Petitions in science?

Hardwicke TE, Ioannidis JPA. Petitions in scientific argumentation: Dissecting the request to retire statistical significance. European Journal of Clinical Investigation. 2019;49 10.1111/eci.13162 [open access].

"One perspective on scientific argumentation states that it is irrelevant who has made an argument; it is the substance of the argument that is of primary importance"

Again on ASA II

Mayo D. On Some Self-Defeating Aspects of the ASA’s (2019) Recommendations on Statistical Significance Tests (ii) [blog]

"In what ways does it make the situation worse? Let me count the ways. The first is in this post. Others will come in following posts, until I become too disconsolate to continue"

⭐️ ASA II: Don’t Say What You Don’t Mean

Mayo D. The 2019 ASA Guide to P-values and Statistical Significance: Don’t Say What You Don’t Mean [blog].

On the ASA II update

Haig B. The ASA’s 2019 update on P-values and significance (ASA II) [blog]

"The claimed benefits of abandoning talk of statistical significance are hopeful conjectures"

Against statistical significance

Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature. 2019; 567: 305-307.

Should we retire statistical significance? Read
Deborah Mayo before deciding!

Sample size for multinomial logistic regression

de Jong VMT, Eijkemans MJC, van Calster B et al. Sample size considerations and predictive performance of multinomial logistic prediction models. Stat Med. 2019 (Open Access).

"In most cases, the calibration and overall predictive performance of the multinomial prediction model is improved by using penalized maximum likelihood regression"

Graphics for uncertainty

Bowman A. Graphics for uncertainty. Journal of the Royal Statistical Society: Series A (Statistics in Society) (Open Access). 2019;182:403-418.

Density strips and other tools to graph uncertainty.

Inappropriate conclusions on body mass index

Flegal KM, Ioannidis JPA, Doehner W. Flawed methods and inappropriate conclusions for health policy on overweight and obesity: the Global BMI Mortality Collaboration meta-analysis. Journal of Cachexia, Sarcopenia and Muscle. 2019 (Open Access).

"The fl
awed conclusion that overweight is uniformly associated with substantially increased risk of death and thus should be combated in any circumstances may lead not only to unjustified treatment efforts and potential harm in a wide range of clinical conditions but also to a tremendous waste of resources"

Sample size for logistic regression

van Smeden M, Moons KG, de Groot JA, Collins GS, Altman DG, Eijkemans MJ, and Reitsma JB. Sample size for binary logistic prediction models: Beyond events per variable criteria. Stat Methods Med Res 962280218784726, 2018.

The event per variable (EPV) criterion has limited value for calculating sample size.

Design of growth charts

Ohuma EO and Altman DG. Design and other methodological considerations for the construction of human fetal and neonatal size and growth charts. Stat Med 2018.

Methodological considerations for the development of growth charts,

In gentle praise of significance tests

Sir David Cox on the p-value controversy (video). I recently gave a talk on p-values directly "quoting" this video.

⭐️ Cargo-cult statistics

Stark PB, Saltelli A. Cargo-cult statistics and scientific crisis. Significance. 2018;15:40-43. 10.1111/j.1740-9713.2018.01174.x [html]

"They [practitioners] invoke statistical terms and procedures as incantations, with scant understanding of the assumptions or relevance of the calculations, or even the meaning of the terminology"

Wondering what is
cargo-cult science ?

Sample size for multivariable prediction - 2

Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 201810.1002/sim.7992.

How to calculate a suitable sample size for development of a prediction model using binary or time-to-event outcomes (part 2).

Sample size for multivariable prediction - 1

Riley RD, Snell KIE, Ensor J, Burke DL, Harrell FE, Moons KGM, Collins GS. Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Stat Med. 201810.1002/sim.7993

"In this article, we build on the previous work of Harrell et al to propose how to calculate a suitable sample size for development of a prediction model using linear regression" (part 1).

What makes a biostatistician?

