BENCHMARKING AND IMPLEMENTATION OF A NEW INDEPENDENT MONTE CARLO DOSE CALCULATION QUALITY ASSURANCE AUDIT TOOL FOR CLINICAL TRIALS

Introduction Commercial treatment planning systems employ a variety of dose calculation algorithms to plan and predict the dose distributions a patient receives during external beam radiation therapy. Traditionally, the Radiological Physics Center has relied on measurements to assure that institutions participating in the National Cancer Institute sponsored clinical trials administer radiation in doses that are clinically comparable to those of other participating institutions. To complement the effort of the RPC, an independent dose calculation tool needs to be developed that will enable a generic method to determine patient dose distributions in three dimensions and to perform retrospective analysis of radiation delivered to patients who enrolled in past clinical trials. Methods A multi-source model representing output for Varian 6 MV and 10 MV photon beams was developed and evaluated. The Monte Carlo algorithm, know as the Dose Planning Method (DPM), was used to perform the dose calculations. The dose calculations were compared to measurements made in a water phantom and in anthropomorphic phantoms. Intensity modulated radiation therapy and stereotactic body radiation therapy techniques were used with the anthropomorphic phantoms. Finally, past patient treatment plans were selected and recalculated using DPM and contrasted against a commercial dose calculation algorithm. Results The multi-source model was validated for the Varian 6 MV and 10 MV photon beams. The benchmark evaluations demonstrated the ability of the model to accurately calculate dose for the Varian 6 MV and the Varian 10 MV source models. The patient calculations proved that the model was reproducible in determining dose under similar conditions described by the benchmark tests. Conclusions The dose calculation tool that relied on a multi-source model approach and used the DPM code to calculate dose was developed, validated, and benchmarked for the Varian 6 MV and 10 MV photon beams. Several patient dose distributions were contrasted against a commercial algorithm to provide a proof of principal to use as an application in monitoring clinical trial activity.

Phase I clinical trials in 56 patients with thyroid cancer: the M. D. Anderson Cancer Center experience.

INTRODUCTION: Thyroid cancer is the most common endocrine malignancy. The outcomes of patients with relapsed thyroid cancer treated on early-phase clinical trials have not been systematically analyzed. PATIENTS AND METHODS: We reviewed the records of consecutive patients with metastatic thyroid cancer referred to the Phase I Clinical Trials Program from March 2006 to April 2008. Best response was assessed by Response Evaluation Criteria in Solid Tumors. RESULTS: Fifty-six patients were identified. The median age was 55 yr (range 35-79 yr). Of 49 patients evaluable for response, nine (18.4%) had a partial response, and 16 (32.7%) had stable disease for 6 months or longer. The median progression-free survival was 1.12 yr. With a median follow-up of 15.6 months, the 1-yr survival rate was 81%. In univariate analysis, factors predicting shorter survival were anaplastic histology (P = 0.0002) and albumin levels less than 3.5 g/dl (P = 0.05). Among 26 patients with tumor decreases, none died (median follow-up 1.3 yr), whereas 52% of patients with any tumor increase died by 1 yr (P = 0.0001). The median time to failure in our phase I clinical trials was 11.5 months vs. 4.1 months for the previous treatment (P = 0.04). CONCLUSION: Patients with advanced thyroid cancer treated on phase I clinical trials had high rates of partial response and prolonged stable disease. Time to failure was significantly longer on the first phase I trial compared with the prior conventional treatment. Patients with any tumor decrease had significantly longer survival than those with any tumor increase.

Is paromomycin an effective and safe treatment against cutaneous leishmaniasis? A meta-analysis of 14 randomized controlled trials.

