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update slides, add tipr
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‎exercises/08-tipr.Rmd‎

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---
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title: "Tipping Point Sensitivity Analyses"
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output: html_document
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---
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```{r}
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library(tipr)
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```
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Using the National Health and Nutrition Examination Survey Data (`nhefs_complete`), we are interested in the relationship between the **exposure**, `qsmk`: whether the participant quit smoking, and the **outcome**, `wt82_71`: their weight change in kilograms.
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## Your turn
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Use the `lm_tip()` function to conduct a sensitivity analysis for the estimate from your previous exercises.
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```{r}
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```
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‎slides/03-pscores.Rmd‎

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title: "Propensity Scores"
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author: "Lucy D'Agostino McGowan"
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institute: "Wake Forest University"
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date: "2020-07-29 (updated: `r Sys.Date()`)"
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date: "2021-09-01 (updated: `r Sys.Date()`)"
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output:
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xaringan::moon_reader:
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css: ["default", "theme.css"]
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## Your turn
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`r countdown::countdown(minutes = 7)`
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`r countdown::countdown(minutes = 10)`
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1. Using the **confounders** identified in the previous DAG, fit a propensity score model for `qsmk`
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2. Stretch: Create two histograms, one of the propensity scores for those that quit smoking and one for those that do not

‎slides/03-pscores.html‎

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<title>Propensity Scores</title>
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<meta charset="utf-8" />
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<meta name="author" content="Lucy D’Agostino McGowan" />
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<script src="libs/header-attrs/header-attrs.js"></script>
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<link href="libs/remark-css/default.css" rel="stylesheet" />
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<script src="libs/countdown/countdown.js"></script>
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<script src="libs/header-attrs-2.10/header-attrs.js"></script>
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<link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
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<link href="libs/countdown-0.3.5/countdown.css" rel="stylesheet" />
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<script src="libs/countdown-0.3.5/countdown.js"></script>
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<link rel="stylesheet" href="theme.css" type="text/css" />
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</head>
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<body>
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# Propensity Scores
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### Lucy D’Agostino McGowan
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### Wake Forest University
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### 2020-07-29 (updated: 2020-12-01)
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### 2021-09-01 (updated: 2021-08-06)
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---
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## Your turn
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<div class="countdown" id="timer_5fc6bbc4" style="right:0;bottom:0;" data-warnwhen="0">
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<code class="countdown-time"><span class="countdown-digits minutes">07</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
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<div class="countdown" id="timer_610d8094" style="right:0;bottom:0;" data-warnwhen="0">
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<code class="countdown-time"><span class="countdown-digits minutes">10</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
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</div>
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1. Using the **confounders** identified in the previous DAG, fit a propensity score model for `qsmk`

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‎slides/04-using-pscores.Rmd‎

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title: "Using Propensity Scores"
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author: "Lucy D'Agostino McGowan"
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institute: "Wake Forest University"
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date: "2020-12-02 (updated: `r Sys.Date()`)"
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date: "2021-09-01 (updated: `r Sys.Date()`)"
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output:
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xaringan::moon_reader:
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css: ["default", "theme.css"]
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## Your Turn
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`r countdown::countdown(minutes = 7)`
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`r countdown::countdown(minutes = 10)`
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1. Using the propensity scores you created in the previous exercise, create a "matched" data set using the ATM method with a caliper of 0.2.
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## Your Turn
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`r countdown::countdown(minutes = 7)`
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`r countdown::countdown(minutes = 10)`
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1. Using the propensity scores you created in the previous exercise, add the ATE weights to your data frame `df`
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‎slides/04-using-pscores.html‎

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