Assessing the disclosure protection provided by misclassification for survey microdata

Government statistical agencies often apply statistical disclosure limitation techniques to survey microdata to protect confidentiality. There is a need for ways to assess the protection provided. This paper develops some simple methods for disclosure limitation techniques which perturb the values of categorical identifying variables. The methods are applied in numerical experiments based upon census data from the United Kingdom which are subject to two perturbation techniques: data swapping and the post randomisation method. Some simplifying approximations to the measure of risk are found to work well in capturing the impacts of these techniques. These approximations provide simple extensions of existing risk assessment methods based upon Poisson log-linear models. A numerical experiment is also undertaken to assess the impact of multivariate misclassification with an increasing number of identifying variables. The methods developed in this paper may also be used to obtain more realistic assessments of risk which take account of the kinds of measurement and other non-sampling errors commonly arising in surveys

Fast Parallel Deterministic and Randomized Algorithms for Model Checking

Model checking is a powerful technique for verification of concurrent systems. One of the potential problems with this technique is state space explosion. There are two ways in which one could cope with state explosion: reducing the search space and searching less space. Most of the existing algorithms are based on the first approach. One of the successful approach for reducing search space uses Binary Decision Diagrams (BDDs) to represent the system. Systems with a large number of states (of the order of 5 x 10 ) have been thus verified. But there are limitations to this heuristic approach. Even systems of reasonable complexity have many more states. Also, the BDD approach might fail even on some simple systems. In this paper we propose the use of parallelism to extend the applicability of BDDs in model checking. In particular we present very fast algorithms for model checking that employ BDDs. The algorithms presented are much faster than the best known previous algorithms. We also describe searching less space as an attractive approach to model checking. In this paper we demonstrate the power of this approach. We also suggest the use of randomization in the design of model checking algorithms

Are Treatment Preferences Relevant in Response to Serotonergic Antidepressants and Cognitive-Behavioral Therapy in Depressed Primary Care Patients? Results from a Randomized Controlled Trial Including a Patients' Choice Arm

Background Little is known about the influence of depressed patients' preferences and expectations about treatments upon treatment outcome We investigated whether better clinical outcome in depressed primary care patients is associated with receiving their preferred treatment Methods Within a randomized placebo-controlled single-centre 10-week trial with 5 arms (sertraline, placebo, cognitive-behavioral group therapy, CBT-G, moderated self-help group control, treatment with sertraline or CBT-G according to patients' choice), outcomes for 145 primary care patients with mild-to-moderate depressive disorders according to DSM-IV criteria were investigated Preference for medication versus psychotherapy was assessed at screening using a single item Post-baseline difference scores for the Hamilton Depression Rating Scale (HAMD-17) were used to assess treatment outcome (mixed-model repeated-measures regression analysis) Results Depressed patients receiving their preferred treatment (n = 63), whether sertraline or CBT-G, responded significantly better than those who did not receive their preferred therapy (n = 54, p = 0 001) The difference in outcome between both groups was 8 0 points on the HAMD-17 for psychotherapy and 2 9 points on the HAMD-17 for treatment with antidepressants Results were not explained by differences in depression severity or dropout rates Conclusions Patients' relative preference for medication versus psychotherapy should be considered when offering a treatment because receiving the preferred treatment conveys an additional and clinically relevant benefit (HAMD-17 +2 9 points for drugs, +8 0 points for CBT-G) in outcome Copyright (C) 2010 S Karger AG Base

Rationale and design of the participant, investigator, observer, and data-analyst-blinded randomized AGENDA trial on associations between gene-polymorphisms, endophenotypes for depression and antidepressive intervention: the effect of escitalopram versus placebo on the combined dexamethasone-corticotrophine releasing hormone test and other potential endophenotypes in healthy first-degree relatives of persons with depression

<p>Abstract</p> <p>Background</p> <p>Endophenotypes are heritable markers, which are more prevalent in patients and their healthy relatives than in the general population. Recent studies point at disturbed regulation of the hypothalamic-pituitary-adrenocortical axis as a possible endophenotype for depression. We hypothesize that potential endophenotypes for depression may be affected by selective serotonin re-uptake inhibitor antidepressants in healthy first-degree relatives of depressed patients. The primary outcome measure is the change in plasma cortisol in the dexamethasone-corticotrophin releasing hormone test from baseline to the end of intervention.</p> <p>Methods</p> <p>The AGENDA trial is designed as a participant, investigator, observer, and data-analyst-blinded randomized trial. Participants are 80 healthy first-degree relatives of patients with depression. Participants are randomized to escitalopram 10 mg per day versus placebo for four weeks. Randomization is stratified by gender and age. The primary outcome measure is the change in plasma cortisol in the dexamethasone-corticotrophin releasing hormone test at entry before intervention to after four weeks of intervention. With the inclusion of 80 participants, a 60% power is obtained to detect a clinically relevant difference in the primary outcome between the intervention and the placebo group. Secondary outcome measures are changes from baseline to four weeks in scores of: 1) cognition and 2) neuroticism. Tertiary outcomes measures are changes from baseline to four weeks in scores of: 1) depression and anxiety symptoms; 2) subjective evaluations of depressive symptoms, perceived stress, quality of life, aggression, sleep, and pain; and 3) salivary cortisol at eight different timepoints during an ordinary day. Assessments are undertaken by assessors blinded to the randomization group.</p> <p>Trial registration</p> <p>Local Ethics Committee: H-KF 307413</p> <p>Danish Medicines Agency: 2612-3162.</p> <p>EudraCT: 2006-001750-28.</p> <p>Danish Data Agency: 2006-41-6737.</p> <p>ClinicalTrials.gov: NCT 00386841</p