Tutorial

class: center, middle, inverse, title-slide

# Tutorial
## 如何使用{gtsummary}快速製作表格
### Jenny Hsu
### 2021-12-20

---

## Overview

#### Descriptive tables

- tbl_summary

- tbl_strata

#### Summarize regression models

- tbl_regression

- tbl_merge

#### Others

- complex survey data

- theme and  print

---

### Data

```r
dta<-readRDS("dmclass.rds")
knitr::kable(head(dta))
```

|ID          |sex    | age|r_smoke |type_1 |type_2 |type_3 |type_4 |
|:-----------|:------|---:|:-------|:------|:------|:------|:------|
|01280000201 |Female |  74|No      |Yes    |Yes    |No     |Yes    |
|01280000401 |Male   |  67|No      |No     |No     |No     |No     |
|01280000402 |Female |  67|No      |No     |Yes    |No     |Yes    |
|01280000403 |Female |  30|No      |No     |No     |No     |No     |
|01280000901 |Male   |  60|No      |No     |No     |No     |No     |
|01280000902 |Female |  45|No      |No     |No     |No     |No     |

---

### Descriptive table

.pull-left[

```r
dt<-split(dta,dta$sex)
dt |> lapply(function(x) age=quantile(x$age, na.rm=T))
```

```
$Male
   0%   25%   50%   75%  100% 
20.00 30.25 44.00 55.00 96.00

$Female
  0%  25%  50%  75% 100% 
  20   35   47   58   98 
```

```r
Z<-CrossTable(dta$sex, dta$r_smoke, digits = 1)
```

```r
round(Z$prop.row*100,1)
```

```
        y
x         Yes   No
  Male   45.2 54.8
  Female  8.0 92.0
```

]

.pull-right[

```r
Rowtable<-dta |>
  select(c(sex, age, r_smoke,type_1)) |>
  tbl_summary(by=sex, missing = "no",
              type = r_smoke ~ "categorical")
```

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</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Male</strong>, N = 4,478<sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Female</strong>, N = 4,492<sup class="gt_footnote_marks">1</sup></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">age</td>
<td class="gt_row gt_center">44 (30, 55)</td>
<td class="gt_row gt_center">47 (35, 58)</td></tr>
    <tr><td class="gt_row gt_left">r_smoke</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Yes</td>
<td class="gt_row gt_center">1,905 (45%)</td>
<td class="gt_row gt_center">338 (8.0%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">No</td>
<td class="gt_row gt_center">2,312 (55%)</td>
<td class="gt_row gt_center">3,893 (92%)</td></tr>
    <tr><td class="gt_row gt_left">type_1</td>
<td class="gt_row gt_center">353 (7.9%)</td>
<td class="gt_row gt_center">452 (10%)</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="3">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          Median (IQR); n (%)
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

---

### Contents of table body

- **.$table_body :** modifying the contents appearance and adding information or statistics

Argument     | Description
-------------|--------------
`label=`     | specify the variable labels printed in table
`type=`      | specify the variable type (e.g. continuous, categorical, etc.)
`statistic=` | change the summary statistics presented
`digits=`    | number of digits the summary statistics will be rounded to
`missing=`   | whether to display a row with the number of missing observations
`percent=`   | print column, row, or cell percentages
`include=`   | list of variables to include in summary table
`add_p()`    | add p-values to the output comparing values across groups
`add_n()`    | add a column with N (or N missing) for each variable

---

### Modify table body
.pull-left[

```r
Table1<-dta |>
  select(c(sex, age, r_smoke,type_1)) |>
  tbl_summary(
    by=sex,
    type = list(r_smoke ~ "categorical", type_1 ~ "categorical"),
    label = list(age ~ "Age",
                 r_smoke ~ "Current smoker",
                 type_1 ~"Uw +  CMRF(Y)"),
    statistic = list(all_continuous() ~ "{mean} ({sd})",
                     all_categorical() ~ "{n} ({p}%)"),
    digits = list(all_continuous() ~ c(1, 2),
                  all_categorical() ~ c(0, 2)),
    missing = "no",
    percent = "row",
    include = everything())|>
  add_p(test = all_continuous() ~ "t.test") |>
  add_n()
```
]