BACKGROUND: High cost, poor compliance, and systemic toxicity have limited the use of pentavalent antimony compounds (SbV), the treatment of choice for cutaneous leishmaniasis (CL). Paromomycin (PR) has been developed as an alternative to SbV, but existing data are conflicting. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed, Scopus, and Cochrane Central Register of Controlled Trials, without language restriction, through August 2007, to identify randomized controlled trials that compared the efficacy or safety between PR and placebo or SbV. Primary outcome was clinical cure, defined as complete healing, disappearance, or reepithelialization of all lesions. Data were extracted independently by two investigators, and pooled using a random-effects model. Fourteen trials including 1,221 patients were included. In placebo-controlled trials, topical PR appeared to have therapeutic activity against the old world and new world CL, with increased local reactions, when used with methylbenzethonium chloride (MBCL) compared to when used alone (risk ratio [RR] for clinical cure, 2.58 versus 1.01: RR for local reactions, 1.60 versus 1.07). In SbV-controlled trials, the efficacy of topical PR was not significantly different from that of intralesional SbV in the old world CL (RR, 0.70; 95% confidence interval, 0.26-1.89), whereas topical PR was inferior to parenteral SbV in treating the new world CL (0.67; 0.54-0.82). No significant difference in efficacy was found between parenteral PR and parenteral SbV in the new world CL (0.88; 0.56-1.38). Systemic side effects were fewer with topical or parenteral PR than parenteral SbV. CONCLUSIONS/SIGNIFICANCE: Topical PR with MBCL could be a therapeutic alternative to SbV in selected cases of the old world CL. Development of new formulations with better efficacy and tolerability remains to be an area of future research.

BAYESIAN PHASE I DOSE FINDING IN CANCER TRIALS

This dissertation explores phase I dose-finding designs in cancer trials from three perspectives: the alternative Bayesian dose-escalation rules, a design based on a time-to-dose-limiting toxicity (DLT) model, and a design based on a discrete-time multi-state (DTMS) model. We list alternative Bayesian dose-escalation rules and perform a simulation study for the intra-rule and inter-rule comparisons based on two statistical models to identify the most appropriate rule under certain scenarios. We provide evidence that all the Bayesian rules outperform the traditional ``3+3'' design in the allocation of patients and selection of the maximum tolerated dose. The design based on a time-to-DLT model uses patients' DLT information over multiple treatment cycles in estimating the probability of DLT at the end of treatment cycle 1. Dose-escalation decisions are made whenever a cycle-1 DLT occurs, or two months after the previous check point. Compared to the design based on a logistic regression model, the new design shows more safety benefits for trials in which more late-onset toxicities are expected. As a trade-off, the new design requires more patients on average. The design based on a discrete-time multi-state (DTMS) model has three important attributes: (1) Toxicities are categorized over a distribution of severity levels, (2) Early toxicity may inform dose escalation, and (3) No suspension is required between accrual cohorts. The proposed model accounts for the difference in the importance of the toxicity severity levels and for transitions between toxicity levels. We compare the operating characteristics of the proposed design with those from a similar design based on a fully-evaluated model that directly models the maximum observed toxicity level within the patients' entire assessment window. We describe settings in which, under comparable power, the proposed design shortens the trial. The proposed design offers more benefit compared to the alternative design as patient accrual becomes slower.

Cancer patients' attitudes toward cancer trials

Background. Accurate measurement of attitudes toward participation in cancer treatment trials (CTs) and cancer prevention trials (CPTs) across varied groups could assist health researchers and educators when addressing attitudinal barriers to participation in these trials. ^ Methods. The Attitudes toward Cancer Trials Scales (ACTS) instrument development was based on a conceptual model developed from research literature, clinical practice experience, and empirical testing of items with a sample of 312 respondents. The ACTS contains two scales, the Cancer Trials (CT) scale (4 components; 18 items) and the Cancer Prevention Trials (CPT) scale (3 components; 16 items). Cronbach's alpha values for the CT and CPT scales, respectively, were 0.86 and 0.89. These two scales along with sociodemographic and cancer trial history variables were distributed in a mail survey of former patients of a large cancer research center. The disproportionate stratified probability sampling procedure yielded 925 usable responses (54% response rate). ^ Results. Prevalence of favorable attitudes toward CTs and CPTs was 66% and 69%, respectively. There were no significant differences in mean scale scores by cancer site or gender, but African Americans had more favorable attitudes toward CTs than European Americans. Multiple regression analysis indicated that older age, lower education level, and prior CT participation history were associated with more favorable attitudes toward CTs. Prior CT participation and prior CPT participation were associated with more favorable attitudes toward CPTs. Results also provided evidence of reliability and construct validity for both scales. ^ Conclusions. Middle age, higher education, and European American ethnicity are associated with less positive attitudes about participating in cancer treatment trials. Availability of a psychometrically sound instrument to measure attitudes may facilitate a better understanding decision making regarding participation in CTs and CPTs. It is this author's intention that the ACTS' scales will be used by other investigators to measure attitudes toward CTs and CPTs in various groups of persons, and that the many issues regarding participation in trials might become more explicit. ^

