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Optimizing the analysis of adherence interventions using logistic generalized estimating equations.

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.

SUBMITTER: Huh D

PROVIDER: S-EPMC3891827 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Optimizing the analysis of adherence interventions using logistic generalized estimating equations.

Huh David D Flaherty Brian P BP Simoni Jane M JM

AIDS and behavior 20120201 2

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 curv ...[more]

PMID: 21553253

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Project description:BackgroundMeteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases.MethodsIn this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (R eff ). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables.ResultsPrecipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases.ConclusionMeteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored.

Dataset Information

Optimizing the analysis of adherence interventions using logistic generalized estimating equations.

Publications

Optimizing the analysis of adherence interventions using logistic generalized estimating equations.

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