.pull-right[
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  font-weight: normal;
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</style>
<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>N</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Male</strong>, N = 4,478<sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Female</strong>, N = 4,492<sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>p-value</strong><sup class="gt_footnote_marks">2</sup></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Age</td>
<td class="gt_row gt_center">8,970</td>
<td class="gt_row gt_center">44.1 (15.89)</td>
<td class="gt_row gt_center">47.0 (16.06)</td>
<td class="gt_row gt_center"><0.001</td></tr>
    <tr><td class="gt_row gt_left">Current smoker</td>
<td class="gt_row gt_center">8,448</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"><0.001</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Yes</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">1,905 (84.93%)</td>
<td class="gt_row gt_center">338 (15.07%)</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">No</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">2,312 (37.26%)</td>
<td class="gt_row gt_center">3,893 (62.74%)</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left">Uw +  CMRF(Y)</td>
<td class="gt_row gt_center">8,899</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"><0.001</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Yes</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">353 (43.85%)</td>
<td class="gt_row gt_center">452 (56.15%)</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">No</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">4,095 (50.59%)</td>
<td class="gt_row gt_center">3,999 (49.41%)</td>
<td class="gt_row gt_center"></td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="5">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          Mean (SD); n (%)
          <br />
        </p>
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>2</em>
          </sup>
           
          Welch Two Sample t-test; Pearson's Chi-squared test
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

---

### Contents of table styling

-  **.$table_styling :** modify and format summary tables.

Function                    | Description
----------------------------|---------------------------------
`modify_header()`           | update column headers
`modify_footnote()`         | update column footnote
`modify_spanning_header()`  | update spanning headers
`modify_caption()`          | update table caption/title
`bold_labels()`             | bold variable labels
`bold_levels()`             | bold variable levels
`italicize_labels()`        | italicize variable labels
`italicize_levels()`        | italicize variable levels
`bold_p()`                  | bold significant p-values

---
### Modify table style
.pull-left[

```r
Table1.s <-Table1|>
  modify_header(list(label~ "**Variable**", all_stat_cols()~ "**{level}** <br> (n ={n})"))|>
  modify_footnote(update = everything() ~ NA) |>
  modify_spanning_header(all_stat_cols() ~ "**Gender**")|>
  modify_caption("Table 1. Characteristics of the participants") |>
  bold_labels() |>
  italicize_levels()|>
  bold_p()
```

]

.pull-right[
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  <thead class="gt_col_headings">
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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1"><strong>Variable</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="2" colspan="1"><strong>N</strong></th>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2">
        <span class="gt_column_spanner"><strong>Gender</strong></span>
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      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Male</strong> <br> (n =4478)</th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Female</strong> <br> (n =4492)</th>
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    <tr><td class="gt_row gt_left" style="font-weight: bold;">Age</td>
<td class="gt_row gt_center">8,970</td>
<td class="gt_row gt_center">44.1 (15.89)</td>
<td class="gt_row gt_center">47.0 (16.06)</td>
<td class="gt_row gt_center" style="font-weight: bold;"><0.001</td></tr>
    <tr><td class="gt_row gt_left" style="font-weight: bold;">Current smoker</td>
<td class="gt_row gt_center">8,448</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center" style="font-weight: bold;"><0.001</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px; font-style: italic;">Yes</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">1,905 (84.93%)</td>
<td class="gt_row gt_center">338 (15.07%)</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px; font-style: italic;">No</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">2,312 (37.26%)</td>
<td class="gt_row gt_center">3,893 (62.74%)</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="font-weight: bold;">Uw +  CMRF(Y)</td>
<td class="gt_row gt_center">8,899</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center" style="font-weight: bold;"><0.001</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px; font-style: italic;">Yes</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">353 (43.85%)</td>
<td class="gt_row gt_center">452 (56.15%)</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px; font-style: italic;">No</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">4,095 (50.59%)</td>
<td class="gt_row gt_center">3,999 (49.41%)</td>
<td class="gt_row gt_center"></td></tr>
  </tbody>
  
  
</table>
</div>

]

---

### Summary regression model

```r
m1<-glm(type_1~ sex+age+r_smoke, data = dta, family=binomial) 
m1
```

```

Call:  glm(formula = type_1 ~ sex + age + r_smoke, family = binomial, 
    data = dta)

Coefficients:
(Intercept)    sexFemale          age    r_smokeNo  
    3.11299     -0.48701     -0.02142      0.65604

Degrees of Freedom: 8384 Total (i.e. Null);  8381 Residual
  (因為不存在，585 個觀察量被刪除了)
Null Deviance:	    5135 
Residual Deviance: 4993 	AIC: 5001
```

```r
exp(cbind(coef(m1), confint(m1)))  
```

```
                            2.5 %     97.5 %
(Intercept) 22.4881362 17.3522048 29.2909726
sexFemale    0.6144593  0.5173148  0.7285857
age          0.9788087  0.9742310  0.9833944
r_smokeNo    1.9271421  1.6074485  2.3085650
```

