Category: Accrual problems in clinical trials (created 2007-08-21). These pages cover some of the issues associated with accrual problems, research studies that accrue patients too slowly. Researchers have the dangerous tendency to provide overly ambitious goals for their clinical trials. They will suggest that they can recruit an unrealistically large number of patients in an unrealistically tight time frame. I am working with a colleague, Byron Gajewski, to develop some Bayesian models for waiting times between successive patients that will allow for more careful planning of the time frame for a clinical trial. These models allow the researchers to track patients accrual rates and react quickly if patient enrollment is suffering. Also see Category: Adverse events in clinical trials, Category: Bayesian statistics. Other entries about accrual problems in clinical trials can be found in the accrual problems page at the StATS website. my old website

2008

  1. P.Mean: Proposed poster for the Missouri Regional Life Sciences Summit (created 2010-02-03). I am preparing a poster for the Missouri Regional Life Science Summit. The poster guidelines are a bit unusual in that there is only room for a four foot by four foot square poster. Normally, these posters can be much wider. The tentative title is "Slipped deadlines, sample size shortfalls, and a proposed Bayesian solution using an informative prior distribution" and here is a proposed abstract.
  2. P.Mean: Data that IRBs should collect about themselves (created 2009-05-22). Somone on the IRBForum (TS) asked about what type of reports that an IRB should provide. There were a lot of good comments. I encouraged a data centric approach to reporting. Here's what I wrote.
  3. P.Mean: Plug for accrual research (created 2008-07-24). I received a request for use of material from my old website. It's a bit tricky right now, but I hope to have things resolved soon. The person inquiring was the owner of a company that specializes in clinical research and clinical data management. I thought it wouldn't hurt to mention some of the work that Byron Gajewski and I have done in accrual rates. Here's what I wrote.
  4. P.Mean: Cytel software has developed a Poisson model for predicting accrual (created 2008-07-09). I attended a web seminar by Jeff Palmer, Cytel Corporation, about Bayesian methods in adaptive clinical trials. It was a very good seminar, and I should try to summarize some of the major points sometime. One of the figures, though, caught my attention. It showed a projection of future accrual based on a Poisson distribution.

Outside resources:

