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PubMed1994

A geometric approach to the analysis of physiological flow data.

Physiological flow data are common in various medical fields. Examples include urinary, blood and expiratory flows. They are widely used in assessing functions in the urinary, circulatory, or pulmonary systems, respectively. Current statistical methods for analysing these flow data in clinical trials are either univariate analyses, which do not utilize all the information together, or some conventional multivariate methods (such as regression analyses) which yield results that do not render clear medical interpretations. This paper presents a new approach to analysing the flow data, using urinary flow as the primary focus. The basic idea and technical steps are applicable to other flow data as well. The proposed method aims to transform the flow measurements back to the shape of the flow graphs. Since the whole geometric pattern of the flow graph provides more information about the patient's flow condition than any individual flow parameter alone, the method is a meaningful way of combining and analysing the flow data in both statistical and clinical senses. The method is a three-stage procedure. Patients are classified into three classes in the first stage and then ranked in sequence in the second stage, according to the geometry of the shape pattern and some clinical criteria. The classification procedure is shown to be very reliable when compared with the clinician's visual evaluation, and hence can be implemented by computer programming to aid clinical trials involving many patients. The whole ranking score is then readily analysed at the third stage for comparing treatment effects by the analysis of covariance method based on ranks, with the post-treatment score as the response variable and the baseline score as the covariate. An example of a urinary flow data set is provided to illustrate the use of the procedure.

Analysis of Variance↗
PubMed1995

Model inconsistency, illustrated by the Cox proportional hazards model.

We consider problems involving the comparison of two or more treatments where we have the opportunity to adjust for relevant covariates either conditionally in a regression model or implicitly in repeated measures data, for example, in crossover trials. It is seen that for data arising from non-Normal distributions there is the possibility that models adjusting for covariates and those not adjusting for covariates will be inconsistent, that is, at most one of the models can be valid. Alternatively, even if conditional and unconditional models are valid, parameters in each model may have different interpretations. We note that this presents difficulties for the specification and interpretation of the analysis. It is also clear that model validation is critical. Specific attention is paid to survival data analysed by the Cox proportional hazards model.

Analysis of Variance↗
PubMed1995

Longitudinal data analysis for linear Gaussian models with random disturbed-highest-derivative-polynomial subject effects.

For linear regression analysis of longitudinal data with Gaussian response, I propose a new model to generalize the traditional class of random effects models in which the random effects are deterministic polynomials with coefficients randomly distributed over subjects with mean zero. The generalization is accomplished by adding zero mean Gaussian 'disturbances' to the highest derivative of each random coefficient subject polynomial, independently at each observation time. The resulting random effects, which have mean zero at each observation time, are called disturbed highest derivative polynomials (DHDPs). The disturbances induce serial correlation and also allow the subject-specific DHDP time trends to be non-linear. I do not estimate the subject-specific DHDP time trends. Analysis is based on the marginal model, that is, the fixed effects or population model obtained by integrating the random polynomial coefficients and all disturbances out of the joint distribution of themselves and the response vector. This allows a 'population averaged' interpretation. One can select the DHDP order by an information criterion. When the population time trend is not correctly modelled, the optimal DHDP order will be larger than when it is correctly modelled. One can make the covariance matrix of the regression coefficients robust to errors in modelling the within-subject dependence. I describe the relationship of a DHDP to a smoothing polynomial spline, and show how to replace the DHDP model with a smoothing polynomial spline model for the within-subject dependence in the marginal model.

Bias↗
PubMed1995

Smoothing splines for longitudinal data.

In a longitudinal data model with fixed and random effects, polynomials are used to model the fixed effects and smoothing polynomial splines are used to model the within-subject random effect curves. The splines are generated by modelling the data for each subject as observations of an integrated random walk with observational error. The initial conditions for each subject's deviation from the fixed effect curve are assumed to have zero mean and arbitrary covariance matrix which is estimated by maximum likelihood, producing an empirical Bayes estimate. This is in contrast to modelling a single curve using a diffuse prior. An example is presented using unbalanced longitudinal data from a pilot study in breast cancer patients.

Bayes Theorem↗
PubMed1995

The design and analysis of randomized trials with recurrent events.

