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Müller et al. BMC Psychiatry 2011, 11:55 http://www.biomedcentral.com/1471-244X/11/55 RESEARCH ARTICLE Open Access The impact of study design and diagnostic approach in a large multi-centre ADHD study: Part 2: Dimensional measures of psychopathology and intelligence Ueli C Müller1,2*, Philip Asherson3, Tobias Banaschewski4,12, Jan K Buitelaar5, Richard P Ebstein6, Jaques Eisenberg6, Michael Gill7, Iris Manor8, Ana Miranda9, Robert D Oades10, Herbert Roeyers11, Aribert Rothenberger12, Joseph A Sergeant13, Edmund JS Sonuga-Barke11,14, Margaret Thompson14, Stephen V Faraone15 and Hans-Christoph Steinhausen1,16,17 Abstract Background: The International Multi-centre ADHD Genetics (IMAGE) project with 11 participating centres from 7 European countries and Israel has collected a large behavioural and genetic database for present and future research. Behavioural data were collected from 1068 probands with ADHD and 1446 unselected siblings. The aim was to describe and analyse questionnaire data and IQ measures from all probands and siblings. In particular, to investigate the influence of age, gender, family status (proband vs. sibling), informant, and centres on sample homogeneity in psychopathological measures. Methods: Conners’ Questionnaires, Strengths and Difficulties Questionnaires, and Wechsler Intelligence Scores were used to describe the phenotype of the sample. Data were analysed by use of robust statistical multi-way procedures. Results: Besides main effects of age, gender, informant, and centre, there were considerable interaction effects on questionnaire data. The larger differences between probands and siblings at home than at school may reflect contrast effects in the parents. Furthermore, there were marked gender by status effects on the ADHD symptom ratings with girls scoring one standard deviation higher than boys in the proband sample but lower than boys in the siblings sample. The multi-centre design is another important source of heterogeneity, particularly in the interaction with the family status. To a large extent the centres differed from each other with regard to differences between proband and sibling scores. Conclusions: When ADHD probands are diagnosed by use of fixed symptom counts, the severity of the disorder in the proband sample may markedly differ between boys and girls and across age, particularly in samples with a large age range. A multi-centre design carries the risk of considerable phenotypic differences between centres and, consequently, of additional heterogeneity of the sample even if standardized diagnostic procedures are used. These possible sources of variance should be counteracted in genetic analyses either by using age and gender adjusted diagnostic procedures and regional normative data or by adjusting for design artefacts by use of covariate statistics, by eliminating outliers, or by other methods suitable for reducing heterogeneity. Keywords: ADHD multi-centre study, sibling design, centre effects * Correspondence: u.c.mueller@bluewin.ch 1Department of Child and Adolescent Psychiatry, University of Zurich, Zurich, Switzerland Full list of author information is available at the end of the article © 2011 Müller 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. Müller et al. BMC Psychiatry 2011, 11:55 http://www.biomedcentral.com/1471-244X/11/55 Background Attention Deficit Hyperactivity Disorder (ADHD), one of the most prevalent disorders in childhood and adoles-cence, is characterized by problems in allocating atten-tion, regulating motor activity, and controlling behavioural impulses [1]. In many subjects, the disorder is accompanied by comorbid conditions including con-duct disorders, oppositional defiant disorders, mood dis-orders, and anxiety disorders [2]. Furthermore, intellectual abilities are often impaired in children with ADHD [3]. The disorder may affect not only all aspects of a child’s life, including familial functioning, but also often persists into adulthood [1,4]. The risk for having ADHD is 2 to 8-fold higher in parents of children with ADHD than in the normal population and is elevated in siblings of children with ADHD [5]. These findings indicate a strong familiality of the disorder. Twin and adoption studies have fre-quently reported a heritability for ADHD of about 75% [1,6,7]. Quite often, siblings of ADHD children are sub-jected to an intermediate level of the disorder that lies between that shown by the affected probands and the