  1. Mark Chang. Classical and adaptive clinical trial designs with ExpDesign Studio. Hoboken, NJ: John Wiley Excerpt: "This book introduces pharmaceutical statisticians, scientists, researchers, and others to ExpDesign Studio software for classical and adaptive designs of clinical trials. It includes the Professional Version 5.0 of ExpDesign Studio software that frees pharmaceutical professionals to focus on drug development and related challenges while the software handles the essential calculations and computations. After a hands-on introduction to the software and an overview of clinical trial designs encompassing numerous variations, Classical and Adaptive Clinical Trial Designs Using ExpDesign Studio: * Covers both classical and adaptive clinical trial designs, monitoring, and analyses * Explains various classical and adaptive designs including groupsequential, sample-size reestimation, dropping-loser, biomarker-adaptive, and response-adaptive randomization designs * Includes instructions for over 100 design methods that have been implemented in ExpDesign Studio and step-by-step demos as well as real-world examples * Emphasizes applications, yet covers key mathematical formulations * Introduces readers to additional toolkits in ExpDesign Studio that help in designing, monitoring, and analyzing trials, such as the adaptive monitor, graphical calculator, the probability calculator, the confidence interval calculator, and more * Presents comprehensive technique notes for sample-size calculation methods, grouped by the number of arms, the trial endpoint, and the analysis basis Written with practitioners in mind, this is an ideal self-study guide for not only statisticians, but also scientists, researchers, and professionals in the pharmaceutical industry, contract research organizations (CROs), and regulatory bodies. It's also a go-to reference for biostatisticians, pharmacokinetic specialists, and principal investigators involved in clinical trials." Available at: http://lccn.loc.gov/2008001358.
  2. Karen Sherman, Rene Hawkes, Laura Ichikawa, et al. Comparing recruitment strategies in a study of acupuncture for chronic back pain. BMC Medical Research Methodology. 2009;9(1):69. Abstract: "BACKGROUND: Meeting recruitment goals is challenging for many clinical trials conducted in primary care populations. Little is known about how the use of different recruitment strategies affects the types of individuals choosing to participate or the conclusions of the study. METHODS: A secondary analysis was performed using data from participants recruited to a clinical trial evaluating acupuncture for chronic back pain among primary care patients in a large integrated health care organization. We used two recruitment methods: mailed letters of invitation and an advertisement in the health plan's magazine. For these two recruitment methods, we compared recruitment success (% randomized, treatment completers, drop outs and losses to follow-up), participant characteristics, and primary clinical outcomes. A linear regression model was used to test for interaction between treatment group and recruitment method. RESULTS: Participants recruited via mailed letters closely resembled those responding to the advertisement in terms of demographic characteristics, most aspects of their back pain history and current episode and beliefs and expectations about acupuncture. No interaction between method of recruitment and treatment group was seen, suggesting that study outcomes were not affected by recruitment strategy. CONCLUSION: In this trial, the two recruitment strategies yielded similar estimates of treatment effectiveness. However, because this finding may not apply to other recruitment strategies or trial circumstances, trials employing multiple recruitment strategies should evaluate the effect of recruitment strategy on outcome." Trial registration: Clinical Trials.gov NCT 00065585. [Accessed October 28, 2009]. Available at: http://www.biomedcentral.com/1471-2288/9/69.
  3. CTriSoft International. ExpDesign Studio: Powerful. User-friendly. Affordable. Excerpt: ExpDesign StudioTM is an integrated environment for designing experiments or clinical trials. It is a powerful and user-friendly statistical software product that has integrated 8 main components: Classical Design, Sequential Design, Multi-Stage Design, Dose-Escalation Design, Adaptive Design, Adaptive Trial Monitoring, Dose-Escalation Trial Monitoring modules, and Adaptive Trial Simulator. In addition, ExpDesign Randomizor can generate random variates from different distributions. ExpDesign Toolkit provides features for distributional calculation, confidence intervals, function and data plotting. [Accessed November 30, 2009]. Available at: http://www.ctrisoft.net/.
  4. Gina Kolata. Lack of Study Volunteers Hobbles Cancer Fight. The New York Times. 2009. Excerpt: "There are more than 6,500 cancer clinical trials seeking adult patients, according to clinicaltrials.gov, a trials registry. But many will be abandoned along the way. More than one trial in five sponsored by the National Cancer Institute failed to enroll a single subject, and only half reached the minimum needed for a meaningful result, Dr. Ramsey and his colleague John Scoggins reported in an editorial in the September 2008 issue of The Oncologist." Also see commentary about this article at www.the-scientist.com/community/posts/list/575.page. [Accessed August 29, 2009]. Available at: http://www.nytimes.com/2009/08/03/health/research/03trials.html.
