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Journal articles on the topic 'Clinical trials analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Geller, N. L. "Planned interim analysis and its role in cancer clinical trials." Journal of Clinical Oncology 5, no. 9 (September 1987): 1485–90. http://dx.doi.org/10.1200/jco.1987.5.9.1485.

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Although interim analyses in cancer clinical trials are commonplace, clinical trials are usually designed with the implicit assumption that data analysis will occur only after the trial is completed. The design of randomized trials with planned interim analyses, "group sequential trials," is described and examples are given. A method to redesign trials in which unplanned interim analyses have been undertaken is described. Planned interim analysis should be considered whenever a cancer clinical trial is designed.

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2

Feizabadi, Mansoureh, Fatima Fahimnia, Alireza Mosavi Jarrahi, Nader Naghshineh, and Shahram Tofighi. "Iranian clinical trials: An analysis of registered trials in International Clinical Trial Registry Platform (ICTRP)." Journal of Evidence-Based Medicine 10, no. 2 (May 2017): 91–96. http://dx.doi.org/10.1111/jebm.12248.

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3

Aartsma-Rus, Annemieke. "Dystrophin Analysis in Clinical Trials." Journal of Neuromuscular Diseases 1, no. 1 (2014): 41–53. http://dx.doi.org/10.3233/jnd-140013.

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4

Cook, David I., Val J. Gebski, and Anthony C. Keech. "Subgroup analysis in clinical trials." Medical Journal of Australia 180, no. 6 (March 2004): 289–91. http://dx.doi.org/10.5694/j.1326-5377.2004.tb05928.x.

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5

Wassertheil-Smoller, Sylvia, and Mimi Y. Kim. "Statistical Analysis of Clinical Trials." Seminars in Nuclear Medicine 40, no. 5 (September 2010): 357–63. http://dx.doi.org/10.1053/j.semnuclmed.2010.04.001.

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6

Caelleigh, A. S. "Clinical trials and meta-analysis." Academic Medicine 72, no. 12 (December 1997): 1030–1. http://dx.doi.org/10.1097/00001888-199712000-00009.

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7

Armitage, Peter. "Interim analysis in clinical trials." Statistics in Medicine 10, no. 6 (June 1991): 925–37. http://dx.doi.org/10.1002/sim.4780100613.

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8

Kassirer, Jerome P. "Clinical Trials and Meta-Analysis." New England Journal of Medicine 327, no. 4 (July 23, 1992): 273–74. http://dx.doi.org/10.1056/nejm199207233270411.

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9

DerSimonian, Rebecca, and Nan Laird. "Meta-analysis in clinical trials." Controlled Clinical Trials 7, no. 3 (September 1986): 177–88. http://dx.doi.org/10.1016/0197-2456(86)90046-2.

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10

Su, Hui-Chun Irene, and Mary D. Sammel. "Interim analysis in clinical trials." Fertility and Sterility 97, no. 3 (March 2012): e9. http://dx.doi.org/10.1016/j.fertnstert.2012.01.107.

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11

Gheorghiade, Mihai, Lonni Schultz, Barbara Tilley, Walter Kao, and Sidney Goldstein. "Subgroup analysis of clinical trials." American Journal of Cardiology 67, no. 4 (February 1991): 330–31. http://dx.doi.org/10.1016/0002-9149(91)90595-c.

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12

Sridharan, Kannan, and Gowri Sivaramakrishnan. "Clinical trials in Ayurveda: Analysis of clinical trial registry of India." Journal of Ayurveda and Integrative Medicine 7, no. 3 (July 2016): 141–43. http://dx.doi.org/10.1016/j.jaim.2016.08.009.

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13

Hirt, Julian, Abeelan Rasadurai, Matthias Briel, Pascal Düblin, Perrine Janiaud, and Lars G. Hemkens. "Clinical trial research on COVID-19 in Germany – a systematic analysis." F1000Research 10 (September 10, 2021): 913. http://dx.doi.org/10.12688/f1000research.55541.1.