healthy controls without a diagnosis of ADHD, e.g. with respect to ADHD symptomatology [5], comorbid condi-tions [8,9], intellectual abilities [10-12], or cognitive tasks performance [13]. The complexity of ADHD, not only in terms of the clinical picture, but also of the underlying pathophysiol-ogy and causes [1] implies that identified causal ‘units’, e.g. single genes, or single environmental factors, have only a small effect on the risk of developing ADHD [14]. Therefore, the investigation of the causes of ADHD needs large and homogeneous samples in order to have the power that is needed for the detection of etiological sources with small effects. The International Multicentre ADHD Genetics (IMAGE) project [14-16] provides a large database for molecular genetic investigations of ADHD. This data-base contains behavioural data from almost 1400 Eur-opean Families with one child affected by ADHD, and one or several unselected siblings. Additionally the DNA of all participants is stored in a cell line repository, enabling almost infinite numbers of molecular genetic ADHD studies in the future. The recruiting and assessment procedure, described in detail in the companion paper [17], included screening with the use of questionnaires, checking for inclusion/ exclusion criteria, procedures for verifying the ADHD diagnosis, ratings from teacher and parent question-naires, IQ measurement, and collection of DNA by blood samples or mouth swabs. Inclusion criteria were Cauca-sian ethnicity; one child with a probable diagnosis of ADHD of the combined type; at least one sibling, regard- Page 2 of 17 four genetic family members, including the proband with ADHD, at least one sibling, and at least one parent; and the age of the children lying between five and seventeen years. Exclusion criteria were IQ<70 in the children, a diagnosis of schizophrenia or autism, including atypical autism; a neurological disorder of the central nervous system, or a genetic disorder that might mimic ADHD. The diagnoses of all probands and of the siblings sus-pected to have ADHD were then verified using a diagnos-tic interview with the parents in combination with a symptom checklist generated from a teacher question-naire. Siblings fulfilling the criteria of ADHD were excluded from the sibling sample. The questionnaires were completed by both the parents and the teachers, except for the questionnaire assessing autistic symptoms, which was completed only by the parents. A short form of an IQ test was applied by trained clinicians. An over-view of studies of the IMAGE project published so far is available in the companion paper of the present contribu-tion [17] or at the periodically updated IMAGE home-page http://image.iop.kcl.ac.uk. The present paper aims to describe and analyse the behavioural phenotype of the IMAGE sample consisting of 1068 probands and 1446 siblings. In contrast to the companion paper [17], which analysed the symptom-based diagnostic characteristics of 1068 probands and 339 siblings, a dimensional approach is chosen in the present paper. Influences of age, gender, family status (proband vs. sibling), informant (parents vs. teacher), and study-centre on questionnaire scores and intelli-gence (IQ) measures are analysed using robust multi-way procedures. This report focuses on the degree of psychopathological heterogeneity caused by these factors and by characteristics of the measures applied and their underlying normative samples. In the first part of this study [17] we argued, that diagnostic criteria based on a defined number of symptoms can mask age- or gender-related distortions in the sample structure, particularly in the associated genotypic structure. Similarly, the pre-sent second part deals with the question of whether and how the questionnaire and IQ findings are biased due to the study design and diagnostic procedures applied. The behavioural measures used in the IMAGE project reflect its main purpose of providing a large database for molecular genetic studies. Intelligence is associated with ADHD and may also be an endophenotype of ADHD [12]. Intelligence quotient (IQ) measures should be assessed and considered as possible covariates in sta-tistical analyses. The Conners’ questionnaires are vali-dated instruments for the assessment of ADHD [18,19]. A symptom checklist as well as dimensional scores can be derived from these questionnaires. Additionally, they include scores for the most common comorbid condi- less of ADHD symptoms; DNA available from at least tions