Polygenic risk for schizophrenia and neurocognitive performance in patients with schizophrenia

Both neurocognitive deficits and schizophrenia are highly heritable. Genetic overlap between neurocognitive deficits and schizophrenia has been observed in both the general population and in the clinical samples. This study aimed to examine if the polygenic architecture of susceptibility to schizophrenia modified neurocognitive performance in schizophrenia patients. Schizophrenia polygenic risk scores (PRSs) were first derived from the Psychiatric Genomics Consortium (PGC) on schizophrenia, and then the scores were calculated in our independent sample of 1130 schizophrenia trios, who had PsychChip data and were part of the Schizophrenia Families from Taiwan project. Pseudocontrols generated from the nontransmitted parental alleles of the parents in these trios were compared with alleles in schizophrenia patients in assessing the replicability of PGC-derived susceptibility variants. Schizophrenia PRS at the P-value threshold (PT) of 0.1 explained 0.2% in the variance of disease status in this Han-Taiwanese samples, and the score itself had a P-value 0.05 for the association test with the disorder. Each patient underwent neurocognitive evaluation on sustained attention using the continuous performance test and executive function using the Wisconsin Card Sorting Test. We applied a structural equation model to construct the neurocognitive latent variable estimated from multiple measured indices in these 2 tests, and then tested the association between the PRS and the neurocognitive latent variable. Higher schizophrenia PRS generated at the PT of 0.1 was significantly associated with poorer neurocognitive performance with explained variance 0.5%. Our findings indicated that schizophrenia susceptibility variants modify the neurocognitive performance in schizophrenia patients.

Using linkage information to weight a genome-wide association of bipolar disorder

Issues of multiple-testing and statistical significance in genomewide association studies (GWAS) have prompted statistical methods utilizing prior data to increase the power of association results. Using prior findings from genome-wide linkage studies on bipolar disorder (BPD), we employed a weighted false discovery approach (wFDR; [Roeder et al. 2006. Am J Hum Genet 78(2): 243–252]) to previously reported GWAS data drawn from the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Using this method, association signals are up or down-weighted given the linkage score in that genomic region. Although no SNPs in our sample reached genome-wide significance through the wFDR approach, the strongest single SNP result from the original GWAS results (rs4939921 in myosin VB) is strongly up-weighted as it occurs on a linkage peak of chromosome 18. We also identify regions on chromosome 9, 17, and 18 where modestly associated SNP clusters coincide with strong linkage scores, implicating them as possible candidate regions for further analysis. Moving forward, we believe the application of prior linkage information will be increasingly useful to future GWAS studies that incorporate rarer variants into their analysis.

Fitting ACE structural equation models to case-control family data

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for members of families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). We describe an ACE model for binary family data; this structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. We then introduce our contribution, a likelihood-based approach to fitting the model to singly ascertained case-control family data. The approach, which involves conditioning on the proband's disease status and also setting prevalence equal to a prespecified value that can be estimated from the data, makes it possible to obtain valid estimates of the A, C, and E variance components from case-control (rather than only from population-based) family data. In fact, simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly made assumptions hold. Further, when our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.

Obesity and weight gain in relation to depression: findings from the Stirling County Study

OBJECTIVE

This study concerns the question of whether obese subjects in a community sample experience depression in a different way from the nonobese, especially whether they overeat to the point of gaining weight during periods of depression.

DESIGN

A representative sample of adults was interviewed regarding depression and obesity.

SUBJECTS

The sample consisted of 1396 subjects whose interviews were studied regarding relationships between obesity and depression and among whom 114 had experienced a major depressive episode at some point in their lives and provided information about the symptoms experienced during the worst or only episode of major depression.

MEASUREMENTS

The Diagnostic Interview Schedule (DIS) was used to identify major depressive episodes. Information was also derived from the section on Depression and Anxiety (DPAX) of the Stirling Study Schedule. Obesity was calculated as a body mass index >30. Logistic regressions were employed to assess relationships, controlling for age and gender, by means of odds ratios and 95% confidence intervals.

RESULTS

In the sample as a whole, obesity was not related to depression although it was associated with the symptom of hopelessness. Among those who had ever experienced a major depressive episode, obese persons were 5 times more likely than the nonobese to overeat leading to weight gain during a period of depression (P<0.002). These obese subjects, compared to the nonobese, also experienced longer episodes of depression, a larger number of episodes, and were more preoccupied with death during such episodes.

CONCLUSIONS

Depression among obese subjects in a community sample tends to be more severe than among the nonobese. Gaining weight while depressed is an important marker of that severity. Further research is needed to understand and possibly prevent the associations, sequences and outcomes among depression, obesity, weight gain and other adversities.

A new statistical screening approach for finding pharmacokinetics-related genes in genome-wide studies

Biomedical researchers usually test the null hypothesis that there is no difference of the population mean of pharmacokinetics (PK) parameters between genotypes by the Kruskal-Wallis test. Although a monotone increasing pattern with a number of alleles is expected for PK-related genes, the Kruskal-Wallis test does not consider a monotonic response pattern. For detecting such patterns in clinical and toxicological trials, a maximum contrast method has been proposed. We show how that method can be used with pharmacogenomics data to a develop test of association. Further, using simulation studies, we compare the power of the modified maximum contrast method to those of the maximum contrast method and the Kruskal-Wallis test. On the basis of the results of those studies, we suggest rules of thumb for which statistics to use in a given situation. An application of all three methods to an actual genome-wide pharmacogenomics study illustrates the practical relevance of our discussion.

Evidence for an Association of the Dopamine D5 Receptor Gene on Age at Onset of Attention Deficit Hyperactivity Disorder

The purpose of this study was to determine whether the single nucleotide polymorphisms (SNPs) within candidate genes for attention deficit hyperactivity disorder (ADHD) are associated with the age at onset for ADHD. One hundred and forty-three SNPs were genotyped across five candidate genes (DRD5, SLC6A3, HTR1B, SNAP25, DRD4) for ADHD in 229 families with at least one affected offspring. SNPs with the highest estimated power to detect an association with age at onset were selected for each candidate gene, using a power-based screening procedure that does not compromise the nominal significance level. A time-to-onset analysis for family-based samples was performed on these SNPs to determine if an association exists with age at onset for ADHD. Seven consecutive SNPs surrounding the D5 dopamine receptor gene (DRD5), were associated with the age at onset for ADHD; FDR adjusted q-values ranged from 0.008 to 0.023. This analysis indicates that individuals with the risk genotype develop ADHD earlier than individuals with any other genotype. A haplotype analysis across the 6 significant SNPs that were in linkage disequilibrium with one another, CTCATA, was also found to be significant (p-value = 0.02). We did not observe significant associations with age at onset for the other candidate loci tested. Although definitive conclusions await independent replication, these results suggest that a variant in DRD5 may affect age at onset for ADHD.

