T.J. Murphy’s research while affiliated with Emory University and other places

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Publications (3)


New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology
  • Article

January 2020

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57 Reads

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46 Citations

Drug Metabolism and Disposition: the Biological Fate of Chemicals

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T.J. Murphy

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The American Society for Pharmacology and Experimental Therapeutics has revised the Instructions to Authors for Drug Metabolism and Disposition, Journal of Pharmacology and Experimental Therapeutics, and Molecular Pharmacology These revisions relate to data analysis (including statistical analysis) and reporting but do not tell investigators how to design and perform their experiments. Their overall focus is on greater granularity in the description of what has been done and found. Key recommendations include the need to differentiate between preplanned, hypothesis-testing, and exploratory experiments or studies; explanations of whether key elements of study design, such as sample size and choice of specific statistical tests, had been specified before any data were obtained or adapted thereafter; and explanations of whether any outliers (data points or entire experiments) were eliminated and when the rules for doing so had been defined. Variability should be described by S.D. or interquartile range, and precision should be described by confidence intervals; S.E. should not be used. P values should be used sparingly; in most cases, reporting differences or ratios (effect sizes) with their confidence intervals will be preferred. Depiction of data in figures should provide as much granularity as possible, e.g., by replacing bar graphs with scatter plots wherever feasible and violin or box-and-whisker plots when not. This editorial explains the revisions and the underlying scientific rationale. We believe that these revised guidelines will lead to a less biased and more transparent reporting of research findings.


New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology

January 2020

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89 Reads

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105 Citations

Molecular Pharmacology

The American Society for Pharmacology and Experimental Therapeutics has revised the Instructions to Authors for Drug Metabolism and Disposition, Journal of Pharmacology and Experimental Therapeutics, and Molecular Pharmacology These revisions relate to data analysis (including statistical analysis) and reporting but do not tell investigators how to design and perform their experiments. Their overall focus is on greater granularity in the description of what has been done and found. Key recommendations include the need to differentiate between preplanned, hypothesis-testing, and exploratory experiments or studies; explanations of whether key elements of study design, such as sample size and choice of specific statistical tests, had been specified before any data were obtained or adapted thereafter; and explanations of whether any outliers (data points or entire experiments) were eliminated and when the rules for doing so had been defined. Variability should be described by S.D. or interquartile range, and precision should be described by confidence intervals; S.E. should not be used. P values should be used sparingly; in most cases, reporting differences or ratios (effect sizes) with their confidence intervals will be preferred. Depiction of data in figures should provide as much granularity as possible, e.g., by replacing bar graphs with scatter plots wherever feasible and violin or box-and-whisker plots when not. This editorial explains the revisions and the underlying scientific rationale. We believe that these revised guidelines will lead to a less biased and more transparent reporting of research findings.


Fig. 1. P-hacking refers to a series of analyses in which the goal is not to answer a specific scientific question, but rather to find a hypothesis and data analysis method that results in a P value less than 0.05.
Fig. 2. Hypothetical data illustrating how data points may appear as outliers on a linear scale but not after log transformation. The five data tests are all randomly drawn from a lognormal distribution. The left panel uses a linear scale. Some of the points look like outliers. The right panel shows the same data on a logarithmic axis. The distribution is symmetrical, as expected for lognormal data. There are no outliers.
Fig. 3. Variability of P values. If the null hypothesis is true, then the distribution of P values is uniform. Half the P values will be less than 0.50, 5% will be less than 0.05, etc. But what if the null hypothesis is false? The figure shows data randomly sampled from two Gaussian populations with the S.D. equal to 5.0 and populations means that differ by 5.0. Top: three simulated experiments. Bottom: the distribution of P values from 2500 such simulated experiments. Not counting the 2.5% highest and lowest P values, the middle 95% of the P values range from 0.00016 to 0.73, a range covering almost 3.5 orders of magnitude!
Fig. 4. Comparison of bar graph (mean and S.D.), box and whiskers, scatter plot, and violin plot for a large data set (n 5 1335). Based on data showing number of micturitions in a group of patients seeking treatment (Amiri et al., 2018). Note that the scale of the y-axis is different for the bar graph than for the other graphs.
Fig. 5. Comparison of error bars. Based on Frazier et al. (2006) showing maximum relaxation of rat urinary bladder by norepinephrine in young and old rats; the left panel shows the underlying raw data for comparison as scatter plot.

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New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology
  • Article
  • Full-text available

January 2020

·

896 Reads

·

93 Citations

Journal of Pharmacology and Experimental Therapeutics

The American Society for Pharmacology and Experimental Therapeutics has revised the Instructions to Authors for Drug Metabolism and Disposition, Journal of Pharmacology and Experimental Therapeutics, and Molecular Pharmacology These revisions relate to data analysis (including statistical analysis) and reporting but do not tell investigators how to design and perform their experiments. Their overall focus is on greater granularity in the description of what has been done and found. Key recommendations include the need to differentiate between preplanned, hypothesis-testing, and exploratory experiments or studies; explanations of whether key elements of study design, such as sample size and choice of specific statistical tests, had been specified before any data were obtained or adapted thereafter; and explanation of whether any outliers (data points or entire experiments) were eliminated and when the rules for doing so had been defined. Variability should be described by S.D. or interquartile range, and precision should be described by confidence intervals; S.E. should not be used. P values should be used sparingly; in most cases, reporting differences or ratios (effect sizes) with their confidence intervals will be preferred. Depiction of data in figures should provide as much granularity as possible, e.g., by replacing bar graphs with scatter plots wherever feasible and violin or box-and-whisker plots when not. This editorial explains the revisions and the underlying scientific rationale. We believe that these revised guidelines will lead to a less biased and more transparent reporting of research findings.

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Citations (3)


... In line with recent guidelines for enhanced robustness of data analysis (25,26), we considered all data to be exploratory, that is, not testing a prespecified statistical null hypothesis; inherently, a posthoc analysis cannot be hypothesis testing as that would require a random sample. Therefore, as recommended by leading statisticians (27,28), we do not report p-values and focus on effect sizes with the presentation of 95% CI. ...

Reference:

Real-world effects of hyoscine butylbromide combined with paracetamol in women with dysmenorrhea: a patient survey
New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology

Journal of Pharmacology and Experimental Therapeutics

... All experiments were exploratory, and statistical results of mean ± standard error of the mean (SEM) are descriptive and are presented to enable evaluation of reproducibility rather than for hypothesis testing (Michel et al., 2020). ...

New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology
  • Citing Article
  • January 2020

Molecular Pharmacology

... Based on the exploratory character of the study and in line with recommendations of leading statisticians [6,7], no hypothesis-testing statistical analysis was performed. Descriptive analyses were performed using Prism 10.1 (GraphPad Software, Los Angeles, CA, USA). ...

New Author Guidelines for Displaying Data and Reporting Data Analysis and Statistical Methods in Experimental Biology
  • Citing Article
  • January 2020

Drug Metabolism and Disposition: the Biological Fate of Chemicals