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This dissertation comprises a series of studies conducted as part of the Cost of Cancer Treatment Study (CCTS). An exploration of the sample size requirements for power and significance levels in clinical trials suggests that proportional representation of subpopulations in trials will often not allow valid inferences to be drawn about differential treatment effects. Where differential treatment effects in subpopulations are suspected, targeted trials should be undertaken. Under-representation of older cancer patients could be accounted for by exclusion criteria based on co-morbid conditions that disproportionately afflict the elderly. The author compared data from patient interviews, medical records abstraction, provider billing records, and Medicare claims as data sources for estimating health care utilization rates and costs; the data were compared in terms of completeness and accessibility. Cost estimates for utilization measures were derived from administrative data using hedonic regression models. Prescription drug costs and out-of-pocket drug expenditures were compared for patients enrolled in cancer trials and for similar cancer patients who did not participate in trials. Trial participation was associated with higher prescription drug costs, but that did not result in any significant difference in out-of-pocket expenditures for participants. These results were robust to a variety of modeling approaches.

This dissertation comprises a series of studies conducted as part of the Cost of Cancer Treatment Study (CCTS). An exploration of the sample size requirements for power and significance levels in clinical trials suggests that proportional representation of subpopulations in trials will often not allow valid inferences to be drawn about differential treatment effects. Where differential treatment effects in subpopulations are suspected, targeted trials should be undertaken. Under-representation of older cancer patients could be accounted for by exclusion criteria based on co-morbid conditions that disproportionately afflict the elderly. The author compared data from patient interviews, medical records abstraction, provider billing records, and Medicare claims as data sources for estimating health care utilization rates and costs; the data were compared in terms of completeness and accessibility. Cost estimates for utilization measures were derived from administrative data using hedonic regression models. Prescription drug costs and out-of-pocket drug expenditures were compared for patients enrolled in cancer trials and for similar cancer patients who did not participate in trials. Trial participation was associated with higher prescription drug costs, but that did not result in any significant difference in out-of-pocket expenditures for participants. These results were robust to a variety of modeling approaches.

This dissertation comprises a series of studies conducted as part of the Cost of Cancer Treatment Study (CCTS). An exploration of the sample size requirements for power and significance levels in clinical trials suggests that proportional representation of subpopulations in trials will often not allow valid inferences to be drawn about differential treatment effects. Where differential treatment effects in subpopulations are suspected, targeted trials should be undertaken. Under-representation of older cancer patients could be accounted for by exclusion criteria based on co-morbid conditions that disproportionately afflict the elderly. The author compared data from patient interviews, medical records abstraction, provider billing records, and Medicare claims as data sources for estimating health care utilization rates and costs; the data were compared in terms of completeness and accessibility. Cost estimates for utilization measures were derived from administrative data using hedonic regression models. Prescription drug costs and out-of-pocket drug expenditures were compared for patients enrolled in cancer trials and for similar cancer patients who did not participate in trials. Trial participation was associated with higher prescription drug costs, but that did not result in any significant difference in out-of-pocket expenditures for participants. These results were robust to a variety of modeling approaches.

This dissertation comprises a series of studies conducted as part of the Cost of Cancer Treatment Study (CCTS). An exploration of the sample size requirements for power and significance levels in clinical trials suggests that proportional representation of subpopulations in trials will often not allow valid inferences to be drawn about differential treatment effects. Where differential treatment effects in subpopulations are suspected, targeted trials should be undertaken. Under-representation of older cancer patients could be accounted for by exclusion criteria based on co-morbid conditions that disproportionately afflict the elderly. The author compared data from patient interviews, medical records abstraction, provider billing records, and Medicare claims as data sources for estimating health care utilization rates and costs; the data were compared in terms of completeness and accessibility. Cost estimates for utilization measures were derived from administrative data using hedonic regression models. Prescription drug costs and out-of-pocket drug expenditures were compared for patients enrolled in cancer trials and for similar cancer patients who did not participate in trials. Trial participation was associated with higher prescription drug costs, but that did not result in any significant difference in out-of-pocket expenditures for participants. These results were robust to a variety of modeling approaches.

The aim of this study is to assess how frequently genetic and or/other omics information has been used in the design, analysis, results and conclusion of the most cited recent trials. Furthermore, our study aims to compare the funding and transparency patterns of the genetics and other –omic identified study when compared to the non-genetic and other –omic trials.

The aim of this study is to assess how frequently genetic and or/other omics information has been used in the design, analysis, results and conclusion of the most cited recent trials. Furthermore, our study aims to compare the funding and transparency patterns of the genetics and other –omic identified study when compared to the non-genetic and other –omic trials.

This article is from Trials , volume 15 . Abstract Background: The proportion of clinical research sponsored by industry will likely continue to expand as federal funds for academic research decreases, particularly in the fields of HIV/AIDS and hepatitis C (HCV). While HIV and HCV continue to burden the US population, insufficient data exists as to how industry sponsorship affects clinical trials involving these infectious diseases. Debate exists about whether pharmaceutical companies undertake more market-driven research practices to promote therapeutics, or instead conduct more rigorous trials than their non-industry counterparts because of increased resources and scrutiny. The ClinicalTrials.gov registry, which allows investigators to fulfill a federal mandate for public trial registration, provides an opportunity for critical evaluation of study designs for industry-sponsored trials, independent of publication status. As part of a large public policy effort, the Clinical Trials Transformation Initiative (CTTI) recently transformed the ClinicalTrials.gov registry into a searchable dataset to facilitate research on clinical trials themselves. Methods: We conducted a cross-sectional analysis of 477 HIV and HCV drug treatment trials, registered with ClinicalTrials.gov from 1 October 2007 to 27 September 2010, to study the relationship of study sponsorship with randomized study design. The likelihood of using randomization given industry (versus non-industry) sponsorship was reported with prevalence ratios (PR). PRs were estimated using crude and stratified tabular analysis and Poisson regression adjusting for presence of a data monitoring committee, enrollment size, study phase, number of study sites, inclusion of foreign study sites, exclusion of persons older than age 65, and disease condition. Results: The crude PR was 1.17 (95% CI 0.94, 1.45). Adjusted Poisson models produced a PR of 1.13 (95% CI 0.82, 1.56). There was a trend toward mild effect measure modification by study phase, but this was not statistically significant. In stratified tabular analysis the adjusted PR was 1.14 (95% CI 0.78, 1.68) among phase 2/3 trials and 1.06 (95% CI 0.50, 2.22) among phase 4 trials. Conclusions: No significant relationship was found between industry sponsorship and use of randomization in trial design in this cross-sectional study. Prospective studies evaluating other aspects of trial design may shed further light on the relationship between industry sponsorship and appropriate trial methodology.