A study of how socioeconomic status moderates the relationship between SNPs encompassing BDNF and ADHD symptom counts in ADHD families

Recent animal research suggests that brain-derived neurotrophic factor (BDNF), may mediate response to different environmental stimuli. In this paper, we evaluated the possible role of BDNF as a moderator of attention deficit hyperactivity disorder (ADHD) in the context of different socioeconomic classes. We genotyped ten single nucleotide polymorphisms (SNPs) in and around BDNF in 229 families and evaluate whether there are SNP-by-socioeconomic status (SES) interactions for attention deficit hyperactivity. We developed three quantitative phenotypes for ADHD from nine inattentive and nine hyperactive-impulsive symptoms that were used in SNP-by-SES interaction analyses using a new methodology implemented in the computer program PBAT. Findings were adjusted for multiple comparisons using the false discovery rate. We found multiple significant SNP-by-SES interactions using the inattentive symptom count. This study suggests that different SES classes may modify the effect of the functional variant(s) in and around BDNF to have an impact on the number of ADHD symptom counts that are observed. The two exons within BDNF represent potential functional variants that may be causing the observed associations.

Approaches to handling incomplete data in family-based association testing

The high throughput of data arising from the complete sequence of the human genome has left statistical geneticists with a rich and extensive information source. The wide availability of software and the increase in computing power has improved the possibilities to access and process such data. One problem is incompleteness of the data: unobserved or partially observed data points due to technical reasons or reasons associated with the patient's status or erroneous measurements of phenotype or genotype, to name a few. When not properly accounted for, these sources of incompleteness may seriously jeopardize the credibility of results from analyses. In this paper we provide some perspectives on the occurrence and analysis of different forms of incomplete data in family-based genetic association testing.

Tests of gene-environment interaction for case-parent triads with general environmental exposures

As knowledge of the human genome continues to grow, more progress is being made towards not only identifying the genes involved in disease susceptibility but also in defining the synergistic role genes play with environmental exposures. The detection of gene-environment interactions is important as it can offer clinicians a potential means of intervention. The discovery of interactions relies heavily on powerful statistical methods. We present a test, FBAT-I, that can be used to investigate gene-environment interaction. The test uses the case-parent triad design and protects the statistical inference from potential spurious results due to population admixture.

Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. 1991

BACKGROUND

As part of an interdisciplinary study of medical injury and malpractice litigation, we estimated the incidence of adverse events, defined as injuries caused by medical management, and of the subgroup of such injuries that resulted from negligent or substandard care.

METHODS

We reviewed 30121 randomly selected records from 51 randomly selected acute care, non-psychiatric hospitals in New York State in 1984. We then developed population estimates of injuries and computed rates according to the age and sex of the patients as well as the specialties of the physicians.

RESULTS

Adverse events occurred in 3.7% of the hospitalizations (95% confidence interval 3.2 to 4.2), and 27.6% of the adverse events were due to negligence (95% confidence interval 22.5 to 32.6). Although 70.5% of the adverse events gave rise to disability lasting less than 6 months, 2.6% caused permanently disabling injuries and 13.6% led to death. The percentage of adverse events attributable to negligence increased in the categories of more severe injuries (Wald test chi(2) = 21.04, p<0.0001). Using weighted totals we estimated that among the 2671863 patients discharged from New York hospitals in 1984 there were 98609 adverse events and 27179 adverse events involving negligence. Rates of adverse events rose with age (p<0.0001). The percentage of adverse events due to negligence was markedly higher among the elderly (p<0.01). There were significant differences in rates of adverse events among categories of clinical specialties (p<0.0001), but no differences in the percentage due to negligence.

CONCLUSIONS

There is a substantial amount of injury to patients from medical management, and many injuries are the result of substandard care.

Anxiety and depression: a 40-year perspective on relationships regarding prevalence, distribution, and comorbidity

OBJECTIVE

Building on a report about the prevalence of depression over time, this paper examines historical trends regarding anxiety in terms of its prevalence, its distribution by age and gender, and its comorbidity with depression. Methods for conducting such time trend analysis are reviewed.

METHOD

Representative samples of adults were selected and interviewed in 1952, 1970, and 1992. Logistic regressions were used for statistical analysis.

RESULTS

Although twice as common as depression, the prevalence of anxiety was equally stable. Anxiety was consistently and significantly more characteristic of women than men. A re-distribution of rates in 1992 indicated that depression but not anxiety had significantly increased among younger women (P = 0.03). Throughout the study, approximately half of the cases of anxiety also suffered depression.

CONCLUSION

The relationships between anxiety and depression remained similar over time with the exception that depression came to resemble anxiety as a disorder to which women were significantly more vulnerable than men. Social and historical factors should be investigated to assess their relevance to this change.

Estimation and tests of haplotype-environment interaction when linkage phase is ambiguous

In the study of complex traits, the utility of linkage analysis and single marker association tests can be limited for researchers attempting to elucidate the complex interplay between a gene and environmental covariates. For these purposes, tests of gene-environment interactions are needed. In addition, recent studies have indicated that haplotypes, which are specific combinations of nucleotides on the same chromosome, may be more suitable as the unit of analysis for statistical tests than single genetic markers. The difficulty with this approach is that, in standard laboratory genotyping, haplotypes are often not directly observable. Instead, unphased marker phenotypes are collected. In this article, we present a method for estimating and testing haplotype-environment interactions when linkage phase is potentially ambiguous. The method builds on the work of Schaid et al. [2002] and is applicable to any trait that can be placed in the generalized linear model framework. Simulations were run to illustrate the salient features of the method. In addition, the method was used to test for haplotype-smoking exposure interaction with data from the Childhood Asthma Management Program.

Family study of the aggregation of eating disorders and mood disorders

BACKGROUND

Family studies have suggested that eating disorders and mood disorders may coaggregate in families. To study further this question, data from a family interview study of probands with and without major depressive disorder was examined.

METHOD

A bivariate proband predictive logistic regression model was applied to data from a family interview study, conducted in Innsbruck, Austria, of probands with (N = 64) and without (N = 58) major depressive disorder, together with 330 of their first-degree relatives.

RESULTS

The estimated odds ratio (OR) for the familial aggregation of eating disorders (anorexia nervosa, bulimia nervosa and binge-eating disorder) was 7.0 (95 % CI 1.4, 28; P = 0.006); the OR for the familial aggregation of mood disorders (major depression and bipolar disorder) was 2.2 (0.92, 5.4; P = 0.076); and for the familial coaggregation of eating disorders with mood disorders the OR was 2.2 (1.1, 4.6; P = 0.035).

CONCLUSIONS

The familial coaggregation of eating disorders with mood disorders was significant and of the same magnitude as the aggregation of mood disorders alone--suggesting that eating disorders and mood disorders have common familial causal factors.

