The GReat-Child TrialTM: A Quasi-Experimental Dietary Intervention among Overweight and Obese Children

Diet composition is a key determinant of childhood obesity. While whole grains and micronutrients are known to decrease the risk of obesity, there are no interventions originating from Southeast Asia that emphasize whole grain as a strategy to improve overall quality of diet in combating childhood obesity. The GReat-Child Trial aimed to improve whole grain intake and quality of diet among overweight and obese children. It is a quasi-experimental intervention based on Social Cognitive Theory. It has a 12-week intervention and 6-month follow-up, consisting of three components that address environmental, personal, and behavioral factors. The intervention consists of: (1) six 30 min lessons on nutrition, using the Malaysian Food Pyramid to emphasize healthy eating, (2) daily deliveries of wholegrain foods to schools so that children can experience and accept wholegrain foods, and (3) diet counseling to parents to increase availability of wholegrain foods at home. Two primary schools with similar demographics in Kuala Lumpur were assigned as control (CG) and intervention (IG) groups. Inclusion criteria were: (1) children aged 9 to 11 years who were overweight/obese; (2) who did not consume whole grain foods; and (3) who had no serious co-morbidity problems. The entire trial was completed by 63 children (31 IG; 32 CG). Study outcomes were measured at baseline and at two time points post intervention (at the 3rd [T1] and 9th [T2] months). IG demonstrated significantly higher intakes of whole grain (mean difference = 9.94, 95%CI: 7.13, 12.75, p < 0.001), fiber (mean difference = 3.07, 95% CI: 1.40, 4.73, p = 0.001), calcium (mean difference = 130.27, 95%CI: 74.15, 186.39, p < 0.001), thiamin (mean difference = 58.71, 95%CI: 26.15, 91.28, p = 0.001), riboflavin (mean difference = 0.84, 95%CI: 0.37, 1.32, p = 0.001), niacin (mean difference = 0.35, 95%CI: 1.91, 5.16, p < 0.001), and vitamin C (mean difference = 58.71, 95%CI: 26.15, 91.28, p = 0.001) compared to CG in T1, after adjusting for covariates. However, T1 results were not sustained in T2 when intervention had been discontinued. The findings indicate that intervention emphasizing whole grains improved overall short-term but not long-term dietary intake among schoolchildren. We hope the present trial will lead to adoption of policies to increase whole grain consumption among Malaysian schoolchildren.

Empirical comparison of four baseline covariate adjustment methods in analysis of continuous outcomes in randomized controlled trials

Background

Although seemingly straightforward, the statistical comparison of a continuous variable in a randomized controlled trial that has both a pre- and posttreatment score presents an interesting challenge for trialists. We present here empirical application of four statistical methods (posttreatment scores with analysis of variance, analysis of covariance, change in scores, and percent change in scores), using data from a randomized controlled trial of postoperative pain in patients following total joint arthroplasty (the Morphine COnsumption in Joint Replacement Patients, With and Without GaBapentin Treatment, a RandomIzed ControlLEd Study [MOBILE] trials).

Methods

Analysis of covariance (ANCOVA) was used to adjust for baseline measures and to provide an unbiased estimate of the mean group difference of the 1-year postoperative knee flexion scores in knee arthroplasty patients. Robustness tests were done by comparing ANCOVA with three comparative methods: the posttreatment scores, change in scores, and percentage change from baseline.

Results

All four methods showed similar direction of effect; however, ANCOVA (−3.9; 95% confidence interval [CI]: −9.5, 1.6; P=0.15) and the posttreatment score (−4.3; 95% CI: −9.8, 1.2; P=0.12) method provided the highest precision of estimate compared with the change score (−3.0; 95% CI: −9.9, 3.8; P=0.38) and percent change (−0.019; 95% CI: −0.087, 0.050; P=0.58).

Conclusion

ANCOVA, through both simulation and empirical studies, provides the best statistical estimation for analyzing continuous outcomes requiring covariate adjustment. Our empirical findings support the use of ANCOVA as an optimal method in both design and analysis of trials with a continuous primary outcome.

Differential effects of the changes of LDL cholesterol and systolic blood pressure on the risk of carotid artery atherosclerosis

BACKGROUND

The effects of baseline and changes in blood pressure and low density lipoprotein (LDL) cholesterol on the carotid intima media thickness (IMT) have not been well documented.

METHODS

A total of 2572 adults (mean age 53.8 years, 54.6% women) in a Taiwanese community undertook three blood pressure and LDL cholesterol examinations over 6 years. Latent growth curve modeling was used to investigate the effects of baseline and change in blood pressure and LDL cholesterol on IMT.