of ADHD. The Strengths and Difficulties Müller et al. BMC Psychiatry 2011, 11:55 http://www.biomedcentral.com/1471-244X/11/55 Questionnaire (SDQ) is another widely used instrument for the assessment of ADHD and comorbidities includ-ing emotional problems, conduct problems, and peer problems [20]. Furthermore, a score measuring prosocial behaviour can be derived from the SDQ. This score in combination with the Social Communications Question-naire [21], is used in screening for autism spectrum dis-orders as autistic features are frequently associated with ADHD [22,23]. The interpretation of the results must bear in mind to which of three categories the data belong. These cate-gories reflect the nature and degree of standardisation applied to the data and lead to different expectations about the effects of independent factors on the data. One category comprises the IQ measures. These result from a direct assessment of the child’s abilities, and the raw scores are translated into standardized scores that take age into account. In addition, it should be noted that different normative samples are used for each lan-guage. A second category comprises the standardized questionnaire measures reflecting the parents’ and tea-chers’ perceptions of the behaviour of the child. These scores are age and gender specific, but, in contrast to the IQ measures, are based on a single normative sam-ple across all centres. The third category comprises all non-standardized questionnaire scores (raw scores) reflecting the parents’ and teachers’ perceptions of the behaviour of the child without formally considering age, gender, language, or other demographic factors. Depending on the characteristics of each category of data, in theory different effects would be expected. Mea-sures belonging to the first category (IQ) would be expected to reveal gender effects, but no effects of age and study-centre, assuming that socio-cultural differ-ences are reflected in the language-specific normative samples. Measures of the second category (the standar-dized questionnaire scores) would be expected to be free of age and gender effects, but probably not of study-centre effects, because only one (US) normative sample is used. Measures of the third category (the non-standardized questionnaire scores) would be expected to reveal age, gender, and study-centre effects. It is impor-tant to consider that all three predictions concern theo-retical assumptions based on a population of unaffected children. Consequently, we expect our sample to deviate from these assumptions because ADHD is not considered to depend linearly on changes in age, gender and other effects [24-26]. In all three categories, we expect to find clear differ-ences in the effect of the family status between probands and siblings in almost all variables, because the siblings of children with ADHD are known to be affected more strongly than healthy controls but less severely than Page 3 of 17 their brothers and sisters with ADHD (see above). These effects may be overlaid with so called contrast effects in the parent ratings leading to a relative overes-timation of symptoms in the probands compared to their siblings and vice versa [27,28]. Furthermore, based on our symptom-based analyses of the IMAGE sample [17] we expect to find considerable of study-centre. Because these study-centre effects were also found between centres in the same countries, we decided to define centres and not countries as recruiting units in the analysis in both papers. Methods Participants The sample for the present analyses consisted of 1068 probands (938 boys and 130 girls) aged 5 - 17 years with a DSM-IV [29] diagnosis of Attention Deficit Hyperactivity Disorder, Combined Type (ADHD-CT), 1446 unselected siblings (730 boys and 716 girls) in the same age range, and their parents. The participating families were recruited within the International Multi-centre ADHD Genetics (IMAGE) project with 11 parti-cipating centres from 7 European countries and Israel, namely Amsterdam (NLD_A), Dublin (IRL_D), Essen (GER_E), Gent (BEL_G), Göttingen (GER_G), Jerusalem (ISR_J), London and Southampton (ENG_L/S), Nijme-gen (NLD_N), Petah Tiqva (ISR_P), Valencia (ESP_V), and Zürich (SWI_Z) between April 2003 and April 2007. The diagnosis was based on both the Parental Account of Childhood Symptom (PACS) [30-33] and the teacher form of the Conners’ questionnaires (CTRS: R-L) [34]. For a more detailed description of the sample, the study design, the inclusion criteria, and the