---

### sjPlot::tab_model

```r
glm(type_1~ sex+age+r_smoke, data=dta, family=binomial) |> sjPlot::tab_model()
```

<table style="border-collapse:collapse; border:none;">
<tr>
<th style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm;  text-align:left; ">&nbsp;</th>
<th colspan="3" style="border-top: double; text-align:center; font-style:normal; font-weight:bold; padding:0.2cm; ">type_1</th>
</tr>
<tr>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  text-align:left; ">Predictors</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">Odds Ratios</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">CI</td>
<td style=" text-align:center; border-bottom:1px solid; font-style:italic; font-weight:normal;  ">p</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">(Intercept)</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">22.49</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">17.35&nbsp;&ndash;&nbsp;29.29</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  "><strong>&lt;0.001</strong></td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">sex [Female]</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.61</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.52&nbsp;&ndash;&nbsp;0.73</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  "><strong>&lt;0.001</strong></td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">age</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.98</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">0.97&nbsp;&ndash;&nbsp;0.98</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  "><strong>&lt;0.001</strong></td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; ">r smoke [No]</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">1.93</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  ">1.61&nbsp;&ndash;&nbsp;2.31</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:center;  "><strong>&lt;0.001</strong></td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm; border-top:1px solid;">Observations</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left; border-top:1px solid;" colspan="3">8385</td>
</tr>
<tr>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; text-align:left; padding-top:0.1cm; padding-bottom:0.1cm;">R<sup>2</sup> Tjur</td>
<td style=" padding:0.2cm; text-align:left; vertical-align:top; padding-top:0.1cm; padding-bottom:0.1cm; text-align:left;" colspan="3">0.021</td>
</tr>

</table>

---

### tbl_regression
.pull-left[

```r
glm(type_1~ sex+age+r_smoke, data=dta, family=binomial)  |> 
  tbl_regression(
    label = list(sex ~ "Sex", age ~ "Age",r_smoke ~ "Current smoker"),
    exponentiate = TRUE,
    pvalue_fun = ~style_pvalue(.x, digits = 2),
    show_single_row= c("r_smoke")) |> 
  add_significance_stars(pattern =  "{estimate} ({conf.low}- {conf.high}){stars}",
                         hide_ci = F, 
                         hide_se = F, 
                         hide_p = F)
```
]

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      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>OR</strong><sup class="gt_footnote_marks">1,2</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>SE</strong><sup class="gt_footnote_marks">2</sup></th>
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    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Sex</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Male</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Female</td>
<td class="gt_row gt_center">0.61 (0.52- 0.73)***</td>
<td class="gt_row gt_center">0.087</td>
<td class="gt_row gt_center">0.52, 0.73</td>
<td class="gt_row gt_center"><0.001</td></tr>
    <tr><td class="gt_row gt_left">Age</td>
<td class="gt_row gt_center">0.98 (0.97- 0.98)***</td>
<td class="gt_row gt_center">0.002</td>
<td class="gt_row gt_center">0.97, 0.98</td>
<td class="gt_row gt_center"><0.001</td></tr>
    <tr><td class="gt_row gt_left">Current smoker</td>
<td class="gt_row gt_center">1.93 (1.61- 2.31)***</td>
<td class="gt_row gt_center">0.092</td>
<td class="gt_row gt_center">1.61, 2.31</td>
<td class="gt_row gt_center"><0.001</td></tr>
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          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          *p<0.05; **p<0.01; ***p<0.001
          <br />
        </p>
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          <sup class="gt_footnote_marks">
            <em>2</em>
          </sup>
           
          OR = Odds Ratio, SE = Standard Error, CI = Confidence Interval
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

---

### tbl_strata by DBM

.pull-left[

```r
FoodL<- dta |>
pivot_longer(cols = starts_with("type"), names_prefix = "type_", names_to = "DBM",
               values_to = "value")|> select(-c(ID))
list_of_labels <- list(sex ~ "Sex", age ~ "Age",r_smoke ~ "Smoker")
headerlist <- list(c(stat_1_1,stat_1_2,stat_1_3,stat_1_4) ~ "**Yes**", 
                   c(stat_2_1,stat_2_2,stat_2_3,stat_2_4) ~ "**No**")
```

```r
Table2<-FoodL |>
    tbl_strata( strata = DBM, .tbl_fun = ~ .x |>
    tbl_summary(by=value,missing ="no",
              type = list(r_smoke ~ "categorical"), 
    label = list_of_labels,
    statistic = list(all_continuous() ~ "{mean} ({sd})", all_categorical() ~ "{n} ({p}%)"),
    digits = list(all_continuous() ~ c(1, 1), all_categorical() ~ c(0, 1)))) |>  
  bold_labels() |>
  modify_header(update= headerlist)|>
  modify_footnote(update = everything() ~ NA)|>
  modify_caption("Table 2. Characteristics of the participants according to DBM")
Table2
```

