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Introducing the At-Risk Average Causal Effect with Application to HealthWise South Africa

Prevention Science volume 13, pages 437–447 (2012)Cite this article

Abstract

Researchers often hypothesize that a causal variable, whether randomly assigned or not, has an effect on an outcome behavior and that this effect may vary across levels of initial risk of engaging in the outcome behavior. In this paper, we propose a method for quantifying initial risk status. We then illustrate the use of this risk-status variable as a moderator of the causal effect of leisure boredom, a non-randomized continuous variable, on cigarette smoking initiation. The data come from the HealthWise South Africa study. We define the causal effects using marginal structural models and estimate the causal effects using inverse propensity weights. Indeed, we found leisure boredom had a differential causal effect on smoking initiation across different risk statuses. The proposed method may be useful for prevention scientists evaluating causal effects that may vary across levels of initial risk.

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Authors’ note

Preparation of this article was supported by NIDA Center Grant P50 DA100075, NIDA R03 DA026543, and NIDDK 5R21DK082858-02. HealthWise was supported by NIDA grant R01 DA01749. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse (NIDA), the National Institute on Diabetes and Digestive and Kidney Diseases (NIDDK), or the National Institutes of Health (NIH).

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Authors and Affiliations

  1. The Methodology Center, The Pennsylvania State University, 204 E. Calder Way, Ste. 400, State College, PA, 16801, USA

    Donna L. Coffman

  2. Dept. of Recreation, Park and Tourism Management, The Pennsylvania State University, 704L Donald H. Ford Bldg., University Park, PA, 16802, USA

    Linda L. Caldwell

  3. Prevention Research Center, The Pennsylvania State University, 105-G Henderson South Bldg., University Park, PA, 16802, USA

    Edward A. Smith

Authors
  1. Donna L. Coffman
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  2. Linda L. Caldwell
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  3. Edward A. Smith
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Corresponding author

Correspondence to Donna L. Coffman.

Appendix

Appendix

R Code

The data set is read into R and is called “dat.” The variable c.bored2 is the mean-centered leisure boredom variable and smoke1, smoke2, and smoke3 are the lifetime smoking variables at baseline, 6 months, and 1 year, respectively.

figure a

SAS Code

The data set is read into SAS. It is originally called “leisure” and is in the library “atrisk.” The variable cbored2 is the mean-centered leisure boredom variable and smoke1, smoke2, and smoke3 are the lifetime smoking variables at baseline, 6 months, and 1 year, respectively.

figure bfigure b

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Coffman, D.L., Caldwell, L.L. & Smith, E.A. Introducing the At-Risk Average Causal Effect with Application to HealthWise South Africa. Prev Sci 13, 437–447 (2012). https://doi.org/10.1007/s11121-011-0271-0

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  • DOI: https://doi.org/10.1007/s11121-011-0271-0

Keywords

  • Causal inference
  • Marginal Structural Models
  • Leisure boredom
  • Cigarette smoking initiation