Validation of the post sleep questionnaire for assessing subjects with restless legs syndrome: results from two double-blind, multicenter, placebo-controlled clinical trials

<p>Abstract</p> <p>Background</p> <p>Because of the subjective nature of Restless Legs Syndrome (RLS) symptoms and the impact of these symptoms on sleep, patient-reported outcomes (PROs) play a prominent role as study endpoints in clinical trials investigating RLS treatments. The objective of this study was to validate a new measure, the Post Sleep Questionnaire (PSQ), to assess sleep dysfunction in subjects with moderate-to-severe RLS symptoms.</p> <p>Methods</p> <p>Pooled data were analyzed from two 12-week, randomized, placebo-controlled trials of gabapentin enacarbil (N = 540). At baseline and Week 12, subjects completed the PSQ and other validated health surveys: IRLS Rating Scale, Clinical Global Impression of Improvement (CGI-I), Profile of Mood States (POMS), Medical Outcomes Study Scale-Sleep (MOS-Sleep), and RLS-Quality of Life (RLSQoL). Pooled data were used <it>post hoc </it>to examine the convergent, divergent, known-group validity and the responsiveness of the PSQ.</p> <p>Results</p> <p>Convergent validity was demonstrated by significant correlations between baseline PSQ items and total scores of IRLS, POMS, RLSQoL, and the MOS-Sleep Scale (p ≤ 0.007 each). Divergent validity was demonstrated through the lack of significant correlations between PSQ items and demographic characteristics. Correlations (p < 0.0001) between RLS severity groups and PSQ items demonstrated known-group validity. Mean changes in investigator- and subject-rated CGI-I scores for each PSQ item (p < 0.0001) demonstrated the PSQ's responsiveness to patient change as reported by their care provider.</p> <p>Conclusions</p> <p>Although these analyses were potentially limited by the use of clinical trial data and not prospective data from a study conducted solely for validation purposes, the PSQ demonstrated robust psychometric properties and is a valid instrument for assessing sleep and sleep improvements in subjects with moderate-to-severe RLS symptoms.</p> <p>Trial Registration</p> <p>This study analyzed data from two registered trials, <a href="http://www.clinicaltrials.gov/ct2/show/NCT00298623">NCT00298623</a> and <a href="http://www.clinicaltrials.gov/ct2/show/NCT00365352">NCT00365352</a>.</p

responder analysis based on patient reported outcomes pros and clinical endpoints ceps in patients pts with metastatic merkel cell carcinoma mmcc treated with avelumab

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Android Security: A Survey of Issues, Malware Penetration, and Defenses

Smartphones have become pervasive due to the availability of office applications, Internet, games, vehicle guidance using location-based services apart from conventional services such as voice calls, SMSes, and multimedia services. Android devices have gained huge market share due to the open architecture of Android and the popularity of its application programming interface (APIs) in the developer community. Increased popularity of the Android devices and associated monetary benefits attracted the malware developers, resulting in big rise of the Android malware apps between 2010 and 2014. Academic researchers and commercial antimalware companies have realized that the conventional signature-based and static analysis methods are vulnerable. In particular, the prevalent stealth techniques, such as encryption, code transformation, and environment-aware approaches, are capable of generating variants of known malware. This has led to the use of behavior-, anomaly-, and dynamic-analysis-based methods. Since a single approach may be ineffective against the advanced techniques, multiple complementary approaches can be used in tandem for effective malware detection. The existing reviews extensively cover the smartphone OS security. However, we believe that the security of Android, with particular focus on malware growth, study of antianalysis techniques, and existing detection methodologies, needs an extensive coverage. In this survey, we discuss the Android security enforcement mechanisms, threats to the existing security enforcements and related issues, malware growth timeline between 2010 and 2014, and stealth techniques employed by the malware authors, in addition to the existing detection methods. This review gives an insight into the strengths and shortcomings of the known research methodologies and provides a platform, to the researchers and practitioners, toward proposing the next-generation Android security, analysis, and malware detection techniques

PERFORMANCE OF TRANSGENIC TgTau-P301L MICE IN A 5-CHOICE SERIAL REACTION TIME TASK (5-CSRTT) AS A MODEL OF ALZHEIMER’S DISEASE

Alzheimer’s disease is increasing to epidemic levels with an estimated 36 million people affected worldwide (Wimo 2010). The aetiology of the disease is not known, which is hindering the progression of the treatment. This study is a longitudinal investigation into the performance of TgTauP301L mice as an animal model of Alzheimer’s disease on the computer automated touchscreen 5- choice serial reaction time task (5-CSRTT). TgTauP301L mice have a single tau mutation in the P301L gene and develop the tau pathology that represents the observed tauopathy in patients with Alzheimer’s disease. The aim of the investigation is to observe if tau pathology in the TgTauP301L mice causes a cognitive impairment in attention and executive function and at what stage this can be identified by the 5-CSRTT task. This will establish if the animals can be used as a therapeutic model for pre-clinical drug trials and help to identify an early indicator and intervention point in patients with Alzheimer’s disease. The animals have previously been studied at 5-months and no differences between performances of the TgTauP301L mice and wild type mice were found (unpublished data). This study measured the performance of the animals at 7- months which is when the tauopathy begins to develop in TgTauP301L mice (Murakami 2005). The results of this study showed that there was no deficit in the performance of the TgTauP301L compared to the wild type mice and there had been no change in the animals’ performance compared to at 5-months. The animals will be retested at 12-months once the pathology has extensively spread to see if the tauopathy causes a deficit in performance

What is the impact of the analysis method used for health state utility values on QALYs in oncology? A simulation study comparing progression-based and time-to-death approaches

Background Health state utility values (‘utilities’) are an integral part of health technology assessment. Though traditionally categorised by disease status in oncology (i.e. progression), several recent assessments have adopted values calculated according to the time that measures were recorded before death. We conducted a simulation study to understand the limitations of each approach, with a focus on mismatches between the way utilities are generated, and analysed. Methods Survival times were simulated based on published literature, with permutations of three utility generation mechanisms (UGMs) and utility analysis methods (UAMs): (1) progression based, (2) time-to-death based, and (3) a ‘combination approach’. For each analysis quality-adjusted life-years (QALYs) were estimated. Goodness of fit was assessed via percentage mean error (%ME) and mean absolute error (%MAE). Scenario analyses were performed varying individual parameters, with complex scenarios mimicking published studies. The statistical code is provided for transparency and to aid future work in the area. Results %ME and %MAE were lowest when the correct analysis form was specified (i.e. UGM and UAM aligned). Underestimates were produced when a time-to-death element was present in the UGM but not included in the UAM, while the ‘combined’ UAM produced overestimates irrespective of the UGM. Scenario analysis demonstrated the importance of the volume of available data beyond the initial time period, for example follow-up. Conclusions We show that the use of an incorrectly or over-specified UAM can result in substantial bias in the estimation of utilities. We present a flowchart to highlight the issues that may be faced. Key Points for Decision Makers A mismatch between the data structure and analysis method results in biased and inaccurate estimates of utility values. Unexpectedly, analysing utilities as a combination of progression- and TTD-based values performed poorly, even if utilities were generated within a corresponding framework. Over-specification of analyses should therefore be avoided. The volume of data available has a marked impact on the accuracy of estimates; this especially means the duration of follow-up and number of long-term survivors