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Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients.
Vols. 2-6 of the CAIB's Final Report contain appendices that provide the supporting documentation for the main text of the Final Report contained in Vol. 1, which was released on Aug. 26, 2003. These appendix materials were working documents. They contain a number of conclusions and proposed recommendations, several of which were adopted by the CAIB in Vol. 1. The other conclusions and proposed recommendations drawn in Vols. 2-6 do not necessarily reflect the views of the CAIB but are included for the record. When there is conflict, Vol. 1 takes precedence. It alone is the CAIB's official statement.
Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.
The Department of Toxic Substances Control (DTSC) of the State of California Environmental Protection Agency is in the process of complying with the Regulatory Structure Update. The Regulatory Structure Update is a comprehensive review and refocusing of California's system for identifying and regulating management of hazardous wastes. As part of this effort, the DTSC proposes to change its current waste classification system that categorizes wastes as hazardous or nonhazardous based on their toxicity. Under the proposed system there would be two risk-based thresholds rather than the single toxicity threshold currently used to distinguish between the wastes. Wastes that contain specific chemicals at concentrations that exceed the upper threshold will be designated as hazardous; those below the lower threshold will be nonhazardous; and those with chemical concentrations between the two thresholds will be "special" wastes and subject to variances for management and disposal. The proposed DTSC system combines toxicity information with short or long-term exposure information to determine the risks associated with the chemicals. Under section 57004 of the California Health and Safety Code, the scientific basis of the proposed waste classification system is subject to external scientific peer review by the National Academy of Sciences, the University of California, or other similar institution of higher learning or group of scientists. This report addresses that regulatory requirement.