]

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<table class="gt_table">
  <caption>Table 2. Characteristics of the participants according to DBM</caption>
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2">
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      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2">
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      </th>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2">
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      </th>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="2">
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      </th>
    </tr>
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Yes</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>No</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Yes</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>No</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Yes</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>No</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Yes</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>No</strong></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left" style="font-weight: bold;">Sex</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Male</td>
<td class="gt_row gt_center">353 (43.9%)</td>
<td class="gt_row gt_center">4,095 (50.6%)</td>
<td class="gt_row gt_center">231 (40.9%)</td>
<td class="gt_row gt_center">4,247 (50.5%)</td>
<td class="gt_row gt_center">871 (42.2%)</td>
<td class="gt_row gt_center">3,607 (52.2%)</td>
<td class="gt_row gt_center">1,252 (43.1%)</td>
<td class="gt_row gt_center">3,210 (53.3%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Female</td>
<td class="gt_row gt_center">452 (56.1%)</td>
<td class="gt_row gt_center">3,999 (49.4%)</td>
<td class="gt_row gt_center">334 (59.1%)</td>
<td class="gt_row gt_center">4,158 (49.5%)</td>
<td class="gt_row gt_center">1,194 (57.8%)</td>
<td class="gt_row gt_center">3,298 (47.8%)</td>
<td class="gt_row gt_center">1,654 (56.9%)</td>
<td class="gt_row gt_center">2,816 (46.7%)</td></tr>
    <tr><td class="gt_row gt_left" style="font-weight: bold;">Age</td>
<td class="gt_row gt_center">50.3 (19.3)</td>
<td class="gt_row gt_center">44.9 (15.4)</td>
<td class="gt_row gt_center">54.8 (17.6)</td>
<td class="gt_row gt_center">44.9 (15.7)</td>
<td class="gt_row gt_center">49.7 (16.0)</td>
<td class="gt_row gt_center">44.3 (15.8)</td>
<td class="gt_row gt_center">49.6 (16.8)</td>
<td class="gt_row gt_center">43.4 (15.2)</td></tr>
    <tr><td class="gt_row gt_left" style="font-weight: bold;">Smoker</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Yes</td>
<td class="gt_row gt_center">258 (33.6%)</td>
<td class="gt_row gt_center">1,973 (25.9%)</td>
<td class="gt_row gt_center">109 (20.3%)</td>
<td class="gt_row gt_center">2,134 (27.0%)</td>
<td class="gt_row gt_center">471 (24.1%)</td>
<td class="gt_row gt_center">1,772 (27.3%)</td>
<td class="gt_row gt_center">699 (25.4%)</td>
<td class="gt_row gt_center">1,538 (27.2%)</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">No</td>
<td class="gt_row gt_center">510 (66.4%)</td>
<td class="gt_row gt_center">5,644 (74.1%)</td>
<td class="gt_row gt_center">427 (79.7%)</td>
<td class="gt_row gt_center">5,778 (73.0%)</td>
<td class="gt_row gt_center">1,482 (75.9%)</td>
<td class="gt_row gt_center">4,723 (72.7%)</td>
<td class="gt_row gt_center">2,056 (74.6%)</td>
<td class="gt_row gt_center">4,121 (72.8%)</td></tr>
  </tbody>
  
  
</table>
</div>
]
]

---

### tbl_merge

```r
# M1<-glm(type_1~ sex+age+r_smoke, data=dta, family=binomial)  |> tbl_regression(exponentiate = TRUE, label = list_of_labels) |> bold_labels()|> add_significance_stars(pattern =  "{estimate} ({conf.low}- {conf.high}){stars}",hide_ci = TRUE, hide_se = TRUE)|>   modify_header(estimate ~ "**OR (95% CI)**")
Mer<-tbl_merge(tbls = list(M1, M2, M3, M4), 
          tab_spanner = c("**Uw +  CMRF(Y)**", "**An +  CMRF(Y)**", "**Other MND +  CMRF(Y)**", "**Total DBM**")) |>  bold_labels() |>    modify_header(list(label~ "**Variables**"))|>    modify_footnote(update = everything() ~ NA)|>   modify_caption("Table 3. logistic regression model of DBM")
```