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Background: In 2020, the COVID-19 pandemic led to an unprecedented volume of almost 3,000 clinical trials registered worldwide. We aimed to describe the COVID-19 clinical trial research agenda in Germany during the first year of the pandemic. Methods: We identified randomized clinical trials assessing interventions to treat or prevent COVID-19 that were registered in 2020 and recruited or planned to recruit participants in Germany. We requested recruitment information from trial investigators as of April 2021. Results: In 2020, 65 trials were completely (n=27) or partially (n=38) conducted in Germany. Most trials investigated interventions to treat COVID-19 (86.2%; 56/65), in hospitalized patients (67.7%; 44/65), with industry funding (53.8%; 35/65). Few trials were completed (21.5%; 14/65). Overall, 187,179 participants were planned to be recruited (20,696 in Germany), with a median number of 106 German participants per trial (IQR 40 to 345). From the planned German participants, 13.4% were recruited (median 15 per trial (IQR 0 to 44). Conclusions: The overall German contribution to the worldwide COVID-19 clinical trial research agenda was modest. Few trials delivered urgently needed evidence. Most trials did not meet recruitment goals. Evaluation and international comparison of the challenges for conducting clinical trials in Germany is needed.

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14

Neto, Ary Serpa, George Tomlinson, Sarina K. Sahetya, Lorenzo Ball, Alistair D. Nichol, Carol Hodgson, Alexandre Biasi Cavalcanti, et al. "Higher PEEP for acute respiratory distress syndrome: a Bayesian meta-analysis of randomised clinical trials." Critical Care and Resuscitation 23, no. 2 (June 7, 2021): 171–82. http://dx.doi.org/10.51893/2021.2.oa4.

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Objective: Benefit or harm of higher positive end expiratory pressure (PEEP) for acute respiratory distress syndrome (ARDS) is controversial. We aimed to assess the impact of higher levels of PEEP in patients with ARDS under a Bayesian framework. Design: Systematic review and Bayesian meta-analysis of randomised clinical trials comparing higher to lower PEEP in adult patients with ARDS. Data sources: MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials from 1996 to 1 March 2020. Review methods: We extracted data from high quality randomised clinical trials comparing higher to lower levels of PEEP in adult patients, using low tidal volume in both arms, and conducted a Bayesian meta-analysis using aggregate data from these studies. Results: Eight clinical trials including 3703 patients (n = 1833 for higher PEEP, n = 1870 for lower PEEP) were included. Under a minimally informative prior, the posterior probability of benefit with higher PEEP was 65% (relative risk, 0.97 [95% credible interval, 0.78–1.14]). In patients with moderate-to-severe ARDS, the posterior probability of benefit with higher PEEP was 77% (relative risk, 0.94 [95% credible interval, 0.77–1.13]). Down-weighting studies that employed a maximum recruitment strategy by 100% increased the posterior probability of benefit to 92% under a minimally informative prior. Conclusions: The probability of benefit or harm from routine use of higher PEEP for patients with ARDS ranges from 27% to 86%, and from 14% to 73% depending on one’s prior, suggesting continued uncertainty and equipoise regarding the benefit of PEEP. If data from trials using a maximum recruitment strategy is discounted to some extent because of uncertainty over the appropriateness of this approach, the available evidence suggests that higher PEEP could be beneficial for moderate-to-severe ARDS. However, well powered randomised clinical trials are needed to confirm these findings.

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15

Tsai, Kao-Tai, and Karl Peace. "Analysis of Subgroup Data of Clinical Trials." Journal of Causal Inference 1, no. 2 (September 10, 2013): 193–207. http://dx.doi.org/10.1515/jci-2012-0008.

