 volunteers, 660 (32.4%) were recruited “actively” and 1375 (67.6%) “passively”. Among people who dropped out of the trial after randomization, there were 19.5% for the “active” and 22.0% for the “passive” method (p = 0.28). The risk of withdrawal was significantly higher in women (OR:1.91; 95% CI:1.17–3.12; p = 0.01), in volunteers older at recruitment (OR:1.25; 95% CI:1.07–1.45; p = 0.004), and in those with a higher BMI at baseline (OR:1.23; 95% CI:1.07–1.43; p = 0.004). Volunteers who lost at least 2 kg (the median weight change) in the first year of intervention were significantly less (53%) likely to withdraw from the trial (OR:0.48; 95% CI:0.30–0.75; p = 0.001). Conclusion Our findings suggest that the “passive” recruitment method was more effective than the “active” one to advance recruitment. The benefits of “passive” recruitment hardly outweighed the drawbacks. Trial registration Trial registration number: EudraCT number: 2012-005427-32. ClinicalTrials.gov ID: NCT02960711.

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Park, Yeonhee. "Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials." Journal of Personalized Medicine 12, no. 3 (March 12, 2022): 448. http://dx.doi.org/10.3390/jpm12030448.

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Personalized medicine has been emerging to take into account individual variability in genes and environment. In the era of personalized medicine, it is critical to incorporate the patients’ characteristics and improve the clinical benefit for patients. The patients’ characteristics are incorporated in adaptive randomization to identify patients who are expected to get more benefit from the treatment and optimize the treatment allocation. However, it is challenging to control potential selection bias from using observed efficacy data and the effect of prognostic covariates in adaptive randomization. This paper proposes a personalized risk-based screening design using Bayesian covariate-adjusted response-adaptive randomization that compares the experimental screening method to a standard screening method based on indicators of having a disease. Personalized risk-based allocation probability is built for adaptive randomization, and Bayesian adaptive decision rules are calibrated to preserve error rates. A simulation study shows that the proposed design controls error rates and yields a much smaller number of failures and a larger number of patients allocated to a better intervention compared to existing randomized controlled trial designs. Therefore, the proposed design performs well for randomized controlled clinical trials under personalized medicine.

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Richard Entsuah, A. "Randomization procedures for analyzing clinical trial data with treatment related withdrawals." Communications in Statistics - Theory and Methods 19, no. 10 (January 1990): 3859–80. http://dx.doi.org/10.1080/03610929008830418.

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Johnson, Karen C., Catherine J. Clarke, Ronald I. Shorr, Christine K. Mauck, Robert L. Summitt, and William B. Applegate. "Will women accept randomization in a clinical trial of hormonal contraceptives?" Clinical Journal of Women[apos ]s Health 1, no. 1 (December 2000): 16–22. http://dx.doi.org/10.1053/cjwh.2000.19164.

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Snowdon, Claire, Diana Elbourne, and Jo Garcia. "Zelen randomization: Attitudes of parents participating in a neonatal clinical trial." Controlled Clinical Trials 19, no. 3 (June 1998): S74—S75. http://dx.doi.org/10.1016/s0197-2456(98)80183-9.

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Yu, A.-Mi, and Jae-Won Lee. "Comparing the Randomization Methods Considering the Covariates in a Clinical Trial." Korean Journal of Applied Statistics 23, no. 6 (December 31, 2010): 1047–56. http://dx.doi.org/10.5351/kjas.2010.23.6.1047.

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Armitage, Peter. "Discussion of “Randomization: The Forgotten Component of the Randomized Clinical Trial”." Statistics in Medicine 38, no. 1 (December 9, 2018): 13. http://dx.doi.org/10.1002/sim.7918.

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Louis, Thomas A. "Discussion of “Randomization: The forgotten component of the randomized clinical trial”." Statistics in Medicine 38, no. 1 (December 9, 2018): 19–22. http://dx.doi.org/10.1002/sim.7923.

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Day, Simon. "Reflections on “Randomization: The Forgotten Component of the Randomized Clinical Trial”." Statistics in Medicine 38, no. 1 (December 9, 2018): 17–18. http://dx.doi.org/10.1002/sim.7924.

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Wittes, Janet Turk. "Commentary on Randomization: The forgotten component of the randomized clinical trial." Statistics in Medicine 38, no. 1 (December 9, 2018): 14–16. http://dx.doi.org/10.1002/sim.7933.

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Zhao, Wenle, and Vance Berger. "Imbalance control in clinical trial subject randomization—from philosophy to strategy." Journal of Clinical Epidemiology 101 (September 2018): 116–18. http://dx.doi.org/10.1016/j.jclinepi.2018.02.022.

