The works of Alexander Pope : in nine volumes, complete

with notes and illustrations by Joseph Warton and others

The works of Lord Byron : complete in one volume

ed. K. L. Broenner

Federally regulated institutional review boards: The aspects of a "model" IRB---Is the current system in need of a tune up or complete overhaul?

Institutional Review Boards (IRBs) are the primary gatekeepers for the protection of ethical standards of federally regulated research on human subjects in this country. This paper focuses on what general, broad measures that may be instituted or enhanced to exemplify a "model IRB". This is done by examining the current regulatory standards of federally regulated IRBs, not private or commercial boards, and how many of those standards have been found either inadequate or not generally understood or followed. The analysis includes suggestions on how to bring about changes in order to make the IRB process more efficient, less subject to litigation, and create standardized educational protocols for members. The paper also considers how to include better oversight for multi-center research, increased centralization of IRBs, utilization of Data Safety Monitoring Boards when necessary, payment for research protocol review, voluntary accreditation, and the institution of evaluation/quality assurance programs. ^ This is a policy study utilizing secondary analysis of publicly available data. Therefore, the research for this paper focuses on scholarly medical/legal journals, web information from the Department of Health and Human Services, Federal Drug Administration, and the Office of the Inspector General, Accreditation Programs, law review articles, and current regulations applicable to the relevant portions of the paper. ^ Two issues are found to be consistently cited by the literature as major concerns. One is a need for basic, standardized educational requirements across all IRBs and its members, and secondly, much stricter and more informed management of continuing research. There is no federally regulated formal education system currently in place for IRB members, except for certain NIH-based trials. Also, IRBs are not keeping up with research once a study has begun, and although regulated to do so, it does not appear to be a great priority. This is the area most in danger of increased litigation. Other issues such as voluntary accreditation and outcomes evaluation are slowing gaining steam as the processes are becoming more available and more sought after, such as JCAHO accrediting of hospitals. ^ Adopting the principles discussed in this paper should promote better use of a local IRBs time, money, and expertise for protecting the vulnerable population in their care. Without further improvements to the system, there is concern that private and commercial IRBs will attempt to create a monopoly on much of the clinical research in the future as they are not as heavily regulated and can therefore offer companies quicker and more convenient reviews. IRBs need to consider the advantages of charging for their unique and important services as a cost of doing business. More importantly, there must be a minimum standard of education for all IRB members in the area of the ethical standards of human research and a greater emphasis placed on the follow-up of ongoing research as this is the most critical time for study participants and may soon lead to the largest area for litigation. Additionally, there should be a centralized IRB for multi-site trials or a study website with important information affecting the trial in real time. There needs to be development of standards and metrics to assess the performance of the IRBs for quality assurance and outcome evaluations. The boards should not be content to run the business of human subjects' research without determining how well that function is actually being carried out. It is important that federally regulated IRBs provide excellence in human research and promote those values most important to the public at large.^

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^