Effect of nonnormal random components on level and power in group-randomized trials

The use of group-randomized trials is particularly widespread in the evaluation of health care, educational, and screening strategies. Group-randomized trials represent a subset of a larger class of designs often labeled nested, hierarchical, or multilevel and are characterized by the randomization of intact social units or groups, rather than individuals. The application of random effects models to group-randomized trials requires the specification of fixed and random components of the model. The underlying assumption is usually that these random components are normally distributed. This research is intended to determine if the Type I error rate and power are affected when the assumption of normality for the random component representing the group effect is violated. ^ In this study, simulated data are used to examine the Type I error rate, power, bias and mean squared error of the estimates of the fixed effect and the observed intraclass correlation coefficient (ICC) when the random component representing the group effect possess distributions with non-normal characteristics, such as heavy tails or severe skewness. The simulated data are generated with various characteristics (e.g. number of schools per condition, number of students per school, and several within school ICCs) observed in most small, school-based, group-randomized trials. The analysis is carried out using SAS PROC MIXED, Version 6.12, with random effects specified in a random statement and restricted maximum likelihood (REML) estimation specified. The results from the non-normally distributed data are compared to the results obtained from the analysis of data with similar design characteristics but normally distributed random effects. ^ The results suggest that the violation of the normality assumption for the group component by a skewed or heavy-tailed distribution does not appear to influence the estimation of the fixed effect, Type I error, and power. Negative biases were detected when estimating the sample ICC and dramatically increased in magnitude as the true ICC increased. These biases were not as pronounced when the true ICC was within the range observed in most group-randomized trials (i.e. 0.00 to 0.05). The normally distributed group effect also resulted in bias ICC estimates when the true ICC was greater than 0.05. However, this may be a result of higher correlation within the data. ^

Bayesian doubly optimal group sequential designs for randomized clinical trials

When conducting a randomized comparative clinical trial, ethical, scientific or economic considerations often motivate the use of interim decision rules after successive groups of patients have been treated. These decisions may pertain to the comparative efficacy or safety of the treatments under study, cost considerations, the desire to accelerate the drug evaluation process, or the likelihood of therapeutic benefit for future patients. At the time of each interim decision, an important question is whether patient enrollment should continue or be terminated; either due to a high probability that one treatment is superior to the other, or a low probability that the experimental treatment will ultimately prove to be superior. The use of frequentist group sequential decision rules has become routine in the conduct of phase III clinical trials. In this dissertation, we will present a new Bayesian decision-theoretic approach to the problem of designing a randomized group sequential clinical trial, focusing on two-arm trials with time-to-failure outcomes. Forward simulation is used to obtain optimal decision boundaries for each of a set of possible models. At each interim analysis, we use Bayesian model selection to adaptively choose the model having the largest posterior probability of being correct, and we then make the interim decision based on the boundaries that are optimal under the chosen model. We provide a simulation study to compare this method, which we call Bayesian Doubly Optimal Group Sequential (BDOGS), to corresponding frequentist designs using either O'Brien-Fleming (OF) or Pocock boundaries, as obtained from EaSt 2000. Our simulation results show that, over a wide variety of different cases, BDOGS either performs at least as well as both OF and Pocock, or on average provides a much smaller trial. ^

Outcome-based adaptive randomization for binary data in clinical trials---Compare and contrast

Monte Carlo simulation has been conducted to investigate parameter estimation and hypothesis testing in some well known adaptive randomization procedures. The four urn models studied are Randomized Play-the-Winner (RPW), Randomized Pôlya Urn (RPU), Birth and Death Urn with Immigration (BDUI), and Drop-the-Loses Urn (DL). Two sequential estimation methods, the sequential maximum likelihood estimation (SMLE) and the doubly adaptive biased coin design (DABC), are simulated at three optimal allocation targets that minimize the expected number of failures under the assumption of constant variance of simple difference (RSIHR), relative risk (ORR), and odds ratio (OOR) respectively. Log likelihood ratio test and three Wald-type tests (simple difference, log of relative risk, log of odds ratio) are compared in different adaptive procedures. ^ Simulation results indicates that although RPW is slightly better in assigning more patients to the superior treatment, the DL method is considerably less variable and the test statistics have better normality. When compared with SMLE, DABC has slightly higher overall response rate with lower variance, but has larger bias and variance in parameter estimation. Additionally, the test statistics in SMLE have better normality and lower type I error rate, and the power of hypothesis testing is more comparable with the equal randomization. Usually, RSIHR has the highest power among the 3 optimal allocation ratios. However, the ORR allocation has better power and lower type I error rate when the log of relative risk is the test statistics. The number of expected failures in ORR is smaller than RSIHR. It is also shown that the simple difference of response rates has the worst normality among all 4 test statistics. The power of hypothesis test is always inflated when simple difference is used. On the other hand, the normality of the log likelihood ratio test statistics is robust against the change of adaptive randomization procedures. ^

