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Optimizing the Analysis of Adherence Interventions Using Logistic Generalized Estimating Equations

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Abstract

Interventions aimed at improving HIV medication adherence could be dismissed as ineffective due to statistical methods that are not sufficiently sensitive. Cross-sectional techniques such as t tests are common to the field, but potentially inaccurate due to increased risk of chance findings and invalid assumptions of normal distribution. In a secondary analysis of a randomized controlled trial, two approaches using logistic generalized estimating equations (GEE)—planned contrasts and growth curves—were examined for evaluating percent adherence data. Results of the logistic GEE approaches were compared to classical analysis of variance (ANOVA). Robust and bootstrapped estimation was used to obtain empirical standard error estimates. Logistic GEE with either planned contrasts or growth curves in combination with robust standard error estimates was superior to classical ANOVA for detecting intervention effects. The choice of longitudinal model led to key differences in inference. Implications and recommendations for applied researchers are discussed.

Resumen

Las intervenciones con el propósito de mejorar la toma de medicamentos para el VIH podrían ser estimadas como ineficaces debido a métodos estadísticos que no son suficientemente sensitivos. Las técnicas transversales tal como la prueba t son comunes en el estudio de campo, pero potencialmente inexactas debido a un aumento del riesgo en los resultados por la casualidad y las suposiciones incorrectas en la distribución normal. A través de un análisis secundario de una prueba controlada aleatoria, fue examinado para evaluar adherencia como porcentaje, el método de la ecuación de estimación generalizada (GEE) logística, a través de dos especificaciones: los contrastes planeados y las curvas de crecimiento. Los resultados de ambas especificaciones fueron comparados con el análisis de la varianza (ANOVA) clásico. La estimación robusta y el bootstrapping fueron usados para obtener cálculos empíricos de error estándar. La GEE logística con estimación robusta, ya sea con contrastes planeados o curvas de crecimiento, fue superior al ANOVA clásico. La elección de método longitudinal produjo diferencias claves con respecto a la inferencia. Las implicaciones y recomendaciones para investigadores aplicados son discutidas.

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Acknowledgment

This research was supported by a National Institute of Mental Health Grant (R01 MH58986) to Jane M. Simoni, Ph.D.

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Authors and Affiliations

  1. Department of Psychology, University of Washington, Campus Box 351525, Seattle, WA, 98195-1525, USA

    David Huh, Brian P. Flaherty & Jane M. Simoni

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  1. David Huh
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  2. Brian P. Flaherty
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  3. Jane M. Simoni
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Correspondence to David Huh.

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Huh, D., Flaherty, B.P. & Simoni, J.M. Optimizing the Analysis of Adherence Interventions Using Logistic Generalized Estimating Equations. AIDS Behav 16, 422–431 (2012). https://doi.org/10.1007/s10461-011-9955-5

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  • DOI: https://doi.org/10.1007/s10461-011-9955-5

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