P.Mean >> 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. The key reference for these ideas is Gajewski BJ, Simon SD, Carlson SE. Predicting accrual in clinical trials with Bayesian posterior predictive distributions. Stat Med. 2008 Jun 15;27(13):2328-40 [PubMed] [Abstract] [PDF]. There are two separate programs written in R based on the Statistics in Medicine paper, covering prediction of the duration of a clinical trial for a fixed sample size and prediction of the sample size of a clinical trial with a fixed duration. I have outlined some of the ideas that are included in that paper, plus some speculations about applications in other areas in some of the webpages listed below. Also see Adverse events in clinical trials, Bayesian statistics.

Most of the new content will be added to my blog (blog.pmean.com).

Review all blog entries related to accrual.


42. P.Mean: R code for estimating the sample size of a clinical trial with a fixed duration (created 2013-07-29). Here is the R code for a simple Bayesian model for patient accrual. It estimates the sample size of a clinical trial for a fixed duration using information from a prior distribution and/or information from an interim review of accrual in the actual study.

41. P.Mean: R code for estimating the duration of a clinical trial with a fixed sample size (created 2013-07-29). Here is the R code for a simple Bayesian model for patient accrual. It estimates the duration of a clinical trial for a fixed sample size using information from a prior distribution and/or information from an interim review of accrual in the actual study.


40. P.Mean: I make an amateur mistake in BUGS (created 2012-05-22). I am just learning how to run BUGS software. I've used WinBUGS, which is a stand-alone program for Windows, and OpenBUGS, which is an Open Source version that runs on Windows and Linux (as well as on the Macintosh with the Windows emulation package WINE). My preference, though, is to run BUGS within R using the BRugs package. I wanted to look at a simple extension of the accrual model, and I made a rather amateurish mistake that I want to document here. BUGS is not a program for the faint-hearted, and, as this lesson reinforces, you need to understand the mathematical foundations of these models if you want to use BUGS successfully.

39. P.Mean: Accrual with refusals, exclusions, or dropouts (created 2012-04-22). A common issue with slow accrual is higher than expected rates of refusals, exclusions, or dropouts. If you have information on these rates, you can incorporate them into a Bayesian model of accrual. Here are the details.

38. P.Mean: The simple accrual model, redefined (created 2012-04-19). I have been writing a bit about the simple homogenous accrual model, but I am having some difficulty with the notation. So I want to redefine the model with some simpler and more consistent notation.

37. P.Mean: BUGS model for the simple Poisson accrual model (created 2012-04-18). I have been working on various extensions to the Bayesian model for patient accrual. Most of these extensions would require the use of the program BUGS. The first step to developing these extensions is to program simple models in BUGS, models where there is a closed form analytical solution. Here is an example of using BUGS to model the simple Poisson accrual model.

36. P.Mean: Fitting the homogenous accrual model in BUGS (created 2012-04-13). Several years ago, I wrote some R code for the homegenous accrual model. This is the simplest case for accrual, with an inverse gamma prior on the waiting time between successive patients. I wanted to fit the same model in BUGS, because I want to look at some extensions and I wanted to start with something simple. I am not great at BUGS yet, but I got it to work in an hour. I'm using the R interface to Open BUGS (BRugs). Here is the code.

35. P.Mean: Iowa talk on accrual (created 2012-04-03). I will be giving a talk "Slipped deadlines and sample size shortfalls in clinical trials: a proposed remedy using a Bayesian model with an informative prior distribution." at the University of Iowa. Here is the handout for my talk.

34. P.Mean: Honorable mention for my R code on accrual (created 2012-01-25). Back in October 2011, I entered a contest sponsored by Revolution Analytics, "Applications of R in Business." I spiffed up a bit of my R code on patient accrual and submitted it with a brief explanation and some simple examples. It turns out that I was one of the five honorable mentions in this contest, which was a pleasant surprise, as I am just an amateur at programming in R.


33. The Monthly Mean: Do you want to help me with some research grants? (September-Novmeber 2011). I am currently writing up two research grants and I thought some of you might be able to help me. There might be something in it for you as well.

