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
-
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.
-
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.
-
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.
- 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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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
- 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.
-
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.
- 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.
-
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.
-
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
-
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.
- 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.
-
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).
- 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
-
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.
- 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.
-
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.
- 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.
-
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.
- 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?
-
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.
- 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.
What now?
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