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#statstab

5 posts5 participants0 posts today
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Dr Mircea Zloteanu 🌼🐝

#statstab #315 What fraction of repeat experiments will have an effect size within the 95% confidence interval of the first experiment?

Thoughts: CV is a great place to learn. We see the same answer as Cumming: 83%

#replicability #confidenceintervals #replication #metascience #estimation #research #science

stats.stackexchange.com/questi

Cross ValidatedWhat fraction of repeat experiments will have an effect size within the 95% confidence interval of the first experiment?Let's stick to an ideal situation with random sampling, Gaussian populations, equal variances, no P-hacking, etc. Step 1. You run an experiment say comparing two sample means, and compute a 95%
Public
Dr Mircea Zloteanu 🌼🐝

#statstab #311 The analysis of continuous data from n-of-1 trials using paired cycles: a simple tutorial

Thoughts: @StephenSenn shows how to treat multiple #nof1 studies as a meta-analysis.

#sced #nof1 #metaanalysis #tutorial #clinical

trialsjournal.biomedcentral.co

BioMed CentralThe analysis of continuous data from n-of-1 trials using paired cycles: a simple tutorial - TrialsN-of-1 trials are defined and the popular paired cycle design is introduced, together with an explanation as to how suitable sequences may be constructed.Various approaches to analysing such trials are explained and illustrated using a simulated data set. It is explained how choosing an appropriate analysis depends on the question one wishes to answer. It is also shown that for a given question, various equivalent approaches to analysis can be found, a fact which may be exploited to expand the possible software routines that may be used.Sets of N-of-1 trials are analogous to sets of parallel group trials. This means that software for carrying out meta-analysis can be used to combine results from N-of-1 trials. In doing so, it is necessary to make one important change, however. Because degrees of freedom for estimating variances for individual subjects will be scarce, it is advisable to estimate local standard errors using pooled variances. How this may be done is explained and fixed and random effect approaches to combining results are illustrated.
Public
Dr Mircea Zloteanu 🌼🐝

#statstab #307 The C-word, the P-word, and realism in epidemiology

Thoughts: A comment on #306. Causal inference in observational research is a confusing matter. Read both.

#causalinference #observational #research #commentary

link.springer.com/article/10.1

SpringerLinkThe C-word, the P-word, and realism in epidemiology - SyntheseThis paper considers an important recent (May 2018) contribution by Miguel Hernán to the ongoing debate about causal inference in epidemiology. Hernán rejects the idea that there is an in-principle epistemic distinction between the results of randomized controlled trials and observational studies: both produce associations which we may be more or less confident interpreting as causal. However, Hernán maintains that trials have a semantic advantage. Observational studies that seek to estimate causal effect risk issuing meaningless statements instead. The POA proposes a solution to this problem: improved restrictions on the meaningful use of causal language, in particular “causal effect”. This paper argues that new restrictions in fact fail their own standards of meaningfulness. The paper portrays the desire for a restrictive definition of causal language as positivistic, and argues that contemporary epidemiology should be more realistic in its approach to causation. In a realist context, restrictions on meaningfulness based on precision of definition are neither helpful nor necessary. Hernán’s favoured approach to causal language is saved from meaninglessness, along with the approaches he rejects.
Public
Dr Mircea Zloteanu 🌼🐝

#statstab #304 {MRCV} Methods for Analyzing Multiple Response Categorical Variables (MRCVs)

Thoughts: These are some of the trickier types of data, but maybe not hopeless for analysis.

#mrcv #analysis #r #package #guide #tutorial #categorical

cran.r-project.org/web/package

cran.r-project.orgMRCV: Methods for Analyzing Multiple Response Categorical Variables (MRCVs)Provides functions for analyzing the association between one single response categorical variable (SRCV) and one multiple response categorical variable (MRCV), or between two or three MRCVs. A modified Pearson chi-square statistic can be used to test for marginal independence for the one or two MRCV case, or a more general loglinear modeling approach can be used to examine various other structures of association for the two or three MRCV case. Bootstrap- and asymptotic-based standardized residuals and model-predicted odds ratios are available, in addition to other descriptive information. Statisical methods implemented are described in Bilder et al. (2000) <<a href="https://doi.org/10.1080%2F03610910008813665" target="_top">doi:10.1080/03610910008813665</a>>, Bilder and Loughin (2004) <<a href="https://doi.org/10.1111%2Fj.0006-341X.2004.00147.x" target="_top">doi:10.1111/j.0006-341X.2004.00147.x</a>>, Bilder and Loughin (2007) <<a href="https://doi.org/10.1080%2F03610920600974419" target="_top">doi:10.1080/03610920600974419</a>>, and Koziol and Bilder (2014) <<a href="https://journal.r-project.org/articles/RJ-2014-014/" target="_top">https://journal.r-project.org/articles/RJ-2014-014/</a>>.
Public
Dr Mircea Zloteanu 🌼🐝

#statstab #299 The role of "max_treedepth" in No-U-Turn?

Thoughts: Once you start using more complex models you will run into issues at some point; this is one; good solution guide.

#brms #bayesian #modeling #stats #issues #solutions #stan #forum

discourse.mc-stan.org/t/the-ro

The Stan Forums · The role of "max_treedepth" in No-U-Turn?Hi, I am working on a model where I saw the treedepth could reach 14, leading to a slow fitting. I wanted to know better the role of this parameter, thus I tested a simpler model with different max_treedepth values. It seems for me that too low max_treedepth would lead to inefficient sampling (low ESS). Could someone explain how max_treedepth works in the algorithm? What is the appropriate value of max_treedepth when use the stan() function in rstan package? Thank you.
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