Univariate and multivariate family-based association analysis of the IL-13 ARG130GLN polymorphism in the Childhood Asthma Management Program

Interleukin 13 (IL-13) has been demonstrated to have a crucial role in animal models of allergy and asthma. In human case-control genetic-association studies, the Arg130Gln polymorphism has been associated with elevated total serum IgE and an asthma diagnosis in atopic and nonatopic individuals (Graves et al. [2000] J. Allergy Clin. Immunol. 105:506-513; Heinzmann et al. [2000] Hum. Mol. Genet. 9:549-559). To apply family-based association methods, we obtained DNA samples from 685 asthmatic children from 640 sibships and their parents in the Childhood Asthma Management Program (CAMP). Six hundred and sixty-six asthmatic children had complete phenotypic information and were used for this analysis. We performed quantitative association analysis using the transmission disequilibrium test (TDT) on 22 individual phenotypes and 5 grouped phenotypes relating to allergy, airway responsiveness, pulmonary function, bronchodilator responsiveness, and asthma severity, using genotypes at the Arg130Gln polymorphism of the IL-13 gene. A positive association was obtained between Arg130Gln and a grouped phenotype of allergy (consisting of the individual phenotypes of eosinophils, IgE, and positive skin tests), using FBAT-GEE, a multivariate extension of the family-based association test (Lange et al. [2002] Biostatistics 1:1-15). The three phenotypes were then evaluated individually and revealed a significant association between total eosinophil count and the Arg130Gln locus; there was a trend for association between total IgE and the Arg130Gln polymorphism. The Arg130Gln polymorphism is associated with an elevated eosinophil count as well as with a grouped allergy phenotype, in children with mild to moderate asthma. No evidence for association was found between Arg130Gln and airway responsiveness, asthma diagnosis, or asthma severity.

Genetic association analysis of behavioral inhibition using candidate loci from mouse models

Genes influence the development of anxiety disorders, but the specific loci involved are not known. Genetic association studies of anxiety disorders are complicated by the complexity of the phenotypes and the difficulty in identifying appropriate candidate loci. We have begun to examine the genetics of behavioral inhibition to the unfamiliar (BI), a heritable temperamental predisposition that is a developmental and familial risk factor for panic and phobic disorders. Specific loci associated with homologous phenotypes in mouse models provide compelling candidate genes for human BI. We conducted family-based association analyses of BI using four genes derived from genetic studies of mouse models with features of behavioral inhibition. The sample included families of 72 children classified as inhibited by structured behavioral assessments. We observed modest evidence of association (P = 0.05) between BI and the glutamic acid decarboxylase gene (65 kDA isoform), which encodes an enzyme involved in GABA synthesis. No significant evidence of association was observed for the genes encoding the adenosine A(1A) receptor, the adenosine A(2A) receptor, or preproenkephalin. This study illustrates the potential utility of using candidate genes derived from mouse models to dissect the genetic basis of BI, a possible intermediate phenotype for panic and phobic disorders.

Tutorial in Biostatistics: Evaluating the impact of 'critical periods' in longitudinal studies of growth using piecewise mixed effects models

Recent developments in modern multivariate methods provide applied researchers with the means to address many important research questions that arise in studies with repeated measures data collected on individuals over time. One such area of applied research is focused on studying change associated with some event or critical period in human development. This tutorial deals with the use of the general linear mixed model for regression analysis of correlated data with a two-piece linear function of time corresponding to the pre- and post-event trends. The model assumes a continuous outcome is linearly related to a set of explanatory variables, but allows for the trend after the event to be different from the trend before it. This task can be accomplished using a piecewise linear random effects model for longitudinal data where the response depends upon time of the event. A detailed example that examines the influence of menarche on changes in body fat accretion will be presented using data from a prospective study of 162 girls measured annually from approximately age 10 until 4 years post menarche.

Multiple informants: mortality associated with psychiatric disorders in the Stirling County Study

This paper applies new statistical procedures for analyzing multiple-source information about the relation of psychiatric diagnoses to mortality. The data come from the Stirling County Study, a longitudinal community investigation of adults, that collected multiple-source reports (self-report and physician-report) about psychiatric disorders. These reports are used as predictors of mortality risk over a 16-year follow-up period (1952-1968). Despite extensive efforts, one or both of these reports were sometimes missing. Missingness of self-report was related to demographic characteristics as well as to physician-reports of psychiatric diagnosis. The statistical procedures used here draw together into a single frame of reference both informant reports for the initial Stirling survey and relate these to mortality risk using weighted generalized estimating equation regression models for time to event data. This unified method has two advantages over traditional approaches: 1) the relative predictiveness of each informant can be assessed and 2) all subjects contribute to the analysis. The methods are applicable to other areas of epidemiology where multiple informant reports are used. The results for self-reports and physician-reports of disorders were comparable: Psychiatric diagnosis was associated with higher mortality, particularly among younger subjects.

Impact of overweight on the risk of developing common chronic diseases during a 10-year period

BACKGROUND

Overweight adults are at an increased risk of developing numerous chronic diseases.

METHODS

Ten-year follow-up (1986-1996) of middle-aged women in the Nurses' Health Study and men in the Health Professionals Follow-up Study to assess the health risks associated with overweight.

RESULTS

The risk of developing diabetes, gallstones, hypertension, heart disease, and stroke increased with severity of overweight among both women and men. Compared with their same-sex peers with a body mass index (BMI) (calculated as weight in kilograms divided by the square of height in meters) between 18.5 and 24.9, those with BMI of 35.0 or more were approximately 20 times more likely to develop diabetes (relative risk [RR], 17.0; 95% confidence interval [CI], 14.2-20.5 for women; RR, 23.4; 95% CI, 19.4-33.2 for men). Women who were overweight but not obese (ie, BMI between 25.0 and 29.9) were also significantly more likely than their leaner peers to develop gallstones (RR, 1.9), hypertension (RR, 1.7), high cholesterol level (RR, 1.1), and heart disease (RR, 1.4). The results were similar in men.

CONCLUSIONS

During 10 years of follow-up, the incidence of diabetes, gallstones, hypertension, heart disease, colon cancer, and stroke (men only) increased with degree of overweight in both men and women. Adults who were overweight but not obese (ie, 25.0 < or = BMI < or = 29.9) were at significantly increased risk of developing numerous health conditions. Moreover, the dose-response relationship between BMI and the risk of developing chronic diseases was evident even among adults in the upper half of the healthy weight range (ie, BMI of 22.0-24.9), suggesting that adults should try to maintain a BMI between 18.5 and 21.9 to minimize their risk of disease.

An alternative parameterization of the general linear mixture model for longitudinal data with non-ignorable drop-outs

This paper considers the mixture model methodology for handling non-ignorable drop-outs in longitudinal studies with continuous outcomes. Recently, Hogan and Laird have developed a mixture model for non-ignorable drop-outs which is a standard linear mixed effects model except that the parameters which characterize change over time depend also upon time of drop-out. That is, the mean response is linear in time, other covariates and drop-out time, and their interactions. One of the key attractions of the mixture modelling approach to drop-outs is that it is relatively easy to explore the sensitivity of results to model specification. However, the main drawback of mixture models is that the parameters that are ordinarily of interest are not immediately available, but require marginalization of the distribution of outcome over drop-out times. Furthermore, although a linear model is assumed for the conditional mean of the outcome vector given time of drop out, after marginalization, the unconditional mean of the outcome vector is not, in general, linear in the regression parameters. As a result, it is not possible to parsimoniously describe the effects of covariates on the marginal distribution of the outcome in terms of regression coefficients. The need to explicitly average over the distribution of the drop-out times and the absence of regression coefficients that describe the effects of covariates on the outcome are two unappealing features of the mixture modelling approach. In this paper we describe a particular parameterization of the general linear mixture model that circumvents both of these problems.

