Effectiveness of two educational interventions based on the theory of planned behavior on oral cancer self-examination in adults: a randomized controlled trial
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Abstract Background The theory of planned behavior (TPB) is recognized as an effective theory for behavior change. The aim of the present study was to investigate the impact of two TPB-based educational interventions on oral self-examination (OSE) behavior and the related TPB constructs among adults in Tehran, Iran, in 2022. Methods This randomized controlled trial involved 400 healthy individuals recruited from 20 urban comprehensive health centers in the southern part of Tehran, Iran. The health centers were randomly assigned to two control (PowerPoint) and intervention (WhatsApp) groups (200 individuals in each group). In the control group (the recipient of the routine care), participants received a 20-minute lecture through a PowerPoint presentation and a pamphlet. In the intervention group (the recipient of an additional intervention alongside the routine care), participants were educated through messages and images on WhatsApp along with having monthly group discussions. Data was collected at baseline, as well as at 1- and 3-month follow-ups using a structured questionnaire. The outcomes assessed included OSE behavior and the related TPB constructs: intention, attitude, subjective norm, and perceived behavioral control. Linear and logistic generalized estimating equations (GEE) regression models were used to evaluate the impact of the interventions with STATA version 17. Results Of the total participants, 151 (37.75%) were men. The mean ± standard deviation (SD) of ages in the PowerPoint and WhatsApp groups were 39.89 ± 13.72 and 39.45 ± 13.90, respectively. OSE and the related TPB constructs showed significant differences between the groups at the 1-month post-intervention assessment. The effect of PowerPoint was more significant in the short-term (one month), while both methods showed similar effectiveness after three months, specifically in relation to OSE and the TPB constructs. At the 3-month post-intervention assessment, there were significant increases in OSE (OR = 28.63), intention (β = 1.47), attitude (β = 0.66), subjective norm (β = 2.82), and perceived behavioral control (β = 1.19) in both groups ( p < 0.001). Conclusions The present study provides evidence of the effectiveness of both educational interventions in improving OSE and the TPB constructs after three months. Therefore, both TPB-based educational methods can be recommended for designing and implementing interventions aimed at preventing oral cancer. Trial registration The trial protocol was registered in the Iranian Registry of Clinical Trials (IRCT) on 04/03/2022 (registration number: IRCT20220221054086N1).Keywords:
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Preventing Chronic Disease (PCD) is a peer-reviewed electronic journal established by the National Center for Chronic Disease Prevention and Health Promotion. PCD provides an open exchange of information and knowledge among researchers, practitioners, policy makers, and others who strive to improve the health of the public through chronic disease prevention.
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This paper illustrates analysis of longitudinal data on students’ academic performance using GEE (Generalize Estimation Equations) under various working correlation assumptions. Many factors account for students’ academic performance in the fulcrum of all levels of education. Hence, any variable that triggers the academic performance of students evoke the awareness of all. The aim of this thesis is to analyze academic performance using application of Generalized Estimating Equation (GEE) Models under various working correlation assumptions. There are various statistical and mathematical models employed in the analyses of students’ academic performance in different level of schools. In this paper, we formulate the Generalized Estimating Equation (GEE) model approach under various correlation assumptions to analyze the probable
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Objective:To explore the application of the generalized estimating equation (GEE)in the repeated measures data.Method:The repeated measures data were analyzed with category variance outcome by GEE and conclusions were drawn according to the estimating of parameter and standard error.Results:The method could efficiently consider the intra-class correlation,advisably deal with the missing value,correctly obtain the estimate and standard error of the each effect,by applying GEE in repeated measures data.Conclusion:It can gain more objective appraise of medication effect by the application of the GEE in the repeated measures data,and to deserve to be generalized.
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Smoking cessation trials generally record information on daily smoking behavior, but base analyses on measures of smoking status at the end of treatment (EOT). We present an alternative approach that analyzes the entire sequence of daily smoking status observations.We analyzed daily abstinence data from a smoking cessation trial, using two longitudinal logistic regression methods: a mixed-effects (ME) model and a generalized estimating equations (GEE) model. We compared results to a standard analysis that takes abstinence status at EOT as outcome. We evaluated time-varying covariates (smoking history and time-varying drug effect) in the longitudinal analysis and compared ME and GEE approaches.We observed some differences in the estimated treatment effect odds ratios across models, with narrower confidence intervals under the longitudinal models. GEE yields similar results to ME when only baseline factors appear in the model, but gives biased results when one includes time-varying covariates. The longitudinal models indicate that the quit probability declines and the drug effect varies over time. Both the previous day's smoking status and recent smoking history predict quit probability, independently of the drug effect.When analysing outcomes of studies from smoking cessation interventions, longitudinal models with multiple outcome data points, rather than just end of treatment, can makes efficient use of the data and incorporate time-varying covariates. The generalized estimating equations approach should be avoided when using time-varying predictors.
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Abstract Background The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations. Methods We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates. Results The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively). Conclusion QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.
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Clustered longitudinal data is often collected as repeated measurements on subjects over time arising in the clusters. Examples include longitudinal community intervention studies, or family studies with repeated measures on each member. Meanwhile, cluster size is sometime informative, which means that the risk for the outcomes is related to the cluster size. Under this situation, generalized estimating equations (GEE) will lead to invalid inferences because GEE assumes that the cluster size is non-informative. In this study, we investigated the performances of generalized estimating equations (GEE), cluster-weighted generalized estimating equations (CWGEE), and within-cluster resampling (WCR) on clustered longitudinal data. Based on our extensive simulation studies, we conclude that all three methods provide comparable estimates when the cluster size is non-informative. But when cluster size is informative, GEE gives biased estimates, while WCR and CWGEE still provide unbiased and consistent estimates under different \\\"working correlation structures\\\" within-subject. However, WCR is a computationally intensive approach, so CWGEE is the best choice for clustered longitudinal data due to its solving only one estimating equation, which is asymptotically equivalent to WCR.
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Abstract The generalized estimating equations (GEE) approach is commonly used to model incomplete longitudinal binary data. When drop‐outs are missing at random through dependence on observed responses (MAR), GEE may give biased parameter estimates in the model for the marginal means. A weighted estimating equations approach gives consistent estimation under MAR when the drop‐out mechanism is correctly specified. In this approach, observations or person‐visits are weighted inversely proportional to their probability of being observed. Using a simulation study, we compare the performance of unweighted and weighted GEE in models for time‐specific means of a repeated binary response with MAR drop‐outs. Weighted GEE resulted in smaller finite sample bias than GEE. However, when the drop‐out model was misspecified, weighted GEE sometimes performed worse than GEE. Weighted GEE with observation‐level weights gave more efficient estimates than a weighted GEE procedure with cluster‐level weights. Copyright © 2002 John Wiley & Sons, Ltd.
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