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Determining directional dependency in causal associations.

ABSTRACT: Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D'Agostino's K(2), and encouraging the use of directional dependency to compare variables only within time points. We offer improved steps for determining directional dependency that fix the problems we note. Next, we discuss how to integrate directional dependency into longitudinal data analysis with two variables. We also examine the accuracy of directional dependency evaluations when several regression assumptions are violated. Directional dependency can suggest the direction of a relation if (a) the regression error in population is normal, (b) an unobserved explanatory variable correlates with any variables equal to or less than .2, (c) a curvilinear relation between both variables is not strong (standardized regression coefficient ? .2), (d) there are no bivariate outliers, and (e) both variables are continuous.

SUBMITTER: Pornprasertmanit S

PROVIDER: S-EPMC3966655 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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Determining directional dependency in causal associations.

Pornprasertmanit Sunthud S Little Todd D TD

International journal of behavioral development 20120701 4

Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D'Agostino's <i>K</i><sup>2</sup>, and encouraging the use of directional dep ...[more]

PMID: 24683282

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Project description:BackgroundPrevious studies have reported associations between attention-deficit/hyperactivity disorder (ADHD) and lower socioeconomic status and intelligence. We aimed to evaluate the causal directions and strengths for these associations by use of a bi-directional two-sample Mendelian randomization (MR) design.MethodsWe used summary-level data from the largest available genome-wide association studies (GWAS) to identify genetic instruments for ADHD, intelligence, and markers of socioeconomic status including the Townsend deprivation index, household income, and educational attainment. Effect estimates from individual genetic variants were combined using inverse-variance weighted regression.ResultsA genetically predicted one standard deviation (SD) increment in the Townsend deprivation index conferred an odds ratio (OR) of 5.29 (95% confidence interval (CI) 1.89-14.76) for an ADHD diagnosis (p<0.001). A genetically predicted one SD higher education level conferred an OR of 0.30 (95% CI 0.25-0.37) (p<0.001), and a genetically predicted one SD higher family income provided an OR of 0.35 (95% CI 0.25-0.49; p<0.001). The associations remained after adjustment for intelligence whereas the lower odds of an ADHD diagnosis with higher intelligence did not persist after adjustment for liability to greater educational attainment (adjusted OR 1.03, 95% CI 0.68-1.56; p=0.87). The MR analysis of the effect of ADHD on socioeconomic markers found that genetic liability to ADHD was statistically associated with each of them (p<0.001) but not intelligence. However, the average change in the socioeconomic markers per doubling of the prevalence of ADHD corresponded only to 0.05-0.06 SD changes.ConclusionsOur results indicate that an ADHD diagnosis may be a direct and strong intelligence-independent consequence of socioeconomic related factors, whereas ADHD appears to lead only to modestly lowered socioeconomic status. Low intelligence seems not to be a major independent cause or consequence of ADHD.

Project description:BackgroundPrevious studies have shown that osteoarthritis (OA) and sarcopenia (SP) are closely related to each other, but the causal relationships between them have not been established. The aim of this study was to investigate the causal associations between OA and SP via a bi-directional Mendelian randomization (MR) approach.MethodsA bi-directional two-sample MR was adopted to research the causal relationship between SP and OA. The instrumental variables for SP and four types of OA: KOA, HOA, total knee replacement (TKR) and total hip replacement (THR) were derived from published large genome-wide association studies (GWAS). The inverse variance weighted (IVW), MR-Egger and weighted median estimator (WME) methods were used to estimate bi-directional causal effects.ResultsLow grip strength (GS) did not have a causal effect on four types of OA (KOA: OR = 1.205, 95% CI 0.837-1.734, p = 0.316; HOA: OR = 1.090, 95% CI 0.924-1.609, p = 0.307; TKR: OR = 1.190, 95% CI 1.084-1.307, p = 0.058; THR: OR = 1.035, 95% CI 0.792-1.353, p = 0.798), while appendicular lean mass (ALM) had a causal effect on four types of OA (KOA: OR = 1.104, 95% CI 1.041-1.171, p = 0.001; HOA: OR = 1.151, 95% CI 1.071-1.237, p < 0.001; TKR: OR = 1.114, 95% CI 1.007-1.232, p < 0.001; THR: OR = 1.203, 95% CI 1.099-1.316, p < 0.001). In the reverse direction, KOA or HOA did not have a significant causal effect on both GS and ALM (KOA-GS: OR = 1.077, 95% CI 0.886-1.309, p = 0.458; KOA-ALM: Beta = 0.004, p = 0.892; HOA-GS: OR = 1.038, 95% CI 0.981-1.099, p = 0.209; HOA-ALM: Beta = - 0.017, p = 0.196; TKR-GS: OR = 0.999, 95% CI 0.739-1.351, p = 0.997; TKR-ALM: Beta = 0.018, p = 0.501; THR-GS: OR = 1.037, 95% CI 0.978-1.101, p = 0.222; THR-ALM: Beta = - 0.023, p = 0.081).ConclusionsThe present study suggests that SP may have a causal effect on OA through changes in muscle composition rather than muscle strength, while little evidence was provided for the causal effect of OA on SP.

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Determining directional dependency in causal associations.

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Determining directional dependency in causal associations.

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