RESULTS

Greater baseline LDL and blood pressure were associated with an increase in IMT (0.005 ± 0.002 mm per 1 mg/dL [p = 0.006] and 0.041 ± 0.004 mm mm Hg [p <0.0001], respectively. Change in blood pressure was associated with a significant increase in IMT (0.047 ± 0.016, P = 0.004), whilst the association between change in LDL and change in IMT was not statistically significant (0.008 ± 0.006, P = 0.20).

CONCLUSIONS

Carotid IMT was associated with baseline blood pressure and LDL cholesterol, yet only changes of blood pressure, not LDL cholesterol, were related to carotid IMT during the 6-year observation.

Comparison of the effects of treatment of peri-implant infection in animal and human studies: systematic review and meta-analysis

OBJECTIVE

The main objective of this systematic review is to compare the effects of treatment of peri-implant infection between animal and human studies.

MATERIAL AND METHODS

A literature search was conducted using the Medline, Cochrane Central Register of Controlled Trials, and Latin American and Caribbean Health Sciences Literature databases up to and including May 2008. In addition, bibliographies of systematic reviews on peri-implant diseases were searched manually. Non-surgical and surgical treatments of peri-implantitis/mucositis in animal models or human studies were compared. Meta-analysis was conducted to investigate the difference between the reported treatment effects in animal and human studies. Changes in probing pocket depth (PPD) and probing attachment level (PAL) from baseline measurements were used as measures of outcome. Single-level and multilevel meta-regression analysis was performed by taking into account the different follow-up times of the studies included.

RESULTS

The single-level and multilevel random-effects meta-analysis showed that the difference in PPD reduction [0.31 mm, 95% confidence interval (CI): -0.27, 0.88] and in PAL gain (0.21 mm, 95% CI: -0.47, 0.88) between animal and human studies was not statistically significant. The random-effects meta-regression suggested that studies with longer follow-up times revealed greater PPD reduction (0.25 mm per month, 95% CI: 0.14, 0.35). However, when the different follow-up times were taken into account, these differences became greater. Substantial heterogeneity between studies was found in the meta-analyses (I(2)=97.6% for animal studies and 99.9% for human studies).

CONCLUSION

There was great heterogeneity between human and animal studies in terms of study designs and treatment procedures. Therefore, the results from this meta-analysis should be interpreted with caution. Heterogeneity between studies and its causes merit further investigations.

An introduction to latent growth curve modelling for longitudinal continuous data in dental research

Many studies in dental research are based on repeated measurements of several continuous variables. Statistical analyses of such data require advanced methods to explore the complexity of information within the data. Currently, the most frequently adopted approach is to undertake multiple univariate tests. Occasionally, more advanced and sophisticated statistical methodologies, such as multilevel modelling and generalized estimating equation, have been used. In the last decade, a novel statistical methodology known as latent growth curve modelling has been developed in the social sciences. Latent growth curve modelling can be considered a special application of structural equation modelling and is generally conducted using structural equation modelling software. Recent development of statistical theory shows that latent growth curve modelling is equivalent to multilevel modelling, and both approaches yield identical results. However, in some study designs latent growth curve modelling can provide a more flexible framework of statistical modelling than multilevel modelling and generalized estimating equation for longitudinal data. The aim of this article was to present a non-technical introduction to latent growth curve modelling for dental researchers. The emphasis was on conceptual understanding, rather than mathematical rigor, so path diagrams were used for visual presentations of various statistical models. When properly applied, latent growth curve modelling has great potential to give new directions for future longitudinal dental research.

Partial least squares path modelling for relations between baseline factors and treatment outcomes in periodontal regeneration

BACKGROUND

Some clinical outcome variables in periodontal research are mathematically coupled, and it is not feasible to include all the mathematically coupled variables in an ordinary least squares (OLS) regression analysis. The simplest solution to this problem is to drop at least one of the mathematically coupled variables. However, this solution is not satisfactory when the mathematically coupled variables have distinctive clinical implications.

MATERIAL AND METHODS

Partial least squares (PLS) methods were used to analyse data from a study on guided tissue regeneration. Relationships between characteristics of baseline lesions and treatment outcomes after 1 year were analysed using PLS, and the results were compared with those from OLS regression.

RESULTS

PLS analysis suggested that there were multiple dimensions in the characteristics of baseline lesion: vertical dimension was positively associated with probing pocket depth (PPD) reduction and clinical attachment level (CAL) gain, whilst horizontal dimension was negatively associated with the outcome. Baseline gingival recession had a negative association with PPD reduction but a small positive one with CAL gain.

CONCLUSION

PLS analysis provides new insights into the relationships between baseline characteristics of infrabony defects and periodontal treatment outcomes. The hypothesis of multiple dimensions in baseline lesions needs to be validated by further analysis of different datasets.