AccrualMaster : software for planning and monitoring accrual rates in clinical trials [abstract]

The most common reason why clinical trials fail is that they fall well below their goals for patient accrual. Researchers will frequently overpromise and underdeliver on the number of patients that they can recruit during the proposed time frame. The result is studies that take far longer than planned and/or that end with fewer patients than planned. This raises serious economic and ethical issues. We have developed a Bayesian model for accrual that will encourage careful planning of accrual rates as well as allow regular monitoring of accrual patterns during the conduct of the clinical trial. We have developed software in R that can show graphically the expected duration of the trial under initial planning estimates of accrual rates and that can adjust those accrual rates as the trial progresses by combing the actual accrual data with the prior beliefs of accrual. This software can be used by individual researchers, by Institution Review Boards during their continuing review of approved projects, and by Data Safety and Monitoring Boards during their interim analysis. We are working on extensions of the software to multi-center trials, to assessing the impact of refusal rates and losses due to exclusion criteria, and to non-uniform accrual rates (e.g., accrual rates in a trial expected to have a slow startup period). We are looking for support and collaborators to make the software available on a R server computer using a simplified front-end interface, to test the software prospectively in a series of clinical trials, and to support research on the extensions to new and important areas

Native American Weight Loss Movement: Pilot Test of a Culturally Tailored Weight Loss Program for American Indians

American Indians (AIs) have higher rates of obesity than other racial/ethnic groups, placing them at heightened risk for cardiovascular diseases, diabetes, and certain cancers. Culturally appropriate weight loss interventions may be the key to reducing risk. The most successful program used in AI communities has been the Diabetes Prevention Program (DPP), which limits enrollment to individuals with a clinical diagnosis of pre-diabetes. The purpose of this pilot project was to modify and culturally tailor a weight loss intervention to AI communities in Kansas to improve weight loss related behaviors among those who do not qualify for the DPP. The Native American Weight Loss Movement (NAWLM) was developed from 2012-2014 using an iterative process with 4 sequential modifications to the program. Group 1 received a slightly modified version of the DPP that was originally tailored to African Americans. Each group received an improved program based on modifications from the previous group. Our analysis shows 36.1% (95% CI: 25.7, 47.5) of all participants (n=72) lost weight; a majority (63.9%, 95% CI: 52.8-75.0) maintained weight, gained weight, or dropped out. Among individuals who completed the program (n=34), 76.5% lost weight (95% CI: 61.4, 91.5). These individuals lost an average of 2.98% body weight (95% CI: 1.58, 4.37), with 6 participants losing \u3e7% body weight. While most participants who completed the program lost weight, more research is needed to determine factors that discourage drop-out and promote behavioral changes. NAWLM shows promise as a weight loss program for AIs who do not qualify for the DPP

Reliability, Responsiveness, and Validity of the Visual Analog Fatigue Scale to Measure Exertion Fatigue in People with Chronic Stroke: A Preliminary Study

Background and Purpose. Post-Stroke Fatigue (PSF) is a prevalent yet commonly neglected issue that impacts daily functions and quality of life in people post-stroke. To date no studies have attempted to validate a clinically-feasible and reliable instrument to quantify PSF. We developed the Visual Analog Fatigue Scale (VAFS) to eliminate difficulties and poor data validity in testing people post-stroke. The purpose of this study was to evaluate the reliability, responsiveness, and validity of the VAFS. Methods. Twenty-one people post-stroke (12 males, age  = 59.5 ± 10.3 years; time post-stroke  = 4.1 ± 3.5 years) participated. Subjects underwent a standardized fatigue-inducing exercise; fatigue level was assessed at rest, immediately after exercise, and after recovery. The same protocol was repeated after 14 days. Results. ICC values for the VAFS at rest was 0.851 (CI = 95%, 0.673 ∼ 0.936, P < .001), immediately after exercise was 0.846 (CI = 95%, 0.663 ∼ 0.934, P < .001), and 15 minutes after exercise was 0.888 (CI = 95%, 0.749 ∼ 0.953, P < .001). The ES values for at-rest to post-exercise and for post-exercise to post-recovery were 14.512 and 0.685, respectively. Using paired t-test, significant difference was found between VAFS scores at-rest and post-exercise (P < .001), and between post-exercise and post-recovery (P < .001). Conclusion. Our data suggests good reliability, responsiveness, and validity of the VAFS to assess exertion fatigue in people post-stroke

Slipped deadlines and sample size shortfalls in clinical trials: a proposed remedy using a Bayesian model with an informative prior distribution [abstract]

Computational Infrastructure and Informatics Poster SessionBackground: The most common reason why clinical trials fail is that they fall well below their goals for patient accrual. Researchers will frequently overpromise and underdeliver on the number of patients that they can recruit during the proposed time frame. The result is studies that take far longer than planned and/or that end with fewer patients than planned. This raises serious economic and ethical issues. Our research efforts have focused on (1) getting reliable data on the scope and magnitude of problems with slow patient accrual in clinical trials, and (2) developing a Bayesian model for accrual that will encourage careful planning of accrual rates as well as allow regular monitoring of accrual patterns during the conduct of the clinical trial. Methods: A random sample of 130 prospective studies approved by the Children's Mercy Hospital (CMH) IRB from 2001 through 2005 were retrospectively reviewed for the proposed and actual accrual rates. At the same time, a Bayesian model for accrual was developed and applied to a clinical trial at Kansas University Medical Center to produce monthly reports projecting estimated final sample sizes with uncertainty limits given the initial projection and currently available enrolment data. Results: 117 (90%) of the studies submitted to the IRB did not specify a start date, a completion date, or both, making it impossible to assess the accrual rate. Of the remaining studies, two failed to list actual start or end dates. Of the remaining 11 studies, 8 took more time than proposed and the average increase in duration in these 8 studies was 100%. Among the 109 studies that included both a target and an actual sample size, 59 (54%) fell short of the proposed sample size. The average shortfall across these 59 studies was 55%. The informative prior used in the Bayesian model was reasonable and produced early estimates of total sample size that were an accurate reflection of the end result. Conclusions: A large number of studies failed to meet the specified sample sizes and the average shortfall among these studies was considerable. The Bayesian model for accrual produced useful reports for a particular study and provided reassurance to the researchers that their accrual rates were on target. The Bayesian model, however, also has the capability of correcting an inaccurate prior distribution as the accumulated accrual patterns provide contradictory results. Future research should focus on collaborations with organizations that conduct large numbers of clinical trials to get more data on existing problems with slipped deadlines and sample size shortfalls and to test the Bayesian accrual model on a wide range of clinical trials