32. The Monthly Mean: Help! My research study is behind schedule! (March/April 2009)

31. P.Mean: Lasagna's Law on patient recruitment (created 2011-10-24). I sent out a question to an email discussion group asking about documentation of sample size shortfalls in clinical research. Someone suggested that I google "Lasagna's Law." What a great suggestion. Here's what I found.

30. P.Mean: Draft grant submission on patient accrual (created 2011-09-13). Here's an early draft of a grant submission on patient accrual.

29. P.Mean: Looking for help to test software for monitoring accrual in a clinical trial (created 2011-06-16). I need some collaborators for a grant I am writing from people who conduct prospective clinical trials. I am working on methods to monitor patient accrual in clinical trials. Accrual means how rapidly do patients enter into a clinical trial. In my experience, researchers overpromise and underdeliver on the time frame in which they expect to recruit a certain number of patients.

28. P.Mean: Small business grant? Maybe not (created 2011-06-16). I want to document on this webpage, a general idea of where we might want to submit a grant to continue our work on accrual models. In particular, I was originally leaning towards an SBIR (Small Business Innovation Research) grant, but now I am not so sure. The impetus was attendance at a webinar on how to write a grant for SBIR.


27. P.Mean: Using the Poisson distribution for modeling accrual (created 2010-12-01). Up to now, I have been modeling accrual times using an exponential distribution. The exponential distribution is a reasonable distribution for waiting times, and the sum of the exponential random variables across the number of patients represents an estimate of the duration of the clinical trial. An attractive alternative is to model the number of patients that appear in a specific time frame using the Poisson distribution. This will give results in different units (counts rather than time) but it is effectively the same analysis. Here's an outline of how you would use the Poisson distribution for accrual and when that approach might be preferred to using the exponential distribution.

26. P.Mean: Ambiguity in the definition of the exponential distribution (created 2010-11-16). I'm trying to run some Bayesian analyses using a program called BUGS (Bayes Using Gibbs Sampler), and this requires me to specify a prior distribution for the parameter associated with an exponential waiting time. I'm having more trouble that I should because the exponential distribution is defined two different ways.

25. P.Mean: Poster presentation at the Missouri Technology conference (created 2010-10-04). I will be presenting a poster about the Bayesian model for accrual at the Missouri Technology conference in Columbia, Missouri. There was some confusion about this, partly because I submitted an abstract at the last minute. Here is the abstract that I turned in.

24. P.Mean: Abstract submitted to Missouri Regional Life Sciences Summit (created 2010-02-13). Yesterday, I submitted the following abstract for a poster session in the Missouri Regional Life Sciences Summit. I'll find out on Monday if it will be accepted. "Slipped deadlines and sample size shortfalls in clinical trials: a proposed remedy using a Bayesian model with an informative prior distribution."

23. 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.


21. 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.


20. 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.

19. 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: (also available at http://www.zotero.org/groups/pmeanreferences/items/collection/2959766)

Lucie Byrne-Davis, Peter Salmon, Katja Gravenhorst, Tim Eden, Bridget Young. Balancing high accrual and ethical recruitment in paediatric oncology: a qualitative study of the 'look and feel' of clinical trial discussions. BMC Medical Research Methodology. 2010;10(1):101. Abstract: "BACKGROUND: High accrual to clinical trials enables new treatment strategies to be tested rapidly, accurately and with generalisability. Ethical standards also must be high so that participation is voluntary and informed. However, this can be difficult to achieve in trials with complex designs and those which are closely embedded in clinical practice. Optimal recruitment requires a balance of both ethical and accrual considerations. In the context of a trial of stratified treatments for children with acute lymphoblastic leukaemia (UKALL2003) we examined how recruitment looked to an observer and how it felt to the parents, to identify how doctors' communication could promote or inhibit optimal recruitment. METHODS: We audio-recorded, transcribed and analysed routine doctor-patient consultations (n=20) and interviews between researchers and parents (n=30 parents) across six UK treatment centres. Analysis was informed by the constant comparative method. For consultation transcripts analysis focussed on how doctors presented the trial. We compared this with analysis of the interview transcripts which focussed on parents' perceptions and understanding of the trial. RESULTS: Parents and doctors discussed the trial in the majority of consultations, even in those that did not involve making a decision about randomisation. Doctors used language allying them both with the trial and with the parent, indicating that they were both an 'investigator' and a 'clinician. They presented the trial both as an empirical study with a scientific imperative and also as offering personalisation of treatment for the child. Parents appeared to understand that trial involvement was voluntary, that it was different from routine care and that they could withdraw from the trial at any time. Some were confused about the significance of the MRD test and the personalisation of treatment. CONCLUSIONS: Doctors communicated in ways that generally promoted optimal recruitment indicating that trials can be embedded into clinical practice. However, parents were unclear about some particular details of the trial's rationale suggesting that recruitment to trials with complicated designs, such as those involving stratified treatments, might need enhanced explanation." [Accessed October 25, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/101.