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AbstractLarge randomized controlled clinical trials are the gold standard to evaluate and compare the effects of treatments. It is common practice for investigators to explore and even attempt to compare treatments, beyond the first round of primary analyses, for various subsets of the study populations based on scientific or clinical interests to take advantage of the potentially rich information contained in the clinical database. Although subjects are randomized to treatment groups in clinical trials, this does not imply the same degree of randomization among sub-populations of the original trials. Therefore, comparisons of treatments in sub-populations may not produce fair and unbiased results without properly addressing this issue. Covariate adjustments in regression analysis and propensity score matching are commonly used to address the non-randomized nature of the sub-populations issue with various degrees of success. However, further improvements to these methods are still possible. In this article, we propose an analysis strategy that shows improvement to conventional methods. Treatment effects and their differences are estimated after adjustment for background imbalances. Treatment groups are then compared using confidence intervals whose limits are determined using the Robbins–Monro stochastic approximation. Data from a recent clinical trial are used to illustrate the methodology.

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16

Zeeneldin, Ahmed A., and Fatma M. Taha. "The Egyptian clinical trials’ registry profile: Analysis of three trial registries (International Clinical Trials Registry Platform, Pan-African Clinical Trials Registry and clinicaltrials.gov)." Journal of Advanced Research 7, no. 1 (January 2016): 37–45. http://dx.doi.org/10.1016/j.jare.2015.01.003.

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17

Brard, Caroline, Gwénaël Le Teuff, Marie-Cécile Le Deley, and Lisa V. Hampson. "Bayesian survival analysis in clinical trials: What methods are used in practice?" Clinical Trials 14, no. 1 (October 11, 2016): 78–87. http://dx.doi.org/10.1177/1740774516673362.

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Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

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18

Garcia-Verdugo, Rosa, Michael Erbach, and Oliver Schnell. "Need for Outcome Scenario Analysis of Clinical Trials in Diabetes." Journal of Diabetes Science and Technology 11, no. 2 (October 5, 2016): 327–34. http://dx.doi.org/10.1177/1932296816670925.

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Since the FDA requirement for cardiovascular safety of all new antihyperglycemic drugs to enter the market, the number and extent of phase 3 clinical trials has markedly increased. Unexpected trial results imply an enormous economic, personal and time cost and has deleterious effects over R&D. To prevent unforeseen developments in clinical trials, we recommend performing a comprehensive prospective outcome scenario analysis before launching the trial. In this commentary, we discuss the most important factors to take in consideration for prediction of clinical trial outcome scenarios and propose a theoretical model for decision making.

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19

Milosevic-Georgiev, Andrijana, Dusanka Krajnovic, Srdjan Milovanovic, Svetlana Ignjatovic, Dusan Djuric, and Valentina Marinkovic. "Analysis of regulatory-ethical framework of clinical trials." Srpski arhiv za celokupno lekarstvo 141, no. 9-10 (2013): 659–66. http://dx.doi.org/10.2298/sarh1310659m.

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Introduction. Every clinical trial has to meet all ethical criteria in addition to the scientific ones. The basic ethical principles in the clinical trials are the following: nonmaleficence, beneficence, respect for autonomy and the principle of justice. Objective. The aim of the study was to analyze clinical cases with the outcomes leading to the changes in regulatory?ethical framework related to the clinical trials, as well as the outcomes of key clinical trials that influenced the introduction of the ethical principles into clinical trials. Methods. This was a descriptive research (methods of analysis and documentation; desk analysis of the secondary data). Results. By analyzing the cases from the secondary sources as well as clinical and ethical outcomes, it may be noticed that the codes, declarations and regulations have been often preceded by certain events that caused their adoption. Moral concern and public awareness of the ethical issues have initiated not only the development of numerous guidelines, codes, and declarations, but also their incorporation into the legislative acts. Conclusion. It is desirable that ethical instruments become legally binding documents, because only in this way will be possible to control all phases of the clinical trials and prevent abuse of the respondents.