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Vogel, Victor G., and Lisa S. Parker. "Ethical Issues of Chemoprevention Clinical Trials." Cancer Control 4, no. 2 (March 1997): 142–49. http://dx.doi.org/10.1177/107327489700400205.

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Background Chemoprevention of malignancy is a new concept in clinical medicine, and little is written about the ethics of identifying and enrolling eligible subjects in chemoprevention clinical trials. Methods The authors identify the ethical issues raised in the conduct of clinical chemoprevention trials and review the ethical considerations that should guide clinical researchers in the design and conduct of this new type of clinical trial. Results The ethics of chemoprevention clinical trials are complicated because (1) chemoprevention lies at the intersection of disease management and health promotion, (2) there are conflicting interests competing in these trials, and (3) multiple values play a role in determining the nature and magnitude of the risks and benefits of chemoprevention of cancer. Ethical questions related to these trials concern the enrollment of healthy individuals rather than cancer patients, confidentiality in recruitment, the enrollment of “high-risk” subjects, randomization, informed consent, trial monitoring, and competing outcomes and toxicities. Conclusions These issues will be resolved with the accumulating clinical experience and ethical deliberations that accompany ongoing clinical chemoprevention research studies.

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ŠAPOKA, Virginijus, Vytautas KASIULEVIČIUS, and Janina DIDŽIAPETRIENĖ. "How should a clinician interpret results of randomized controlled trials?" Acta medica Lituanica 17, no. 1-2 (January 1, 2010): 30–34. http://dx.doi.org/10.15388/amed.2010.21689.

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Randomized controlled trials (RCTs) and systematic reviews are the most reliable methods of determining the effects of treatment. The randomization procedure gives a randomized controlled trial its strength. Random allocation means that all participants have the same chance of being assigned to each of the study groups. The choice of which end point(s) to select is critical to any study design. Intention-to-treat is the preferred approach to the analysis of clinical trials. Sample size calculations and data analyses have an important impact on the planning, interpretation, and conclusions of randomized trials. In this article, we discuss the problematic areas that can affect the outcome of a trial, such as blinding, sample size calculation, randomization; concealment allocation; intention of treating the analysis; selection of end points; selection of traditional versus equivalence testing, early stopped trials, selective publications. Keywords: randomized controlled trials, sample size, outcomes, type of analyses

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Heo, Moonseong, Paul Meissner, Alain H. Litwin, Julia H. Arnsten, M. Diane McKee, Alison Karasz, Paula McKinley, et al. "Preference option randomized design (PORD) for comparative effectiveness research: Statistical power for testing comparative effect, preference effect, selection effect, intent-to-treat effect, and overall effect." Statistical Methods in Medical Research 28, no. 2 (November 9, 2017): 626–40. http://dx.doi.org/10.1177/0962280217734584.

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Comparative effectiveness research trials in real-world settings may require participants to choose between preferred intervention options. A randomized clinical trial with parallel experimental and control arms is straightforward and regarded as a gold standard design, but by design it forces and anticipates the participants to comply with a randomly assigned intervention regardless of their preference. Therefore, the randomized clinical trial may impose impractical limitations when planning comparative effectiveness research trials. To accommodate participants’ preference if they are expressed, and to maintain randomization, we propose an alternative design that allows participants’ preference after randomization, which we call a “preference option randomized design (PORD)”. In contrast to other preference designs, which ask whether or not participants consent to the assigned intervention after randomization, the crucial feature of preference option randomized design is its unique informed consent process before randomization. Specifically, the preference option randomized design consent process informs participants that they can opt out and switch to the other intervention only if after randomization they actively express the desire to do so. Participants who do not independently express explicit alternate preference or assent to the randomly assigned intervention are considered to not have an alternate preference. In sum, preference option randomized design intends to maximize retention, minimize possibility of forced assignment for any participants, and to maintain randomization by allowing participants with no or equal preference to represent random assignments. This design scheme enables to define five effects that are interconnected with each other through common design parameters—comparative, preference, selection, intent-to-treat, and overall/as-treated—to collectively guide decision making between interventions. Statistical power functions for testing all these effects are derived, and simulations verified the validity of the power functions under normal and binomial distributions.

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Chen, Chen-Hsin, Pesus Chou, Han-Hwa Hu, and Julia J. Tsuei. "Further Analysis of a Pilot Study for Planning an Extensive Clinical Trial in Traditional Medicine - with an Example of Acupuncture Treatment for Stroke." American Journal of Chinese Medicine 22, no. 02 (January 1994): 127–36. http://dx.doi.org/10.1142/s0192415x94000164.

