08-addhealth.Rmd

# National Longitudinal Study of Adolescent to Adult Health (ADDHEALTH) {-}

[![Build Status](https://travis-ci.org/asdfree/addhealth.svg?branch=master)](https://travis-ci.org/asdfree/addhealth) [![Build status](https://ci.appveyor.com/api/projects/status/github/asdfree/addhealth?svg=TRUE)](https://ci.appveyor.com/project/ajdamico/addhealth)

The National Longitudinal Study of Adolescent to Adult Health follows a cohort of teenagers from the 1990s into adulthood.

* Many tables, most with one row per sampled youth respondent.

* A complex sample survey designed to generalize to adolescents in grades 7-12 in the United States during the 1994-95 school year.

* Released at irregular intervals, with 1994-1995, 1996, 2001-2002, and 2008-2009 available and 2016-2018 forthcoming.

* Administered by the [Carolina Population Center](http://www.cpc.unc.edu/) and funded by [a consortium](http://www.cpc.unc.edu/projects/addhealth/about/funders).

## Simplified Download and Importation {-}

The R `lodown` package easily downloads and imports all available ADDHEALTH microdata by simply specifying `"addhealth"` with an `output_dir =` parameter in the `lodown()` function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

```{r eval = FALSE }
library(lodown)
lodown( "addhealth" , output_dir = file.path( path.expand( "~" ) , "ADDHEALTH" ) , 
	your_email = "email@address.com" , 
	your_password = "password" )
```

`lodown` also provides a catalog of available microdata extracts with the `get_catalog()` function. After requesting the ADDHEALTH catalog, you could pass a subsetted catalog through the `lodown()` function in order to download and import specific extracts (rather than all available extracts).

```{r eval = FALSE , results = "hide" }
library(lodown)
# examine all available ADDHEALTH microdata files
addhealth_cat <-
	get_catalog( "addhealth" ,
		output_dir = file.path( path.expand( "~" ) , "ADDHEALTH" ) , 
		your_email = "email@address.com" , 
		your_password = "password" )

# wave i only
addhealth_cat <- subset( addhealth_cat , wave == "wave i" )
# download the microdata to your local computer
addhealth_cat <- lodown( "addhealth" , addhealth_cat , 
	your_email = "email@address.com" , 
	your_password = "password" )
```

## Analysis Examples with the `survey` library \ {-}

Construct a complex sample survey design:

```{r eval = FALSE }

```

```{r eval = FALSE }
options( survey.lonely.psu = "adjust" )

library(survey)

addhealth_df <- 
	readRDS( 
		file.path( path.expand( "~" ) , "ADDHEALTH" , 
		"wave i consolidated.rds" ) 
	)

addhealth_design <- 
	svydesign( 
		id = ~cluster2 , 
		data = addhealth_df , 
		weights = ~ gswgt1 , 
		nest = TRUE 
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
addhealth_design <- 
	update( 
		addhealth_design , 
		
		one = 1 ,
		
		male = as.numeric( as.numeric( bio_sex ) == 1 ) ,
		
		hours_of_computer_games = ifelse( h1da10 > 99 , NA , h1da10 ) ,
		
		hours_of_television = ifelse( h1da8 > 99 , NA , h1da8 )
		
	)
```

### Unweighted Counts {-}

Count the unweighted number of records in the survey sample, overall and by groups:
```{r eval = FALSE , results = "hide" }
sum( weights( addhealth_design , "sampling" ) != 0 )

svyby( ~ one , ~ h1gh25 , addhealth_design , unwtd.count )
```

### Weighted Counts {-}
Count the weighted size of the generalizable population, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ one , addhealth_design )

svyby( ~ one , ~ h1gh25 , addhealth_design , svytotal )
```

### Descriptive Statistics {-}

Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ hours_of_computer_games , addhealth_design , na.rm = TRUE )

svyby( ~ hours_of_computer_games , ~ h1gh25 , addhealth_design , svymean , na.rm = TRUE )
```

Calculate the distribution of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ h1gh24 , addhealth_design , na.rm = TRUE )

svyby( ~ h1gh24 , ~ h1gh25 , addhealth_design , svymean , na.rm = TRUE )
```

Calculate the sum of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ hours_of_computer_games , addhealth_design , na.rm = TRUE )

svyby( ~ hours_of_computer_games , ~ h1gh25 , addhealth_design , svytotal , na.rm = TRUE )
```

