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 undeliver 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.
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.
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.