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Statistical methods for evaluating the effects of treatments and prognostic factors in clinical trials are discussed. Exploratory data analysis, nonparametric methods, regression modeling, and regression diagnostics of influential cases are applied to the analysis of a pilot 'randomized' controlled trial on the treatment of acute stroke with acupuncture. The utility of this analysis for modifying patient eligibility criteria, determining required sample size and utilizing stratified randomization in a future extensive stroke trial is discussed.

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Law, Zhe Kang, Jason P. Appleton, Polly Scutt, Ian Roberts, Rustam Al-Shahi Salman, Timothy J. England, David J. Werring, et al. "Brief Consent Methods Enable Rapid Enrollment in Acute Stroke Trial: Results From the TICH-2 Randomized Controlled Trial." Stroke 53, no. 4 (April 2022): 1141–48. http://dx.doi.org/10.1161/strokeaha.121.035191.

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Background: Seeking consent rapidly in acute stroke trials is crucial as interventions are time sensitive. We explored the association between consent pathways and time to enrollment in the TICH-2 (Tranexamic Acid in Intracerebral Haemorrhage-2) randomized controlled trial. Methods: Consent was provided by patients or by a relative or an independent doctor in incapacitated patients, using a 1-stage (full written consent) or 2-stage (initial brief consent followed by full written consent post-randomization) approach. The computed tomography-to-randomization time according to consent pathways was compared using the Kruskal-Wallis test. Multivariable logistic regression was performed to identify variables associated with onset-to-randomization time of ≤3 hours. Results: Of 2325 patients, 817 (35%) gave self-consent using 1-stage (557; 68%) or 2-stage consent (260; 32%). For 1507 (65%), consent was provided by a relative (1 stage, 996 [66%]; 2 stage, 323 [21%]) or a doctor (all 2-stage, 188 [12%]). One patient did not record prerandomization consent, with written consent obtained subsequently. The median (interquartile range) computed tomography-to-randomization time was 55 (38–93) minutes for doctor consent, 55 (37–95) minutes for 2-stage patient, 69 (43–110) minutes for 2-stage relative, 75 (48–124) minutes for 1-stage patient, and 90 (56–155) minutes for 1-stage relative consents ( P <0.001). Two-stage consent was associated with onset-to-randomization time of ≤3 hours compared with 1-stage consent (adjusted odds ratio, 1.9 [95% CI, 1.5–2.4]). Doctor consent increased the odds (adjusted odds ratio, 2.3 [1.5–3.5]) while relative consent reduced the odds of randomization ≤3 hours (adjusted odds ratio, 0.10 [0.03–0.34]) compared with patient consent. Only 2 of 771 patients (0.3%) in the 2-stage pathways withdrew consent when full consent was sought later. Two-stage consent process did not result in higher withdrawal rates or loss to follow-up. Conclusions: The use of initial brief consent was associated with shorter times to enrollment, while maintaining good participant retention. Seeking written consent from relatives was associated with significant delays. Registration: URL: https://www.isrctn.com ; Unique identifier: ISRCTN93732214.

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Angelidou, Konstantia, Mary Glenn Fowler, Pat Flynn, Anne Coletti, Katie McCarthy, Renee Browning, James McIntyre, Sean S. Brummel, David E. Shapiro, and Camlin Tierney. "Enrollment and transition challenges in the International Maternal Pediatric and Adolescent AIDS Clinical Trials (IMPAACT) network’s PROMISE trial for resource-limited regions." Clinical Trials 17, no. 4 (March 19, 2020): 437–47. http://dx.doi.org/10.1177/1740774520912428.

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Background: We describe enrollment and accrual challenges in the “Promoting Maternal and Infant Survival Everywhere” (PROMISE) trial conducted in resource-limited countries, as well as the challenges in transitioning participants from the antepartum to the postpartum components of the study. Methods: PROMISE was a large multi-national randomized controlled trial of the safety and efficacy of interventions to reduce perinatal transmission of HIV-1 (HIV) during pregnancy and breastfeeding and of interventions to preserve maternal health after cessation of perinatal transmission risk. The PROMISE study included two protocols for HIV-infected pregnant women in resource-limited countries who intended to either breastfeed or formula-feed their infants and did not meet country criteria for antiretroviral treatment. The PROMISE breastfeeding protocol (1077BF) used a sequential randomization design with up to three randomizations (Antepartum, Postpartum, and Maternal Health). The PROMISE formula-feeding protocol (1077FF) had two randomizations (Antepartum and Maternal Health). Women presenting to the clinic during early or active labor or in the immediate postpartum period were registered as Late Presenters and screened to determine whether eligible to participate in the Postpartum randomization. Results: The study was conducted at 14 sites in seven countries and opened to enrollment in April 2011. A total of 3259 pregnant women intending to breastfeed and an additional 284 pregnant women intending to formula feed were randomized in the Antepartum component. A total of 204 Late Presenters were registered during labor or after delivery. Enrollment was high among breastfeeding women (representing 96% of the target of 3400 women) but was lower than expected among women intending to formula feed (28% of 1000 expected) and late-presenting women (8% of 2500 expected). The successful overall enrollment and final primary study analyses results were attributed to substantial preparation before the study opened, collaboration among all stakeholders, close study monitoring during implementation and the flexibility to change and streamline the protocol. Conclusions: Experiences from the PROMISE study illustrate the challenges of enrolling in longer term studies in the setting of rapidly evolving prevention and treatment standards priorities. The lessons learned will help the community, site investigators, and study coordinators in the design and implementation of future clinical trials.