Journal article: Sue Ross, Adrian Grant, Carl Counsell, William Gillespie, Ian Russell, Robin Prescott. Barriers to Participation in Randomised Controlled Trials: A Systematic Review Journal of Clinical Epidemiology. 52(12):1143-1156. Abstract: "Method: A systematic review of three bibliographic databases from 1986 to 1996 identified 78 papers reporting barriers to recruitment of clinicians and patients to randomised controlled trials. Results: Clinician barriers included: time constraints, lack of staff and training, worry about the impact on the doctor-patient relationship, concern for patients, loss of professional autonomy, difficulty with the consent procedure, lack of rewards and recognition, and an insufficiently interesting question. Patient barriers included: additional demands of the trial, patient preferences, worry caused by uncertainty, and concerns about information and consent. Conclusions: To overcome barriers to clinician recruitment, the trial should address an important research question and the protocol and data collection should be as straightforward as possible. The demands on clinicians and patients should be kept to a minimum. Dedicated research staff may be required to support clinical staff and patients. The recruitment aspects of a randomised controlled trial should be carefully planned and piloted. Further work is needed to quantify the extent of problems associated with clinician and patient participation, and proper evaluation is required of strategies to overcome barriers." [Accessed on January 20, 2012]. http://www.jclinepi.com/article/S0895-4356(99)00141-9/abstract.

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.

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.

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/.

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.

Journal article: Frederick L. Brancati, Mary Evans, Curt D. Furberg, Nancy Geller, Steven Haffner, Steven E. Kahn, Peter G. Kaufmann, Cora E. Lewis, David M. Nathan, et al. Midcourse Correction to a Clinical Trial When the Event Rate Is Underestimated: The Look AHEAD (Action for Health in Diabetes) Study. Clin Trials. 2012;9(1):113–124. Abstract: "The Look AHEAD (Action for Health in Diabetes) Study is a long-term clinical trial that aims to determine the cardiovascular disease (CVD) benefits of an intensive lifestyle intervention (ILI) in obese adults with type 2 diabetes. The study was designed to have 90% statistical power to detect an 18% reduction in the CVD event rate in the ILI Group compared to the Diabetes Support and Education (DSE) Group over 10.5 years of follow-up. The original power calculations were based on an expected CVD rate of 3.125% per year in the DSE group; however, a much lower-than-expected rate in the first 2 years of follow-up prompted the Data and Safety Monitoring Board (DSMB) to recommend that the Steering Committee undertake a formal blinded evaluation of these design considerations. The Steering Committee created an Endpoint Working Group (EPWG) that consisted of individuals masked to study data to examine relevant issues. The EPWG considered two primary options: (1) expanding the definition of the primary endpoint and (2) extending follow-up of participants. Ultimately, the EPWG recommended that the Look AHEAD Steering Committee approve both strategies. The DSMB accepted these modifications, rather than recommending that the trial continue with inadequate statistical power. Trialists sometimes need to modify endpoints after launch. This decision should be well justified and should be made by individuals who are fully masked to interim results that could introduce bias. This article describes this process in the Look AHEAD study and places it in the context of recent articles on endpoint modification and recent trials that reported endpoint modification." [Accessed on May 24, 2012]. http://ctj.sagepub.com/content/9/1/113.

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.

