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Data Issues (data + issues)

Distribution by Scientific Domains

Medical Sciences	60%

Selected Abstracts

STrengthening the REporting of Genetic Association studies (STREGA) , an extension of the STROBE statement

EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, Issue 4 2009
Julian Little
Abstract Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy,Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis. [source]

STrengthening the REporting of Genetic Association Studies (STREGA),an extension of the STROBE statement,

GENETIC EPIDEMIOLOGY, Issue 7 2009
Julian Little
Abstract Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy-Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis. Genet. Epidemiol. 33:581,598, 2009. © 2009 Wiley-Liss, Inc. [source]

Evaluation of gestational age and admission date assumptions used to determine prenatal drug exposure from administrative data,

PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 12 2005
Marsha A. Raebel PharmD
Abstract Objective Our aim was to evaluate the 270-day gestational age and delivery date assumptions used in an administrative dataset study assessing prenatal drug exposure compared to information contained in a birth registry. Study Design and Setting Kaiser Permanente Colorado (KPCO), a member of the Health Maintenance Organization (HMO) Research Network Center for Education and Research in Therapeutics (CERTs), previously participated in a CERTs study that used claims data to assess prenatal drug exposure. In the current study, gestational age and deliveries information from the CERTs study dataset, the Prescribing Safely during Pregnancy Dataset (PSDPD), was compared to information in the KPCO Birth Registry. Sensitivity and positive predictive value (PPV) of the claims data for deliveries were assessed. The effect of gestational age and delivery date assumptions on classification of prenatal drug exposure was evaluated. Results The mean gestational age in the Birth Registry was 273 (median,=,275) days. Sensitivity of claims data at identifying deliveries was 97.6%, PPV was 98.2%. Of deliveries identified in only one dataset, 45% were related to the gestational age assumption and 36% were due to claims data issues. The effect on estimates of prevalence of prescribing during pregnancy was an absolute change of 1% or less for all drug exposure categories. For Category X, drug exposures during the first trimester, the relative change in prescribing prevalence was 13.7% (p,=,0.014). Conclusion Administrative databases can be useful for assessing prenatal drug exposure, but gestational age assumptions can result in a small proportion of misclassification. Copyright © 2005 John Wiley & Sons, Ltd. [source]

The demand for alcohol: a meta-analysis of elasticities,

AUSTRALIAN JOURNAL OF AGRICULTURAL & RESOURCE ECONOMICS, Issue 2 2007
Craig A. Gallet
Numerous studies have estimated elasticities of alcohol demand using different procedures. Because of widespread differences in demand estimates, however, it is difficult to synthesise the literature into coherent meaning. This study improves our understanding of alcohol demand by reporting results from a meta-analysis of 132 studies. Specifically, regressing estimated price, income and advertising elasticities of alcohol on variables accounting for study characteristics, we find alcohol elasticities to be particularly sensitive to demand specification, data issues and various estimation methods. Furthermore, compared to other alcoholic beverages, beer elasticities tend to be more inelastic. [source]

Monitoring of acromegaly: what should be performed when GH and IGF-1 levels are discrepant?

CLINICAL ENDOCRINOLOGY, Issue 2 2009
Pamela U. Freda
Summary Monitoring of a patient with acromegaly requires periodic evaluation of levels of GH and IGF-1, the biochemical markers of this disease. Although the results of these two tests are usually concordant, they can be discrepant and how to proceed when they are can be a challenging clinical problem. In some cases, IGF-1 levels are normal yet GH suppression after oral glucose is abnormal; this pattern may be due to persistent GH dysregulation despite remission. In other cases, IGF-1 levels are elevated yet GH suppression appears to be normal; this pattern may be observed if the cutoff for GH suppression is inappropriately high for the GH assay being used. Various conditions known to alter GH and IGF-1 including malnutrition, thyroid disease and oestrogen use as well as the potential for methodological or normative data issues with the GH and IGF-1 assays should be considered in the interpretation of discrepant results. When a known cause of the discrepancy other than acromegaly is not identified, a clinical decision about the patient's therapy needs to be made. We adjust treatment in most patients whose results are discrepant based on the IGF-1 level, continuing current treatment if it is persistently normal or modifying this if it is elevated. The clinical picture of the patient, however, also needs to be incorporated into this decision. All patients should have continued periodic surveillance of both GH and IGF-1 levels. [source]