1,721,052 research outputs found
15+ MILLION TOP 1% MOST CITED SCIENTIST 12.2% AUTHORS AND EDITORS FROM TOP 500 UNIVERSITIES Epidemiology of Glioma
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An Epidemiologic Approach for Estimating Risk Reduction and Asymptotic Power on the Log-Difference Scale
No full textWhen comparing the efficacy or harmfulness of two groups (e.g., drugs, devices, assays, interventions, environmental toxins), it is important to minimize bias by making this comparison with respect to a common referent-control group, assuming random allocation. Under such a scenario, one can estimate risk reduction for a new therapy on a log-difference, relative effect scale. The current manuscript reviews the large-sample framework for this conditionally independent comparison and demonstrates how to estimate test power for a given sample size
The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves
No full textOften in the planning phase of a clinical trial, a researcher will need to choose between a standard versus weighted log-rank test (LRT) for investigating right-censored survival data. While a standard LRT is optimal for analyzing evenly distributed but distinct survival events (proportional hazards), an appropriately weighted LRT test may be better suited for handling non-proportional, delayed treatment effects. The “a priori” misspecification of this alternative may result in a substantial loss of power when determining the effectiveness of an experimental drug. In this paper, the standard unweighted and inverse log-rank tests (iLRTs) are compared with the multiple weight, default Max-Combo procedure for analyzing differential late survival outcomes. Unlike combination LRTs that depend on the arbitrary selection of weights, the iLRT by definition is a single weight test and does not require implicit multiplicity correction. Empirically, both weighted methods have reasonable flexibility for assessing continuous survival curve differences from the onset of a study. However, the iLRT may be preferable for accommodating delayed separating survival curves, especially when one arm finishes first. Using standard large-sample methods, the power and sample size for the iLRT are easily estimated without resorting to complex and timely simulations
An Efficient Gatekeeper Algorithm for Detecting GxE
No full text The risk for many complex diseases is believed to be a result of the interactive effects of genetic and environmental factors. Developing efficient techniques to identify gene-environment interactions (GxE) is important for unraveling the etiologic basis of many modern day diseases including cancer. The problem of false positives and false negatives continues to pose significant roadblocks to detecting GxE and informing targeted public health screening and intervention. A heuristic gatekeeper method is presented to guide the selection of single nucleotide polymorphisms (SNPs) in the design phase of a GxE study. </jats:p
An Efficient Gatekeeper Algorithm for Detecting GxE
No full textThe risk for many complex diseases is believed to be a result of the interactive effects of genetic and environmental factors. Developing efficient techniques to identify gene-environment interactions (GxE) is important for unraveling the etiologic basis of many modern day diseases including cancer. The problem of false positives and false negatives continues to pose significant roadblocks to detecting GxE and informing targeted public health screening and intervention. A heuristic gatekeeper method is presented to guide the selection of single nucleotide polymorphisms (SNPs) in the design phase of a GxE study
An efficient gatekeeper algorithm for detecting GxE. Cancer Inform
No full textAbstract: The risk for many complex diseases is believed to be a result of the interactive effects of genetic and environmental factors. Developing efficient techniques to identify gene-environment interactions (GxE) is important for unraveling the etiologic basis of many modern day diseases including cancer. The problem of false positives and false negatives continues to pose significant roadblocks to detecting GxE and informing targeted public health screening and intervention. A heuristic gatekeeper method is presented to guide the selection of single nucleotide polymorphisms (SNPs) in the design phase of a GxE study
Epidemiology of Rounding Error
No full textThis work represents a significant contribution to understanding the importance of appropriately rounding numbers with minimal error. That is, to reduce inexact rounding and data truncation error and simultaneously eliminate unintentional misleading findings in epidemiological studies. The rounding of numbers represents a compromise solution that attempts to find a balance between the loss of information from reporting too few significant digits versus retaining more digits than necessary. Substituting a rounded number for its original value may be acceptable and practical in many applied situations if an adequate degree of accuracy is retained. On the other hand, numeric error may result from improper rounding or data truncation which, in effect, compromises the credibility of study findings and may lead to a false sense of discovery. Performing complex computations on such values, especially when sequential or composite operations are involved, can lead to error propagation and inaccurate results. Having an overall awareness of the nature and impact of rounding error, including preventive actions, can contribute greatly to the integrity of research, yielding more reliable and accurate conclusions. Heuristic examples are provided to illustrate the consequences of rounding and data truncation error in epidemiology studies, specifically those pertaining to relative effect estimation
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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