Journal article: R J Winn. Obstacles to the accrual of patients to clinical trials in the community setting Semin. Oncol. 1994;21(4 Suppl 7):112-117. Abstract: "Data indicate that substantial pool of candidates exists, especially in the community setting, for enrollment in clinical oncologic trials. However, only a small proportion of cancer patients are actually enrolled. Obstacles to accrual include physician determinants, patient determinants, organizational issues, and health care system factors. Physicians can influence patient enlistment in their capacity as either referring physician or study investigator. A practitioner's medical orientation may impact the likelihood of referral. Physicians reluctant to enroll patients cite logistic and practical problems and the potential for disrupting patient relationships; a trial's protocol also may be questioned. Patient refusals to participate in clinical trials may stem from practical concerns, psychosocial issues, or a wariness of toxic treatment effects. Organizational issues affecting patient accrual have not been extensively studied; an institutional review board's view of research may be a major factor. Health care system factors such as escalating costs and prohibitive reimbursement policies of third-party payers also adversely affect accrual, and will require a new commitment to minimizing protocol costs. Continued evaluation of physician and patient barriers to accrual is warranted. Once these barriers are recognized, randomized intervention trials will be required to identify ways to overcome them." [Accessed on September 29, 2011]. http://www.ncbi.nlm.nih.gov/pubmed/8091236.

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.

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.

Journal article: Anneke T Schroen, Gina R Petroni, Hongkun Wang, Robert Gray, Xiaofei F Wang, Walter Cronin, Daniel J Sargent, Jacqueline Benedetti, Donald L Wickerham, et al. Preliminary evaluation of factors associated with premature trial closure and feasibility of accrual benchmarks in phase III oncology trials Clin Trials. 2010;7(4):312-321. Abstract: "BACKGROUND: A major challenge for randomized phase III oncology trials is the frequent low rates of patient enrollment, resulting in high rates of premature closure due to insufficient accrual. PURPOSE: We conducted a pilot study to determine the extent of trial closure due to poor accrual, feasibility of identifying trial factors associated with sufficient accrual, impact of redesign strategies on trial accrual, and accrual benchmarks designating high failure risk in the clinical trials cooperative group (CTCG) setting. METHODS: A subset of phase III trials opened by five CTCGs between August 1991 and March 2004 was evaluated. Design elements, experimental agents, redesign strategies, and pretrial accrual assessment supporting accrual predictions were abstracted from CTCG documents. Percent actual/predicted accrual rate averaged per month was calculated. Trials were categorized as having sufficient or insufficient accrual based on reason for trial termination. Analyses included univariate and bivariate summaries to identify potential trial factors associated with accrual sufficiency. RESULTS: Among 40 trials from one CTCG, 21 (52.5%) trials closed due to insufficient accrual. In 82 trials from five CTCGs, therapeutic trials accrued sufficiently more often than nontherapeutic trials (59% vs 27%, p = 0.05). Trials including pretrial accrual assessment more often achieved sufficient accrual than those without (67% vs 47%, p = 0.08). Fewer exclusion criteria, shorter consent forms, other CTCG participation, and trial design simplicity were not associated with achieving sufficient accrual. Trials accruing at a rate much lower than predicted (<35% actual/predicted accrual rate) were consistently closed due to insufficient accrual. LIMITATIONS: This trial subset under-represents certain experimental modalities. Data sources do not allow accounting for all factors potentially related to accrual success. CONCLUSION: Trial closure due to insufficient accrual is common. Certain trial design factors appear associated with attaining sufficient accrual. Defining accrual benchmarks for early trial termination or redesign is feasible, but better accrual prediction methods are critically needed. Future studies should focus on identifying trial factors that allow more accurate accrual predictions and strategies that can salvage open trials experiencing slow accrual." [Accessed on September 27, 2011]. http://www.ncbi.nlm.nih.gov/pubmed/20595245.

