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      Comparison of the discriminative ability of a generic and a condition-specific OHRQoL measure in adolescents with and without normative need for orthodontic treatment

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          Abstract

          BackgroundAt present, there is no evidence on whether using condition-specific Oral Health-Related Quality of Life (OHRQoL) measures provides more reliable information than generic measures for needs assessment. Therefore, the objective was to assess the discriminative ability of one generic and one condition-specific OHRQoL measure, namely, respectively, the short form of the Oral Health Impact Profile (OHIP-14) and the Condition-Specific form of the Oral Impacts on Daily Performances (CS-OIDP) attributed to malocclusion, between adolescents with and without normative need for orthodontic treatment.Methods200 16–17-year-old adolescents were randomly selected from 957 schoolchildren attending a Sixth Form College in London, United Kingdom. The impact of their oral conditions on quality of life during the last 6 months was assessed using two OHRQoL measures; OHIP-14 and OIDP. Adolescents were also examined for normative orthodontic treatment need using the Index of Orthodontic Treatment Need (IOTN) and the Dental Aesthetic Index (DAI). Discriminative ability was assessed comparing the overall scores and prevalence of oral impacts, calculated using each OHRQoL measure, between adolescents with and without normative need. Using the prevalence of oral impacts allowed adjusting for covariates.ResultsThere were significant differences in overall scores for CS-OIDP attributed to malocclusion between adolescents with and without normative need for orthodontic treatment when IOTN or DAI were used to define need (p = 0.029 or 0.011 respectively), and in overall scores for OHIP-14 when DAI, but not IOTN was used to define need (p = 0.029 and 0.080 respectively). For the prevalence of impacts, only the prevalence of CS-OIDP attributed to malocclusion differed significantly between adolescents with and without normative need, even after adjusting for covariates (p = 0.017 and 0.049 using IOTN and DAI to define need).ConclusionCS-OIDP attributed to malocclusion was better able than OHIP-14 to discriminate between adolescents with and without normative needs for orthodontic treatment.

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          Most cited references 45

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          Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio

          Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.
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            Statistical Power Analysis for the Behavioral Sciences

             Jacob Cohen (2013)
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              Effect sizes for interpreting changes in health status.

              Health status measures are being used with increasing frequency in clinical research. Up to now the emphasis has been on the reliability and validity of these measures. Less attention has been given to the sensitivity of these measures for detecting clinical change. As health status measures are applied more frequently in the clinical setting, we need a useful way to estimate and communicate whether particular changes in health status are clinically relevant. This report considers effect sizes as a useful way to interpret changes in health status. Effect sizes are defined as the mean change found in a variable divided by the standard deviation of that variable. Effect sizes are used to translate "the before and after changes" in a "one group" situation into a standard unit of measurement that will provide a clearer understanding of health status results. The utility of effect sizes is demonstrated from four different perspectives using three health status data sets derived from arthritis populations administered the Arthritis Impact Measurement Scales (AIMS). The first perspective shows how general and instrument-specific benchmarks can be developed and how they can be used to translate the meaning of clinical change. The second perspective shows how effect sizes can be used to compare traditional clinical measures with health status measures in a standard clinical drug trial. The third application demonstrates the use of effect sizes when comparing two drugs tested in separate drug trials and shows how they can facilitate this type of comparison. Finally, our health status results show how effect sizes can supplement standard statistical testing to give a more complete and clinically relevant picture of health status change. We conclude that effect sizes are an important tool that will facilitate the use and interpretation of health status measures in clinical research in arthritis and other chronic diseases.
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                Author and article information

                Affiliations
                [1 ]Unidad de investigación en Salud Pública Dental, Departamento de Odontología Social, Universidad Peruana Cayetano Heredia, Lima, Perú
                [2 ]Department of Epidemiology and Public Health, University College London, London, UK
                Contributors
                Journal
                Health Qual Life Outcomes
                Health and Quality of Life Outcomes
                BioMed Central
                1477-7525
                2008
                21 August 2008
                : 6
                : 64
                2533301
                1477-7525-6-64
                18718004
                10.1186/1477-7525-6-64
                Copyright © 2008 Bernabé et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Research

                Health & Social care

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