Continuous safety screens for randomized controlled clinical trials with blinded treatment information

Standard methods for testing safety data are needed to ensure the safe conduct of clinical trials. In particular, objective rules for reliably identifying unsafe treatments need to be put into place to help protect patients from unnecessary harm. DMCs are uniquely qualified to evaluate accumulating unblinded data and make recommendations about the continuing safe conduct of a trial. However, it is the trial leadership who must make the tough ethical decision about stopping a trial, and they could benefit from objective statistical rules that help them judge the strength of evidence contained in the blinded data. We design early stopping rules for harm that act as continuous safety screens for randomized controlled clinical trials with blinded treatment information, which could be used by anyone, including trial investigators (and trial leadership). A Bayesian framework, with emphasis on the likelihood function, is used to allow for continuous monitoring without adjusting for multiple comparisons. Close collaboration between the statistician and the clinical investigators will be needed in order to design safety screens with good operating characteristics. Though the math underlying this procedure may be computationally intensive, implementation of the statistical rules will be easy and the continuous screening provided will give suitably early warning when real problems were to emerge. Trial investigators and trial leadership need these safety screens to help them to effectively monitor the ongoing safe conduct of clinical trials with blinded data.^

Interim analysis of clinical trials: Simulation studies of fractional Brownian motion

Interim clinical trial monitoring procedures were motivated by ethical and economic considerations. Classical Brownian motion (Bm) techniques for statistical monitoring of clinical trials were widely used. Conditional power argument and α-spending function based boundary crossing probabilities are popular statistical hypothesis testing procedures under the assumption of Brownian motion. However, it is not rare that the assumptions of Brownian motion are only partially met for trial data. Therefore, I used a more generalized form of stochastic process, called fractional Brownian motion (fBm), to model the test statistics. Fractional Brownian motion does not hold Markov property and future observations depend not only on the present observations but also on the past ones. In this dissertation, we simulated a wide range of fBm data, e.g., H = 0.5 (that is, classical Bm) vs. 0.5< H <1, with treatment effects vs. without treatment effects. Then the performance of conditional power and boundary-crossing based interim analyses were compared by assuming that the data follow Bm or fBm. Our simulation study suggested that the conditional power or boundaries under fBm assumptions are generally higher than those under Bm assumptions when H > 0.5 and also matches better with the empirical results. ^

A systematic review of community-based colorectal cancer screening randomized controlled trials with multi-ethnic groups

Background. The CDC estimates that 40% of adults 50 years of age or older do not receive time-appropriate colorectal cancer screening. Sixty percent of colorectal cancer deaths could be prevented by regular screening of adults 50 years of age and older. Yet, in 2000 only 42.5% of adults age 50 or older in the U.S. had received recommended screening. Disparities by health care, nativity status, socioeconomic status, and race/ethnicity are evident. Disparities in minority, underserved populations prevent us from attaining Goal 2 of Healthy People 2010 to “eliminate health disparities.” This review focuses on community-based screening research among underserved populations that includes multiple ethnic groups for appropriate disparities analysis. There is a gap in the colorectal cancer screening literature describing the effectiveness of community-based randomized controlled trials. ^ Objective. To critically review the literature describing community-based colorectal cancer screening strategies that are randomized controlled trials, and that include multiple racial/ethnic groups. ^ Methods. The review includes a preliminary disparities analysis to assess whether interventions were appropriately targeted in communities to those groups experiencing the greatest health disparities. Review articles are from an original search using Ovid Medline and a cross-matching search in Pubmed, both from January 2001 to June 2009. The Ovid Medline literature review is divided into eight exclusionary stages, seven electronic, and the last stage consisting of final manual review. ^ Results. The final studies (n=15) are categorized into four categories: Patient mailings (n=3), Telephone outreach (n=3), Electronic/multimedia (n=4), and Counseling/community education (n=5). Of 15 studies, 11 (73%) demonstrated that screening rates increased for the intervention group compared to controls, including all studies (100%) from the Patient mailings and Telephone outreach groups, 4 of 5 (80%) Counseling/community education studies, and 1 of 4 (25%) Electronic/multimedia interventions. ^ Conclusions. Patient choice and tailoring education and/or messages to individuals have proven to be two important factors in improving colorectal cancer screening adherence rates. Technological strategies have not been overly successful with underserved populations in community-based trials. Based on limited findings to date, future community-based colorectal cancer screening trials should include diverse populations who are experiencing incidence, survival, mortality and screening disparities. ^

