Data Submission Standards and Evidence Requirements

Presented are recommendations of the Data Submission Standards and Evidence Requirements panel from the Conference on Clinical Cancer Research along with the U.S. Food and Drug Administration's response to these recommendations

Challenges and Opportunities to Updating Prescribing Information for Longstanding Oncology Drugs.

A number of important drugs used to treat cancer-many of which serve as the backbone of modern chemotherapy regimens-have outdated prescribing information in their drug labeling. The Food and Drug Administration is undertaking a pilot project to develop a process and criteria for updating prescribing information for longstanding oncology drugs, based on the breadth of knowledge the cancer community has accumulated with the use of these drugs over time. This article highlights a number of considerations for labeling updates, including selecting priorities for updating; data sources and evidentiary criteria; as well as the risks, challenges, and opportunities for iterative review to ensure prescribing information for oncology drugs remains relevant to current clinical practice

Statistical challenges in the development and evaluation of marker-based clinical tests

Exciting new technologies for assessing markers in human specimens are now available to evaluate unprecedented types and numbers of variations in DNA, RNA, proteins, or biological structures such as chromosomes. These markers, whether viewed individually, or collectively as a 'signature', have the potential to be useful for disease risk assessment, screening, early detection, prognosis, therapy selection, and monitoring for therapy effectiveness or disease recurrence. Successful translation from basic research findings to clinically useful test requires basic, translational, and regulatory sciences and a collaborative effort among individuals with varied types of expertise including laboratory scientists, technology developers, clinicians, statisticians, and bioinformaticians. The focus of this commentary is the many statistical challenges in translational marker research, specifically in the development and validation of marker-based tests that have clinical utility for therapeutic decision-making

Improved Classification of Alzheimer's Disease Data via Removal of Nuisance Variability

Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making

The Natural History of Severe Acute Liver Injury.

OBJECTIVES: Acute liver failure (ALF) is classically defined by coagulopathy and hepatic encephalopathy (HE); however, acute liver injury (ALI), i.e., severe acute hepatocyte necrosis without HE, has not been carefully defined nor studied. Our aim is to describe the clinical course of specifically defined ALI, including the risk and clinical predictors of poor outcomes, namely progression to ALF, the need for liver transplantation (LT) and death. METHODS: 386 subjects prospectively enrolled in the Acute Liver Failure Study Group registry between 1 September 2008 through 25 October 2013, met criteria for ALI: International Normalized Ratio (INR)≥2.0 and alanine aminotransferase (ALT)≥10 × elevated (irrespective of bilirubin level) for acetaminophen (N-acetyl-p-aminophenol, APAP) ALI, or INR≥2.0, ALT≥10x elevated, and bilirubin≥3.0 mg/dl for non-APAP ALI, both groups without any discernible HE. Subjects who progressed to poor outcomes (ALF, death, LT) were compared, by univariate analysis, with those who recovered. A model to predict poor outcome was developed using the random forest (RF) procedure. RESULTS: Progression to a poor outcome occurred in 90/386 (23%), primarily in non-APAP (71/179, 40%) vs. only 14/194 (7.2%) in APAP patients comprising 52% of all cases (13 cases did not have an etiology assigned; 5 of whom had a poor outcome). Of 82 variables entered into the RF procedure: etiology, bilirubin, INR, APAP level and duration of jaundice were the most predictive of progression to ALF, LT, or death. CONCLUSIONS: A majority of ALI cases are due to APAP, 93% of whom will improve rapidly and fully recover, while non-APAP patients have a far greater risk of poor outcome and should be targeted for early referral to a liver transplant center

Coagulopathy, Bleeding Events, and Outcome According to Rotational Thromboelastometry in Patients With Acute Liver Injury/Failure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/169284/1/hep31767_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169284/2/hep31767-sup-0001-Supinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/169284/3/hep31767.pd

Vitamin D supplementation and breast cancer prevention : a systematic review and meta-analysis of randomized clinical trials

In recent years, the scientific evidence linking vitamin D status or supplementation to breast cancer has grown notably. To investigate the role of vitamin D supplementation on breast cancer incidence, we conducted a systematic review and meta-analysis of randomized controlled trials comparing vitamin D with placebo or no treatment. We used OVID to search MEDLINE (R), EMBASE and CENTRAL until April 2012. We screened the reference lists of included studies and used the “Related Article” feature in PubMed to identify additional articles. No language restrictions were applied. Two reviewers independently extracted data on methodological quality, participants, intervention, comparison and outcomes. Risk Ratios and 95% Confident Intervals for breast cancer were pooled using a random-effects model. Heterogeneity was assessed using the I2 test. In sensitivity analysis, we assessed the impact of vitamin D dosage and mode of administration on treatment effects. Only two randomized controlled trials fulfilled the pre-set inclusion criteria. The pooled analysis included 5372 postmenopausal women. Overall, Risk Ratios and 95% Confident Intervals were 1.11 and 0.74–1.68. We found no evidence of heterogeneity. Neither vitamin D dosage nor mode of administration significantly affected breast cancer risk. However, treatment efficacy was somewhat greater when vitamin D was administered at the highest dosage and in combination with calcium (Risk Ratio 0.58, 95% Confident Interval 0.23–1.47 and Risk Ratio 0.93, 95% Confident Interval 0.54–1.60, respectively). In conclusions, vitamin D use seems not to be associated with a reduced risk of breast cancer development in postmenopausal women. However, the available evidence is still limited and inadequate to draw firm conclusions. Study protocol code: FARM8L2B5L