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  <thead class="gt_col_headings">
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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="2" colspan="1"><strong>Variables</strong></th>
      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="1">
        <span class="gt_column_spanner"><strong>Uw +  CMRF(Y)</strong></span>
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      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="1">
        <span class="gt_column_spanner"><strong>An +  CMRF(Y)</strong></span>
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      <th class="gt_center gt_columns_top_border gt_column_spanner_outer" rowspan="1" colspan="1">
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        <span class="gt_column_spanner"><strong>Total DBM</strong></span>
      </th>
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    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>OR (95% CI)</strong><sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>OR (95% CI)</strong><sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>OR (95% CI)</strong><sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>OR (95% CI)</strong><sup class="gt_footnote_marks">1</sup></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left" style="font-weight: bold;">Sex</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Male</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Female</td>
<td class="gt_row gt_center">0.61 (0.52- 0.73)***</td>
<td class="gt_row gt_center">0.79 (0.65- 0.95)*</td>
<td class="gt_row gt_center">0.67 (0.60- 0.76)***</td>
<td class="gt_row gt_center">0.65 (0.58- 0.72)***</td></tr>
    <tr><td class="gt_row gt_left" style="font-weight: bold;">Age</td>
<td class="gt_row gt_center">0.98 (0.97- 0.98)***</td>
<td class="gt_row gt_center">0.96 (0.96- 0.97)***</td>
<td class="gt_row gt_center">0.98 (0.98- 0.98)***</td>
<td class="gt_row gt_center">0.98 (0.97- 0.98)***</td></tr>
    <tr><td class="gt_row gt_left" style="font-weight: bold;">Smoker</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Yes</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">No</td>
<td class="gt_row gt_center">1.93 (1.61- 2.31)***</td>
<td class="gt_row gt_center">0.84 (0.66- 1.06)</td>
<td class="gt_row gt_center">1.07 (0.94- 1.22)</td>
<td class="gt_row gt_center">1.18 (1.05- 1.33)**</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="5">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          OR = Odds Ratio
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>

---

### Complex survey data

```r
dta2<-readRDS("dmclas2s.rds")
knitr::kable(head(dta2))
```

|ID          |sex    | age|r_smoke |type_1 |type_2 |type_3 |type_4 | psurec_ses| strrec_ses|   g_fwgt|
|:-----------|:------|---:|:-------|:------|:------|:------|:------|----------:|----------:|--------:|
|01280000201 |Female |  74|No      |Yes    |Yes    |No     |Yes    |          4|          1| 1390.639|
|01280000401 |Male   |  67|No      |No     |No     |No     |No     |          4|          1| 1683.325|
|01280000402 |Female |  67|No      |No     |Yes    |No     |Yes    |          4|          1| 1571.633|
|01280000403 |Female |  30|No      |No     |No     |No     |No     |          4|          1| 2606.608|
|01280000901 |Male   |  60|No      |No     |No     |No     |No     |          4|          1| 1647.134|
|01280000902 |Female |  45|No      |No     |No     |No     |No     |          4|          1| 1908.051|

---

### tbl_svysummary

.pull-left[

```r
Rowtable<-dta2 |>
  select(c(sex, age, r_smoke,type_1)) |>
  tbl_summary(by=sex, 
              list_of_labels, missing = "no")
```

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<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Male</strong>, N = 4,478<sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Female</strong>, N = 4,492<sup class="gt_footnote_marks">1</sup></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Age</td>
<td class="gt_row gt_center">44 (30, 55)</td>
<td class="gt_row gt_center">47 (35, 58)</td></tr>
    <tr><td class="gt_row gt_left">Smoker</td>
<td class="gt_row gt_center">1,905 (45%)</td>
<td class="gt_row gt_center">338 (8.0%)</td></tr>
    <tr><td class="gt_row gt_left">type_1</td>
<td class="gt_row gt_center">353 (7.9%)</td>
<td class="gt_row gt_center">452 (10%)</td></tr>
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        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          Median (IQR); n (%)
          <br />
        </p>
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    </tr>
  </tfoot>
</table>
</div>
]

.pull-right[

```r
svydta<-survey::svydesign(data=dta2, id=~psurec_ses, strata=~strrec_ses, weights=~g_fwgt, nest=TRUE)
options(survey.lonely.psu="adjust")
svytable<-svydta |>
    tbl_svysummary(by=sex, include=c(sex, age, r_smoke,type_1), list_of_labels, missing = "no")
```