  5. Vladimir V. Anisimov, Valerii V. Fedorov. Modelling, prediction and adaptive adjustment of recruitment in multicentre trials. Statistics in Medicine. 2007;26(27):4958-4975. Abstract: "This paper is focused on statistical modelling, prediction and adaptive adjustment of patient recruitment in multicentre clinical trials. We consider a recruitment model, where patients arrive at different centres according to Poisson processes, with recruitment rates viewed as a sample from a gamma distribution. A statistical analysis of completed studies is provided and properties of a few types of parameter estimators are investigated analytically and using simulation. The model has been validated using many real completed trials. A statistical technique for predictive recruitment modelling for ongoing trials is developed. It allows the prediction of the remaining recruitment time together with confidence intervals using current enrolment information, and also provision of an adaptive adjustment of recruitment by calculating the number of additional centres required to accomplish a study up to a certain deadline with a pre-specified probability. Results are illustrated for different recruitment scenarios." Copyright © 2007John Wiley & Sons, Ltd. [Accessed November 30, 2009]. Available at: http://dx.doi.org/10.1002/sim.2956.
  6. Mei-Wei Chang, Roger Brown, Susan Nitzke. Participant recruitment and retention in a pilot program to prevent weight gain in low-income overweight and obese mothers. BMC Public Health. 2009;9(1):424. Abstract: "Background: Recruitment and retention are key functions for programs promoting nutrition and other lifestyle behavioral changes in low-income populations. This paper describes strategies for recruitment and retention and presents predictors of early (two-month post intervention) and late (eight-month post intervention) dropout (non retention) and overall retention among young, low-income overweight and obese mothers participating in a community-based randomized pilot trial called Mothers In Motion. Methods: Low-income overweight and obese African American and white mothers ages 18 to 34 were recruited from the Special Supplemental Nutrition Program for Women, Infants, and Children in southern Michigan. Participants (n = 129) were randomly assigned to an intervention (n = 64) or control (n = 65) group according to a stratification procedure to equalize representation in two racial groups (African American and white) and three body mass index categories (25.0-29.9 kg/m2, 30.0-34.9 kg/m2, and 35.0-39.9 kg/m2). The 10-week theory-based culturally sensitive intervention focused on healthy eating, physical activity, and stress management messages that were delivered via an interactive DVD and reinforced by five peer-support group teleconferences. Forward stepwise multiple logistic regression was performed to examine whether dietary fat, fruit and vegetable intake behaviors, physical activity, perceived stress, positive and negative affect, depression, and race predicted dropout as data were collected two- month and eight-month after the active intervention phase. Results: Trained personnel were successful in recruiting subjects. Increased level of depression was a predictor of early dropout (odds ratio = 1.04; 95% CI = 1.00, 1.08; p = 0.03). Greater stress predicted late dropout (odds ratio = 0.20; 95% CI = 0.00, 0.37; p = 0.01). Dietary fat, fruit, and vegetable intake behaviors, physical activity, positive and negative affect, and race were not associated with either early or late dropout. Less negative affect was a marginal predictor of participant retention (odds ratio = 0.57; 95% CI = 0.31, 1.03; p = 0.06). CONCLUSIONS: Dropout rates in this study were higher for participants who reported higher levels of depression and stress." Trial registration: Current Controlled Trials NCT00944060 [Accessed November 30, 2009]. Available at: http://www.biomedcentral.com/1471-2458/9/424.
  7. KM Taylor, RG Margolese, CL Soskolne. Physicians' reasons for not entering eligible patients in a randomized clinical trial of surgery for breast cancer. N Engl J Med. 1984;310(21):1363-1367. Abstract: "We studied the reasons surgical principal investigators chose not to enter patients in a large, multicenter trial sponsored by a cooperative group. In 1976 the National Surgical Adjuvant Project for Breast and Bowel Cancers (NSABP) initiated a clinical trial to compare segmental mastectomy and postoperative radiation, or segmental mastectomy alone, with total mastectomy. Because the low rates of accrual were threatening to close the trial prematurely, we mailed a questionnaire to the 94 NSABP principal investigators, asking why they were not entering eligible patients in the trial. A response rate of 97 per cent was achieved. Physicians who did not enter all eligible patients offered the following explanations: (1) concern that the doctor-patient relationship would be affected by a randomized clinical trial (73 per cent), (2) difficulty with informed consent (38 per cent), (3) dislike of open discussions involving uncertainty (22 per cent), (4) perceived conflict between the roles of scientist and clinician (18 per cent), (5) practical difficulties in following procedures (9 per cent), and (6) feelings of personal responsibility if the treatments were found to be unequal (8 per cent). Further investigation into the behavioral aspects of the investigator-patient relationship is particularly pressing, since fear of change in this relationship was the most common reason given for not entering eligible patients in the trial." [Accessed November 30, 2009]. Available at: http://content.nejm.org/cgi/content/abstract/310/21/1363.
  8. Oliver Herber, Wilfried Schnepp, Monika Rieger. Recruitment rates and reasons for community physicians' non-participation in an interdisciplinary intervention study on leg ulceration. BMC Medical Research Methodology. 