Fred Andersen, Torgeir Engstad, Bjorn Straume, et al. Recruitment methods in Alzheimer's disease research: general practice versus population based screening by mail. BMC Medical Research Methodology. 2010;10(1):35. Abstract: "BACKGROUND: In Alzheimer's disease (AD) research patients are usually recruited from clinical practice, memory clinics or nursing homes. Lack of standardised inclusion and diagnostic criteria is a major concern in current AD studies. The aim of the study was to explore whether patient characteristics differ between study samples recruited from general practice and from a population based screening by mail within the same geographic areas in rural Northern Norway. METHODS: An interventional study in nine municipalities with 70000 inhabitants was designed. Patients were recruited from general practice or by population based screening of cognitive function by mail. We sent a questionnaire to 11807 individuals [greater than or equal to] 65 years of age of whom 3767 responded. Among these, 438 individuals whose answers raised a suspicion of cognitive impairment were invited to extended cognitive testing and a clinical examination. Descriptive statistics, chi-square, independent sample t-test and analyses of covariance adjusted for possible confounders were used. RESULTS: The final study samples included 100 patients recruited by screening and 87 from general practice. Screening through mail recruited younger and more self-reliant male patients with a higher MMSE sum score, whereas older women with more severe cognitive impairment were recruited from general practice. Adjustment for age did not alter the statistically significant differences of cognitive function, self-reliance and gender distribution between patients recruited by screening and from general practice. CONCLUSIONS: Different recruitment procedures of individuals with cognitive impairment provided study samples with different demographic characteristics. Initial cognitive screening by mail, preceding extended cognitive testing and clinical examination may be a suitable recruitment strategy in studies of early stage AD. Registration: ClinicalTrial.gov Identifier: NCT00443014" [Accessed May 06, 2010]. Available at http://www.biomedcentral.com/1471-2288/10/35.

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.

MK Campbell, C Snowdon, D Francis, D Elbourne, AM McDonald, R Knight, V Entwistle, J Garcia, I Roberts and A Grant. Recruitment to randomised trials: strategies for trial enrolment and participation study. The STEPS study. Health Technology Assessment 2007; Vol. 11: No. 48.

Journal article: Xiaoxi Zhang, Qi Long. Stochastic modeling and prediction for accrual in clinical trials Stat Med. 2010;29(6):649-658. Abstract: "Patient accrual in clinical trials is a topic of interest for important practical reasons. It has implications in both the initial planning and ongoing monitoring of trials. Slow accrual is of particular concern when it leads to reduced sample size. Although accrual in clinical trials has been studied and its estimation has been proposed and implemented, the existing methods are usually over-simplified by assuming a constant or piecewise constant accrual rate, and more flexible and realistic methods are needed. In this paper, we discuss a principled framework to monitor and predict trial accrual. We model trial accrual using a non-homogeneous Poisson process and model the underlying time-dependent accrual rate using cubic B-splines. The statistical inference and prediction procedure for the model are studied in a Bayesian paradigm. We conduct simulation studies to investigate the performance of the proposed approach and compare with a constant accrual rate model discussed by Gajewski et al. (Statist. Med. 2008; 27: 2328-2340). With satisfactory results, we illustrate the proposed method using accrual data from a real oncology trial. Our results show that the proposed model is more robust and achieves substantially better performance compared with the existing methods." [Accessed on October 20, 2011].

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.

Journal article: Patrina H. Y. Caldwell, Sana Hamilton, Alvin Tan, Jonathan C. Craig. Strategies for Increasing Recruitment to Randomised Controlled Trials: Systematic Review PLoS Med. 2010;7(11):e1000368. Excerpt: "Patrina Caldwell and colleagues performed a systematic review of randomized studies that compared methods of recruiting individual study participants into trials, and found that strategies that focus on increasing potential participants' awareness of the specific health problem, and that engaged them, appeared to increase recruitment." [Accessed on January 20, 2012]. http://dx.doi.org/10.1371/journal.pmed.1000368,http://dx.doi.org/10.1371/journal.pmed.1000368.

