Publications by Qingyang Shi, Yiling Zhou, Jaroslav Krc, Yao Lin, and Nathan Ribeiro
Causal inference Lab 1
The perfect doctor Lab specs can be found here Exercise 1 Question: Recreate table 1 shown below, inserting values appropriately for the three empty colums: (i) The column labeled \(\delta_i\): please enter each patient’s treatment effect (ii) The column labeled \(D\): the optimal treatment for this patient (iii) The column labeled \(Y\): the ob...
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Intro to meta regression 2
Introduction to Meta-regression Author Qingyang Shi Traditional method: meta-analysis and meta-regression in a multilevel generalized linear model framework Standard meta-analysis with arm-based likelihood The most common method to combine the aggregate data from multiple randomized trials is meta-analysis, which accounts for the heterogeneity...
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statistical simulation 1
PART II Statistical Simulation - SEC 1 Binary outcome Author Qingyang Shi Introduction This is the document for the statistical simulation part of the project 1 - towards causally-interpretable meta-analysis with aggregate data using individual-level simulation modeling with likelihood-free inference. In this part, We use statistical simulation...
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Team 13 lab work (Final project)
Simulating the data In the first part of this lab we are going to replicate some of the ideas presented in a post written by Michael Clark. We are going to use the faux package to simulate nested data (observations nested within clusters). First, we define the number of clusters n_cluster and the number of observations in each cluster, obs_per_clus...
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Team 13 lab work
Exploratory data analysis Exercise 1 Create a table1 and use GGally::ggpairs to explore your data. # create the table 1 label(nsduh$demog_sex) <- "Sex" label(nsduh$alc_agefirst) <- "Alcohol (age first used) " label(nsduh$demog_age_cat6) <- "Age" label(nsduh$demog_income) <- "Income" table1(~ demog_sex + alc_agefirst + demog_age_cat...
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Team 13 lab work
# load the following libraries: # tidyverse, broom, table1, GGally, car, easystats, datawizard, and modelsummary lapply( list('tidyverse', 'broom', 'table1', 'GGally', 'car', 'easystats', 'datawizard', 'modelsummary'), require, character.only = TRUE ) ## Loading required package: tidyverse ## ── Attaching core tidyverse packages ──�...
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