2009;9(1):61. Abstract: "BACKGROUND: This article describes the challenges a research team experienced recruiting physicians within a randomised controlled trial about leg ulcer care that seeks to foster the cooperation between the medical and nursing professions. Community-based physicians in North Rhine-Westphalia, Germany, were recruited for an interdisciplinary intervention designed to enhance leg ulcer patients' self-care agency. The aim of this article is to investigate the success of different recruitment strategies employed and reasons for physicians' non-participation. METHODS: The first recruitment phase stressed the recruitment of GPs, the second the recruitment of specialists. Throughout the recruitment process data were collected through phone conversations with GP practices who indicated reasons for non-participation. RESULTS: Despite great efforts to recruit physicians, the recruitment rate reached only 26 out of 1549 contacted practices (1.7%) and 12 out of 273 (4.4%) practices during the first and second recruitment phase respectively. The overall recruitment rate over the 16-month recruitment period was 2%. With a target recruitment rate of n = 300, only 45 patients were enrolled in the study, not meeting study projections. Various reasons for community physicians' non-participation are presented as stated spontaneously during phone conversations that might explain low recruitment rates. The recruitment strategy utilised is discussed against the background of factors associated with high participation rates from the international literature. CONCLUSION: Time, money, and effort needed during the planning and recruitment phase of a study must not be underestimated to avoid higher than usual rates of refusal and lack of initial contact. Pilot studies prior to a study start-up may provide some evidence on whether the target recruitment rate is feasible. TRIAL REGISTRATION: Current Controlled Trials ISRCTN42122226." [Accessed November 30, 2009]. Available at: http://www.biomedcentral.com/1471-2288/9/61.
  9. Jane Dyas, Tanefa Apeky, Michelle Tilling, A Siriwardena. Strategies for improving patient recruitment to focus groups in primary care: a case study reflective paper using an analytical framework. BMC Medical Research Methodology. 2009;9(1):65. Abstract: "BACKGROUND: Recruiting to primary care studies is complex. With the current drive to increase numbers of patients involved in primary care studies, we need to know more about successful recruitment approaches. There is limited evidence on recruitment to focus group studies, particularly when no natural grouping exists and where participants do not regularly meet. The aim of this paper is to reflect on recruitment to a focus group study comparing the methods used with existing evidence using a resource for research recruitment, PROSPeR (Planning Recruitment Options: Strategies for Primary Care). METHODS: The focus group formed part of modelling a complex intervention in primary care in the Resources for Effective Sleep Treatment (REST) study. Despite a considered approach at the design stage, there were a number of difficulties with recruitment. The recruitment strategy and subsequent revisions are detailed. RESULTS: The researchers' modifications to recruitment, justifications and evidence from the literature in support of them are presented. Contrary evidence is used to analyse why some aspects were unsuccessful and evidence is used to suggest improvements. Recruitment to focus group studies should be considered in two distinct phases; getting potential participants to contact the researcher, and converting those contacts into attendance. The difficulty of recruitment in primary care is underemphasised in the literature especially where people do not regularly come together, typified by this case study of patients with sleep problems. CONCLUSIONS: We recommend training GPs and nurses to recruit patients during consultations. Multiple recruitment methods should be employed from the outset and the need to build topic related non-financial incentives into the group meeting should be considered. Recruitment should be monitored regularly with barriers addressed iteratively as a study progresses." [Accessed September 29, 2009]. Available at: http://www.biomedcentral.com/1471-2288/9/65.
  10. Kerrie Sanders, Amanda Stuart, Elizabeth Merriman, et al. Trials and tribulations of recruiting 2,000 older women onto a clinical trial investigating falls and fractures: Vital D study. BMC Medical Research Methodology. 2009;9(1):78. Abstract: "BACKGROUND: Randomised, placebo-controlled trials are needed to provide evidence demonstrating safe, effective interventions that reduce falls and fractures in the elderly. The quality of a clinical trial is dependent on successful recruitment of the target participant group. This paper documents the successes and failures of recruiting over 2,000 women aged at least 70 years and at higher risk of falls or fractures onto a placebo-controlled trial of six years duration. The characteristics of study participants at baseline are also described for this study. METHODS: The Vital D Study recruited older women identified at high risk of fracture through the use of an eligibility algorithm, adapted from identified risk factors for hip fracture. Participants were randomised to orally receive either 500,000 IU vitamin D3 (cholecalciferol) or placebo every autumn for three to five consecutive years. A variety of recruitment strategies were employed to attract potential participants. RESULTS: Of the 2,317 participants randomised onto the study, 74% (n= 1716/ 2317) were consented onto the study in the last five months of recruiting. This was largely due to the success of a targeted mail-out. Prior to this only 541 women were consented in the 18 months of recruiting. A total of 70% of all participants were recruited as a result of targeted mail-out. The response rate from the letters increased from 2 to 7% following revision of the material by a public relations company. Participant demographic or risk factor profile did not differ between those recruited by targeted mail-outs compared with other methods. CONCLUSIONS: The most successful recruitment strategy was the targeted mail-out and the response rate was no higher in the local region where the study had extensive exposure through other recruiting strategies. The strategies that were labour-intensive and did not result in successful recruitment include the activities directed towards the GP medical centres. Comprehensive recruitment programs employ overlapping strategies simultaneously with ongoing assessment of recruitment rates. In our experience, and others direct mail-outs work best although rights to privacy must be respected." Trial registration: ISRCTN83409867 and ACTR12605000658617. [Accessed November 30, 2009]. Available at: http://www.biomedcentral.com/1471-2288/9/78.

