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Biomedical subjects

N M Laird

Publications and source records attributed to N M Laird.

At least 37 records · Page 2Linked to original sources
PubMed2000

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.

Adolescent↗
PubMed2000

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.

Alleles↗
PubMed2000

The transmission/disequilibrium test and parental-genotype reconstruction for X-chromosomal markers.

Family-based association methods have recently been introduced that allow testing for linkage in the presence of linkage disequilibrium between a marker and a disease even if there is only incomplete parental-marker information. No such tests are currently available for X-linked markers. This report fills this methodological gap by presenting the X-linked sibling transmission/disequilibrium test (XS-TDT) and the X-linked reconstruction-combination transmission/disequilibrium test (XRC-TDT). As do their autosomal counterparts (S-TDT and RC-TDT), these tests make no assumption about the mode of inheritance of the disease and the ascertainment of the sample. They protect against spurious association due to population stratification. The two tests were compared by simulations, which show that (1) the X-linked RC-TDT is, in general, considerably more powerful than the X-linked S-TDT and (2) the lack of parental-genotype information can be offset by the typing of a sufficient number of sibling controls. A freely available SAS implementation of these tests allows the calculation of exact P values.

Alleles↗
PubMed2000

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.

Journal Article↗
PubMed2000

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.

Anti-HIV Agents↗
PubMed1999

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.

Computer Simulation↗
PubMed1999

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.

Adult↗
PubMed1999

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.

Adolescent↗
PubMed1999

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.

Algorithms↗
PubMed1998

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.

Age of Onset↗
PubMed1998

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.

Alleles↗
PubMed1998

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.

Acquired Immunodeficiency Syndrome↗
PubMed1998

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.

Antineoplastic Agents↗
PubMed1998

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.

Acquired Immunodeficiency Syndrome↗
PubMed1997

Using the general linear mixed model to analyse unbalanced repeated measures and longitudinal data.

The general linear mixed model provides a useful approach for analysing a wide variety of data structures which practising statisticians often encounter. Two such data structures which can be problematic to analyse are unbalanced repeated measures data and longitudinal data. Owing to recent advances in methods and software, the mixed model analysis is now readily available to data analysts. The model is similar in many respects to ordinary multiple regression, but because it allows correlation between the observations, it requires additional work to specify models and to assess goodness-of-fit. The extra complexity involved is compensated for by the additional flexibility it provides in model fitting. The purpose of this tutorial is to provide readers with a sufficient introduction to the theory to understand the method and a more extensive discussion of model fitting and checking in order to provide guidelines for its use. We provide two detailed case studies, one a clinical trial with repeated measures and dropouts, and one an epidemiological survey with longitudinal follow-up.

Adolescent↗
PubMed1997

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.

Bipolar Disorder↗
PubMed1997

Regression models for mixed discrete and continuous responses with potentially missing values.

In this paper a likelihood-based method for analyzing mixed discrete and continuous regression models is proposed. We focus on marginal regression models, that is, models in which the marginal expectation of the response vector is related to covariates by known link functions. The proposed model is based on an extension of the general location model of Olkin and Tate (1961, Annals of Mathematical Statistics 32, 448-465), and can accommodate missing responses. When there are no missing data, our particular choice of parameterization yields maximum likelihood estimates of the marginal mean parameters that are robust to misspecification of the association between the responses. This robustness property does not, in general, hold for the case of incomplete data. There are a number of potential benefits of a multivariate approach over separate analyses of the distinct responses. First, a multivariate analysis can exploit the correlation structure of the response vector to address intrinsically multivariate questions. Second, multivariate test statistics allow for control over the inflation of the type I error that results when separate analyses of the distinct responses are performed without accounting for multiple comparisons. Third, it is generally possible to obtain more precise parameter estimates by accounting for the association between the responses. Finally, separate analyses of the distinct responses may be difficult to interpret when there is nonresponse because different sets of individuals contribute to each analysis. Furthermore, separate analyses can introduce bias when the missing responses are missing at random (MAR). A multivariate analysis can circumvent both of these problems. The proposed methods are applied to two biomedical datasets.

Air Pollution↗