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Good statistical practice in pharmacology. Problem 2.

ABSTRACT: BACKGROUND AND PURPOSE: This paper is intended to assist pharmacologists to make the most of statistical analysis and in avoid common errors. APPROACH: A scenario is presented where an experimenter performed an experiment to test the effects of two drugs on cultured cells. Analysis of the results, expressed as percentage of control, by a one-way ANOVA yielded P=0.058 and the experimenter concluded that neither drug was effective. The data were expressed as percentage of control because of pairing of the data within each experimental run, a common feature in cell culture experiments. Such data can be analysed with potentially more powerful ANOVA methods equivalent to the paired t-test. Monte Carlo simulations are presented to compare the power of relevant analyses. RESULTS: For data correlated within experimental run (i.e. paired values), transformation to percentage of control improved the power of a one-way ANOVA to detect a real effect, but a randomized block ANOVA (equivalent to a 2-way ANOVA with experiment and treatment as factors) using the raw values was substantially more powerful. The randomized block ANOVA performed well even with uncorrelated data, being only marginally less powerful than the one-way ANOVA. CONCLUSIONS AND IMPLICATIONS: A randomized block ANOVA is far superior to the one-way ANOVA with correlated data, and with uncorrelated data it is only marginally less powerful. Thus where there is, or might reasonably be, such a correlation (e.g. relatedness among the data within a single experimental run, or within a multi-well culture plate, or within an animal, et cetera), use the more powerful randomized block ANOVA rather than one-way ANOVA.

SUBMITTER: Lew M

PROVIDER: S-EPMC2042947 | biostudies-other | 2007 Oct

REPOSITORIES: biostudies-other

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Project description:BackgroundThe Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patients' outcomes. One of the core criteria for "Meaningful Use" of EHRs is to have a problem list that shows the most important health problems faced by a patient. The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting.MethodsIn this study, using sampled clinical documents associated with a cohort of patients who received their primary care at Mayo Clinic, we investigated the associations between problem list and practice setting through natural language processing (NLP) and topic modeling techniques. Specifically, after practice settings and problem lists were normalized, statistical χ2 test, term frequency-inverse document frequency (TF-IDF) and enrichment analysis were used to choose representative concepts for each setting. Then Latent Dirichlet Allocations (LDA) were used to train topic models and predict potential practice settings using similarity metrics based on the problem concepts representative of practice settings. Evaluation was conducted through 5-fold cross validation and Recall@k, Precision@k and F1@k were calculated.ResultsOur method can generate prioritized and meaningful problem lists corresponding to specific practice settings. For practice setting prediction, recall increases from 0.719 (k = 2) to 0.931 (k = 10), precision increases from 0.882 (k = 2) to 0.931 (k = 10) and F1 increases from 0.790 (k = 2) to 0.931 (k = 10).ConclusionTo our best knowledge, our study is the first attempting to discover the association between the problem lists and hospital practice settings. In the future, we plan to investigate how to provide more tailored care by utilizing the association between problem list and practice setting revealed in this study.

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Good statistical practice in pharmacology. Problem 2.

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