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<table class="gt_table">
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Male</strong>, N = 9,323,757<sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>Female</strong>, N = 9,404,074<sup class="gt_footnote_marks">1</sup></th>
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    <tr><td class="gt_row gt_left">Age</td>
<td class="gt_row gt_center">40 (29, 52)</td>
<td class="gt_row gt_center">42 (31, 54)</td></tr>
    <tr><td class="gt_row gt_left">Smoker</td>
<td class="gt_row gt_center">4,017,598 (46%)</td>
<td class="gt_row gt_center">711,268 (8.1%)</td></tr>
    <tr><td class="gt_row gt_left">type_1</td>
<td class="gt_row gt_center">662,243 (7.1%)</td>
<td class="gt_row gt_center">852,594 (9.1%)</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="3">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          Median (IQR); n (%)
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

---

### svyglm+tbl_regression

.pull-left[

```r
svyglm1<-survey::svyglm(type_1~ sex+age, design = svydta, family=quasibinomial)
options(survey.lonely.psu="adjust")
exp(cbind(coef(svyglm1), confint(svyglm1)))
```

```
                            2.5 %     97.5 %
(Intercept) 25.5978046 15.2379036 43.0011648
sexFemale    0.7862816  0.6051227  1.0216751
age          0.9844758  0.9742211  0.9948384
```

```r
svyglm1|> sjPlot::tab_model(svyglm1) args(tab_model)
```

- Error in match.arg(method, choices = c("wald", "residual", "normal", "likelihood")) : 'arg' 應當是 “wald”, “residual”, “normal”, “likelihood” 其中的一個

]

.pull-right[

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<table class="gt_table">
  <caption>Table . complex survey data svyglm result</caption>
  
  <thead class="gt_col_headings">
    <tr>
      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>N</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>OR</strong><sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>95% CI</strong><sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>p-value</strong></th>
    </tr>
  </thead>
  <tbody class="gt_table_body">
    <tr><td class="gt_row gt_left">Sex</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Male</td>
<td class="gt_row gt_center">9,273,609</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Female</td>
<td class="gt_row gt_center">9,340,991</td>
<td class="gt_row gt_center">0.79</td>
<td class="gt_row gt_center">0.61, 1.02</td>
<td class="gt_row gt_center">0.061</td></tr>
    <tr><td class="gt_row gt_left">Age</td>
<td class="gt_row gt_center">18,614,600</td>
<td class="gt_row gt_center">0.98</td>
<td class="gt_row gt_center">0.97, 0.99</td>
<td class="gt_row gt_center" style="font-weight: bold;">0.018</td></tr>
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        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          OR = Odds Ratio, CI = Confidence Interval
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>

]

---

### tbl_regression vs tbl_uvregression

.pull-left[

```r
glm(type_1~ sex+age+r_smoke, data=dta, family=binomial) |>
  tbl_regression(exponentiate = TRUE, label = list_of_labels) |> add_n()
```

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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>N</strong></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>OR</strong><sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>95% CI</strong><sup class="gt_footnote_marks">1</sup></th>
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<td class="gt_row gt_center">8,385</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Male</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Female</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">0.61</td>
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<td class="gt_row gt_center"><0.001</td></tr>
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<td class="gt_row gt_center">0.98</td>
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<td class="gt_row gt_center"><0.001</td></tr>
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<td class="gt_row gt_center">8,385</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
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<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">No</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">1.93</td>
<td class="gt_row gt_center">1.61, 2.31</td>
<td class="gt_row gt_center"><0.001</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="5">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          OR = Odds Ratio, CI = Confidence Interval
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

.pull-right[

```r
dta[,c("sex","age","r_smoke","type_1")] %>%
tbl_uvregression(y = type_1, method = glm, method.args = list(family = binomial), exponentiate = T, label = list_of_labels) 
```

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      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>N</strong></th>
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<td class="gt_row gt_center">8,899</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Male</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Female</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">0.76</td>
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<td class="gt_row gt_center"><0.001</td></tr>
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<td class="gt_row gt_center">8,899</td>
<td class="gt_row gt_center">0.98</td>
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<td class="gt_row gt_center"><0.001</td></tr>
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<td class="gt_row gt_center">8,385</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
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<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">No</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center">1.45</td>
<td class="gt_row gt_center">1.23, 1.69</td>
<td class="gt_row gt_center"><0.001</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="5">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          OR = Odds Ratio, CI = Confidence Interval
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