Creative Commons License All of the material above this paragraph is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2010-02-03. The material below this paragraph links to my old website, StATS. Although I wrote all of the material listed below, my ex-employer, Children's Mercy Hospital, has claimed copyright ownership of this material. The brief excerpts shown here are included under the fair use provisions of U.S. Copyright laws.

2008

  1. StATS: Eliciting a prior distribution for rejection/refusal rates (June 7, 2008). I got a question about the Bayesian model for rejection/refusal rates. I had used three prior distributions in my calculations, a Beta(10,40), a Beta(45,5), and a Beta(25,25). The question was, how did I select those prior distributions.
  2. StATS: A simple Bayesian model for exponential accrual times (May 26, 2008). Here is a simple Bayesian model for exponential accrual times. This model will help researchers to plan the estimated duration of a clinical trial. The same model will also allow the researcher to monitor the accrual during the trial itself and develop revised estimates for the duration or the sample size.
  3. StATS: Why does a Bayesian approach make sense for monitoring accrual? (May 8, 2008). I'm working with Byron Gajewski to develop some models for monitoring the progress of clinical trials. Too many researchers overpromise and underdeliver on the planned sample size and the planned completion date of their research This leads to serious delays in the research and inadequate precision and power when the research is completed. We want to develop some tools that will let researchers plan the pattern of patient accrual in their studies. These tools will also let the researchers carefully monitor the progress of their studies and let them take action quickly if accrual rates are suffering. We've adopted a Bayesian approach for these tools. While a Bayesian approach to Statistics is controversial, we feel that there should be no controversy with regard to using Bayesian models in modeling accrual.
  4. StATS: Slipped deadlines and sample size shortfalls in a random sample of research studies (May 7, 2008). There is a limited amount of data out there that suggests that many researchers overpromise on the planned sample size and completion date and underdeliver. About a year ago, I received a small grant to study the proportion of studies at Children's Mercy Hospital (CMH) that failed to meet the proposed completion deadlines, that failed to recruit the promised number of patients or both. Here is a brief summary of these results.
  5. StATS: Monitoring refusals and exclusions in a clinical trial (May 1, 2008). Someone sent me an email asking about the work that Byron Gajewski and I have done on monitoring accrual patterns in clinical trials. She had been doing something similar at her job and wanted to see if we could collaborate. In her situation, the major issue was the number of patients who made an initial contact but did not keep their first appointment, the number of patients who kept the appointment, but refused to sign the consent form once they realized what the study was about, and the number of patients who did sign the consent form, but who did not meet the inclusion criteria once the initial screening was done.

