Kahnvelez2626
There is an increasing demand for utilization of external data, such as historical study data and patient registry data, to augment the control group in a randomized controlled trial. While such a study design could reduce the time and cost, how to maintain the study validity and integrity is one major statistical challenge that needs to be carefully addressed. We discuss a study design quality process to enhance the study validity and integrity when using this approach. The discussed quality process is tailored to the confirmatory study using a 2-stage design with an emphasis on the interaction process among stakeholders. In an example, the quality process covers a 2-step assessment of the similarity in patient characteristics between the current study and the external data source, and between the treatment and augmented control groups.BACKGROUND The 2006 FDA's Unapproved Drug Initiative (UDI) aimed to improve safety and public health by decreasing the availability of drug products that never obtained FDA approval (unapproved drug products) in the market and incentivizing manufacturers to emphasize that these products must obtain FDA approval. The objective of this study was to measure changes in the prices, sales, and quantities sold of drug products approved under the FDA-UDI. METHODS Drug products that obtained voluntary approval under FDA-UDI from 2006 to 2015 were identified and trends in prices, sales, and units sold were analyzed using the IQVIA National Sales Perspective database. RESULTS Eleven drug products were included in the final analysis. Relative to baseline levels 2 years before approval, a steep increase in price and sales was observed 2 years postapproval for all except 2 of the drug categories-with median percent change of 245% (range -37% to 9618%) for price and 238% (range -4% to 6707%) for sales. Substantial variance was observed in the changes in units sold. CONCLUSION A marked increase was seen in postapproval prices and sales for the vast majority of drug products approved in the FDA-UDI with mixed results in changes in units sold. In addition to increased information on safety, the policy's impact on postapproval drug prices and associated effects on units sold should be considered in assessing the policy, especially when substantial price increases and decreases in units sold may negatively impact health.Using a measure of agreement that does not distinguish the "positive" outcome from the "negative" outcome can be sometimes misleading in assessing resemblance. To alleviate this concern, some new indices, including the "positive" and "negative" conditional synchrony measures (CSM) (or the conditional discordant measures [CDM]), as well as their related measures, have been recently proposed elsewhere. We show that one can easily derive exact confidence limits for these new indices. Using Monte Carlo simulation, we find that the asymptotic interval estimator derived from the score test and these exact interval estimators can all perform well in a variety of situations, while the asymptotic interval estimator based on Wald's statistic can lose accuracy. We use the data taken from a cross-sectional validation study assessing the diagnostic performance of the Whooley questions for major depression disorder (MDD) among older adults to illustrate the use of these interval estimators developed here.BACKGROUND Medical information departments are responsible for maintaining standard response letters to address health care providers' inquiries. Several factors, including Food and Drug Administration regulations, insufficient diversity in clinical trials, and stringent exclusion criteria, might limit the information available to respond to unsolicited requests. However, if new data becomes available for an inquiry that was previously unanswered, it is not common practice for medical information departments to provide an updated response to health care providers. Therefore, the purpose of this study is to evaluate the impact of reviewing literature to provide an updated response to health care providers. METHODS We conducted a 1-year retrospective review of medical inquiries regarding a Bristol-Myers Squibb oncology product. We identified medical inquiry responses that were missing data via our metrics reporting software and conducted an internal and external literature search to assess if new data became available. RESULTS Of 21,264 unsolicited global inquiries, data were unavailable for 531 (2.7%). The 3 most frequently observed inquiry topics were "use in special populations" (32%), "drug interactions" (27%), and "adverse events and safety" (23%). After performing an internal and external literature review, we developed standard response letters for 30% of medical inquiries that were previously unanswered. CONCLUSIONS Medical information departments serve as a resource to answer product-related questions for health care providers. However, data are not always available to provide a response. On discovery of new data, if medical information departments followed up with health care providers to share new data, this could potentially increase patient safety, build stronger relationships with health care providers, and obtain insights that could influence strategies in future clinical trials and publications.In contemporary clinical trials, often evaluated simultaneously are multiple new treatments or the same treatment at multiple dose levels. Cytoskeletal Signaling inhibitor These treatments are first compared with a control, and the best candidate with sufficient activity is then picked for the following trial for further investigation. When the primary outcome is binary, several testing procedures including Dunnett's test, have been proposed for the assessment of hypotheses. The sample size of each group is predetermined; thus, an unconditional exact approach is aligned with the study design. The exact unconditional approach based on maximization has been studied for comparing multiple treatments with a control. The newly developed exact unconditional approach based on estimation and maximization could possibly increase the effectiveness of exact approaches by smoothing the tail probability surface. We compare these 2 exact unconditional approaches based on 3 commonly used test statistics under various design settings. Based on results from numerical studies, we provide recommendations on the usage of these exact approaches.