An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

Bayesian methods have garnered huge interest in cognitive science as an approach to models of cognition and perception. On the other hand, Bayesian methods for data analysis have not yet made much headway in cognitive science against the institutionalized inertia of 20th century null hypothesis significance testing (NHST). Ironically, specific Bayesian models of cognition and perception may not long endure the ravages of empirical verification, but generic Bayesian methods for data analysis will eventually dominate. It is time that Bayesian data analysis became the norm for empirical methods in cognitive science. This article reviews a fatal flaw of NHST and introduces the reader to some benefits of Bayesian data analysis. The article presents illustrative examples of multiple comparisons in Bayesian analysis of variance and Bayesian approaches to statistical power. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.

Bayesian data analysis.

Overwhelming evidence shows the quality of reporting of randomised controlled trials (RCTs) is not optimal. Without transparent reporting, readers cannot judge the reliability and validity of trial findings nor extract information for systematic reviews. Recent methodological analyses indicate that inadequate reporting and design are associated with biased estimates of treatment effects. Such systematic error is seriously damaging to RCTs, which are considered the gold standard for evaluating interventions because of their ability to minimise or avoid bias. A group of scientists and editors developed the CONSORT (Consolidated Standards of Reporting Trials) statement to improve the quality of reporting of RCTs. It was first published in 1996 and updated in 2001. The statement consists of a checklist and flow diagram that authors can use for reporting an RCT. Many leading medical journals and major international editorial groups have endorsed the CONSORT statement. The statement facilitates critical appraisal and interpretation of RCTs. During the 2001 CONSORT revision, it became clear that explanation and elaboration of the principles underlying the CONSORT statement would help investigators and others to write or appraise trial reports. A CONSORT explanation and elaboration article was published in 2001 alongside the 2001 version of the CONSORT statement. After an expert meeting in January 2007, the CONSORT statement has been further revised and is published as the CONSORT 2010 Statement. This update improves the wording and clarity of the previous checklist and incorporates recommendations related to topics that have only recently received recognition, such as selective outcome reporting bias. This explanatory and elaboration document-intended to enhance the use, understanding, and dissemination of the CONSORT statement-has also been extensively revised. It presents the meaning and rationale for each new and updated checklist item providing examples of good reporting and, where possible, references to relevant empirical studies. Several examples of flow diagrams are included. The CONSORT 2010 Statement, this revised explanatory and elaboration document, and the associated website (www.consort-statement.org) should be helpful resources to improve reporting of randomised trials.

https://www.bmj.com/content/bmj/340/bmj.c869.full.pdf

CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials

In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment. This paper introduces marginal structural models, a new class of causal models that allow for improved adjustment of confounding in those situations. The parameters of a marginal structural model can be consistently estimated using a new class of estimators, the inverse-probability-of-treatment weighted estimators.

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Marginal Structural Models and Causal Inference in Epidemiology

BackgroundNivolumab, a programmed death 1 (PD-1) checkpoint inhibitor, was associated with encouraging overall survival in uncontrolled studies involving previously treated patients with advanced renal-cell carcinoma. This randomized, open-label, phase 3 study compared nivolumab with everolimus in patients with renal-cell carcinoma who had received previous treatment. MethodsA total of 821 patients with advanced clear-cell renal-cell carcinoma for which they had received previous treatment with one or two regimens of antiangiogenic therapy were randomly assigned (in a 1:1 ratio) to receive 3 mg of nivolumab per kilogram of body weight intravenously every 2 weeks or a 10-mg everolimus tablet orally once daily. The primary end point was overall survival. The secondary end points included the objective response rate and safety. ResultsThe median overall survival was 25.0 months (95% confidence interval [CI], 21.8 to not estimable) with nivolumab and 19.6 months (95% CI, 17.6 to 23.1) with everolimus. The haz...

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Nivolumab versus Everolimus in Advanced Renal-Cell Carcinoma

Bayesian statistical methods are being used increasingly in clinical research because the Bayesian approach is ideally suited to adapting to information that accrues during a trial, potentially allowing for smaller more informative trials and for patients to receive better treatment. Accumulating results can be assessed at any time, including continually, with the possibility of modifying the design of the trial, for example, by slowing (or stopping) or expanding accrual, imbalancing randomization to favour better-performing therapies, dropping or adding treatment arms, and changing the trial population to focus on patient subsets that are responding better to the experimental therapies. Bayesian analyses use available patient-outcome information, including biomarkers that accumulating data indicate might be related to clinical outcome. They also allow for the use of historical information and for synthesizing results of relevant trials. Here, I explain the rationale underlying Bayesian clinical trials, and discuss the potential of such trials to improve the effectiveness of drug development.

Bayesian clinical trials

1. Statistics and the Scientific Method 2. Displaying and Summarizing Data 3. Designing Experiments 4. Probability and Uncertainty 5. Conditional Probability and Bayes' Rule 6. Models for Proportions 7. Densities for Proportions 8. Comparing Two Proportions 9. Densities for Two Proportions 10. General Samples and Population Means 11. Densities for Means 12. Comparing Two or More Means 13. Data Transformations and Nonparametric Methods 14. Regression Analysis Answers to Selected Exercises

Statistics: A Bayesian Perspective

Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models.- Marginal structural models versus structural nested models as tools for causal inference.- Nonparametric locally efficient estimation of the treatment specific survival distribution with right censored data and covariates in observational studies.- Estimation of disease rates in small areas: A new mixed model for spatial dependence.- Markov chain Monte Carlo methods for clustering in case event and count data in spatial epidemiology.- A simulation study of the epidemiological impact of air pollution: Diagnostics of the confounding effects for generalized linear models.- The use of reference priors and Bayes factors in the analysis of clinical trials.- Surrogate endpoints in cancer clinical trials.- List of Participants.

Statistical models in epidemiology, the environment, and clinical trials

Preface Contributors. 1. Basic Principles in Designing and Analyzing Clinical Studies. 2. Bioavailability: Designs and Analysis. 3. Analysis of Repeated-Measures Designs. 4. Dose-Response: Relating Doses and Plasma Levels to Efficacy and Adverse Experiences. 5. Population Models. 6. Linear and Nonlinear Regression. 7. Design and Analysis of Multicenter Trials. 8. Crossover Versus Parallel Designs. 9. Handling Dropouts and Related Issues. 10. Group Sequential Methods in Clinical Trials. 11. Survival Analysis. 12. Robust Data Analysis. 13. Categorical Data Analysis. 14. Causality Assessment for Adverse Drug Reactions. 15. Bayesian Metaanalysis. 16. Inferential Problems in Postmarketing Surveillance. Index

Statistical Methodology in the Pharmaceutical Sciences

A method is presented for choosing an additive constant c when transforming data x to y = log(x + c) The method preserves Type I error probability and power in ANOVA under the assumption that the x + c for some c are log-normally distributed The method has advantages similar to those of rank transformations--namely, it is easy to use and is resistant to extreme observations Since the special case c----infinity corresponds in ANOVA to y = x, the method is a useful generalization of least squares

https://www.jstor.org/stable/pdfplus/2531826.pdf

Donald A. Berry

Papers

Bayesian clinical trials

Statistics: A Bayesian Perspective

Statistical models in epidemiology, the environment, and clinical trials

Statistical Methodology in the Pharmaceutical Sciences

Logarithmic transformations in ANOVA.