Zapf A, Huebner M, Rauch G, Kieser M. What makes a biostatistician?. Stat Med. 201810.1002/sim.7998

"We discuss the required professional expertise for
the main areas of applications in the medical field as well as the necessary soft skill competencies of a biostatistician"

Inappropriate Analysis and Reporting

Wang MQ, Yan AF, Katz RV. Researcher Requests for Inappropriate Analysis and Reporting: A U.S. Survey of Consulting Biostatisticians. Ann Intern Med. 2018;169:554-558. 10.7326/M18-1230

"Researchers frequently make inappropriate requests of their biostatistical consultants regarding the analysis and reporting of their data"

Predictive Value of New Measurements

"When the outcome variable Y is continuous, there are only three measures of added value that are commonly used: increase in R2, decrease in mean squared prediction error, and decrease in mean absolute prediction error. Why have so many measures been invented when Y is binary or censored?" Blog post

Lack of group-to-individual generalizability

Fisher AJ, Medaglia JD, Jeronimus BF. Lack of group-to-individual generalizability is a threat to human subjects research. Proceedings of the National Academy of Sciences. 2018;115:E6106-E6115.

"This suggests that literatures in social and medical sciences may overestimate the accuracy of aggregated statistical estimates".

I recently gave a
talk on this *big* problem.

Food for thought: a conference (BMJ)

"Nutrition, which is often neglected, is one of the biggest drivers of the global epidemic of chronic lifestyle diseases, including obesity and diabetes. However, the evidence base for dietary advice is beset with poor quality science and unresolved controversy". Conference.

Perils of the NNT

S. Senn. Personal perils: are numbers needed to treat misleading us as to the scope for personalised medicine? (blog post). See also.

Bootstrap Inference using boottest

Roodman D, MacKinnon JG, Nielsen MO, Webb MD. Fast and Wild: Bootstrap Inference in Stata Using boottest. Queen’s Economics Department. Working Paper No. 1406

"We review the main ideas of the wild cluster bootstrap, o
er tips for use, explain why it is particularly amenable to computational optimization, state the syntax of boottest, artest, scoretest, and waldtest, and present several empirical examples for illustration"

Study size based on precision

Rothman KJ, Greenland S. Planning Study Size Based on Precision Rather than Power. Epidemiology. 2018

"Focusing on precision may help to avert some of the problems related to reliance on statistical hypothesis testing"

Obesity: Contrary Evidence and Consilience

Archer E, Lavie CJ, Hill JO. The Contributions of Diet, Genes, and Physical Activity to the Etiology of Obesity: Contrary Evidence and Consilience. Prog Cardiovasc Dis. 2018

The "Metabolic Tipping Point" paradigm and the "Maternal Resources Hypothesis" provide comprehensive mechanistic explanatory narratives that answer the two "Fundamental Questions of Obesity".

Supplements for CVD Prevention and Treatment

Jenkins DJA, Spence JD, Giovannucci EL et al. Supplemental Vitamins and Minerals for CVD Prevention and Treatment. J Am Coll Cardiol. 2018;71:2570-2584.

"Conclusive evidence for the benefit of any supplement across all dietary backgrounds (including deficiency and sufciency) was not demonstrated"

Big Data and Predictive Analytics

Shah ND, Steyerberg EW, Kent DM. Big Data and Predictive Analytics: Recalibrating Expectations. JAMA. 2018

On the importance of model calibration.

Linear regression and the normality assumption

Schmidt AF, Finan C. Linear regression and the normality assumption. Journal of Clinical Epidemiology. 2018;98:146-151.

Outcome transformations may change the target estimate and hence bias results.

Initial data analysis

Huebner M, le Cessie S, Schmidt C, Vach W. A Contemporary Conceptual Framework for Initial Data Analysis. Observational Studies. 2018;4:171-192.

Proposal of a framework for IDA from the IDA group of the
STRATOS initiative.

Case-control matching

Mansournia MA, Jewell NP, Greenland S. Case-control matching: effects, misconceptions, and recommendations. Eur J Epidemiol. 2018;33:5-14.

On the limitations of matching in case-control studies.

Abandon statistical significance

Amrhein V, Trafimow D, Greenland S. Abandon statistical inference. Peer J Preprints. 2018

"We reccomend that we should use, communicate, and teach our statistical methods as being descriptive of logical relations between assumptions and data, rather than allowing specified generalized inferences about universal populations".