A new combined endpoint using U-statistic in analysis in clinical trials

Common endpoints can be divided into two categories. One is dichotomous endpoints which take only fixed values (most of the time two values). The other is continuous endpoints which can be any real number between two specified values. Choices of primary endpoints are critical in clinical trials. If we only use dichotomous endpoints, the power could be underestimated. If only continuous endpoints are chosen, we may not obtain expected sample size due to occurrence of some significant clinical events. Combined endpoints are used in clinical trials to give additional power. However, current combined endpoints or composite endpoints in cardiovascular disease clinical trials or most clinical trials are endpoints that combine either dichotomous endpoints (total mortality + total hospitalization), or continuous endpoints (risk score). Our present work applied U-statistic to combine one dichotomous endpoint and one continuous endpoint, which has three different assessments and to calculate the sample size and test the hypothesis to see if there is any treatment effect. It is especially useful when some patients cannot provide the most precise measurement due to medical contraindication or some personal reasons. Results show that this method has greater power then the analysis using continuous endpoints alone. ^

Ascertainment, meta-analysis and reporting of adverse reactions in clinical trials of antibiotics

The ascertainment and analysis of adverse reactions to investigational agents presents a significant challenge because of the infrequency of these events, their subjective nature and the low priority of safety evaluations in many clinical trials. A one year review of antibiotic trials published in medical journals demonstrates the lack of standards in identifying and reporting these potentially fatal conditions. This review also illustrates the low probability of observing and detecting rare events in typical clinical trials which include fewer than 300 subjects. Uniform standards for ascertainment and reporting are suggested which include operational definitions of study subjects. Meta-analysis of selected antibiotic trials using multivariate regression analysis indicates that meaningful conclusions may be drawn from data from multiple studies which are pooled in a scientifically rigorous manner. ^

Behavioral facets of cancer chemoprevention trials

Few studies have explored factors related to participation in cancer chemoprevention trials. The purpose of this dissertation was to conduct investigations in this emerging field by studying aspects of participation at three phases of cancer chemoprevention trials: at enrollment, during a placebo run-in period, and post-trial. In all three studies, subjects had a history of cancer and were at high risk of recurrence or second primary tumors.^ The first study explored correlates of enrollment in a head and neck cancer chemoprevention trial by comparing participants and eligible nonparticipants. Of 148 subjects who met the trial's preliminary eligibility criteria, 40% enrolled. In multivariate analysis, enrollment was positively associated with being male (OR 2.36) and being employed (OR 2.73). The most commonly cited reason for declining participation among nonparticipants was transportation.^ The second study examined outcomes of an eight-week placebo run-in period in a head and neck cancer chemoprevention trial. Of 391 subjects, 91.3% were randomized after the run-in. Adherence to drug capsules ranged from 0% to 120.3% (mean $\pm$ SD, 95.8% $\pm$ 15.1). In multivariate analysis, the main variable predicting run-in outcome was race; white subjects were 3.45 times more likely to be randomized than non-white subjects. Subjects with Karnofsky scores of 100 were 2.13 times more likely to be randomized than were subjects with lower scores.^ The third study used post-trial questionnaires to assess subjects' (n = 64) perceptions of participation in a cancer chemoprevention trial. The most highly rated trial benefit was the perception of potential colon cancer prevention, and the most troublesome barrier was erroneous billing for study visits. Perceived benefits were positively associated with interest in participating in future trials of the same (p = 0.05) and longer (p = 0.02) duration, and difficulty with trial pills and procedures was inversely related to interest in future placebo-controlled trials (p = 0.01).^ These are among the first behavioral studies to be completed in the rapidly growing field of cancer chemoprevention. Much work has yet to be done, however, to advance our understanding of the complex issues relating to chemoprevention trial participation. ^