    2007
     
  6. StATS: Case study of accrual in a clinical trial (September 11, 2007). I received additional accrual data on a clinical trial I am monitoring. To review, the trial started on August 28, 2007 and will continue until January 31, 2008, for a total of 22 weeks. The researcher thinks that he might be able to get 3 patients per week over a 22 week trial (66 total), but he is very confident that he would get at least 2 patients per week (44 total). The confidence in the estimate of 3 patients per week was rated as 5 on a 10 point scale. After one week, a single patient has entered the study. No patients enter on weeks 2, 3, or 4. On week 5, three patients enter the study. On week 6, one more patient enters for a total of 5 patients.
  7. StATS: An alternate way of viewing accrual (October 2, 2007). I was talking about a project with a fellow in Emergency Medicine and during the discussion realized a different way of looking at accrual in a clinical trial. She plans to look how accurately EKGs are read by physicians in the Emergency Room. I showed her some of the work that Byron Gajewski and I had done on planning and monitoring accrual rates. She pointed at that accrual was not a problem here in that the number of EKGs that are processed in the ER is known with very high precision. The problem, of course, is that the physicians who participate in the study have to fill out a small amount of additional paperwork for the research. While this is not an intrusive amount of work and she is going to work hard to promote this research project, there will some physicians at some times who will not fill out the extra research paperwork, or will fill it out so incompletely as to make the EKG unusable in the research. The ER is a busy and hectic place and it is difficult to get complete data, even when the ER doctors are trying their best to help with the research.
  8. StATS: Case study of accrual in a clinical trial (September 11, 2007). Someone came by today with a project where he wants to monitor the accrual in a clinical trial. The trial started on August 28, 2007 and will continue until January 31, 2008, for a total of 22 weeks. He thinks that he might be able to get 3 patients per week over a 22 week trial (66 total), but he is very confident that he would get at least 2 patients per week (44 total).
  9. StATS: Accrual grant, Round 3 (August 21, 2007). Last year, I applied for a Kansas City Area Life Sciences Institute (KCALSI) Research Development grant. It was not funded, but a subsequent grant that I submitted to the Katherine B. Richardson foundation was funded. Both grants are rather small, intended as seed money to encourage development of a larger scale project which might attract funding from the NIH or a large foundation. I want to revise the KCALSI grant and re-submit it for the 2007 cycle.

    2006
     
  10. StATS: A simple Bayesian model for accrual (November 17, 2006). Suppose you are a researcher in charge of a long term study. You plan to collect data on 120 patients. The goal is to finish your study in ten years, which means getting 12 patients per year or one every thirty days on average. Recruiting patients though appears to be harder than you had expected. You recruited your first patient on day 56, 26 days behind schedule. The second patient is not recruited until day 93. About two years into the study (day 768), you have just recruited your 10th patient. It looks like recruitment might be behind schedule. Is it time to take action? A Bayesian model of accrual times can help you to discern whether recruitment is behind schedule and project an estimated completion date allowing for uncertainty.
  11. StATS: My second grant, part 3 (October 2, 2006). I just finished my second grant, which I gave the title "Estimating delays in completion of IRB approved and KBR supported research studies" The two acronyms, IRB and KBR should be familiar to the group I am applying to. IRB stands for Institutional Review Board and KBR represents an internal grant mechanism here at Children's Mercy Hospital to support initial research efforts. The initials KBR stand for Katherine Berry Richardson, who is one of the initial founders in Children's Mercy Hospital.
  12. StATS: My second grant, part 2 (September 13, 2006). I took a three day workshop on grant writing and prepared a draft grant as part of the student exercises in that class. It's not in the format that I need to use, but it outlines most of the goals and efforts of my proposed work. I wrote about accrual problems in clinical trials.
  13. StATS: My second grant (July 26, 2006). I'm in the final stretch of writing a grant to submit to the Kansas City Area Life Sciences Institute. I am already thinking "what is my next step?" One possibility would be to run a small study that will provide hard numbers to support a commonly expressed belief that most research studies fall behind schedule and fail to get anything close to the targeted sample sizes.
  14. StATS: Initial work on the KCALSI grant (July 17, 2006). I am submitting a grant in response to a KCALSI RFP. According to the RFP, the general structure of the grant should follow the structure used by NIH. Here is a review of the structure of a typical NIH grant.
  15. StATS: Early detection of accrual problems in clinical trials (June 30, 2006). The most common reason why clinical trials fail is that they fall well below their goals for patient accrual. Institutional Review Boards (IRBs) are charged with the continual monitoring of clinical trials and they need to identify when these trials encounter problems with accrual. When do they "jump the shark" so to speak?
  16. StATS: Applications of the CUSUM chart (June 20, 2006). I am interested in investigating the use of CUSUM charts in monitoring accrual rates, drop out rates, and adverse event rates in a clinical trial. Some references which I might cite in a literature review are listed here.
  17. StATS: Monitoring accrual rates (May 30, 2006). This scenario is based on real data, but has been adapted slightly to serve as an illustration of the use of control charts in monitoring a clinical trial. Suppose a clinical trial was set up in 1997 and the goal was to recruit one patient per month over a ten year period, for a total sample size of 120 patients. Here are the dates of recruitment for the first 42 patients.

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