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20

Drummond, Michael F., and Linda Davies. "Economic Analysis Alongside Clinical Trials: Revisiting the Methodological Issues." International Journal of Technology Assessment in Health Care 7, no. 4 (1991): 561–73. http://dx.doi.org/10.1017/s0266462300007121.

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AbstractControlled clinical trials are recognized as the best source of data on the efficacy of health care interventions and technologies. Because economic evaluation is dependent on the quality of the underlying medical evidence, clinical trials have increasingly been viewed as a natural vehicle for economic analysis. However, the closer integration of economic and clinical research raises many methodological issues. This paper discusses these issues in trial design, collection of resource use data, collection of outcome data, and interpretation and extrapolation of results. Some guidelines are suggested for economic analysts wishing to undertake evaluations alongside clinical trials.

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21

Katayama, Erryk Stephan, Jonathan J. Hue, David Lawrence Bajor, Lee Mayer Ocuin, John Brian Ammori, Jeffrey Hardacre, and Jordan Michael Winter. "Clinical trials in pancreatic cancer: A comprehensive analysis." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e16730-e16730. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e16730.

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e16730 Background: Pancreatic cancer is the most aggressive of common cancers and desperately in need of novel therapies. Unlike many other common cancers, there have been no new paradigm-changing therapies in the past 40 years beyond chemotherapy. The most urgent question in the field then is what is coming down the pike? In this study, we perform the first comprehensive analysis of the current clinical trial landscape in pancreatic cancer to better understand the pipeline of new therapies. Methods: We queried clinicaltrials.gov for registered pancreatic cancer clinical trials. Studies were curated and categorized according to the phase of study, the clinical stage of the study population, the type of the intervention under investigation, and the biologic mechanism targeted by the therapy. This compendium revealed the full landscape of investigational therapeutic trials. Results: As of May 18, 2019, there were 440 total trials testing 600 distinct interventions. 104 of these trials spanned multiple phases, yielding 544 trials across phases. These included 38 trials (8.6%) in phase III testing, 269 (61%) in phase II, and 237 (54%) in phase I. With respect to therapeutic category, 186 (31%) were investigating immunotherapies, 66 (11%) targeted cell signaling pathways, 140 (23%) targeted cell cycle or DNA biology, and 32 (5%) targeted metabolic pathways. Of the 38 phase III trials, only 10 are currently testing novel drugs that are not already FDA approved or routinely used in patients for another indication. Conclusions: A large number of novel therapeutic strategies are currently under investigation. They include a broad range of therapies targeting diverse biologic processes. However, only a small number of novel therapies are in late-stage testing, suggesting that future progress is likely several years away, and dependent on the success of early-stage trials. [Table: see text]

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22

Chowta, MuktaN, PrabhaM Adhikari, KV Ramesh, and AshokK Shenoy. "Pharmacogenomics in clinical trials: An analysis." Indian Journal of Medical Sciences 61, no. 10 (2007): 574. http://dx.doi.org/10.4103/0019-5359.35808.

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23

van der Graaf, Yolanda. "Clinical trials: study design and analysis." European Journal of Radiology 27, no. 2 (May 1998): 108–15. http://dx.doi.org/10.1016/s0720-048x(97)00159-9.

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24

Imai, Kumiko, and Ping Zhang. "Integrating economic analysis into clinical trials." Lancet 365, no. 9473 (May 2005): 1749–50. http://dx.doi.org/10.1016/s0140-6736(05)66390-8.

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25

Pihlstrom, Bruce. "Analysis and Reporting of Clinical Trials." Journal of Clinical Periodontology 35, no. 8 (August 2008): 680. http://dx.doi.org/10.1111/j.1600-051x.2008.01251.x.

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26

Chilet-Rosell, Elisa, Ma Teresa Ruiz-Cantero, and Ma Angeles Pardo. "Gender Analysis of Moxifloxacin Clinical Trials." Journal of Women's Health 23, no. 1 (January 2014): 77–104. http://dx.doi.org/10.1089/jwh.2012.4171.