---

### Plot model Odds Ratio (OR)

.pull-left[

```r
# tbl_regression
glm(type_1~ sex+age+r_smoke, data=dta, family=binomial)|> 
  tbl_regression(exponentiate = TRUE, label = list_of_labels) |>
  plot(remove_reference_rows = TRUE)
```
]

.pull-right[
![](1220_files/figure-html/unnamed-chunk-35-1.png)
]

---

### Theme

.pull-left[

```r
glm(type_1~ sex+age+r_smoke, data=dta, family=binomial)  |> 
  tbl_regression(exponentiate = TRUE, label = list_of_labels) |>   bold_p() 
```

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<table class="gt_table">
  
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      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>95% CI</strong><sup class="gt_footnote_marks">1</sup></th>
      <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1"><strong>p-value</strong></th>
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  </thead>
  <tbody class="gt_table_body">
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<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Male</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Female</td>
<td class="gt_row gt_center">0.61</td>
<td class="gt_row gt_center">0.52, 0.73</td>
<td class="gt_row gt_center" style="font-weight: bold;"><0.001</td></tr>
    <tr><td class="gt_row gt_left">Age</td>
<td class="gt_row gt_center">0.98</td>
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<td class="gt_row gt_center" style="font-weight: bold;"><0.001</td></tr>
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<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
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<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">No</td>
<td class="gt_row gt_center">1.93</td>
<td class="gt_row gt_center">1.61, 2.31</td>
<td class="gt_row gt_center" style="font-weight: bold;"><0.001</td></tr>
  </tbody>
  
  <tfoot>
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      <td colspan="4">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          OR = Odds Ratio, CI = Confidence Interval
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>
]

.pull-right[

```r
theme_gtsummary_journal(journal = "jama")
glm(type_1~ sex+age+r_smoke, data=dta, family=binomial)  |> tbl_regression(exponentiate = TRUE, label = list_of_labels) |>  bold_p()
```

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  <thead class="gt_col_headings">
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      <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1"><strong>Characteristic</strong></th>
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  <tbody class="gt_table_body">
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<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Male</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Female</td>
<td class="gt_row gt_center">0.61 (0.52 to 0.73)</td>
<td class="gt_row gt_center" style="font-weight: bold;"><0.001</td></tr>
    <tr><td class="gt_row gt_left">Age</td>
<td class="gt_row gt_center">0.98 (0.97 to 0.98)</td>
<td class="gt_row gt_center" style="font-weight: bold;"><0.001</td></tr>
    <tr><td class="gt_row gt_left">Smoker</td>
<td class="gt_row gt_center"></td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">Yes</td>
<td class="gt_row gt_center">—</td>
<td class="gt_row gt_center"></td></tr>
    <tr><td class="gt_row gt_left" style="text-align: left; text-indent: 10px;">No</td>
<td class="gt_row gt_center">1.93 (1.61 to 2.31)</td>
<td class="gt_row gt_center" style="font-weight: bold;"><0.001</td></tr>
  </tbody>
  
  <tfoot>
    <tr class="gt_footnotes">
      <td colspan="3">
        <p class="gt_footnote">
          <sup class="gt_footnote_marks">
            <em>1</em>
          </sup>
           
          OR = Odds Ratio, CI = Confidence Interval
          <br />
        </p>
      </td>
    </tr>
  </tfoot>
</table>
</div>

]

---

### as_flex_table

- particularly useful when combined with R markdown with Word output

```r
# set_table_properties(layout = "autofit") 
# print(Mertb1,preview="docx")
Mer |>  as_flex_table() 
```

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1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-3bdc41c4{width:138.2pt;background-color:transparent;vertical-align: middle;border-bottom: 1pt solid rgba(0, 0, 0, 1.00);border-top: 1pt solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table class='cl-3be9e116'><caption class="Table Caption">