Calculate the weighted sum of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ h1gh24 , addhealth_design , na.rm = TRUE )

svyby( ~ h1gh24 , ~ h1gh25 , addhealth_design , svytotal , na.rm = TRUE )
```

Calculate the median (50th percentile) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svyquantile( ~ hours_of_computer_games , addhealth_design , 0.5 , na.rm = TRUE )

svyby( 
	~ hours_of_computer_games , 
	~ h1gh25 , 
	addhealth_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE ,
	keep.var = TRUE ,
	na.rm = TRUE
)
```

Estimate a ratio:
```{r eval = FALSE , results = "hide" }
svyratio( 
	numerator = ~ hours_of_computer_games , 
	denominator = ~ hours_of_television , 
	addhealth_design ,
	na.rm = TRUE
)
```

### Subsetting {-}

Restrict the survey design to self-reported fair or poor health:
```{r eval = FALSE , results = "hide" }
sub_addhealth_design <- subset( addhealth_design , as.numeric( h1gh1 ) %in% c( 4 , 5 ) )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ hours_of_computer_games , sub_addhealth_design , na.rm = TRUE )
```

### Measures of Uncertainty {-}

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:
```{r eval = FALSE , results = "hide" }
this_result <- svymean( ~ hours_of_computer_games , addhealth_design , na.rm = TRUE )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
	svyby( 
		~ hours_of_computer_games , 
		~ h1gh25 , 
		addhealth_design , 
		svymean ,
		na.rm = TRUE 
	)
	
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )
```

Calculate the degrees of freedom of any survey design object:
```{r eval = FALSE , results = "hide" }
degf( addhealth_design )
```

Calculate the complex sample survey-adjusted variance of any statistic:
```{r eval = FALSE , results = "hide" }
svyvar( ~ hours_of_computer_games , addhealth_design , na.rm = TRUE )
```

Include the complex sample design effect in the result for a specific statistic:
```{r eval = FALSE , results = "hide" }
# SRS without replacement
svymean( ~ hours_of_computer_games , addhealth_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ hours_of_computer_games , addhealth_design , na.rm = TRUE , deff = "replace" )
```

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See `?svyciprop` for alternatives:
```{r eval = FALSE , results = "hide" }
svyciprop( ~ male , addhealth_design ,
	method = "likelihood" )
```

### Regression Models and Tests of Association {-}

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( hours_of_computer_games ~ male , addhealth_design )
```

Perform a chi-squared test of association for survey data:
```{r eval = FALSE , results = "hide" }
svychisq( 
	~ male + h1gh24 , 
	addhealth_design 
)
```

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		hours_of_computer_games ~ male + h1gh24 , 
		addhealth_design 
	)

summary( glm_result )
```

## Analysis Examples with `srvyr` \ {-}

The R `srvyr` library calculates summary statistics from survey data, such as the mean, total or quantile using [dplyr](https://github.com/tidyverse/dplyr/)-like syntax. [srvyr](https://github.com/gergness/srvyr) allows for the use of many verbs, such as `summarize`, `group_by`, and `mutate`, the convenience of pipe-able functions, the `tidyverse` style of non-standard evaluation and more consistent return types than the `survey` package. [This vignette](https://cran.r-project.org/web/packages/srvyr/vignettes/srvyr-vs-survey.html) details the available features. As a starting point for ADDHEALTH users, this code replicates previously-presented examples:

```{r eval = FALSE , results = "hide" }
library(srvyr)
addhealth_srvyr_design <- as_survey( addhealth_design )
```
Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
addhealth_srvyr_design %>%
	summarize( mean = survey_mean( hours_of_computer_games , na.rm = TRUE ) )

addhealth_srvyr_design %>%
	group_by( h1gh25 ) %>%
	summarize( mean = survey_mean( hours_of_computer_games , na.rm = TRUE ) )
```

---

## Replication Example {-}

A member of the AddHealth staff at UNC ran the mean hours of television using the wave one public use file with stata to confirm that the calculations presented on this page follow the correct methodology.

	svyset cluster2 [pweight=gswgt1]
	svy: mean w1hrtv

	Survey: Mean estimation

	Number of strata = 1 Number of obs = 6477
	Number of PSUs = 132 Population size = 22159516
									 Design df = 131

	--------------------------------------------------------------
				 | Linearized
				 | Mean Std. Err. [95% Conf. Interval]
	-------------+------------------------------------------------
		 w1hrtv | 15.64193 .3902066 14.87 16.41385
	--------------------------------------------------------------

	
```{r eval = FALSE , results = "hide" }

result <-
	svymean( 
		~ hours_of_television ,
		addhealth_design ,
		na.rm = TRUE
	)
	
stopifnot( round( coef( result ) , 3 ) == 15.642 )
stopifnot( round( SE( result ) , 4 ) == 0.3902 )

```