The family based association test method: strategies for studying general genotype--phenotype associations

With possibly incomplete nuclear families, the family based association test (FBAT) method allows one to evaluate any test statistic that can be expressed as the sum of products (covariance) between an arbitrary function of an offspring's genotype with an arbitrary function of the offspring's phenotype. We derive expressions needed to calculate the mean and variance of these test statistics under the null hypothesis of no linkage. To give some guidance on using the FBAT method, we present three simple data analysis strategies for different phenotypes: dichotomous (affection status), quantitative and censored (eg, the age of onset). We illustrate the approach by applying it to candidate gene data of the NIMH Alzheimer Disease Initiative. We show that the RC-TDT is equivalent to a special case of the FBAT method. This result allows us to generalise the RC-TDT to dominant, recessive and multi-allelic marker codings. Simulations compare the resulting FBAT tests to the RC-TDT and the S-TDT. The FBAT software is freely available.

Multivariate logistic regression for familial aggregation of two disorders. II. Analysis of studies of eating and mood disorders

Family studies have suggested that eating disorders and mood disorders may coaggregate within families. Previous studies, however, have been limited by use of univariate modeling techniques and failure to account for the correlation of observations within families. To provide a more efficient analysis and to illustrate multivariate logistic regression models for familial aggregation of two disorders, the authors analyzed pooled data from two previously published family studies (conducted in Massachusetts in 1984-1986 and 1986-1987) by using multivariate proband predictive and family predictive models. Both models demonstrated a significant familial aggregation of mood disorders and familial coaggregation of eating and mood disorders. The magnitude of the coaggregation between eating and mood disorders was similar to that of the aggregation of mood disorders. Similar results were obtained with alternative models, including a traditional univariate proband predictive model. In comparison with the univariate model, the multivariate models provided greater flexibility, improved precision, and wider generality for interpreting aggregation effects.

Maximum likelihood analysis of logistic regression models with incomplete covariate data and auxiliary information

This article presents a new method for maximum likelihood estimation of logistic regression models with incomplete covariate data where auxiliary information is available. This auxiliary information is extraneous to the regression model of interest but predictive of the covariate with missing data. Ibrahim (1990, Journal of the American Statistical Association 85, 765-769) provides a general method for estimating generalized linear regression models with missing covariates using the EM algorithm that is easily implemented when there is no auxiliary data. Vach (1997, Statistics in Medicine 16, 57-72) describes how the method can be extended when the outcome and auxiliary data are conditionally independent given the covariates in the model. The method allows the incorporation of auxiliary data without making the conditional independence assumption. We suggest tests of conditional independence and compare the performance of several estimators in an example concerning mental health service utilization in children. Using an artificial dataset, we compare the performance of several estimators when auxiliary data are available.

Multivariate logistic regression for familial aggregation of two disorders. I. Development of models and methods

The question of whether two disorders cluster together, or coaggregate, within families often arises. This paper considers how to analyze familial aggregation of two disorders and presents two multivariate logistic regression methods that model both disorder outcomes simultaneously. The first, a proband predictive model, predicts a relative's outcomes (the presence or absence of each of the two disorders) by using the proband's disorder status. The second, a family predictive model derived from the quadratic exponential model, predicts a family member's outcomes by using all of the remaining family members' disorder statuses. The models are more realistic, flexible, and powerful than univariate models. Methods for estimation and testing account for the correlation of outcomes among family members and can be implemented by using commercial software.

Implementing a unified approach to family-based tests of association

We describe a broad class of family-based association tests that are adjusted for admixture; use either dichotomous or measured phenotypes; accommodate phenotype-unknown subjects; use nuclear families, sibships or a combination of the two, permit multiple nuclear families from a single pedigree; incorporate di- or multi-allelic marker data; allow additive, dominant or recessive models; and permit adjustment for covariates and gene-by-environment interactions. The test statistic is basically the covariance between a user-specified function of the genotype and a user-specified function of the trait. The distribution of the statistic is computed using the appropriate conditional distribution of offspring genotypes that adjusts for admixture.

Family-based tests of association in the presence of linkage

Linkage analysis may not provide the necessary resolution for identification of the genes underlying phenotypic variation. This is especially true for gene-mapping studies that focus on complex diseases that do not exhibit Mendelian inheritance patterns. One positional genomic strategy involves application of association methodology to areas of identified linkage. Detection of association in the presence of linkage localizes the gene(s) of interest to more-refined regions in the genome than is possible through linkage analysis alone. This strategy introduces a statistical complexity when family-based association tests are used: the marker genotypes among siblings are correlated in linked regions. Ignoring this correlation will compromise the size of the statistical hypothesis test, thus clouding the interpretation of test results. We present a method for computing the expectation of a wide range of association test statistics under the null hypothesis that there is linkage but no association. To standardize the test statistic, an empirical variance-covariance estimator that is robust to the sibling marker-genotype correlation is used. This method is widely applicable: any type of phenotypic measure or family configuration can be used. For example, we analyze a deletion in the A2M gene at the 5' splice site of "exon II" of the bait region in Alzheimer disease (AD) discordant sibships. Since the A2M gene lies in a chromosomal region (chromosome 12p) that consistently has been linked to AD, association tests should be conducted under the null hypothesis that there is linkage but no association.

[Validity and reproducibility of a questionnaire on physical activity and non-activity for school children in Mexico City]

OBJECTIVE

To assess the validity and reproducibility of a self-reported questionnaire on physical activity and inactivity, developed for children aged 10-14 in Mexico City.

MATERIAL AND METHODS

Between May and December 1996, a self-reported physical activity and inactivity questionnaire was developed and applied twice to a sample of 114 students aged 10 to 14, from a low and middle income population of Mexico City. The children's mothers completed the same questionnaire, and two 24-hour recalls of physical activity were used for comparison. Statistical analysis consisted of central tendency and dispersion measures and Pearson's correlation coefficient.

RESULTS

Correlations between hours per day spent in physical activity and inactivity from the children's questionnaire and the 24-hour recall data, were 0.03 for moderate activity, 0.15 for vigorous activity, and 0.51 (p = 0.001) for watching television, adjusted by age, gender, town, and illness prior to the administration of the questionnaire. Compared to the 24-hour recall data, the questionnaire overestimated the time spent watching television, reading or participating in vigorous activity, and underestimated the time engaged in moderate activity. Statistically significant (p < 0.05) six-month reproducibility values were observed for watching television (r = 0.53), sleeping (r = 0.40), moderate (r = 0.38), and vigorous activity (r = 0.55).

