Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Good statistical practice in pharmacology. Problem 1.

ABSTRACT: BACKGROUND AND PURPOSE: This paper is intended to assist pharmacologists in making the most of statistical analysis and in avoiding common errors that can lead to false conclusions. APPROACH: A scenario is presented where a pathway inhibitor increased blood pressure responses to an agonist by about one third. The fictional experimenter concludes that the inhibitor enhanced the responses to the agonist, but has not applied any statistical analysis. Questions are asked of the reader, and a discussion of the author's answers is presented. RESULTS: The agonist responses have unequal standard errors, as often seen in data like these concentration-response curves with responses expressed as change from baseline. The uneven variability (heteroscedasticity) violates an assumption of conventional parametric statistical analyses, but can be corrected by data transformation. Expressing the data as absolute blood pressure and then transforming it to log blood pressure eliminated the heteroscedasticity, but made evident an effect of the inhibitor on baseline blood pressure. CONCLUSIONS AND IMPLICATIONS: Statistical analysis is a sensible precaution against mistakes, but cannot protect against all erroneous conclusions. In this scenario, the inhibitor reduced the blood pressure and increased responses to the agonist. However, it is likely that the latter effect was a consequence of the former and thus no conclusion can be safely drawn about any direct interaction between the agonist and the pathway inhibitor from this experiment. Where results are awkward to interpret because of confounding factors such as an altered baseline, statistical analysis may not be very useful in supporting a safe conclusion.

SUBMITTER: Lew M

PROVIDER: S-EPMC2042955 | biostudies-other | 2007 Oct

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Good statistical practice in pharmacology. Problem 2.

Project description: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.

| S-EPMC2042947 | biostudies-other

On contemporaneous controls, unlikely outcomes, boxes and replacing the 'Student': good statistical practice in pharmacology, problem 3.

Project description:This paper is intended to assist pharmacologists to make the most of statistical analysis and avoid common errors. A scenario, in which an experimenter performed an experiment in two separate stages, combined the control groups for analysis and found some surprising results, is presented. The consequences of combined controls are discussed, appropriate display and analysis of the data are described, and an analysis of the likelihood of erroneous conclusions is made. Comparisons between data from separately conducted experimental series are hazardous when there is any possibility that the properties of the experimental units have changed between the series. Experiments that have been performed independently should be analyzed independently. Unlikely or surprising results should be treated with caution and a high standard of evidence should be required, and verification by repeated experiments should be performed and reported. Box and whisker plots contain more information than plots more commonly used to display for qualitative variables and should be used where the sample size is large enough (say, n > or = 5). In most biomedical experiments the observations are not random samples from large populations as assumed by conventional parametric analyses such as Student's t-test, and so permutation tests, which do not lose their validity when a sampled population is non-normal or when the data are not random samples, should frequently be used instead of Student's t-tests.

| S-EPMC2597241 | biostudies-other

Partial identification in the statistical matching problem.

Project description:The statistical matching problem involves the integration of multiple datasets where some variables are not observed jointly. This missing data pattern leaves most statistical models unidentifiable. Statistical inference is still possible when operating under the framework of partially identified models, where the goal is to bound the parameters rather than to estimate them precisely. In many matching problems, developing feasible bounds on the parameters is equivalent to finding the set of positive-definite completions of a partially specified covariance matrix. Existing methods for characterising the set of possible completions do not extend to high-dimensional problems. A Gibbs sampler to draw from the set of possible completions is proposed. The variation in the observed samples gives an estimate of the feasible region of the parameters. The Gibbs sampler extends easily to high-dimensional statistical matching problems.

| S-EPMC5423529 | biostudies-literature

Good Clinical Trials by removing defensive interpretation of Good Clinical Practice guidelines.

Project description: Not available

| S-EPMC9292589 | biostudies-literature

Good clinical practice in resource-limited settings: translating theory into practice.

Project description:A Good Clinical Practices (GCPs) course, based on the combination of theoretical modules with a practical training in real-life conditions, was held in 2010 in Burkina Faso. It was attended by 15 trainees from nine African, Asian, and Latin American countries. There were some discrepancies between the average good results at the end of the theoretical phase and the GCP application during the first days of the practical phase, underlying the difficulties of translating theoretical knowledge into good practices. Most of the findings were not unexpected and reflected the challenges commonly faced by clinical investigators in resource-poor contexts (i.e., the high workload at peripheral health facilities, the need to conciliate routine clinical activities with clinical research, and the risk of creating a double standard among patients attending the same health facility [free care for recruited patients versus user fees for non-recruited patients with the same medical condition]). Even if limited in number and time, these observations suggest that a theoretical training alone may not be sufficient to prepare trainees for the challenges of medical research in real-life settings. Conversely, when a practical phase immediately follows a theoretical one, trainees can immediately experience what the research methodology implicates in terms of work organization and relationship with recruited and non-recruited patients. This initial experience shows the complexity of translating GCP into practice and suggests the need to rethink the current conception of GCP training.

| S-EPMC3617841 | biostudies-literature

Toward Good Read-Across Practice (GRAP) guidance.