Journal article: Shaun Treweek, Elizabeth Mitchell, Marie Pitkethly, Jonathan Cook, Monica Kjeldstrøm, Marit Johansen, Taina K Taskila, Frank Sullivan, Sue Wilson, et al. Strategies to improve recruitment to randomised controlled trials Cochrane Database of Systematic Reviews. 2010. Abstract: "Background: Recruiting participants to trials can be extremely difficult. Identifying strategies that improve trial recruitment would benefit both trialists and health research. Objectives: To quantify the effects of strategies to improve recruitment of participants to randomised controlled trials. Search strategy: We searched the Cochrane Methodology Review Group Specialised Register (CMR) 2010, Issue 2, part of The Cochrane Library (online) www.thecochranelibrary.com (searched 16 April 2010); MEDLINE, Ovid (1950 to March Week 5 2010) (searched 14 April 2010); EMBASE, Ovid (1980 to 2010 Week 14) (searched 14 April 2010); ERIC, CSA (1966 to 14 April 2010); Science Citation Index Expanded, ISI Web of Science (1975 to 14 April 2010); Social Sciences Citation Index, ISI Web of Science (1975 to 14 April 2010); National Research Register (online) (Issue 3 2007) (searched 3 September 2007); C2-SPECTR (searched 9 April 2008) and PubMed 'Related citations' (searched 4 June 2010). Selection criteria: Randomised and quasi-randomised controlled trials of methods to increase recruitment to randomised controlled trials. This includes non-healthcare studies and studies recruiting to hypothetical trials. We excluded studies aiming to increase response rates to questionnaires or trial retention, or which evaluated incentives and disincentives for clinicians to recruit patients. Data collection and analysis: We extracted data on: the method evaluated; country in which the study was carried out; nature of the population; nature of the study setting; nature of the study to be recruited into; randomisation or quasi-randomisation method; and numbers and proportions in each intervention group. We used a risk or odds ratio and their 95% confidence interval (CI) to describe the effect in individual trials. We assessed heterogeneity between trial results. Main results: We identified 45 eligible trials (18 new to this update) with more than 41,239 participants. There were 40 studies involving interventions aimed directly at trial participants, while five evaluated interventions aimed at people recruiting participants. All studies were in health care. Some interventions were effective in increasing recruitment: telephone reminders to non-respondents (odds ratio (OR) 1.95, 95% CI 1.04 to 3.66; two trials, 1058 participants), use of opt-out, rather than opt-in, procedures for contacting potential trial participants (RR 1.39, 95% CI 1.06 to 1.84; one study, 152 participants) and open designs where participants know which treatment they are receiving in the trial (RR 1.22, 95% CI 1.09 to 1.36; two studies, 4833 participants). However, some of these strategies have disadvantages, which may limit their widespread use. For example, opt-out procedures are controversial and open designs are by definition unblinded. The effects of many other recruitment strategies are unclear; examples include the use of video to provide trial information to potential participants and modifying the training of recruiters. Many studies looked at recruitment to hypothetical trials and it is unclear how applicable these results are to real trials. Authors' conclusions: There are promising strategies for increasing recruitment to trials: telephone reminders; requiring potential participants to opt-out of being contacted by the trial team regarding taking part in a trial, rather than them having to opt-in, and open designs. Some strategies (e.g. open trial designs) need to be considered carefully before use because they also have disadvantages. For example, opt-out procedures are controversial and open designs are by definition unblinded." [Accessed on January 20, 2012]. http://summaries.cochrane.org/MR000013/strategies-to-recruit-participants-to-randomised-trials.

Carol A. Townsley, Rita Selby, and Lillian L. Systematic Review of Barriers to the Recruitment of Older Patients With Cancer Onto Clinical Trials. Siu JCO 23(13), pp 3112-24 ,2005

