On Constructing Statistics and Reporting Data

Wolfe, Douglas A.; Hogg, Robert V.

doi:10.1080/00031305.1971.10477278

Cited by 59 publications

(62 citation statements)

References 6 publications

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“…At each age and sleep state, the mean Vt/Ttot in aborted SIDS infants is not significantly different from I. 30 that in normal infants by either the Welch test or the MannWhitney test. Moreover, neither the differences in the mean Vt/ ; Ttot (Fig.…”

Section: R E S P~r R T O R T Cycle Tipementioning

confidence: 94%

Ventilation and Ventilatory Pattern During Sleep in Aborted Sudden Infant Death Syndrome

et al. 1981

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SummaryT o assess ventilatory control during sleep in infants at risk for the sudden infant death syndrome (SIDS), we made serial measurements of resting tidal volume (Vt), respiratory cycle time (Ttot), and the ventilatory changes resulting from inhalation of 2% Con in aborted S l D S infants in rapid eye movement and quiet sleep and compared them to a group of normal infants during the first 4 months of life. Ventilation was measured by the barometric method, and sleep was staged using electroencephalogram, electrooculogram, and electromyogram and behavioral criteria.Although resting instantaneous minute ventilation (Vt/Ttot) was virtually the same in both groups of infants, Vt tended to be smaller (by up to 50% in the first 2 months) and Ttot tended to be shorter in aborted S l D S than in normal infants in both rapid eye movement and quiet sleep. The increase in the mean Vt/Ttot with 2% COz is greater by about 5 to 20% in aborted SIDS than in normal infants at 3 and 4 months of age in both sleep states. These findings, together with our previous findings that aborted S l D S infants have an increase in heart rate and a shortening of the QT interval, provide indirect evidence that infants a t high risk for S l D S may have increased sympathoadrenal activity.

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Section: R E S P~r R T O R T Cycle Tipementioning

confidence: 94%

Ventilation and Ventilatory Pattern During Sleep in Aborted Sudden Infant Death Syndrome

et al. 1981

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“…Wolfe and Hogg (1971) discussed a variety of distribution-free statistics with this property. Whether or not there is interference between units, we know how D behaved in the actual experiment, and because D is distributionfree, we know how it would have behaved in the uniformity trial, and D is easy to interpret as a measure of magnitude of effect.…”

Section: Confidence Statements For Effects With Interferencementioning

confidence: 99%

Interference Between Units in Randomized Experiments

Rosenbaum

2007

Journal of the American Statistical Association

313

269

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In a randomized experiment comparing two treatments, there is interference between units if applying the treatment to one unit may affect other units. Interference implies that treatment effects are not comparisons of two potential responses that a unit may exhibit, one under treatment and the other under control, but instead are inherently more complex. Interference is common in social settings where people communicate, compete, or spread disease; in studies that treat one part of an organism using a symmetrical part as control; in studies that apply different treatments to the same organism at different times; and in many other situations. Available statistical tools are limited. For instance, Fisher's sharp null hypothesis of no treatment effect implicitly entails no interference, and so his randomization test may be used to test no effect, but conventional ways of inverting the test to obtain confidence intervals, say for an additive effect, are not applicable with interference. Another commonly used approach assumes that interference is of a simple parametric form confined to units that are near one another in time or space; this is useful when applicable but is of little use when interference may be widespread and of uncertain form. Exact, nonparametric methods are developed for inverting randomization tests to obtain confidence intervals for magnitudes of effect assuming nothing at all about the structure of the interference between units. The limitations of these methods are discussed. To illustrate the general approach, two simple methods and two simple empirical examples are discussed. Extension to randomization based covariance adjustment is briefly described.

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“…In more general contexts, the AUC is also used as a measure of difference between distributions Wolfe and Hogg (1971). ROC curves and the AUC have been studied under both parametric and nonparametric assumptions.…”

Section: An Example: Prostate Cancer Datamentioning

confidence: 99%

An overview of robust methods in medical research

Farcomeni

Ventura

2010

Stat Methods Med Res

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Robust statistics is an extension of classical parametric statistics that specifically takes into account the fact that the assumed parametric models used by the researchers are only approximate. In this paper we review and outline how robust inferential procedures may routinely be applied in practice in the biomediacal research. Numerical illustrations are given for the t-test, regression models, logistic regression, survival analysis and ROC curves, showing that robust methods are often more appropriate than standard procedures.

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On Constructing Statistics and Reporting Data

Cited by 59 publications

References 6 publications

Ventilation and Ventilatory Pattern During Sleep in Aborted Sudden Infant Death Syndrome

Ventilation and Ventilatory Pattern During Sleep in Aborted Sudden Infant Death Syndrome

Interference Between Units in Randomized Experiments

An overview of robust methods in medical research

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