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Ibsen, Eric, and Jeff Kumer. "Pre-clinical studies of inflammation: automating the most common methods of animal randomization and distribution (EDU1P.254)." Journal of Immunology 192, no. 1_Supplement (May 1, 2014): 49.8. http://dx.doi.org/10.4049/jimmunol.192.supp.49.8.

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Abstract To more closely emulate clinical research methodology and improve clinical relevance, researchers are increasingly utilizing more clinically-similar group randomization and distribution methods for Inflammation studies in animals. A common pre-clinical approach is paradoxically the least clinically-relevant; a random number generator in a spreadsheet, which is subject to manual and transcription errors and provides no easy way to exclude animals from distribution based on other parameters. Pre-clinical trial software is available which automates the most recommended randomization and distribution methods based on tumor volume or other parameters: deterministic and pair-matched distribution, pure randomization, stratified sampling randomization and randomization across multiple numeric parameters. Subjects may be excluded initially based on recorded clinical observations and comments and then by acceptable value ranges for any numeric parameter. Animals can be randomized iteratively in a rolling or staggered way and can be re-randomized into new groups as required on drug-resistance studies. Proposed randomization results by ANOVA can be displayed instantly to ensure that the group means are acceptably similar. Standardizing and automating methods of randomization and distribution which are similar to this used clinically can potentially improve the ease, clinical relevance, quality and outcome of pre-clinical Inflammation studies relative to spreadsheet-based methods.

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Kundt, G. "An Alternative Proposal for “Mixed Randomization” by Schulz and Grimes." Methods of Information in Medicine 44, no. 04 (2005): 572–76. http://dx.doi.org/10.1055/s-0038-1634009.

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Summary Objective: Randomization is an important part of clinical trials. Using permuted-block randomization for forcing equal group sizes potentially harms the unpredictability of treatment assignments. This can allow bias to creep into a trial. As an alternative, Schulz and Grimes suggest a “Mixed randomization” scheme which introduces more complexity to realize randomization. The objective of our research was to work out a model for randomization which is easier to handle than “Mixed randomization”, with an equal level of performance in unpredictability and balance. Methods: We analyzed a “Mixed randomization” procedure regarding the degree of unpredictability and balancing power and compared performance using permuted-block randomization with very large block size in a worst case scenario. Our work was done by the application of Blackwell-Hodges model for evaluation of the unpredictability of treatment assignments. Results: Regarding unpredictability, performance of permuted-block randomization with block size b = 36 was very similar to that of “Mixed randomization”. Regarding balancing power it was more favourable than “Mixed randomization”. Conclusion: Results of Schulz and Grimes are very important as they emphasized that mildly unequal sample sizes of therapy groups don’t cause problems. But the suggested scheme of “Mixed randomization” to a large extent adds complexity and we do not believe that this proposal is very feasible. Basically, we rather recommend the use of only one restricted randomization procedure in the best way. This can be permuted-block randomization with optimum choice of a large block size.

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Walsh, J. H., and D. Sahasrabudhe. "Knowledge of cancer clinical trials in a population being screened for cancer." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 6095. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.6095.

