Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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).</p> <p>Conclusion</p> <p>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.</p

Parenting-by-gender interactions in child psychopathology: attempting to address inconsistencies with a Canadian national database

<p>Abstract</p> <p>Background</p> <p>Research has shown strong links between parenting and child psychopathology. The moderating role of child gender is of particular interest, due to gender differences in socialization history and in the prevalence of psychiatric disorders. Currently there is little agreement on how gender moderates the relationship between parenting and child psychopathology. This study attempts to address this lack of consensus by drawing upon two theories (self-salience vs. gender stereotyped misbehaviour) to determine how child gender moderates the role of parenting, if at all.</p> <p>Methods</p> <p>Using generalized estimating equations (GEE) associations between three parenting dimensions (hostile-ineffective parenting, parental consistency, and positive interaction) were examined in relationship to child externalizing (physical aggression, indirect aggression, and hyperactivity-inattention) and internalizing (emotional disorder-anxiety) dimensions of psychopathology. A sample 4 and 5 year olds from the National Longitudinal Survey of Children and Youth (NLSCY) were selected for analysis and followed over 6 years (N = 1214). Two models with main effects (Model 1) and main effects plus interactions (Model 2) were tested.</p> <p>Results</p> <p>No child gender-by-parenting interactions were observed for child physical aggression and indirect aggression. The association between hostile-ineffective parenting and child hyperactivity was stronger for girls, though this effect did not reach conventional levels of statistical significance (<it>p </it>= .059). The associations between parenting and child emotional disorder did vary as a function of gender, where influences of parental consistency and positive interaction were stronger for boys.</p> <p>Discussion</p> <p>Despite the presence of a few significant interaction effects, hypotheses were not supported for either theory (i.e. self-salience or gender stereotyped misbehaviour). We believe that the inconsistencies in the literature regarding child gender-by-parenting interactions is due to the reliance on gender as an indicator of a different variable which is intended to explain the interactions. This may be problematic because there is likely within-gender and between-sample variability in such constructs. Future research should consider measuring and modelling variables that are assumed to explain such interactions when conducting gender-by-parenting research.</p

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Bibliography: p. 61-6

Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database-1

<p><b>Copyright information:</b></p><p>Taken from "Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database"</p><p>http://www.biomedcentral.com/1471-2288/8/28</p><p>BMC Medical Research Methodology 2008;8():28-28.</p><p>Published online 9 May 2008</p><p>PMCID:PMC2396173.</p><p></p

Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database-0

<p><b>Copyright information:</b></p><p>Taken from "Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database"</p><p>http://www.biomedcentral.com/1471-2288/8/28</p><p>BMC Medical Research Methodology 2008;8():28-28.</p><p>Published online 9 May 2008</p><p>PMCID:PMC2396173.</p><p></p