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27

Jeong, Jong-Hyeon. "Statistical analysis in cancer clinical trials." Anti-Cancer Drugs 19, Supplement 1 (February 2008): S9—S10. http://dx.doi.org/10.1097/01.cad.0000277609.31000.10.

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28

Allin, Paul, and C. L. Meinhert. "Clinical Trials: Design, Conduct, and Analysis." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 1 (1988): 233. http://dx.doi.org/10.2307/2982209.

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29

Cornell, Richard G., and Curtis L. Meinert. "Clinical Trials: Design, Conduct, and Analysis." Journal of the American Statistical Association 83, no. 403 (September 1988): 923. http://dx.doi.org/10.2307/2289356.

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30

Baker, Laurence H. "Clinical Trials: Design, Conduct, and Analysis." JAMA: The Journal of the American Medical Association 257, no. 9 (March 6, 1987): 1247. http://dx.doi.org/10.1001/jama.1987.03390090119041.

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31

Cesana, Bruno Mario. "Statistical Analysis Plans for Clinical Trials." JAMA 319, no. 18 (May 8, 2018): 1938. http://dx.doi.org/10.1001/jama.2018.2581.

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32

Chuang-Stein, Christy. "Safety Analysis in Controlled Clinical Trials." Drug Information Journal 32, no. 1_suppl (October 1998): 1363S—1372S. http://dx.doi.org/10.1177/00928615980320s132.

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33

Chan, Yick-Kwong, Anne P. Cross, and Gerald L. Wolf. "Cost-effectiveness analysis in clinical trials." Controlled Clinical Trials 7, no. 3 (September 1986): 231. http://dx.doi.org/10.1016/0197-2456(86)90062-0.

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34

Rodda, Bruce E. "Clinical trials: Design, conduct, and analysis." Controlled Clinical Trials 8, no. 4 (December 1987): 406–8. http://dx.doi.org/10.1016/0197-2456(87)90162-0.

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35

Wisniewski, Stephen R., and James E. Bost. "P86 Cost analysis in clinical trials." Controlled Clinical Trials 16, no. 3 (June 1995): 121S. http://dx.doi.org/10.1016/0197-2456(95)90566-n.

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36

Klebanoff, Mark A. "Subgroup analysis in obstetrics clinical trials." American Journal of Obstetrics and Gynecology 197, no. 2 (August 2007): 119–22. http://dx.doi.org/10.1016/j.ajog.2007.02.030.

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37

Zhang, Xiao, and Gary Cutter. "Bayesian interim analysis in clinical trials." Contemporary Clinical Trials 29, no. 5 (September 2008): 751–55. http://dx.doi.org/10.1016/j.cct.2008.05.007.

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38

DerSimonian, Rebecca, and Nan Laird. "Meta-analysis in clinical trials revisited." Contemporary Clinical Trials 45 (November 2015): 139–45. http://dx.doi.org/10.1016/j.cct.2015.09.002.

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39

Jacobsen, P. H. "Design and analysis of clinical trials." Journal of Dentistry 16, no. 5 (October 1988): 215–18. http://dx.doi.org/10.1016/0300-5712(88)90073-5.

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40

Olesen, J. "Clinical trials. Design, conduct and analysis." Pain 30, no. 1 (July 1987): 138–39. http://dx.doi.org/10.1016/0304-3959(87)90109-6.

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41

Emmerson, J., and J. M. Brown. "Understanding Survival Analysis in Clinical Trials." Clinical Oncology 33, no. 1 (January 2021): 12–14. http://dx.doi.org/10.1016/j.clon.2020.07.014.

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42

Sormani, Maria Pia. "Subgroup analysis in MS trials." Multiple Sclerosis Journal 23, no. 1 (July 11, 2016): 34–35. http://dx.doi.org/10.1177/1352458515625808.