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6095 Background: Only 2.5 percent of adult cancer patients enrolled in cooperative group clinical trials between 1998 and 1999. Increasing participation in clinical trials is important in efforts to improve cancer care. Methods: We administered an anonymous true/false questionnaire that assessed knowledge of clinical trials to 117 men seeking prostate cancer screening. Questions involved the rationale for conducting clinical trials, regulatory oversight of clinical trials, informed consent, randomization, blinding, use of placebos, and safeguards built into clinical trial design. We performed multi-way ANOVA and t-test analyses to compare the average proportion of correct responses by group. Results: On 71 percent of test questions, participants chose the correct answer 80 percent or more of the time. Descriptive general statements about the conduct of clinical trials were answered correctly 88 percent of the time. Questions in which participants scored less than 80 percent involved randomization, blinding, and the use of clinical trials to improve cancer treatments. 45 percent incorrectly answered a question about placebo use. Results of the univariate t-tests and multi-way ANOVA suggest that white men and employed men are significantly more knowledgeable about clinical trials. College educated men also appear somewhat more knowledgeable about clinical trials, although this effect does not attain statistical significance when other predictors are controlled. Marital status, age, health insurance status and income do not have a significant effect on men’s knowledge. Conclusions: Percent of participants answering correctly decreased for concepts such as randomization, placebo use, and blinding. A belief persists that placebos will be used in cancer clinical trials. This may play a role in low accrual, and may be an area in which educational interventions could improve accrual. This study identifies specific concepts, beliefs, and populations that have to be addressed to improve enrollment on cancer clinical trials. [Table: see text] No significant financial relationships to disclose.

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Rahman, Rifaquat, Lorenzo Trippa, Geoffrey Fell, Eudocia Quant Lee, Isabel Arrillaga-Romany, Mehdi Touat, Jan Drappatz, et al. "Evaluating the benefit of adaptive randomization in the CC-115 arm of the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT): A phase II randomized Bayesian adaptive platform trial in newly diagnosed MGMT unmethylated glioblastoma." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): 2006. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.2006.

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2006 Background: Adaptive randomization adjusts enrollment rates based upon early trial results, which can allow for decreased enrollment for therapies less likely to meet the primary endpoint of a trial. CC-115, a CNS-penetrant, oral inhibitor of mammalian target of rapamycin kinase (mTOR) and deoxyribonucleic acid-dependent protein kinase (DNA-PK), was evaluated in the Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT) trial. As CC-115 was discontinued due to concerns about toxicity and unfavorable risk-to-benefit ratio, we sought to investigate the impact of adaptive randomization in its testing. Methods: In INSIGhT, adults with newly diagnosed MGMT-unmethylated glioblastoma and available genomic data are adaptively randomized to an experimental arm or the control arm of standard radiotherapy with concurrent and adjuvant temozolomide. Patients randomized to CC-115 received it (10mg po BID) with radiotherapy and as adjuvant monotherapy, and a safety lead-in 3+3 design was used for this arm. By simulating the INSIGhT trial with standard uniform randomization, we estimated the reduction of enrollment rate and sample size of the CC-115 arm that was attributable to adaptive randomization. Results: Twelve patients were randomized to CC-115; 58% (n = 7) patients had possible treatment-related CTCAE grade > 3 toxicity. Compared to the control arm, there was no significant difference in progression-free survival (PFS, HR 0.66, 95% CI 0.32-1.36, p = 0.3) or overall survival (OS, HR 0.93, 95% CI 0.43-2.03, p = 0.8). Based on early PFS results, randomization probability to CC-115 decreased from 25% to 16%. At the time of the CC-115 arm closure, 14% of enrolled INSIGhT patients had been randomized to this arm. Compared to average expected enrollment by standard randomization, the use of adaptive randomization decreased the number of patients randomized to CC-115 by 50% (12 patients vs. 18 patients [95% CI 11-25 patients]). Conclusions: The INSIGhT trial, designed with adaptive randomization, facilitated more efficient testing of CC-115 and decreased the number of patients allocated to the CC-115 arm relative to a standard randomization design. Clinical trial information: NCT02977780.

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Scharfstein, Daniel O., and Aidan McDermott. "Global sensitivity analysis of clinical trials with missing patient-reported outcomes." Statistical Methods in Medical Research 28, no. 5 (March 20, 2018): 1439–56. http://dx.doi.org/10.1177/0962280218759565.

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Randomized trials with patient-reported outcomes are commonly plagued by missing data. The analysis of such trials relies on untestable assumptions about the missing data mechanism. To address this issue, it has been recommended that the sensitivity of the trial results to assumptions should be a mandatory reporting requirement. In this paper, we discuss a recently developed methodology (Scharfstein et al., Biometrics, 2018) for conducting sensitivity analysis of randomized trials in which outcomes are scheduled to be measured at fixed points in time after randomization and some subjects prematurely withdraw from study participation. The methodology is explicated in the context of a placebo-controlled randomized trial designed to evaluate a treatment for bipolar disorder. We present a comprehensive data analysis and a simulation study to evaluate the performance of the method. A software package entitled SAMON (R and SAS versions) that implements our methods is available at www.missingdatamatters.org .

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Journal articles on the topic 'Clinical Trial Randomization'

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