CONCLUSIONS

Among children of Mexico City aged 10-14, the questionnaire showed acceptable validity in estimating the time watching television, and acceptable reproducibility of the time watching television, vigorous and moderate activity.

A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information

A general approach to family-based examinations of association between marker alleles and traits is proposed. The approach is based on computing p values by comparing test statistics for association to their conditional distributions given the minimal sufficient statistic under the null hypothesis for the genetic model, sampling plan and population admixture. The approach can be applied with any test statistic, so any kind of phenotype and multi-allelic markers may be examined, and covariates may be included in analyses. By virtue of the conditioning, the approach results in correct type I error probabilities regardless of population admixture, the true genetic model and the sampling strategy. An algorithm for computing the conditional distributions is described, and the results of the algorithm for configurations of nuclear families are presented. The algorithm is applicable with all pedigree structures and all patterns of missing marker allele information.

Probabilistic diagnosis in linkage analysis of bipolar disorder: putting weights on the fringe

We explored the utility of probabilistic weighting of fringe phenotypes in linkage analysis of bipolar disorder for the GAW10 chromosome 18 data. Four liability classes were assigned probabilistic weights based on the estimated probability that the case was a true bipolar. The weights were incorporated in parametric and nonparametric, single and multipoint analyses.

A mixture model for longitudinal data with application to assessment of noncompliance

In clinical trials of a self-administered drug, repeated measures of a laboratory marker, which is affected by study medication and collected in all treatment arms, can provide valuable information on population and individual summaries of compliance. In this paper, we introduce a general finite mixture of nonlinear hierarchical models that allows estimates of component membership probabilities and random effect distributions for longitudinal data arising from multiple subpopulations, such as from noncomplying and complying subgroups in clinical trials. We outline a sampling strategy for fitting these models, which consists of a sequence of Gibbs, Metropolis-Hastings, and reversible jump steps, where the latter is required for switching between component models of different dimensions. Our model is applied to identify noncomplying subjects in the placebo arm of a clinical trial assessing the effectiveness of zidovudine (AZT) in the treatment of patients with HIV, where noncompliance was defined as initiation of AZT during the trial without the investigators' knowledge. We fit a hierarchical nonlinear change-point model for increases in the marker MCV (mean corpuscular volume of erythrocytes) for subjects who noncomply and a constant mean random effects model for those who comply. As part of our fully Bayesian analysis, we assess the sensitivity of conclusions to prior and modeling assumptions and demonstrate how external information and covariates can be incorporated to distinguish subgroups.

Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies

This paper presents a method for analysing longitudinal data when there are dropouts. In particular, we develop a simple method based on generalized linear mixture models for handling nonignorable dropouts for a variety of discrete and continuous outcomes. Statistical inference for the model parameters is based on a generalized estimating equations (GEE) approach (Liang and Zeger, 1986). The proposed method yields estimates of the model parameters that are valid when nonresponse is nonignorable under a variety of assumptions concerning the dropout process. Furthermore, the proposed method can be implemented using widely available statistical software. Finally, an example using data from a clinical trial of contracepting women is used to illustrate the methodology.

Incidence of depression in the Stirling County Study: historical and comparative perspectives

BACKGROUND

The Stirling County Study provides a 40-year perspective on the epidemiology of psychiatric disorders in an adult population in Atlantic Canada. Across samples selected in 1952, 1970 and 1992 current prevalence of depression was stable. This paper concerns time trends in annual incidence as assessed through cohorts selected from the first two samples.

METHODS

Consistent interview data were analysed by a computerized diagnostic algorithm. The cohorts consisted of subjects at risk for a first depression: Cohort-1 (N = 575) was followed 1952-1970; Cohort-2 (N = 639) was followed 1970-1992. Life-table methods were used to calculate incidence rates and proportional hazards procedures were used for statistical assessment.

RESULTS

Average annual incidence of depression was 4.5 per 1000 for Cohort-1 and 3.7 for Cohort-2. Differences by gender, age and time were not statistically significant. The stability of incidence and the similarity of distribution by gender and age in these two cohorts corresponds to findings about the two early samples. In contrast, current prevalence in the recent sample was distributed differently and showed an increase among women under 45 years.

CONCLUSIONS

The stability of the incidence of depression emphasizes the distinctive characteristics of current prevalence in the recent sample and suggests that the dominance of women in rates of depression may have occurred among those born after the Second World War. The results offer partial support for the interpretation of an increase in depression based on retrospective data in other recent studies but they indicate that the increase is specific to women.

A 40-year perspective on the prevalence of depression: the Stirling County Study

BACKGROUND

According to epidemiologic studies that use recall of lifetime episodes, the prevalence of depression is increasing. This report from the Stirling County Study compares rates of current depression among representative samples of adults from a population in Atlantic Canada.

METHODS

Sample sizes were 1003, 1201, and 1396 in 1952, 1970, and 1992, respectively. The depression component of the study's method, the DPAX (DP for depression and AX for anxiety), was employed. The original procedure (DPAX-1) was applied in all years. A revision (DPAX-2) was used in 1970 and 1992. The Diagnostic Interview Schedule (DIS) was also used in 1992.

RESULTS

With the DPAX-1, the overall prevalence of current depression was steady at 5% over the 2 early samples but declined in 1992 because of vernacular changes referring to dysphoria. The DPAX-2 gave a stable overall prevalence of 5% in the 2 recent samples, but indicated that women and younger people were at greater risk in 1992 than in 1970. The DIS, like the DPAX-2, found a current 1992 rate of 5% for major depressive episodes combined with dysthymia. Recalled lifetime rates using the DIS showed the same profile interpreted in other studies as suggesting an increase in depression over time.

CONCLUSIONS

Three samples over a 40-year period showed a stable current prevalence of depression using the DPAX methods that was comparable in 1992 with the current rates using the DIS. This casts doubt on the interpretation that depression is generally increasing. Within the overall steady rate observed in this study, historical change was a matter of redistribution by sex and age, with a higher rate among younger women being of recent origin.

A comparison of diagnostic interviews for depression in the Stirling County study: challenges for psychiatric epidemiology

BACKGROUND

High prevalence rates in psychiatric epidemiologic studies raise questions about whether data-gathering procedures identify transient responses rather than clinical disorders. This issue is explored relevant to depression using data from the Stirling County Study.

METHODS

The study's customary method, the DPAX (DP for depression and AX for anxiety) was compared with the Diagnostic Interview Schedule (DIS), both of which were administered to a sample of 1396 subjects selected in 1992. Reasons for discordance were analyzed, and demographic correlates of responses to questions about dysphoria were examined. These lay-administered interviews were then compared with clinician-administered interviews that used the Structured Clinical Interview for DSM-III-R (SCID) with 139 subjects. The kappa statistic and logistic regression were used for statistical assessment.