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Subgroup analysis is often conducted as a post-hoc evaluation of clinical trials. The aim of a subgroup analysis is the evaluation of the treatment effect that was tested in the trial, in a specific subgroups of patients. It can be run both on positive trials (to provide information about patients receiving the highest benefit from the treatment) and on negative trials (to test whether the treatment that had no effect on the overall population can be of any benefit in a specific subset of patients). A subgroup analysis is aimed at generating hypotheses for future research. Subgroup analyses have statistical challenges involving multiple testing and unplanned and low powered analyses; however the main issue, at least in subgroup analysis conducted so far in MS studies, seems to be related to the reporting and interpretation of results. In this viewpoint I will try to show the misleading ways of reporting subgroup analysis in MS trials, along with the correct approach based on an interaction test.

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43

Schoenfeld, David Alan, Dianne M. Finkelstein, Eric Macklin, Neta Zach, David L. Ennist, Albert A. Taylor, and Nazem Atassi. "Design and analysis of a clinical trial using previous trials as historical control." Clinical Trials 16, no. 5 (July 1, 2019): 531–38. http://dx.doi.org/10.1177/1740774519858914.

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Background/AimsFor single arm trials, a treatment is evaluated by comparing an outcome estimate to historically reported outcome estimates. Such a historically controlled trial is often analyzed as if the estimates from previous trials were known without variation and there is no trial-to-trial variation in their estimands. We develop a test of treatment efficacy and sample size calculation for historically controlled trials that considers these sources of variation.MethodsWe fit a Bayesian hierarchical model, providing a sample from the posterior predictive distribution of the outcome estimand of a new trial, which, along with the standard error of the estimate, can be used to calculate the probability that the estimate exceeds a threshold. We then calculate criteria for statistical significance as a function of the standard error of the new trial and calculate sample size as a function of difference to be detected. We apply these methods to clinical trials for amyotrophic lateral sclerosis using data from the placebo groups of 16 trials.ResultsWe find that when attempting to detect the small to moderate effect sizes usually assumed in amyotrophic lateral sclerosis clinical trials, historically controlled trials would require a greater total number of patients than concurrently controlled trials, and only when an effect size is extraordinarily large is a historically controlled trial a reasonable alternative. We also show that utilizing patient level data for the prognostic covariates can reduce the sample size required for a historically controlled trial.ConclusionThis article quantifies when historically controlled trials would not provide any sample size advantage, despite dispensing with a control group.

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44

Bajpai, Vikas. "Rise of Clinical Trials Industry in India: An Analysis." ISRN Public Health 2013 (July 31, 2013): 1–17. http://dx.doi.org/10.1155/2013/167059.

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Clinical trials industry has seen a phenomenal increase in last ten years or so, and India has emerged as one of the foremost global destinations for clinical trials. Changed intellectual property regimen after WTO has been the prime mover of the phenomenon, and maximizing profits rather than serving any altruistic motives forms the main ideological underpinning of the rise of clinical trial industry in India. The paper examines the ideological underpinnings of the rise of clinical trials industry in the country in detail and how the ruling classes of India have tried to capitalize on this as a great economic opportunity. In the process the interests of India’s poor have been the main casualty.

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45

Jin, Susan, Richard Pazdur, and Rajeshwari Sridhara. "Re-Evaluating Eligibility Criteria for Oncology Clinical Trials: Analysis of Investigational New Drug Applications in 2015." Journal of Clinical Oncology 35, no. 33 (November 20, 2017): 3745–52. http://dx.doi.org/10.1200/jco.2017.73.4186.

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Clinical trial eligibility criteria are necessary to define the patient population under study and improve trial safety. However, there are concerns that eligibility criteria for cancer clinical trials are too restrictive and limit patient enrollment in clinical trials. Recently, there have been initiatives to re-examine and modernize eligibility criteria for oncology clinical trials. To assess current eligibility requirements for cancer clinical trials, we have conducted a comprehensive review of eligibility criteria for commercial investigational new drug clinical trial applications submitted to the US Food and Drug Administration Office of Hematology and Oncology Products in 2015. Our findings suggest that eligibility criteria for current cancer clinical trials tend to narrowly define the study population and limit the study to lower-risk patients, which may not be reflective of the greater patient population outside of the study. We discuss potential areas for expanding eligibility criteria to include more patients in clinical trials and design options for clinical trials incorporating expanded eligibility criteria. The broadening of clinical trial eligibility criteria can be considered to better reflect the real-world patient population, improve clinical trial participation, and increase patient access to new investigational treatments.

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46

Renfro, Lindsay A., Axel M. Grothey, James Paul, Irene Floriani, Franck Bonnetain, Donna Niedzwiecki, Takeharu Yamanaka, Ioannis Souglakos, Greg Yothers, and Daniel J. Sargent. "Projecting Event-Based Analysis Dates in Clinical Trials: An Illustration Based on the International Duration Evaluation of Adjuvant Chemotherapy (IDEA) Collaboration. Projecting Analysis Dates for the IDEA Collaboration." Forum of Clinical Oncology 5, no. 2 (December 10, 2014): 1–7. http://dx.doi.org/10.2478/fco-2014-0006.

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Abstract Purpose: Clinical trials are expensive and lengthy, where success of a given trial depends on observing a prospectively defined number of patient events required to answer the clinical question. The point at which this analysis time occurs depends on both patient accrual and primary event rates, which typically vary throughout the trial's duration. We demonstrate real-time analysis date projections using data from a collection of six clinical trials that are part of the IDEA collaboration, an international preplanned pooling of data from six trials testing the duration of adjuvant chemotherapy in stage III colon cancer, and we additionally consider the hypothetical impact of one trial's early termination of follow-up. Patients and Methods: In the absence of outcome data from IDEA, monthly accrual rates for each of the six IDEA trials were used to project subsequent trial-specific accrual, while historical data from similar Adjuvant Colon Cancer Endpoints (ACCENT) Group trials were used to construct a parametric model for IDEA's primary endpoint, disease-free survival, under the same treatment regimen. With this information and using the planned total accrual from each IDEA trial protocol, individual patient accrual and event dates were simulated and the overall IDEA interim and final analysis times projected. Projections were then compared with actual (previously undisclosed) trial-specific event totals at a recent census time for validation. The change in projected final analysis date assuming early termination of follow-up for one IDEA trial was also calculated. Results: Trial-specific predicted event totals were close to the actual number of events per trial for the recent census date at which the number of events per trial was known, with the overall IDEA projected number of events only off by eight patients. Potential early termination of follow-up by one IDEA trial was estimated to postpone the overall IDEA final analysis date by 9 months. Conclusions: Real-time projection of the final analysis time during a trial, or the overall analysis time during a trial collaborative such as IDEA, has practical implications for trial feasibility when these projections are translated into additional time and resources required.

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Kannan, Sridharan, and S. Gowri. "Clinical trials in dentistry in India: Analysis from trial registry." Perspectives in Clinical Research 8, no. 2 (2017): 95. http://dx.doi.org/10.4103/2229-3485.203039.

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Radulovic, Sinisa. "Good clinical practice: International quality standard for clinical trials." Serbian Dental Journal 50, no. 1 (2003): 34–38. http://dx.doi.org/10.2298/sgs0301034r.

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A clinical trial is one of the most important examples of experimental studies. Clinical trials represent an indispensable tool for testing, in a rigorous scientific manner, the efficacy of new therapies. Good Clinical Practice is an international ethical and scientific quality standard for clinical trials, concerning the design, conduct, performance, monitoring auditing, recording, analysis and reporting. This is an assurance to the public that the rights, safety and well-being of trial subjects are protected, and that clinical trial data is credible. The above definitions are consistent with the principles that have their origin in the declaration of Helsinki. The objectives of Good Clinical Practice are to protect the rights of trial subjects, to enhance credibility of data and to improve the quality of science.

