Managing the risks of medical technology: Do hazard surveillance systems provide an early warning of medical device problems?

The Food and Drug Administration (FDA) is responsible for risk assessment and risk management in the post-market surveillance of the U.S. medical device industry. One of the FDA regulatory mechanisms, the Medical Device Reporting System (MDR) is an adverse event reporting system intended to provide the FDA with advance warning of device problems. It includes voluntary reporting for individuals, and mandatory reporting for device manufacturers. ^ In a study of alleged breast implant safety problems, this research examines the organizational processes by which the FDA gathers data on adverse events and uses adverse event reporting systems to assess and manage risk. The research reviews the literature on problem recognition, risk perception, and organizational learning to understand the influence highly publicized events may have on adverse event reporting. Understanding the influence of an environmental factor, such as publicity, on adverse event reporting can provide insight into the question of whether the FDA's adverse event reporting system operates as an early warning system for medical device problems. ^ The research focuses on two main questions. The first question addresses the relationship between publicity and the voluntary and mandatory reporting of adverse events. The second question examines whether government agencies make use of these adverse event reports. ^ Using quantitative and qualitative methods, a longitudinal study was conducted of the number and content of adverse event reports regarding breast implants filed with the FDA's medical device reporting system during 1985–1991. To assess variation in publicity over time, the print media were analyzed to identify articles related to breast implant failures. ^ The exploratory findings suggest that an increase in media activity is related to an increase in voluntary reporting, especially following periods of intense media coverage of the FDA. However, a similar relationship was not found between media activity and manufacturers' mandatory adverse event reporting. A review of government committee and agency reports on the FDA published during 1976–1996 produced little evidence to suggest that publicity or MDR information contributed to problem recognition, agenda setting, or the formulation of policy recommendations. ^ The research findings suggest that the reporting of breast implant problems to FDA may reflect the perceptions and concerns of the reporting groups, a barometer of the volume and content of media attention. ^

Clinical relevance of polymorphic DNA copy number variation (CNV) in African American women with stage I-II breast cancer

Objectives. The chief goal of this study was to analyze copy number variation (CNV) in breast cancer tumors from 25 African American women with early stage breast cancer (BC) using molecular inversion probes (MIP) in order to: (1) compare the degree of CNV in tumors compared to normal lymph nodes, and (2) determine whether gains and/or losses of genes in specific chromosomes differ between pathologic subtypes of breast cancer defined by known prognostic markers, (3) determine whether gains/losses in CN are associated with known oncogenes or tumor suppressor genes, and (4) determine whether increased gains/losses in CN for specific chromosomes were associated with differences in breast cancer recurrence. ^ Methods. Twenty to 37 nanograms of DNA extracted from 25 formalin-fixed paraffin embedded (FFPE) tumor samples and matched normal lymph nodes were added to individual tubes. Oligonucleotide probes with recognition sequences at each terminus were hybridized with a genomic target sequence to form a circular structure. Probes are released from genomic DNA obtained from FFPE samples, and those which have been correctly "circularized" in the proper allele/nucleotide reaction combination are amplified using polymerase chain reaction (PCR) primers. Amplicons were fluorescently labeled and the tag sequences released from the genome homology regions by treatment with uracil-N-glycosylase to cleave the probe at the site where uracils are present, and detected using a complementary tag array developed by Affymetrix. ^ Results. Analysis of CN gains and losses from tumors and normal tissues showed marked differences in tumors with numerous chromosomes affected. Similar changes were not observed in normal lymph nodes. When tumors were stratified into four groups based on expression or lack of expression of the estrogen receptor and HER2/neu, distinct patterns of CNV for different chromosomes were observed. Gains or losses in CN for specific chromosomes correlated with amplifications/deletions of particular oncogenes or tumor suppressor genes (i.e. such as found on chromosome 17) known to be associated with aggressive tumor phenotype and poor prognosis. There was a trend for increases in CN observed for chromosome 17 to correlate inversely with time to recurrence of BC (p=0.14 for trend). CNV was also observed for chromosomes 5, 8, 10, 11, and 16, which are known sites for several breast cancer susceptibility alleles. ^ Conclusions. This study is the first to validate the MIP technique, to correlate differences in gene expression with known prognostic tumor markers, and to correlate significant increases/decreases in CN with known tumor markers associated with prognosis. The results of this study may have far reaching public health implications towards identifying new high-risk groups based on genomic differences in CNP, both with respect to prognosis and response to therapy, and to eventually identify new therapeutic targets for prevention and treatment of this disease. ^

Early stage breast cancer and comorbidities: Their impact on breast cancer disease-free survival differences by ethnicity

The retrospective cohort study examined the association between the presence of comorbidities and breast cancer disease-free survival rates among racial/ethnic groups. The study population consisted of 2389 women with stage I and II invasive breast cancer who were diagnosed and treated at the M.D. Anderson Cancer Center between 1985 and 2000. It has been suggested that as the number of comorbidities increases, breast cancer mortality increases. It is known that African Americans and Hispanics are considered to be at a higher risk for comorbid conditions such as hypertension and diabetes compared to Caucasian women (23) (10). When compared to Caucasian women, African American women also have a higher breast cancer mortality rate (1). As a result, the study also examined whether comorbid conditions contribute to racial differences in breast cancer disease-free survival. Among the study population, 24% suffered from breast cancer recurrence, 6% died from breast cancer and 24% died from all causes. The mean age was 56 with 41% of the population being women between the ages of 40-55. One or more comorbidities were reported in 84 (36%) African Americans (OR 1.57; 95% CI 1.19-2.10), 58 (31%) Hispanics (OR 1.25; 95% CI 0.90-1.74) compared to the reference group of 531 (27%) Caucasians. Additionally, African American women were significantly more likely to suffer from either a breast cancer recurrence or breast cancer death (OR 1.5; 95% CI 0.70-1.41) when compared to Caucasian women. Multivariate analysis found hypertension (HR 1.22; 95% CI 0.99-1.49; p<0.05) to be statistically significant and a potential prognostic tool for disease-free survival with African American women (OR 2.96; 95% 2.25-3.90) more likely to suffer from hypertension when compared to Caucasian women. When compared to Caucasian women, Hispanics were also more likely to suffer from hypertension (OR 1.33; 95% CI 0.96-1.83). This suggests that comorbid conditions like hypertension could account for the racial disparities that exist when comparing breast cancer survival rates. Future studies should investigate this relationship further.^

Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.