HDs with 1-on-1 Matching
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1 How to Peel Oranges into Apples: Finding Causes and Effects of Health Disparities with Difference Scores Built by 1-on-1 Matching May 24, 2017 Emil N. Coman 1 Helen Wu 2 Wizdom A. Powell 1 1 UConn Health Disparities Institute, 2 UConn Health Please cite as Presented at the Modern Modeling conference, May 22-24, 2017 Modern Modeling conference, May 22-24, HDs with 1-on-1 Matching Goals Introduce a new method to estimate HDs Interpret results Suggest uses/extensions Modern Modeling conference, May 22-24,
2 HDs with 1-on-1 Matching Understanding the underlying causes of health disparities (HD) is a major public health objective HDs are defined as differences in health resulting from avoidable social forces, not due to unavoidable biological (or genetic) differences Challenge: compare the comparable to confidently reach causal inference Modern Modeling conference, May 22-24, HDs and causes - example Solar, O., & Irwin, A. (2007). A conceptual framework for action on the social determinants of health. from Modern Modeling conference, May 22-24,
3 HDs with 1-on-1 Matching on what White (%) N Black (%) N All (%) N Total Employment Unemployed Homemaker Part-time Fulltime Education 0 < Grade < Grade = Grade > Marital status Married <.001 Co-habitating Not married w/ boyfriend Not married w/o boyfriend Means White SDs Black SDs All SDs p t test Age Modern Modeling conference, May 22-24, on-1 matching approach Deceptively simple: 1. Get probabilities/propensities for all in logistic e.g. model Black vs. white regressed on SES = Match on SES factors 2. Apply a rule for matching 1-on-1 3. Create dyads 1. One has now 4 groups: B/W matched, and B/W unmatched 4. Analyze as repeated measures 5. Build difference (latent, why not) scores, and find its predictors 6. Matching can be done in clusters/strata too. Modern Modeling conference, May 22-24,
4 1-on-1 matching approach Modern Modeling conference, May 22-24, on-1 matching approach AMOS version There are then 4 groups: matched (comparison and reference) and unmatched (comparison and reference) Modern Modeling conference, May 22-24,
5 Anxiety scores Frequency BIS_Anxiety_AVG Graphs by blvswh The distribution of Y scores in the 2 groups (initial, unmatched). Modern Modeling conference, May 22-24, on-1 matching approach Mean SE p SEM B vs. W p LDS Dyadic' correlations Anxiety - whites NS Anxiety - Blacks HD-Anxiety LDS We can compare before matching (92 white, 145 Black women) and then after matching 61 dyads. Black women report lower BIS anxiety than white women, indicating a HD favoring Blacks, by about.40 standard deviation of the combined measure. Unmatched matched sample (n = 115) Mean SE Anxiety - whites Anxiety - Blacks p SEM B vs. W Modern Modeling conference, May 22-24,
6 Simplified model of HD causes ΔBehavior (D-A) ΔGenetics (D-A) effect ΔG effect ΔEnvironment ΔE (D-A) effect ΔB ΔHealth (D-A) D=Disadvantaged group; A= advantaged. ΔG, ΔE, and ΔB are the effects of the differences in genetics, environment, and behavior on health disparities (HD, differences in health). Modern Modeling conference, May 22-24, on-1 matching approach Intuitive model LDS/LCS McArdle s Y WHITE X BLACK HD = Y=LDS BLACK-WHITE Computing the difference: possible in the raw data Y WHITE +1 X BLACK α Y = 0 HD = Y=LDS BLACK-WHITE +1 σ ε2 = 0 Coman, E. N., K. Picho, et al. (2013). "The paired t-test as a simple latent change score model." Frontiers in Quantitative Psychology and Measurement 4, Article 738. LDS is a DV now: we estimate intercepts, hence centering is crucial: the tests should be done at the predictors values set to 0. Modern Modeling conference, May 22-24,
7 1-on-1 matching approach - intuition One gets 2 variables out of any Y: Y group 1 & Y group 2 It turns the data from independent groups to repeated tests one can run change in nature: t-test and others It changes the logic: Being in group 2 (vs reference group 1) leads to an difference between Y averages: Y 2 Y 1 to: There are varying differences between similar group 2 and group 1 pairs of cases. ( Y j, j are dyads), hence we get Y Modern Modeling conference, May 22-24, on-1 matching HDs AMOS [setup and] results YLDS has a mean and a variance. WAnx was centered (not needed unless It points to YLDS). Modern Modeling conference, May 22-24,
8 Cluster matching in HD mixed y binary clust13: y Coef. Std. Err. z P> z [95% Conf. Interval] binary We can also compare the 2 groups (binary) in terms of the matched clusters Y averages in comparable Black and white clusters (13 here). Modern Modeling conference, May 22-24, Correlations between three disparity measures +0.62*.006 (+1.196/0.437) ΔNeighborhood Disorder (Black-White) ΔAnxiety (Black-White) NS.291 (+2.193/2.076) Larger Neighborhood pairwise differences translate into larger Anxiety pairwise differences *.019 ( /5.687) ΔPerceived Stress (Black-White) Notes: Correlation p value (covariance/standard error); * : p <.05; NS = non-significant (p >.05). Modern Modeling conference, May 22-24,
9 Effects among three disparity measures ΔAnxiety (Black-White) ΔNeighborhood Disorder (Black-White) +0.62*.001 (+1.777/0.507) -0.10*.698 (-1.555/4.009) +0.52*.031 (+2.709/1.256) ΔPerceived Stress (Black-White) Notes: Correlation p value (covariance/standard error); * : p <.05; NS = non-significant (p >.05). Modern Modeling conference, May 22-24, Conclusions for >: comanus@gmail.com 1. Capturing causal processes responsible for health disparities (by any groupings) may be served by models explaining actual such disparities. 2. The latent difference score (LCS) method of J. McArdle is uniquely qualified 3. Extensions can be envisioned: combinations of difference and change scores, partialling out group specific unreliabilities, etc. Modern Modeling conference, May 22-24,
Health Disparities (HD): It s just about comparing two groups
A review of modern methods of estimating the size of health disparities May 24, 2017 Emil Coman 1 Helen Wu 2 1 UConn Health Disparities Institute, 2 UConn Health Modern Modeling conference, May 22-24,
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