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fix tipr function
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‎exercises/08-tipr.Rmd‎

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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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Use the `tip_coef()` function to conduct a sensitivity analysis for the estimate from your previous exercises.
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```{r}
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‎slides/raw/08-tipr.Rmd‎

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## Main function
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# `lm_tip()`
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# `tip_coef()`
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* `d`: a data frame that includes the observed confidence bounds
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* `effect`: observed exposure - outcome effect
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---
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## Main function
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# `lm_tip()`
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# `tip_coef()`
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* `smd`: scaled mean difference between the unmeasured confounder in the exposed and unexposed population
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## Main function
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# `lm_tip()`
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# `tip_coef()`
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* `outcome_association`: association between the unmeasured confounder and outcome
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---
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## Main function
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# `lm_tip()`
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# `tip_coef()`
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* `d`
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* `effect`
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* `smd`
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* `outcome_association`
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---
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## Main function
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# `lm_tip()`
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# `tip_coef()`
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### **specify** one, it will **estimate** the other
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```{r, eval = TRUE}
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library(tipr)
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lm_tip(data.frame(conf.low = 2.4,
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conf.high = 4.4),
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smd = 0.3)
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tip_coef(effect = 2.4,
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smd = 0.3)
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```
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--
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The observed effect (2.4, 4.4) WOULD be tipped by 1 unmeasured confounder
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with the following specifications:
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*estimated standardized mean difference between the unmeasured confounder
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in the exposed population and unexposed population: 0.3*
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*estimated association between the unmeasured confounder and the outcome: 8*
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*estimated standardized mean difference between the unmeasured confounder
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in the exposed population and unexposed population: 0.3*
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*estimated association between the unmeasured confounder and the outcome: 8*
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---
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## Your turn
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`r countdown::countdown(minutes = 10)`
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1. Use the `lm_tip()` function to conduct a sensitivity analysis for the estimate from your previous exercises.
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1. Use the `tip_coef()` function to conduct a sensitivity analysis for the estimate from your previous exercises.

‎slides/raw/08-tipr.html‎

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<title>Tipping Point Sensitivity Analyses</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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<link href="libs/countdown/countdown.css" rel="stylesheet" />
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<script src="libs/countdown/countdown.js"></script>
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<script src="libs/header-attrs-2.14/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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<textarea id="source">
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class: center, middle, inverse, title-slide
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.title[
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# Tipping Point Sensitivity Analyses
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]
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.author[
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### Lucy D’Agostino McGowan
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]
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.institute[
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### Wake Forest University
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### 2021-09-01 (updated: 2021-10-21)
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]
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.date[
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### 2021-09-01 (updated: 2022-06-07)
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]
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---
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## Main function
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# `lm_tip()`
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# `tip_coef()`
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* `d`: a data frame that includes the observed confidence bounds
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* `effect`: observed exposure - outcome effect
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---
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## Main function
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# `lm_tip()`
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# `tip_coef()`
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* `smd`: scaled mean difference between the unmeasured confounder in the exposed and unexposed population
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## Main function
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# `lm_tip()`
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# `tip_coef()`
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* `outcome_association`: association between the unmeasured confounder and outcome
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## Main function
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# `lm_tip()`
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# `tip_coef()`
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* `d`
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* `effect`
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* `smd`
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* `outcome_association`
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---
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## Main function
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# `lm_tip()`
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# `tip_coef()`
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### **specify** one, it will **estimate** the other
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```r
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library(tipr)
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lm_tip(data.frame(conf.low = 2.4,
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conf.high = 4.4),
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smd = 0.3)
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tip_coef(effect = 2.4,
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smd = 0.3)
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```
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```
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## # A tibble: 1 × 5
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## observed_lb observed_ub smd outcome_association
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## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
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## 1 2.4 4.4 0.3 8
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## # … with 1 more variable: n_unmeasured_confounders &lt;dbl&gt;
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## # A tibble: 1 × 4
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## observed_effect smd outcome_association n_unmeasured_confounders
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## &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
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## 1 2.4 0.3 8 1
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```
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--
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The observed effect (2.4, 4.4) WOULD be tipped by 1 unmeasured confounder
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with the following specifications:
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*estimated standardized mean difference between the unmeasured confounder
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in the exposed population and unexposed population: 0.3*
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*estimated association between the unmeasured confounder and the outcome: 8*
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*estimated standardized mean difference between the unmeasured confounder
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in the exposed population and unexposed population: 0.3*
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*estimated association between the unmeasured confounder and the outcome: 8*
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---
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## Your turn
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<div class="countdown" id="timer_61718391" style="right:0;bottom:0;" data-warnwhen="0">
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<div class="countdown" id="timer_629fa62c" 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. Use the `lm_tip()` function to conduct a sensitivity analysis for the estimate from your previous exercises.
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1. Use the `tip_coef()` function to conduct a sensitivity analysis for the estimate from your previous exercises.
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</textarea>
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<style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>
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<script src="https://remarkjs.com/downloads/remark-latest.min.js"></script>
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deleted = true;
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});
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})();
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// add `data-at-shortcutkeys` attribute to <body> to resolve conflicts with JAWS
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// screen reader (see PR #262)
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(function(d) {
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let res = {};
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d.querySelectorAll('.remark-help-content table tr').forEach(tr => {
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const t = tr.querySelector('td:nth-child(2)').innerText;
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tr.querySelectorAll('td:first-child .key').forEach(key => {
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const k = key.innerText;
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if (/^[a-z]$/.test(k)) res[k] = t; // must be a single letter (key)
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});
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});
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d.body.setAttribute('data-at-shortcutkeys', JSON.stringify(res));
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})(document);
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(function() {
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"use strict"
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// Replace <script> tags in slides area to make them executable

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