Katharine Barnard, Louise Dent, Andrew Cook. A systematic review of models to predict recruitment to multicentre clinical trials. BMC Medical Research Methodology. 2010;10(1):63. Abstract: "BACKGROUND: Less than one third of publicly funded trials managed to recruit according to their original plan often resulting in request for additional funding and/or time extensions. The aim was to identify models which might be useful to a major public funder of randomised controlled trials when estimating likely time requirements for recruiting trial participants. The requirements of a useful model were identified as usability, based on experience, able to reflect time trends, accounting for centre recruitment and contribution to a commissioning decision. METHODS: A systematic review of English language articles using MEDLINE and EMBASE. Search terms included: randomised controlled trial, patient, accrual, predict, enrol, models, statistical; Bayes Theorem; Decision Theory; Monte Carlo Method and Poisson. Only studies discussing prediction of recruitment to trials using a modelling approach were included. Information was extracted from articles by one author, and checked by a second, using a pre-defined form. RESULTS: Out of 326 identified abstracts, only 8 met all the inclusion criteria. Of these 8 studies examined, there are five major classes of model discussed: the unconditional model, the conditional model, the Poisson model, Bayesian models and Monte Carlo simulation of Markov models. None of these meet all the pre-identified needs of the funder. CONCLUSIONS: To meet the needs of a number of research programmes, a new model is required as a matter of importance. Any model chosen should be validated against both retrospective and prospective data, to ensure the predictions it gives are superior to those currently used." [Accessed July 11, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/63.

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.

Jonathan Graffy, Peter Bower, Elaine Ward, et al. Trials within trials? Researcher, funder and ethical perspectives on the practicality and acceptability of nesting trials of recruitment methods in existing primary care trials. BMC Medical Research Methodology. 2010;10(1):38. Abstract: "BACKGROUND: Trials frequently encounter difficulties in recruitment, but evidence on effective recruitment methods in primary care is sparse. A robust test of recruitment methods involves comparing alternative methods using a randomized trial, 'nested' in an ongoing 'host' trial. There are potential scientific, logistical and ethical obstacles to such studies. METHOD: Telephone interviews were undertaken with four groups of stakeholders (funders, principal investigators, trial managers and ethics committee chairs) to explore their views on the practicality and acceptability of undertaking nested trials of recruitment methods. These semi-structured interviews were transcribed and analyzed thematically. RESULTS: Twenty people were interviewed. Respondents were familiar with recruitment difficulties in primary care and recognised the case for 'nested' studies to build an evidence base on effective recruitment strategies. However, enthusiasm for this global aim was tempered by the challenges of implementation. Challenges for host studies included increasing complexity and management burden; compatibility between the host and nested study; and the impact of the nested study on trial design and relationships with collaborators. For nested recruitment studies, there were concerns that host study investigators might have strong preferences, limiting the nested study investigators' control over their research, and also concerns about sample size which might limit statistical power. Nested studies needed to be compatible with the main trial and should be planned from the outset. Good communication and adequate resources were seen as important. CONCLUSIONS: Although research on recruitment was welcomed in principle, the issue of which study had control of key decisions emerged as critical. To address this concern, it appeared important to align the interests of both host and nested studies and to reduce the burden of hosting a recruitment trial. These findings should prove useful in devising a programme of research involving nested studies of recruitment interventions." [Accessed May 6, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/38.

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. Anything below this paragraph represents material from my old website, StATS. Until recently (June 2012), this material was available through Children's Mercy Hospital, but is no longer available there. Although I do not hold clear copyright for this material, I am reproducing it here as a service. See my old website page for more details.


18. 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.

17. 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.

16. 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.

15. 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.

14. 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.


13. 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.

11. 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.

10. 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.


8. 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.

7. 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.

6. 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.

5. 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.

4. 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.

3a. StATS: Possible sources of funding for my grant (July 6, 2006). The NIH has a Request for Application (RFA) titled Research on Research Integrity (R01). The goal of this RFA is to foster empirical research on research integrity. The sponsoring programs are particularly interested in research that will provide clear evidence (rates of occurrence and impacts) of potential problems areas as well as societal, organizational, group, and individual factors that affect, both positively and negatively, integrity in research. Applications must have clear relevance to biomedical, behavioral health sciences, and health services research. I have written on my weblog about some research that I want to get grant funding for and this RFA might be a place where I could apply for funding. This RFA seems to be focused predominantly on research misconduct, and that might be a problem. Accrual problems may actually represent sloppy planning rather than misconduct.

3. 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?

2. 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.

1a. StATS: Seminar on control charts and adverse events (June 5, 2006). I took some time to expand my May 30, 2005 weblog entry on accrual rates and developed a seminar which I will present to the Statistics journal club at KUMC today. The handout for this talk combines that weblog entry with a brief tutorial on quality control.

1. 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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