diagnos-tic protocol see part 1 of this contribution [17]. Measures The children’s behaviour was assessed by teacher and parent forms of the Conners’ questionnaire (CTRS:R-L and CPRS:R-L) [34], the Strengths and Difficulties Ques-tionnaire (SDQ) [20], and by the parent form of the Social Communication Questionnaire (SCQ) [21]. The parent version of the Conners’ questionnaires, the CPRS-R:L [34], contains 80 questions and the teacher version, the CTRS-R:L [35], contains 59 questions which are grouped into the following 14 scales: (1) opposi-tional, (2) cognitive problems/inattention, (3) hyperactiv-ity, (4) anxious/shy, (5) perfectionism, (6) social problems, (7) psychosomatic, (8) Conners’ ADHD index, (9) Conners’ global index: emotional lability, (10) Con-ners’ global index: impulsivity, (11) Conners’ global index: total, (12) DSM-IV ADHD symptoms: inattention, (13) DSM-IV ADHD symptoms: hyperactivity/impulsiv-ity, and (14) DSM-IV ADHD symptoms: total. In the Müller et al. BMC Psychiatry 2011, 11:55 http://www.biomedcentral.com/1471-244X/11/55 present study, standardized scores (T-scores) based on the US normative sample were used [36]. The Strength and Difficulties Questionnaire SDQ [20] comprises 25 questions and allows computation of raw scores for the following five scales: emotional symptoms, conduct problems, hyperactivity (and inattention), peer problems, and prosocial behaviour. The Social Communication Questionnaire SCQ [21] contains 40 questions dealing with autism spectrum dis-order symptoms. The number of positively answered questions adds up to a total score with a cut-off value of 14 for autism spectrum disorder and 21 for classical autism. In addition to the behavioural assessments, intelli-gence was assessed with the WISC-III [37] (age<17) or the WAIS-III [38](age> = 17). The following subtests were assessed: vocabulary, similarities, block design, pic-ture completion, and digit span. Scaled scores of each subtest were calculated using validated versions of the WISC/WAIS according to the language of the test per-son. The intelligence quotient (IQ) was prorated from two verbal subtests (vocabulary and similarities) and two performance subtests (picture completion and block design) using an algorithm based on correlations among the subtests [39]. Digit span was chosen as a measure of working memory. Statistical procedures The distributions of the data in the samples and sub-samples deviated markedly from normality and symme-try and the subsamples had unequal variances and sample sizes, as emphasized in part I [17]. Moreover, comparisons between subsamples (e.g. probands vs. sib-lings) were often skewed in opposite direction. There-fore, in the present contribution we applied methods which are robust to deviations from normality, symme-try, equal sample sizes, and homogeneity of variance. The following statistical procedures were used: - The percentile bootstrap procedure trimpb [40,41], with 2000 bootstrap samples, was applied to com-pute robust confidence intervals for means and trimmed means in R [42]. - Robust three-way analyses were calculated in R [42] by applying the procedure t3way [41,43], a het-eroscedastic method for trimmed means with esti-mates of standard errors and degrees of freedom adjusted for the amount of trimming, unequal var-iances and unequal sample sizes. This method pro-vides a test value (’Q’) which can be used to test null-hypotheses of main effects and interactions and adjusted critical values (’crit.’) for the 1-alpha quan-tile of a chi-square distribution. Page 4 of 17 - Robust post-hoc pairwise comparisons were com-puted in R [42] by using the bootstrap procedure linconb6 [44], an expansion of the procedure lincon [43], which allows unequal variances; 599 bootstrap samples were taken by default; familywise 95% confi-dence intervals, corresponding to a 5% probability of making at least one Type I error when performing multiple tests, were calculated. - The adapted robust ‘between × between × within ANOVA’ procedure bbtwin [41,44] was applied to compare two dependent groups (parents and teacher ratings) when including two dichotomous covariates (gender and family status) with respect to 20% trimmed means. - The residuals of linear regression analyses on age [45] were used instead of raw scores in order to adjust statistics for age effects. - Effect sizes are reported in units of standard devia-tions, calculated by converting the T-scores (Con-ners’ questionnaires), the prorated IQ, and the standard scores of the IQ subscales, or by use of the scores of a British normative sample in the case of the SDQ [46]. Results Conners questionnaires Conners’ questionnaire data were available from 1068 probands with ADHD-CT and their 1446 unselected siblings. The male to female ratios were 7.2:1 for the probands and 1.0:1 for the siblings (for a detailed analy-sis of demographic data, see the companion paper [17]. Table S1 (additional file 1) shows quartiles with 95% confidence intervals of trimmed population means for all Conners’ scales, divided by informant, gender, and family status. Overlaid histograms of the sample distri-butions for each scale of the Conners’ questionnaires, divided by family status and informant, are displayed in Figure S1 (additional file 2). Although the T-scores for the Conners’ subscales are adjusted for age (and gender), there were small, but sig-nificant correlations between age and almost all Con-ners’ T-scores, both in the parents’ (average rho = .06) and in the teachers’ ratings (average rho = .10; see Table 1). The three-way analyses of centre-, status-, and gender effects, therefore, were performed on the basis of age corrected scores (residuals of the scores’ linear regression on age). Status effects (siblings vs. probands) When looking globally at all 14 symptoms, there was a strong average effect of family status as evident in the difference between the teachers’ average trimmed mean scores in probands (66.9) and in siblings (52.9), and even more strongly in the parents’ ratings (70.8 in Table 1 Conners’ Questionnaires: Effects of age, centre, status, and gender Parent ratings Age Centre1° Status° Gender° Centre × Status° Centre × Gender° Status × Gender° Centre × Status × Gender° rho p A 0.063 0.002 B 0.002 0.919 C 0.099 0.000 D 0.076 0.001 E -0.073 0.000 F -0.002 0.938 G 0.020 0.312 H 0.053 0.008 I 0.080 0.000 J 0.125 0.000 K 0.099 0.000 L 0.045 0.027 M 0.072 0.000 N 0.070 0.001 Mean § 0.0628 Q Crit Sig 69.2 22.9 *** 13.8 22.7 12.3 20.8 16.8 25.0 168.0 23.8 *** 31.4 24.7 * 21.6 24.7 32.3 20.7 ** 17.1 21.3 65.7 23.4 *** 17.8 21.8 15.9 22.3 14.8 21.1 10.0 20.6 36.2 22.6 Q Crit Sig 595.9 4.01 *** 881.2 4.05 *** 2001 3.94 *** 82.1 4.05 *** 76.06 4.11 *** 371.3 4.03 *** 69.78 4.03 *** 1327 4.00 *** 1343 3.99 *** 299.7 4.11 *** 1133 4.00 *** 1057 3.98 *** 1850 3.96 *** 1823 3.95 *** 922.1 4.0 Q Crit Sig Q Crit Sig 0.55 4.01 103 22.9 *** 28.7 4.05 *** 81.4 22.7 *** 1.71 3.94 69.0 20.8 *** 3.84 4.05 42.5 25.0 ** 1.74 4.11 32.8 23.8 ** 6.41 4.03 * 43.0 24.7 ** 8.75 4.03 ** 12.4 24.7 19.5 4.00 *** 109 20.7 *** 4.94 3.99 * 95.3 21.3 *** 0.44 4.11 79.4 23.4 *** 1.68 4.00 104 21.8 *** 27.8 3.98 *** 108 22.3 *** 3.90 3.96 66.1 21.1 *** 17.5 3.95 *** 101 20.6 *** 9.1 4.0 74.7 22.6 Q Crit Sig Q 17.9 22.9 16.5 6.87 22.7 70.3 8.43 20.8 53.1 8.42 25.0 14.6 14.1 23.8 1.67 24.0 24.7 15.6 14.2 24.7 6.22 9.5 20.7 90.6 3.08 21.3 59.0 15.7 23.4 12.9 6.29 21.8 50.2 6.95 22.3 78.0 6.79 21.1 46.7 8.14 20.6 84.9 10.7 22.6 42.9 Crit Sig Q Crit Sig 4.01 *** 22.0 22.9 4.05 *** 10.6 22.7 3.94 *** 14.3 20.8 4.05 *** 11.8 25.0 4.11 17.7 23.8 4.03 *** 26.6 24.7 * 4.03 * 6.69 24.7 4.00 *** 20.5 20.7 3.99 *** 7.79 21.3 4.11 ** 18.5 23.4 4.00 *** 11.8 21.8 3.98 *** 15.3 22.3 3.96 *** 11.2 21.1 3.95 *** 16.9 20.6 4.0 15.1 22.6 Teacher ratings Age Centre° Status° Gender° Centre × Status° Centre × Gender° Status × Gender° Centre × Status × Gender° rho p A 0.060 0.004 B 0.122 0.000 C 0.114 0.000 D 0.151 0.000 E -0.024 0.254 F 0.057 0.006 H 0.134 0.000 I 0.115 0.000 J 0.087 0.000 K 0.102 0.000 Q Crit Sig 85.5 25.1 *** 55.2 23.8 *** 32.9 22.1 ** 30.3 26.1 * 104 22.2 *** 30.3 25.1 * 74.0 21.3 *** 43.1 21.0 *** 48.3 22.7 *** 59.4 21.0 *** Q Crit Sig 159.7 4.22 *** 235.1 3.97 *** 560.0 3.96 *** 43.42 4.27 *** 39.09 4.02 *** 146.2 4.20 *** 647.7 3.95 *** 622.8 3.98 *** 224.2 3.96 *** 618.4 3.94 *** Q Crit Sig Q 0.68 4.22 19.5 9.94 3.97 ** 12.1 33.7 3.96 *** 10.4 1.06 4.27 4.6 6.82 4.02 * 8.8 6.23 4.20 * 24.9 30.9 3.95 *** 8.8 27.0 3.98 *** 11.6 0.01 3.96 36.5 14.1 3.94 *** 11.5 Crit Sig 25.1 23.8 22.1 26.1 22.2 25.1 21.3 21.0 22.7 ** 21.0 Q Crit Sig Q 8.28 25.1 4.90 16.3 23.8 18.4 19.8 22.1 29.8 5.82 26.1 1.64 9.66 22.2 1.54 40.3 25.1 ** 6.63 24.97 21.3 * 44.3 27.2 21.0 * 43.1 51.4 22.7 *** 2.58 27.6 21.0 * 30.1 Crit Sig Q Crit Sig 4.22 * 11.2 25.1 3.97 *** 10.5 23.8 3.96 *** 18.5 22.1 4.27 9.6 26.1 4.02 6.90 22.2 4.20 * 37.1 25.1 ** 3.95 *** 16.4 21.3 3.98 *** 14.4 21.0 3.96 59.4 22.7 *** 3.94 *** 17.6 21.0 ... - tailieumienphi.vn
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