Using an Anchor to Improve Linear Predictions with Application to Predicting Disease Progression

Linear models are some of the most straightforward and commonly used modelling approaches. Consider modelling approximately monotonic response data arising from a time-related process. If one has knowledge as to when the process began or ended, then one may be able to leverage additionalassumed data to reduce prediction error. This assumed data, referred to as the anchor, is treated as an additional data-point generated at either the beginning or end of the process. The response value of the anchor is equal to an intelligently selected value of the response (such as the upper bound, lower bound, or 99th percentile of the response, as appropriate). The anchor reduces the variance of prediction at the cost of a possible increase in prediction bias, resulting in a potentially reduced overall mean-square prediction error. This can be extremely eective when few individual data-points are available, allowing one to make linear predictions using as little as a single observed data-point. We develop the mathematics showing the conditions under which an anchor can improve predictions, and also demonstrate using this approach to reduce prediction error when modelling the disease progression of patients with amyotrophic lateral sclerosis.Modelos lineales son los modelos más fáciles de usar y comunes en modelamiento. Si se considera el modelamiento de una respuesta aprosimadamente monótona que surge de un proceso relacionado al tiempo y se sabe cuándo el proceso inició o terminó, es posible asumir datos adicionales como palanca para reducir el error de predicción. Estos datos adicionales son llamados de ``anclaje'' y son datos generados antes del inicion o después del final del proceso. El valor de respuesta del anclaje es igual a un valor de respuesta escogido de manera inteligente (como por ejemplo la cota superior, iferior o el percentil 99, según conveniencia). Este anclaje reduce la varianza de la predicción a costo de un posible sesgo en la misma, lo cual resulta en una reducción potencial del error medio de predicción. Lo anterior puede ser extremadamente efectivo cuando haypocos datos individuales, permitiendo hacer predicciones con muy pocos datos. En este trabajo presentamos en desarrollo matemático demostrando las condiciones bajo las cuales el anclaje puede mejorar predicciones y también demostramos una reducción del error de predicción aplicando el método a la modelación de progresión de enfermedad en pacientes con esclerosis lateral amiotrófica

Reliability of Peak Treadmill Exercise Tests in Mild Alzheimer Disease

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Neuroscience on August 2011, available online: http://www.tandfonline.com/10.3109/00207454.2011.574762.The prevalence of Alzheimer disease (AD) doubles every 5 years after the age of 65, reaching nearly 50% after age 85 (Evans et al., 1989). This, along with an unprecedented growth in the elderly population, is leading to dramatic increases in the incidence of AD. Thus, effective strategies for promoting healthy brain aging and preventing AD are increasingly important. One strategy that appears promising in promoting healthy brain aging is exercise and physical activity. Evidence is accumulating that endurance exercise is beneficial to brain health (Laurin, Verreault, Lindsay, MacPherson, & Rockwood, 2001), and increased cardiorespiratory fitness is associated with increased brain volume in subjects with very mild to mild AD (Burns et al., 2008). While enhancing cardiorespiratory fitness may be a strategy for preventing cognitive decline in AD, there is limited information available on the validity and reliability of cardiorespiratory fitness measures in this population. The gold standard measure of cardiorespiratory fitness is maximum oxygen consumption (VO2max) (Frankin, 2001), the highest rate of oxygen uptake attainable during maximal or exhaustive exercise (American College of Sports Medicine, 2005). If the subject becomes exhausted and ends the test prior to reaching the physiologic VO2max, the end of the test is called peak oxygen consumption (VO2peak). It is unknown if advanced age and cognitive difficulties in people with AD would limit their ability to fully participate in a standard graded exercise test to reliably assess VO2max or VO2peak. Treadmill exercise testing has been found to be reliable in subjects with traumatic brain injury and mental retardation, although these subjects were very young (Fernhall, Millar, Tymeson, & Burkett, 1990; Mossberg & Greene, 2005). Traumatic brain injury and mental retardation are different disease processes than AD and would be expected to result in static rather than progressive cognitive symptoms. With AD, memory is impaired as is the ability to follow commands, however patients in the earliest stages of AD would be expected to respond to prompting and reminders to follow testing procedures. To our knowledge, no studies have assessed the reliability of peak treadmill exercise testing in subjects with AD. In our previous research on patients with very mild to mild AD (Burns, et al., 2008), we have found them to be capable of ambulating on a treadmill and completing peak treadmill exercise testing with 3 participants out of 74 (126 total peak exercise tests) identified as having EKG changes during testing. All 3 participants had negative follow-up testing in cardiology. The purpose of this study was to investigate the reliability of a graded peak treadmill exercise test in elderly people with early AD

Bayesian accrual prediction for interim review of clinical studies: open source R package and smartphone application

Illustration of use of R accrual package. (DOCX 917 kb