Using correspondence analysis in pharmacy practice

Inciardi, John F.; Stijnen, Theo; McMahon, Kay

doi:10.1093/ajhp/59.10.968

Cited by 3 publications

(2 citation statements)

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“…As was demonstrated in past research correspondence analysis is a useful tool for researchers examining prescription medication data [25]. Combining LCA with CA provides researchers a powerful tool for data reduction analysis.…”

Section: Resultsmentioning

confidence: 99%

Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population

Hardigan

Schwartz²,

Hardigan

2013

BMC Med Inform Decis Mak

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BackgroundFalls among the elderly are a major public health concern. Therefore, the possibility of a modeling technique which could better estimate fall probability is both timely and needed. Using biomedical, pharmacological and demographic variables as predictors, latent class analysis (LCA) is demonstrated as a tool for the prediction of falls among community dwelling elderly.MethodsUsing a retrospective data-set a two-step LCA modeling approach was employed. First, we looked for the optimal number of latent classes for the seven medical indicators, along with the patients’ prescription medication and three covariates (age, gender, and number of medications). Second, the appropriate latent class structure, with the covariates, were modeled on the distal outcome (fall/no fall). The default estimator was maximum likelihood with robust standard errors. The Pearson chi-square, likelihood ratio chi-square, BIC, Lo-Mendell-Rubin Adjusted Likelihood Ratio test and the bootstrap likelihood ratio test were used for model comparisons.ResultsA review of the model fit indices with covariates shows that a six-class solution was preferred. The predictive probability for latent classes ranged from 84% to 97%. Entropy, a measure of classification accuracy, was good at 90%. Specific prescription medications were found to strongly influence group membership.ConclusionsIn conclusion the LCA method was effective at finding relevant subgroups within a heterogenous at-risk population for falling. This study demonstrated that LCA offers researchers a valuable tool to model medical data.

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Section: Resultsmentioning

confidence: 99%

Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population

Hardigan

Schwartz²,

Hardigan

2013

BMC Med Inform Decis Mak

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“…Moreover, based on niche overlap, hierarchical cluster analysis was used to sort different sucking louse species' resource utilization of similar kind into respective categories in a way that measures association between two species. Secondly, correspondence analysis (CA) has been applied to a variety of areas from social science to natural science such as violence against woman, health survey, synonymous codon usage in bacteria, pharmacy practice, microarray data displaying associations between genes and experiments (Fellenberg et al 2001;Inciardi et al 2002;Mota et al 2008;Suzuki et al 2008), and several ranges of parasite-host systems such as genus of Ficus and their species-specific pollinator wasps (Kerdelhué et al 2000), but the sucking lice-mammal associations were rarely reported. Consequently, correspondence analysis was applied to visualize associations between parasitic sucking lice and their small mammalian hosts, which was based on constituted ratio, and it was used to search for patterns in the sucking louse species distribution on their hosts.…”

Section: Introductionmentioning

confidence: 99%

Host selection and niche differentiation in sucking lice (Insecta: Anoplura) among small mammals in southwestern China

et al. 2010

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Understanding factors that shape host selection has been a classic issue in ecology, evolutionary biology, and epidemiological investigation. During the survey from 2000 to 2009, a total of 11,216 individuals of small mammals were captured from Yunnan Province in southwestern China. The captured small mammalian hosts belong to five orders, ten families, 35 genera, and 65 species and from their body surface, 38,885 individuals of ectoparasitic sucking lice were collected, which represent five families, seven genera, and 31 species. Based on niche overlap of dominant sucking lice on their primary hosts, we used hierarchical cluster analysis to sort different sucking louse species' resource utilizations of similar kind into respective categories. Given λ<5, there are only two groups clustered, however, sucking louse species' resource utilization was sorted into eight respective categories at λ=15. The results revealed that most species of sucking lice usually had high host specificity and a certain species of sucking louse usually restricted to one or few small mammalian species as their dominant hosts. Correspondence analysis was used to visualize associations between parasitic sucking lice and their small mammalian hosts, which suggested three different patterns of host resource utilization: species specialists, genera generalists, and multiple selections. For example, Sathrax durus (Johnson) only parasitized on species of Tupaia belangeri (Wagner), Hoplopleura edentula (Fahredholz) predominatly on genus of Eothenomys, and Polyplax reclinata (Nitzsch) on Family of Soricidae. Our results demonstrate that sucking lice have high host specificity and this might be due to coevolution between sucking lice and their hosts.

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A Perspective on Drug-Nutrient Interactions

Boullata¹,

Barber²

2004

Handbook of Drug-Nutrient Interactions

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Using correspondence analysis in pharmacy practice

Cited by 3 publications

References 0 publications

Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population

Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population

Host selection and niche differentiation in sucking lice (Insecta: Anoplura) among small mammals in southwestern China

A Perspective on Drug-Nutrient Interactions

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