RESULTS

For the level of agreement between the DPAX and the DIS for current and lifetime depression, kappa = 0.40 and kappa = 0.33, respectively. Subjects diagnosed only by the DPAX tended to have less education than those diagnosed only by the DIS. Some idioms for dysphoria seemed to work better than others. Using SCID interviews as a clinical standard, the DPAX had 15% sensitivity and 96% specificity and the DIS had 25% sensitivity and 98% specificity.

CONCLUSIONS

Comprehension of an interview can be improved by using multiple questions for dysphoria and a simpler mode of inquiry. Clinician-administered interviews tend to corroborate disorders identified in lay-administered interviews but suggest that survey methods underestimate prevalence. Further research is needed to evaluate the validity of both types of interviews, but evidence from a 16-year follow-up evaluation indicates that depression diagnosed by the DPAX is a serious disorder in terms of morbidity and mortality.

Family-based tests of association and linkage that use unaffected sibs, covariates, and interactions

We extend the methodology for family-based tests of association and linkage to allow for both variation in the phenotypes of subjects and incorporation of covariates into general-score tests of association. We use standard association models for a phenotype and any number of predictors. We then construct a score statistic, using likelihoods for the distribution of phenotype, given genotype. The distribution of the score is computed as a function of offspring genotypes, conditional on parental genotypes and trait values for offspring and parents. This approach provides a natural extension of the transmission/disequilibrium test to any phenotype and to multiple genes or environmental factors and allows the study of gene-gene and gene-environment interaction. When the trait varies among subjects or when covariates are included in the association model, the score statistic depends on one or more nuisance parameters. We suggest two approaches for obtaining parameter estimates: (1) choosing the estimate that minimizes the variance of the test statistic and (2) maximizing the statistic over a nuisance parameter and using a corrected P value. We apply our methods to a sample of families with attention-deficit/hyperactivity disorder and provide examples of how covariates and gene-environment and gene-gene interactions can be incorporated.

Patient risk factors for adverse drug events in hospitalized patients. ADE Prevention Study Group

BACKGROUND

Adverse drug events (ADEs) are common in hospitalized patients, but few empirical data are available regarding the strength of patient risk factors for ADEs.

METHODS

We performed a nested case-control study within a cohort that included 4108 admissions to a stratified random sample of 11 medical and surgical units in 2 tertiary care hospitals during a 6-month period. Analyses were conducted on 2 levels: (1) using a limited set of variables available for all patients using computerized data available from 1 hospital and (2) using a larger set of variables for the case patients and matched controls from both hospitals. Case patients were patients with an ADE, and the matched control for each case patient was the patient on the same unit as the case patient with the most similar prevent length of stay. Main outcome measures were presence of an ADE, preventable ADE, or severe ADE.

RESULTS

In the cohort analysis, electrolyte concentrates (odds ratio [OR], 1.7), diuretics (OR, 1.7), and medical admission (OR, 1.6) were independent correlates of ADEs. Independent correlates of preventable ADEs in the cohort analysis were low platelet count (OR, 4.5), antidepressants (OR, 3.3), antihypertensive agents (OR, 2.9), medical admission (OR, 2.2), and electrolyte concentrates (OR, 2.1). In the case-control analysis, exposure to psychoactive drugs (OR, 2.1) was an independent correlate of an ADE, and use of cardiovascular drugs (OR, 2.4) was independently correlated with severe ADEs. For preventable ADEs, no independent predictors were retained after multivariate analysis.

CONCLUSIONS

Adverse drug events occurred more frequently in sicker patients who stayed in the hospital longer. However, after controlling for level of care and preevent length of stay, few risk factors emerged. These results suggest that, rather than targeting ADE-prone individuals, prevention strategies should focus on improving medication systems.

Correcting for non-compliance in randomized trials: an application to the ATBC Study

Different methods for estimating the effect of treatment actually received in a longitudinal placebo-controlled trial with non-compliance are discussed. Total mortality from the ATBC Study is used as an illustrative example. In the ATBC Study some 25 per cent of the participants dropped out from active follow-up prior to the scheduled end of the study. The 'intention-to-treat' analysis showed an increased death risk in the beta-carotene arm when compared with the no beta-carotene arm. Owing to considerable non-compliance it is also of interest to estimate the effect of beta-carotene actually received. We use a simple model for the treatment action and discuss three methods for estimation of the treatment effect under the model - the 'intention-to-treat' approach, the 'as-treated' approach and the g-estimation approach. These approaches are compared in a simulation study under different settings for non-compliance. Finally, the data from the ATBC Study are analysed using the proposed methods.

Weight cycling, weight gain, and risk of hypertension in women

To assess prospectively the relation between body mass index, weight gain, repeated intentional weight losses, and the risk of self-reported hypertension, the authors studied 46,224 women who were participants in the Nurses Health Study II, who were free of hypertension in 1993, and who completed questions on intentional weight losses between 1989 and 1993. Women who reported they had intentionally lost > or =20 lbs (9 kg) > or =3 times were classified as severe weight cyclers. Women who had intentionally lost > or =10 lbs (4.5 kg) > or =3 times, but who did not meet the criteria for severe weight cycling, were classified as mild weight cyclers. Between 1993 and 1995, 1,107 incident cases of diagnosed hypertension were reported. Body mass index and weight gain, but not weight cycler status, were independently associated with the development of hypertension. For each 10 lb (4.5 kg) gain in weight between 1989 and 1993, the risk of hypertension increased 20% (odds ratio (OR) = 1.20, 95% confidence interval (CI) 1.15, 1.24). After adjustment for body mass index and weight gain, the risks associated with mild weight cycling (OR = 1.15, 95% CI 1.00, 1.33) and severe weight cycling (OR = 1.13, 95% CI 0.79, 1.61) were small and not significant. Thus, the results of this study offer support for the current weight guidelines and provide further evidence of the health risks associated with excessive weight and weight gain. However, these data do not suggest an independent effect of weight cycling on risk of hypertension.

Impact of a school-based interdisciplinary intervention on diet and physical activity among urban primary school children: eat well and keep moving

OBJECTIVE

To evaluate the impact of a school-based interdisciplinary health behavior intervention on diet and physical activity among children in grades 4 and 5.

DESIGN

A quasiexperimental field trial with 6 intervention and 8 matched control schools. Outcomes were assessed longitudinally using preintervention (fall 1995) and follow-up (spring 1997) student survey food frequency and activity measures and follow-up 24-hour recall measures of diet and activity. Change was also assessed using yearly repeated cross-sectional surveys of all grade 5 students from 1995 through 1997.

PARTICIPANTS

Longitudinal data were collected from 479 students initially in grade 4 in Baltimore, Md, public schools; 91% were African American. Repeated 24-hour recall measures in 1997 were collected for a random subsample of 336 students. Cross-sectional survey data were collected from all grade 5 students in 1995,1996, and 1997 (n = 2103).

