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Results for “hypothesis testing”

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PubMed1993

Cognitive strategies and hypothesis testing during discrimination learning in Parkinson's disease.

This study investigated the nature and extent of impairments in the use of hypotheses and cognitive strategies in medicated subjects with idiopathic Parkinson's disease (PD) and matched control subjects. PD subjects did not differ from controls in solving one- or two-dimensional discrimination learning problems, but showed impairment on four-dimensional problems which did not appear to be attributable to memory deficits. They achieved fewer correct solutions, used fewer hypotheses, and were less likely to use appropriate lose-shift strategies following negative feedback. The pattern of findings was similar to those previously reported for subjects with frontal lobe lesions.

Aged↗
PubMed1990

Evolutionary relationships between "Q-type" photosynthetic reaction centres: hypothesis-testing using parsimony.

Hypotheses concerning the evolutionary relationships between "Q-type" photosynthetic reaction centres are tested using amino acid parsimony analysis of subunit sequences and an alignment based on dot matrix comparisons. Strong evidence is found for independent gene duplications having produced the L and M subunits of the photosynthetic purple bacterial reaction centre and D1 and D2 of Photosystem-II. Much support is also found for the L and M subunits of the green filamentous bacterium Chloroflexus aurantiacus arising from the same gene duplication as the purple bacterial subunits, suggesting there was an ancestral bacterial heterodimeric reaction centre. These conclusions caution against over-extrapolation from the purple bacterial reaction centre to Photosystem-II, and suggest that the latter is more ancient than previously supposed.

Bacteria↗
PubMed1999

Carvedilol and the Food and Drug Administration (FDA) approval process: the FDA paradigm and reflections on hypothesis testing.

Carvedilol (Coreg), a beta- and alpha-blocker and an antioxidant drug, was evaluated for moderate to severe heart failure patients in a program containing four United States and one Australia/New Zealand study. The data were evaluated twice by the Cardiovascular and Renal Drugs Advisory Committee of the U.S. Food and Drug Administration (FDA). These meetings resulted in opposite decisions by the advisory committee. The crux of the argumentation was the two-positive-trial FDA paradigm. Carvedilol did not meet the usual paradigm because an exercise end point was not statistically different from placebo in three U.S. trials. Most other end points were highly significant, and death, which was monitored across the U.S. program, was different with p < 0.0001. Here we argue that the usual paradigm is very useful but not an absolute principle, that the usual paradigm can sometimes miss the strength of evidence even in the primary end points, and that rational decision making requires on occasion that other evidence must lead to approval. Control of the type I error rate should be taken very seriously, should rarely be violated, and serves the biomedical community well. It is not an absolute principle, however, but rather must be considered in context.

Adrenergic alpha-Antagonists↗
PubMed2000

Hypothesis testing in clinical trials.

In designing and analyzing any clinical trial, two issues related to patient heterogeneity must be considered: (1) the effect of chance and (2) the effect of bias. These issues are addressed by enrolling adequate numbers of patients in the study and using randomization for treatment assignment. An "intention-to-treat" analysis of outcome data includes all individuals randomized and counted in the group to which they are randomized. There is an increased risk of spurious results with a greater number of subgroup analyses, particularly when these analyses are data derived. Factorial designs are sometimes appropriate and can lead to efficiencies by addressing more than one comparison of interventions in a single trial.

Clinical Trials as Topic↗
PubMed1997

Applications of abduction: hypothesis testing of neuroendocrinological qualitative compartmental models.

It is difficult to assess hypothetical models in poorly measured domains such as neuroendocrinology. Without a large library of observations to constrain inference, the execution of such incomplete models implies making assumptions. Mutually exclusive assumptions must be kept in separate worlds. We define a general abductive multiple-worlds engine that assesses such models by (i) generating the worlds and (ii) tests if these worlds contain known behaviour. World generation is constrained via the use of relevant envisionment. We describe QCM, a modeling language for compartmental models that can be processed by this inference engine. This tool has been used to find faults in theories published in international refereed journals; i.e. QCM can detect faults which are invisible to other methods. The generality and computational limits of this approach are discussed. In short, this approach is applicable to any representation that can be compiled into an and-or graph, provided the graphs are not too big or too intricate (fanout < 7).

Computer Simulation↗
PubMed2003

Non-parametric hypothesis testing and confidence intervals with doubly censored data.

The non-parametric maximum likelihood estimator (NPMLE) of the distribution function with doubly censored data can be computed using the self-consistent algorithm (Tumbull, 1974). We extend the self-consistent algorithm to include a constraint on the NPMLE. We then show how to construct confidence intervals and test hypotheses based on the NPMLE via the empirical likelihood ratio. Finally, we present some numerical comparisons of the performance of the above method with another method that makes use of the influence functions.

Algorithms↗
PubMed2004

Randomization-based hypothesis testing from event-related data.

Methods are described for non-parametric significance testing from event-related encephalographic data, using randomization tests. These methods may be applied in both signal space and source space. The methods include within-subject between-condition comparisons, paired and unpaired comparisons, and within-group and between-group comparisons. Test statistics are also derived for comparing the spatial or temporal response patterns, independent of specific changes at individual locations. Novel methods for testing peak-height significance, and also for making map-wide comparisons, are described. These methods have been validated using simulated data.

Brain Mapping↗
PubMed1998

Data monitoring: a hypothesis-testing approach for treatment-outcome research.

Traditional inferential statistics require that hypotheses be evaluated at only 1 sample size. That is, researchers must choose how many participants will be included in a study before conducting analyses; they are not allowed to add data if initial results are not significant. This requirement forces researchers to choose among including more participants than necessary, risking inconclusive results, or violating the requirement by adding participants. This study presents a more flexible approach, called data monitoring, that allows repeating an analysis as the sample increases. First, the cost of the uncorrected data monitoring that researchers sometimes do is estimated. Second, the correction that is needed to allow data monitoring while holding an overall alpha at a desired level is estimated. Third, the power of data monitoring is compared with traditional approaches. This study also provides an example of the use of data monitoring. At least in some circumstances, data monitoring can reduce Type II error or the number of participants needed without sacrificing Type I error.

Animals↗