weightedRank (version 0.5.1)

aHDLe: HDL Cholesterol and Light Daily Alcohol 2013-2020

Description

A blocked observational study of light daily alcohol consumption and HDL cholesterol from NHANES 2013-2020. This is an enlarged version of the aHDL data in this package, adding data from NHANES 2017-2020.

Usage

data("aHDLe")

Format

A data frame with 2888 observations on the following 10 variables.

nh

NHANES 2013-2014 is 1314, NHANES 2015-2016 is 1516, and NHANES 2017-2020 is 1720

SEQN

NHANES ID number

age

Age in years

female

1=female, 0=male

education

1 is <9th grade, 3 is high school, 5 is a BA degree

z

1=light almost daily alcohol, 0=little or no alcohol last year.

grpL

Treated group and control groups. D=Daily=light almost daily alcohol, Never=never drinks -- see note, Rarely drinks -- see note, B = PastBinge = a past history of binge drinking on most days, but currently drinks once a week or less. For details, see Rosenbaum (2023, Appendix).

hdl

HDL cholesterol level mg/dL

mmercury

Methylmercury level ug/L

mset

Matched set indicator, 1, 2, ..., 722. The 2888 observations are in 722 matched sets, each of size 4.

Details

This data set enlarges the aHDL data in this package to include also data from NHANES 2017-2020. The aHDL data included 406 blocks of 4 people from NHANES 2013-2016, and they are included in aHDLe. From NHANES 2017-2020, an additional 316 blocks of 4 people were added, making 722 blocks of size 4 in total.

The alcohol questions changed slightly in NHANES 2017-2020, forcing small changes in the definitions of two of the control groups. See the Note for specifics.

There is a debate about whether light daily alcohol consumption -- a single glass of red wine -- shortens or lengthens life. LoConte et al. (2018) emphasize that alcohol is a carcinogen. Suh et al. (1992) claim reduced cardiovascular mortality brought about by an increase in high density high-density lipoprotein (HDL) cholesterol, the so-called good cholesterol. There is on-going debate about whether there are cardiovascular benefits, and if they exist, whether they are large enough to offset an increased risk of cancer. This example looks at a small corner of the larger debate, namely the effect on HDL cholesterol.

The example contains several attempts to detect unmeasured confounding bias, if present. There is a secondary outcome thought to be unaffected by alcohol consumption, namely methylmercury levels in the blood, likely an indicator of the consumption of fish, not of alcohol; see Pedersen et al. (1994) and WHO (2021). There are also three control groups, all with little present alcohol consumption, but with different uses of alcohol in the past; see the definition of variable grp above.

The appendix to Rosenbaum (2023) describes the 2013-2016 data and matching in detail. See also the documentation in this package for the aHDL data.

References

LoConte, N. K., Brewster, A. M., Kaur, J. S., Merrill, J. K., and Alberg, A. J. (2018). Alcohol and cancer: a statement of the American Society of Clinical Oncology. Journal of Clinical Oncology 36, 83-93. <doi:10.1200/JCO.2017.76.1155>

Pedersen, G. A., Mortensen, G. K. and Larsen, E. H. (1994) Beverages as a source of toxic trace element intake. Food Additives and Contaminants, 11, 351–363. <doi:10.1080/02652039409374234>

Rosenbaum, P. R. (1987) <doi:10.1214/ss/1177013232> The role of a second control group in an observational study. Statistical Science, 2, 292-306. Discusses multiple control groups, as in this example.

Rosenbaum, P. R. (1989) <doi:10.2307/2531497> The role of known effects in observational studies. Biometrics, 45, 557-569. Discusses a known effect, such as the effect of alcohol on methylmercury.

Rosenbaum, P. R. (1989) <doi:10.1214/aos/1176347131> On permutation tests for hidden biases in observational studies. The Annals of Statistics, 17, 643-653. Abstractly discusses multiple control groups and known effects.

Rosenbaum, P. R. (2014) <doi:10.1080/01621459.2013.879261> Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls. Journal of the American Statistical Association, 109(507), 1145-1158. An alternative to weighted rank statistics for weighted block analyses.

Rosenbaum, P. R. (2023) <doi:10.1111/biom.13558> Sensitivity analyses informed by tests for bias in observational studies. Biometrics 79, 475–487. Uses the known effect of alchol on methylmercury. Data appendix contains detail information about the NHANES data.

Rosenbaum, P. R. (2024) <doi:10.1080/01621459.2023.2221402> Bahadur efficiency of observational block designs. Journal of the American Statistical Association. Discusses properties of weighted rank statistics.

Suh, I., Shaten, B. J., Cutler, J. A., and Kuller, L. H. (1992) <doi:10.7326/0003-4819-116-11-881> Alcohol use and mortality from coronary heart disease: the role of high-density lipoprotein cholesterol. Annals of Internal Medicine 116, 881-887.

World Health Organization (2021). Mercury and Health, <https://www.who.int/news-room/fact-sheets/detail/mercury-and-health>, (Accessed 30 August 2021).

Examples

data(aHDLe)
nh20172020<-aHDLe$nh==1720
table(nh20172020)
table(nh20172020,aHDLe$grpL)
par(mfrow=c(1,2))
boxplot(aHDLe$hdl[!nh20172020]~aHDLe$grpL[!nh20172020],main="NHANES 2013-2016",
    ylim=c(15,230),ylab="HDL Cholesterol",xlab="Group",las=1)
boxplot(aHDLe$hdl[nh20172020]~aHDLe$grpL[nh20172020],main="NHANES 2017-2020",
    ylim=c(15,230),ylab="HDL Cholesterol",xlab="Group",las=1)
par(mfrow=c(1,1))