INTERVENTION

The Eat Well and Keep Moving Program was taught by classroom teachers over 2 years in math, science, language arts, and social studies classes. Materials provided links to school food services and families and provided training and wellness programs for teachers and other staff members. Intervention materials focused on decreasing consumption of foods high in total and saturated fat and increasing fruit and vegetable intake, as well as reducing television viewing and increasing physical activity.

MAIN OUTCOME MEASURES

Dietary intake and physical activity measured via repeated 24-hour recall were primary end points, with additional food frequency and activity measures.

RESULTS

The 24-hour recall measures indicated that, after controlling for baseline covariates, the percentages of total energy from fat and saturated fat were reduced among students in intervention compared with control schools (-1.4%; 95% confidence interval [CI], -2.8 to -0.04; P = .04 and -0.60%; 95% CI, -1.2 to -0.01; P = .05). There was an increase in fruit and vegetable intake (0.36 servings/4184 kJ; 95% CI, 0.10-0.62; P=.01), in vitamin C intake (8.8 mg/4184 kJ; 95% CI, 2.0-16; P=.01), and in fiber consumption (0.7 g/4184 kJ; 95% CI, 0.0-1.4; P=.05). Television viewing was marginally reduced (-0.55 h/d; 95% CI, -1.04 to 0.04; P=.06). Analysis of longitudinal and repeated cross-sectional food frequency data indicated similar significant decreases in the percentages of total energy from fat and saturated fat.

CONCLUSION

Evaluation of the Eat Well and Keep Moving Program indicates effectiveness in improving dietary intake of students and reducing television viewing.

Association of obesity with physical activity, television programs and other forms of video viewing among children in Mexico city

OBJECTIVE

To assess the association of physical activity, television program viewing and other forms of video viewing with the prevalence of obesity among school children.

DESIGN

Cross-sectional study.

SUBJECTS

712 children, 9-16 y old, from a low- and a middle-income town in the Mexico City area.

MEASUREMENTS

Children completed a self administered questionnaire to assess time spent in physical activity and television viewing, and diet. Height weight and triceps skinfolds were measured. The outcome variable was obesity, and the covariates were hours of television programs and other video viewing, physical activity, energy intake, percentage of energy from fat, town of location of school, age, gender and perception of mother's weight status.

RESULTS

Among 461 children with complete information, 24% were classified as obese. Children reported an average of 4.1 +/- 2.2 h/d watching television (2.4 +/- 1.5 h/d for TV programs and 1.7 +/- 1.5 h/d for video cassette recorder (VCR) or videogames), and 1.8 +/- 1.3 h/d in moderate and vigorous physical activities. Odds ratios (OR) of obesity were 12% higher for each hour of television program viewing per day (OR = 1.12, 95% confidence interval (CI) 1.02,-1.22), and 10% lower for each hour of moderate/vigorous physical activity per day (OR = 0.90, 95% CI 0.83-0.98), controlling for age, gender, town and perception of mother's weight status. Children in the middle-income town had higher adjusted odds of obesity (OR = 2.58, 95% CI 1.47-4.54).

CONCLUSION

Physical activity and television viewing, but not VCR/videogames use, were related to obesity prevalence in Mexican children 9-16 y old.

Reducing obesity via a school-based interdisciplinary intervention among youth: Planet Health

OBJECTIVE

To evaluate the impact of a school-based health behavior intervention known as Planet Health on obesity among boys and girls in grades 6 to 8.

DESIGN

Randomized, controlled field trial with 5 intervention and 5 control schools. Outcomes were assessed using preintervention (fall 1995) and follow-up measures (spring 1997), including prevalence, incidence, and remission of obesity.

PARTICIPANTS

A group of 1295 ethnically diverse grade 6 and 7 students from public schools in 4 Massachusetts communities.

INTERVENTION

Students participated in a school-based interdisciplinary intervention over 2 school years. Planet Health sessions were included within existing curricula using classroom teachers in 4 major subjects and physical education. Sessions focused on decreasing television viewing, decreasing consumption of high-fat foods, increasing fruit and vegetable intake, and increasing moderate and vigorous physical activity.

MAIN OUTCOME MEASURES

Obesity was defined as a composite indicator based on both a body mass index and a triceps skinfold value greater than or equal to age- and sex-specific 85th percentiles. Because schools were randomized, rather than students, the generalized estimating equation method was used to adjust for individual-level covariates under cluster randomization.

RESULTS

The prevalence of obesity among girls in intervention schools was reduced compared with controls, controlling for baseline obesity (odds ratio, 0.47; 95% confidence interval, 0.24-0.93; P = .03), with no differences found among boys. There was greater remission of obesity among intervention girls vs. control girls (odds ratio, 2.16; 95% confidence interval, 1.07-4.35; P = .04). The intervention reduced television hours among both girls and boys, and increased fruit and vegetable consumption and resulted in a smaller increment in total energy intake among girls. Reductions in television viewing predicted obesity change and mediated the intervention effect. Among girls, each hour of reduction in television viewing predicted reduced obesity prevalence (odds ratio, 0.85; 95% confidence interval, 0.75-0.97; P = .02).

CONCLUSION

Planet Health decreased obesity among female students, indicating a promising school-based approach to reducing obesity among youth.

Maximum likelihood analysis of generalized linear models with missing covariates

Missing data is a common occurrence in most medical research data collection enterprises. There is an extensive literature concerning missing data, much of which has focused on missing outcomes. Covariates in regression models are often missing, particularly if information is being collected from multiple sources. The method of weights is an implementation of the EM algorithm for general maximum-likelihood analysis of regression models, including generalized linear models (GLMs) with incomplete covariates. In this paper, we will describe the method of weights in detail, illustrate its application with several examples, discuss its advantages and limitations, and review extensions and applications of the method.

Angiotensinogen gene polymorphisms M235T/T174M: no excess transmission to hypertensive Chinese

The gene encoding angiotensinogen (AGT) has been widely studied as a candidate gene for hypertension. Most studies to date have relied on case-control analysis to test for an excess of AGT variants among hypertensive cases compared with normotensive controls. However, with this design, nothing guarantees that a positive finding is due to actual allelic association as opposed to an inappropriate control population. To avoid this difficulty in our study of essential hypertension in Anqing, China, we tested AGT variants using the transmission/disequilibrium test, a procedure that bypasses the need for a control sample by testing for excessive transmission of a genetic variant from parents heterozygous for that variant. We analyzed two AGT polymorphisms, M235T and T174M, which have been associated with essential hypertension in whites and Japanese, using data on 335 hypertensive subjects from 315 nuclear families and their parents. Except in the group of subjects younger than 25 years, M235 and T174 were the more frequently transmitted alleles. We found that 194 parents heterozygous for M235T transmitted M235 106 times (P=0.22) and that 102 parents heterozygous for T174M transmitted T174 60 times (P=0.09). Stratifying offspring by gender, M235 and T174 were transmitted 60 of 106 times (P=0.21) and 44 of 75 times (P=0.17), respectively, in men, and 46 of 88 times (P=0.75) and 16 of 27 times (P=0.44), respectively, in women. Our results were also negative in all age groups and for the affected offspring with blood pressure values >/=160/95 mm Hg. Thus, this study provides no evidence that either allele of M235T or T174M contributes to hypertension in this Chinese population.