Project description:Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislations such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document aims to summarize the state-of-the-art, summarizes insights learned from reviewing ECHA published decisions as far as the relative successes/pitfalls surrounding read-across under REACH and compile the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.

| S-EPMC5581000 | biostudies-literature

Nephrological Complications in Hemoglobinopathies: SITE Good Practice.

Project description:BackgroundHemoglobinopathies, among which thalassemic syndromes (transfusion-dependent and non-transfusion dependent thalassemias) and sickle cell disease (SCD), are the most widespread monogenic diseases worldwide. Hemoglobinopathies are endemic and spread-out all-over Italy, as result of internal and external migration flows. Nowadays, the increase therapeutic options associated to the general aging of patients with hemoglobinopathies related to the improvement in clinical management, contribute to the abnormalities in kidney function going from blood and urine test alterations to chronic kidney disease and end stage renal disease.MethodsHere, we carried out a revision of the literature as panel of recognized experts in hemoglobinopathies with the consultancy and the revision of two nephrologists on kidney alteration and kidney disease in patients with TDT, NTDT and SCD. This is part of the action of the Italian society for the study of thalassemia and hemoglobinopties (SITE). The purpose of this "good practice (GP)" is to provide recommendations for follow-up and therapy for the management of kidney alterations in patients with TDT, NTDT and SCD. The literature review covers the period 1.1.2016 to 31.12.2022. In consideration of the rarity of these diseases, the analysis was extended from 5 to 7 years. Moreover, in the absence of relevant scientific papers in the identified time frame, we referred to pivotal or population studies, when available. Finally, in the absence of evidence-based data from prospective and randomized trials, the authors had to refer to expert opinion (expert consensus) for many topics.ResultsWe generated question and answer boxes to offer a friendly consultation, using color code strategy and focused answers.ConclusionsThe present GP will help in improving the clinical management, and the quality of care of patients with hemoglobinopathies.

| S-EPMC10707125 | biostudies-literature

Development of genetic quality tests for good manufacturing practice-compliant induced pluripotent stem cells and their derivatives

Project description:Purpose : We investigated RNA-seq-based broad-range genetic quality tests on GMP-compliant human leucocyte antigen (HLA)-homozygous hiPSCs and their derivatives under postdistribution conditions to investigate whether sequencing data could provide a basis for future quality control. Methods : Transcriptome profiling of the differentiated cell lines in three lineages, including neuronal cells, cardiomyocytes and hepatocytes, at early and late passages, respectively, were carried out using RNA sequencing. Results : We performed SNV, indel, CNV, ekaryotyping, HLA genotyping and transcriptomic analyses in which differentiated cells were compared with hiPSCs using RNA-seq data from undifferentiated and differentiated cultures returned from multiple centres. In particular, we evaluated whether prolonged culture conditions have the potential to give rise to genomic and phenotypic changes in the CMC3 hiPSC line, a candidate line for manufacturing cell therapies. Conclusion : RNA-seq analysis appears to offer an informative genetic quality testing approach for cell types and allows the early screening of candidate hiPSC seed stocks for clinical use by facilitating safety and potential risk evaluation

2020-04-13 | GSE148517 | GEO

How Good Are Statistical Models at Approximating Complex Fitness Landscapes?

Project description:Fitness landscapes determine the course of adaptation by constraining and shaping evolutionary trajectories. Knowledge of the structure of a fitness landscape can thus predict evolutionary outcomes. Empirical fitness landscapes, however, have so far only offered limited insight into real-world questions, as the high dimensionality of sequence spaces makes it impossible to exhaustively measure the fitness of all variants of biologically meaningful sequences. We must therefore revert to statistical descriptions of fitness landscapes that are based on a sparse sample of fitness measurements. It remains unclear, however, how much data are required for such statistical descriptions to be useful. Here, we assess the ability of regression models accounting for single and pairwise mutations to correctly approximate a complex quasi-empirical fitness landscape. We compare approximations based on various sampling regimes of an RNA landscape and find that the sampling regime strongly influences the quality of the regression. On the one hand it is generally impossible to generate sufficient samples to achieve a good approximation of the complete fitness landscape, and on the other hand systematic sampling schemes can only provide a good description of the immediate neighborhood of a sequence of interest. Nevertheless, we obtain a remarkably good and unbiased fit to the local landscape when using sequences from a population that has evolved under strong selection. Thus, current statistical methods can provide a good approximation to the landscape of naturally evolving populations.

| S-EPMC4989103 | biostudies-literature

ESHRE good practice recommendations on recurrent implantation failure.

Project description: Not available

| S-EPMC10270320 | biostudies-literature