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Stanulovic, Vid, Mirjana Djeric, and Jovan Popovic. "Clinical trials of statins and fibrates: A meta-analysis." Medical review 59, no. 5-6 (2006): 213–18. http://dx.doi.org/10.2298/mpns0606213s.

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Introduction. Several clinical trials of hypolipidemics showed a decrease in mortality by 30-40%, while others showed detrimental or no effects. The question remains: which trial should be the basis of clinical decision making in the choice of hypolipidemic therapy? Material and methods. Meta-analysis is a method for combining research results of several studies. Effects of statins and fibrates with respect to placebo, were assessed by systematic literature review and meta-analysis. Medline and CENTRAL databases were searched using the following keywords: hyperlipoproteinemia, hypolipidemic agents and individual drug names. The main inclusion criteria were as follows: statin or fibrate, placebo controlled randomized trial, at least one year treatment on average, at least 100 patients per study arm and reported mortality. Results. Fibrates showed almost complete absence of treatment effects on mortality with odds ratio of 0.99 and 95% confidence interval 0.80-1.11. The odds f or statins were 0.87, 0.80-0.9 5. Discussion. Despite the absence of treatment effects of fibrates, it is note?worthy that inclusion criteria of early fibrate trials focused mainly on cholesterol with recent identification of elevated triglycerides as an independent risk factor. As fibrates exert the most pronounced effect on triglycerides, they still may show effect in target populations. Effects of statins are confirmed, but they are noticeably lower than in individual trials which are given most publicity. Conclusion. Even after several decades of fibrate use, conclusive evidence of their beneficial effects still needs to be elucidated in appropriately designed trials. However, a beneficial effect of statins on mortality decrease has been proven. Meta-analysis has an important role in estimating true treatment effects and in the practice of evidence-based medicine. .

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E, Abinaya, Saradha S, Ilamathi K. R., Ruckmani A, and Arunkumar R. "Description and Analysis of Characteristics of COVID-19 Clinical Trials Registered in the Clinical Trials Registry-India (CTRI)." Biomedical and Pharmacology Journal 14, no. 1 (March 30, 2021): 15–32. http://dx.doi.org/10.13005/bpj/2096.

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Many clinical trials are ongoing in India to evaluate the efficacy and safety of various interventions in COVID-19. It is mandatory that the clinical trials be registered in the Clinical Trials Registry-India (CTRI) before enrollment of study participants. The present study was carried out with the objective of collecting, compiling and analyzing various types of trial data such as study design, interventions, outcomes and study sites. The clinical trial data were collected from the CTRI web portal using the key word “COVID-19” on 14 July 2020. The CTRI data output of every study registered till 14 July 2020 was stored as PDF document and the data were transcribed into a validated excel sheet based on the pre-defined methods and categories and analyzed. A total of 293 clinical studies have been registered in CTRI as on 14 July 2020. Among them, 188 (64.16%) are interventional and 105 (35.83%) are observational studies. The interventions being evaluated are modern medications including drugs and biologicals, AYUSH formulations, Nutraceuticals, Yoga and Naturopathy. Most of the interventions are already in clinical use for non-COVID indications and undergoing repurposing evaluations for COVID-19 in the clinical studies. Trials with AYUSH formulations constitute more than half of the interventional studies (51.06%) while modern medications in 31.09% of the studies. 119 trials (63.3%) of the interventional studies are randomized studies. Large numbers of trials are conducted in the states where the incidence of COVID-19 is high. 146 interventional studies out of 188 are expected to be completed within 6 months and the outcomes of these studies may provide valid information on the potential treatments in COVID-19.

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