A discordant-sibship test for disequilibrium and linkage: no need for parental data

The sibship disequilibrium test (SDT) is designed to detect both linkage in the presence of association and association in the presence of linkage (linkage disequilibrium). The test does not require parental data but requires discordant sibships with at least one affected and one unaffected sibling. The SDT has many desirable properties: it uses all the siblings in the sibship; it remains valid if there are misclassifications of the affectation status; it does not detect spurious associations due to population stratification; asymptotically it has a chi2 distribution under the null hypothesis; and exact P values can be easily computed for a biallelic marker. We show how to extend the SDT to markers with multiple alleles and how to combine families with parents and data from discordant sibships. We discuss the power of the test by presenting sample-size calculations involving a complex disease model, and we present formulas for the asymptotic relative efficiency (which is approximately the ratio of sample sizes) between SDT and the transmission/disequilibrium test (TDT) for special family structures. For sib pairs, we compare the SDT to a test proposed both by Curtis and, independently, by Spielman and Ewens. We show that, for discordant sib pairs, the SDT has good power for testing linkage disequilibrium relative both to Curtis's tests and to the TDT using trios comprising an affected sib and its parents. With additional sibs, we show that the SDT can be more powerful than the TDT for testing linkage disequilibrium, especially for disease prevalence >.3.

Effect of computerized physician order entry and a team intervention on prevention of serious medication errors

CONTEXT

Adverse drug events (ADEs) are a significant and costly cause of injury during hospitalization.

OBJECTIVES

To evaluate the efficacy of 2 interventions for preventing nonintercepted serious medication errors, defined as those that either resulted in or had potential to result in an ADE and were not intercepted before reaching the patient.

DESIGN

Before-after comparison between phase 1 (baseline) and phase 2 (after intervention was implemented) and, within phase 2, a randomized comparison between physician computer order entry (POE) and the combination of POE plus a team intervention.

SETTING

Large tertiary care hospital.

PARTICIPANTS

For the comparison of phase 1 and 2, all patients admitted to a stratified random sample of 6 medical and surgical units in a tertiary care hospital over a 6-month period, and for the randomized comparison during phase 2, all patients admitted to the same units and 2 randomly selected additional units over a subsequent 9-month period.

INTERVENTIONS

A physician computer order entry system (POE) for all units and a team-based intervention that included changing the role of pharmacists, implemented for half the units.

MAIN OUTCOME MEASURE

Nonintercepted serious medication errors.

RESULTS

Comparing identical units between phases 1 and 2, nonintercepted serious medication errors decreased 55%, from 10.7 events per 1000 patient-days to 4.86 events per 1000 (P=.01). The decline occurred for all stages of the medication-use process. Preventable ADEs declined 17% from 4.69 to 3.88 (P=.37), while nonintercepted potential ADEs declined 84% from 5.99 to 0.98 per 1000 patient-days (P=.002). When POE-only was compared with the POE plus team intervention combined, the team intervention conferred no additional benefit over POE.

CONCLUSIONS

Physician computer order entry decreased the rate of nonintercepted serious medication errors by more than half, although this decrease was larger for potential ADEs than for errors that actually resulted in an ADE.

Alpha-2 macroglobulin is genetically associated with Alzheimer disease

Alpha-2-macroglobulin (alpha-2M; encoded by the gene A2M) is a serum pan-protease inhibitor that has been implicated in Alzheimer disease (AD) based on its ability to mediate the clearance and degradation of A beta, the major component of beta-amyloid deposits. Analysis of a deletion in the A2M gene at the 5' splice site of 'exon II' of the bait region (exon 18) revealed that inheritance of the deletion (A2M-2) confers increased risk for AD (Mantel-Haenzel odds ratio=3.56, P=0.001). The sibship disequilibrium test (SDT) also revealed a significant association between A2M and AD (P=0.00009). These values were comparable to those obtained for the APOE-epsilon4 allele in the same sample, but in contrast to APOE-epsilon4, A2M-2 did not affect age of onset. The observed association of A2M with AD did not appear to account for the previously published linkage of AD to chromosome 12, which we were unable to confirm in this sample. A2M, LRP1 (encoding the alpha-2M receptor) and the genes for two other LRP ligands, APOE and APP (encoding the amyloid beta-protein precursor), have now all been genetically linked to AD, suggesting that these proteins may participate in a common neuropathogenic pathway leading to AD.

A Bayesian framework for intent-to-treat analysis with missing data

In longitudinal clinical trials, one analysis of interest is an intention-to-treat analysis, which groups subjects according to the randomized treatment regardless of whether they stayed on that treatment or not. When in addition to going off the randomized treatment subjects may also drop out of the study and be lost to follow-up, it is unclear what an intention-to-treat analysis should be. If measurements are made after treatment drop-out on a random sample of subjects who drop the treatment, then Hogan and Laird (1996, Biometrics 52, 1002-1017) present a random effects model, well suited to this type of analysis, which fits a two-piece linear spline to the data with the knot at the time the assigned treatment is dropped. This article presents a Bayesian approach to fitting a similar two-piece linear spline model and shows how the model can be applied to data that have no off-treatment observations.

Increasing efficiency from censored survival data by using random effects to model longitudinal covariates

When estimating a survival time distribution, the loss of information due to right censoring results in a loss of efficiency in the estimator. In many circumstances, however, repeated measurements on a longitudinal process which is associated with survival time are made throughout the observation time, and these measurements may be used to recover information lost to censoring. For example, patients in an AIDS clinical trial may be measured at regular intervals on CD4 count and viral load. We describe a model for the joint distribution of a survival time and a repeated measures process. The joint distribution is specified by linking the survival time to subject-specific random effects characterizing the repeated measures, and is similar in form to the pattern mixture model for multivariate data with nonignorable nonresponse. We also describe an estimator of survival derived from this model. We apply the methods to a long-term AIDS clinical trial, and study properties of the survival estimator. Monte Carlo simulation is used to estimate gains in efficiency when the survival time is related to the location and scale of the random effects distribution. Under relatively light censoring (20%), the methods yield a modest gain in efficiency for estimating three-year survival in the AIDS clinical trial. Our simulation study, which mimics characteristics of the clinical trial, indicates that much larger gains in efficiency can be realized under heavier censoring or with studies designed for long term follow up on survival.

Tests for homogeneity of the risk difference when data are sparse

Test statistics for the homogeneity of the risk difference for a series of 2 x 2 tables when the data are sparse is proposed. A weighted least squares statistic is commonly used to test for equality of the risk difference over the tables; however, when the data are sparse, this statistic can have anticonservative Type I error rates. Simulation is used to compare the proposed test statistics to the weighted least squares statistic. The weighted least squares statistic has the most anticonservative Type I error rates of all the statistics compared. We suggest the use of one of our proposed test statistics instead of the weighted least squares statistic.