Table 3. logistic regression model of DBM

</caption><thead><tr style="overflow-wrap:break-word;"><td class="cl-3bdc41c2"><p class="cl-3bdb7ed2"><span class="cl-3bdb57d6"> </span></p></td><td class="cl-3bdc41c3"><p class="cl-3bdb7ed3"><span class="cl-3bdb57d6">Uw +  CMRF(Y)</span></p></td><td class="cl-3bdc41c3"><p class="cl-3bdb7ed3"><span class="cl-3bdb57d6">An +  CMRF(Y)</span></p></td><td class="cl-3bdc41c4"><p class="cl-3bdb7ed3"><span class="cl-3bdb57d6">Other MND +  CMRF(Y)</span></p></td><td class="cl-3bdc41c3"><p class="cl-3bdb7ed3"><span class="cl-3bdb57d6">Total DBM</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3bdc41c2"><p class="cl-3bdb7ed2"><span class="cl-3bdb57d6">Variables</span></p></td><td class="cl-3bdc41c3"><p class="cl-3bdb7ed3"><span class="cl-3bdb57d6">OR (95% CI)</span><span class="cl-3bdb57d7">1</span></p></td><td class="cl-3bdc41c3"><p class="cl-3bdb7ed3"><span class="cl-3bdb57d6">OR (95% CI)</span><span class="cl-3bdb57d7">1</span></p></td><td class="cl-3bdc41c4"><p class="cl-3bdb7ed3"><span class="cl-3bdb57d6">OR (95% CI)</span><span class="cl-3bdb57d7">1</span></p></td><td class="cl-3bdc41c3"><p class="cl-3bdb7ed3"><span class="cl-3bdb57d6">OR (95% CI)</span><span class="cl-3bdb57d7">1</span></p></td></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-3bdc1aba"><p class="cl-3bdb7ed4"><span class="cl-3bdb57d8">Sex</span></p></td><td class="cl-3bdc1ab8"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6"></span></p></td><td class="cl-3bdc1ab8"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6"></span></p></td><td class="cl-3bdc1ab9"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6"></span></p></td><td class="cl-3bdc1ab8"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6"></span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3bdc1abd"><p class="cl-3bdb7ed6"><span class="cl-3bdb57d6">Male</span></p></td><td class="cl-3bdc1abb"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">—</span></p></td><td class="cl-3bdc1abb"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">—</span></p></td><td class="cl-3bdc1abc"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">—</span></p></td><td class="cl-3bdc1abb"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">—</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3bdc1abe"><p class="cl-3bdb7ed6"><span class="cl-3bdb57d6">Female</span></p></td><td class="cl-3bdc1abf"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">0.61 (0.52- 0.73)***</span></p></td><td class="cl-3bdc1abf"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">0.79 (0.65- 0.95)*</span></p></td><td class="cl-3bdc1ac0"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">0.67 (0.60- 0.76)***</span></p></td><td class="cl-3bdc1abf"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">0.65 (0.58- 0.72)***</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3bdc1abe"><p class="cl-3bdb7ed4"><span class="cl-3bdb57d8">Age</span></p></td><td class="cl-3bdc1abf"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">0.98 (0.97- 0.98)***</span></p></td><td class="cl-3bdc1abf"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">0.96 (0.96- 0.97)***</span></p></td><td class="cl-3bdc1ac0"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">0.98 (0.98- 0.98)***</span></p></td><td class="cl-3bdc1abf"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">0.98 (0.97- 0.98)***</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3bdc1aba"><p class="cl-3bdb7ed4"><span class="cl-3bdb57d8">Smoker</span></p></td><td class="cl-3bdc1ab8"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6"></span></p></td><td class="cl-3bdc1ab8"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6"></span></p></td><td class="cl-3bdc1ab9"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6"></span></p></td><td class="cl-3bdc1ab8"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6"></span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3bdc1abd"><p class="cl-3bdb7ed6"><span class="cl-3bdb57d6">Yes</span></p></td><td class="cl-3bdc1abb"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">—</span></p></td><td class="cl-3bdc1abb"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">—</span></p></td><td class="cl-3bdc1abc"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">—</span></p></td><td class="cl-3bdc1abb"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">—</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-3bdc1ac2"><p class="cl-3bdb7ed6"><span class="cl-3bdb57d6">No</span></p></td><td class="cl-3bdc1ac1"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">1.93 (1.61- 2.31)***</span></p></td><td class="cl-3bdc1ac1"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">0.84 (0.66- 1.06)</span></p></td><td class="cl-3bdc41be"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">1.07 (0.94- 1.22)</span></p></td><td class="cl-3bdc1ac1"><p class="cl-3bdb7ed5"><span class="cl-3bdb57d6">1.18 (1.05- 1.33)**</span></p></td></tr></tbody><tfoot><tr style="overflow-wrap:break-word;"><td  colspan="5"class="cl-3bdc41c1"><p class="cl-3bdb7ed7"><span class="cl-3bdb57d7">1</span><span class="cl-3bdb57d6">OR = Odds Ratio</span></p></td></tr></tfoot></table></div></template>
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### Conclusion

**{gtsummary} presents the results ready for publication**

- reproducible

- customizable

- reduce copy paste error and save time

- easier to discuss preliminary results with others