Key result
Continuous interaction modeling of Type D personality avoids bias and false positives versus subgroup methods.
Why the study?
Methods used to analyze Type D personality have been criticized, prompting an investigation into whether these methods adequately detect a Type D effect.
Type D personality effects should be modeled using a continuous interaction approach rather than subgroup methods to avoid bias and false positive findings.
Continuous modeling of Type D effects is required to avoid bias; leaves open validity of prior subgroup-based cardiovascular associations.
OBJECTIVE: In research on Type D personality, its subcomponents negative affectivity (NA) and social inhibition (SI) are hypothesized to have a synergistic effect on various medical and psychosocial outcomes. As some methods to analyze Type D personality have been criticized, this study investigated whether these methods adequately detect a Type D effect. METHOD: We used a simulation and two empirical illustrations to investigate each method's performance (bias, power and false positives) in detecting the Type D effect. RESULTS: Our simulation showed that the two most commonly used methods to assess the Type D effect (subgroup methods) were primarily picking up the presence of NA or SI main effects, indicating that these methods might falsely suggest synergistic Type D effects. Moreover, these methods failed to detect the combined presence of the NA and SI main effects, resulting in significant Type D effects when only one of the NA/SI main effects was present. The method that best detected Type D effects modeled the continuous NA/SI main effects and their statistical interaction in a regression analysis. Reanalysis of two empirical Type D personality datasets confirmed the patterns found in our simulation. CONCLUSION: This study showed that Type D effects should be modeled with a continuous interaction approach. Other approaches showed either more bias, more false positive findings or lower power. We recommend against using subgroup approaches to operationalize Type D personality, as these methods are biased, regardless of whether the Type D effect is synergistic or additive in nature.
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Paul Lodder (2020) studied Type D personality. Continuous interaction approach (regression analysis) vs. Subgroup methods was evaluated on Performance (bias, power and false positives) in detecting the Type D effect. Modeling Type D personality effects with a continuous interaction approach avoided the bias and false positive findings associated with commonly used subgroup methods.
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