Dr Mircea Zloteanu 🌺🌞🍃#statstab #559 A Framework for Descriptive Epidemiology
Thoughts: Saying "the effect of X, while controlling for A, B, & C" is a silly answer to the wrong question.
#descriptive #description #covariates #nuissance #modelling #estimand #tutorial
#statstab #510 A Note on Dropping Experimental Subjects who Fail a Manipulation Check
Thoughts: Another paper to consider when "removing participants who failed our manipulation check"🤷♂️
#manipulationcheck #estimand #experiment #design #bias #guide #assumptions #missingdata
The ObservationsReading a paper on the estimands framework and in one of the examples, analysis strategies to deal with data from patients dying before the end of the clinical trial are considered.
A hypothetical strategy what if all patients would have survived, is considered but dismissed because
“… no mechanism to avoid death exists …”
And I really do like how methodological papers suddenly turn to deadpan humor, just for a part of a sentence.
#statstab #497 On the Statistical Analysis of Experiments
With Manipulation Checks
Thoughts: All psychologists reading this title will panic. Yes, you can't just delete data and assume all is well.
#assumptions #QRPs #estimand #causalinference #ITT #ATE #bias
https://journals.sagepub.com/doi/pdf/10.1177/25152459241297537
Chris. Bart.The estimand framework, formalized in ICH E9(R1) regulatory guidance, provides a structured approach to define scientific objectives with precision. We apply the estimand framework to dose–exposure–response analyses. ... strategy to improve exposure–response analyses for dose selection, particularly when the relevant evidence includes data from multiple studies.
#estimand #exposure-response #dose-response #causal #pharmacometrics #pmx
#statstab #485 Bayesian ANCOVA and the ATE
Thoughts: Still grappling with the implications of using the causal inference approach to randomized experiments. But it's interesting.
#ATE #causalinference #ancova #ANOVA #rstats #estimand #counterfactuals
#statstab #471 Analysis Resources for N-of-1 research
Thoughts: Some cool and some questionable stuff, but a good place to start looking.
#Nof1 #analysis #resources #estimand #methods #sced #stats #smallsample #scd
#statstab #457 Race as a Bundle of Sticks: Designs that Estimate Effects of Seemingly Immutable Characteristics
Thoughts: The theoretical framework a researcher uses will affect the causal inference they can make.
#estimand #causalinference #rubin
https://www.annualreviews.org/content/journals/10.1146/annurev-polisci-032015-010015
Race as a Bundle of Sticks: Designs that Estimate Effects of Seemingly Immutable Characteristics
Although understanding the role of race, ethnicity, and identity is central to political science, methodological debates persist about whether it is possible to estimate the effect of something immutable. At the heart of the debate is an older theoretical question: Is race best understood under an essentialist or constructivist framework? In contrast to the “immutable characteristics” or essentialist approach, we argue that race should be operationalized as a “bundle of sticks” that can be disaggregated into elements. With elements of race, causal claims may be possible using two designs: (a) studies that measure the effect of exposure to a racial cue and (b) studies that exploit within-group variation to measure the effect of some manipulable element. These designs can reconcile scholarship on race and causation and offer a clear framework for future research.
#statstab #456 Shall we count the living or the dead?
Thoughts: survival ratio -> if the intervention increases risk of the outcome
risk ratio -> if the intervention reduces risk of the outcome
Shall we count the living or the dead?
In the 1958 paper "Shall we count the living or the dead?", Mindel C. Sheps proposed a principled solution to the familiar problem of asymmetry of the relative risk. We provide causal models to clarify the scope and limitations of Sheps' line of reasoning, and show that her preferred variant of the relative risk will be stable between patient groups under certain biologically interpretable conditions. Such stability is useful when findings from an intervention study must be generalized to support clinical decisions in patients whose risk profile differs from the participants in the study. We show that Sheps' approach is consistent with a substantial body of psychological and philosophical research on how human reasoners carry causal information from one context to another, and that it can be implemented in practice using van der Laan et al's Switch Relative Risk, or equivalently, using Baker and Jackson's Generalized Relative Risk Reduction (GRRR).