This paper describes a method for planning the duration of a randomized parallel group study in which the response of interest is a potentially recurrent event. At the design stage we assume patients accrue at a constant rate, we model events via a homogeneous Poisson process, and we utilize an independent exponential censoring mechanism to reflect loss to follow-up. We derive the appropriate study duration to ensure satisfaction of power requirements for the effect size of interest under a Poisson regression model. An application to a kidney transplant study illustrates the potential savings of the Poisson-based design relative to a design based on the time to the first event. Revised design criteria are also derived to accommodate overdispersed Poisson count data. We examine the frequency properties of two non-parametric tests recently proposed by Lawless and Nadeau for trials based on the above design criteria. In simulation studies involving homogeneous and non-homogeneous Poisson processes they performed well with respect to their type I error rate and power. Results from supplementary simulation studies indicate that these tests are also robust to extra-Poisson variation and to clustering in the event times, making these tests attractive in their generality. We illustrate both tests by application to data from a completed kidney transplant study.

Algorithms↗
PubMed1995

Longitudinal models for analysis of respiratory function.

We compare the results of fitting three longitudinal models, two autoregressive models (the serial correlation model and a damped autoregressive model) and a compound symmetry model, to data on a cohort of 1154 adult men in Boston. The serial correlation model assumes that the error terms are autocorrelated with correlation of the form lambda t for visits t years apart while the damped autoregressive assumes that the correlation between error terms of observations t years apart is of the form lambda t theta. The compound symmetry model assumes that the errors are correlated, with the same correlation regardless of how far apart observations are in time. These three models are all related in that the serial correlation and compound symmetry models are particular cases of the damped autoregressive models (that is, theta = 1 corresponds to the serial correlation model and theta = 0 corresponds to the compound symmetry model). For current smokers, the damped autoregressive model provided a significantly better fit than either of the other two models (p < 0.001); for never smokers the damped autoregressive and compound symmetry models were almost identical with both providing a significantly better fit than the serial correlation model (p < 0.001).

Adult↗
PubMed2001

Effects of covariance model assumptions on hypothesis tests for repeated measurements: analysis of ovarian hormone data and pituitary-pteryomaxillary distance data.

In the analysis of repeated measurements, multivariate methods which account for the correlations among the observations from the same experimental unit are widely used. Two commonly-used multivariate methods are the unstructured multivariate approach and the mixed model approach. The unstructured multivariate approach uses MANOVA types of models and does not require assumptions on the covariance structure. The mixed model approach uses multivariate linear models with random effects and requires covariance structure assumptions. In this paper, we describe the characteristics of tests based on these two methods of analysis and investigate the performance of these tests. We focus particularly on tests for group effects and parallelism of response profiles.

Adolescent↗
PubMed2001

Data augmentation priors for Bayesian and semi-Bayes analyses of conditional-logistic and proportional-hazards regression.

Data augmentation priors have a long history in Bayesian data analysis. Formulae for such priors have been derived for generalized linear models, but their accuracy depends on two approximation steps. This note presents a method for using offsets as well as scaling factors to improve the accuracy of the approximations in logistic regression. This method produces an exceptionally simple form of data augmentation that allows it to be used with any standard package for conditional-logistic or proportional-hazards regression to perform Bayesian and semi-Bayes analyses of matched and survival data. The method is illustrated with an analysis of a matched case-control study of diet and breast cancer.

Algorithms↗
PubMed1993

Quit and win smoking cessation contests: how should effectiveness be evaluated?

BACKGROUND: In societies where there are both multiple influences on smoking cessation and a downward secular cessation trend, the attribution of cessation effects to particular interventions poses challenging evaluation problems. Quit smoking lotteries are gaining popularity as mass-reach smoking cessation strategies. Most published evaluations of the lotteries have reported impressive cessation rates within samples of entrants. However, none has considered the possibility that the lotteries merely concentrate a secular quitting trend around a researched event or whether they increase the cessation rate of the whole community from which entrants derive. RESULTS: Results from a lottery run in a smoking population (n = 101,277) are presented. Of the 1,167 people who entered, 29.2% self-reported being smoke-free at 4 months. These results are considered against a prediction that the campaign might increase the cumulative background 4-month quit rate (708/101,277 or 0.7%) by a minimum of 10%. CONCLUSION: It is concluded that such a realistic hope, even if achieved, could in practice never be measured. Implications for evaluating the impact of discrete health promotion evaluations in large communities are discussed in terms of the dilemmas posed by the case study.

Adult↗
PubMedDate not supplied

The effectiveness of Drug Abuse Resistance Education (project DARE): 5-year follow-up results.

BACKGROUND: This article reports the results of a 5-year, longitudinal evaluation of the effectiveness of Drug Abuse Resistance Education (DARE), a school-based primary drug prevention curriculum designed for introduction during the last year of elementary education. DARE is the most widely disseminated school-based prevention curriculum in the United States. METHOD: Twenty-three elementary schools were randomly assigned to receive DARE and 8 were designated comparison schools. Students in the DARE schools received 16 weeks of protocol-driven instruction and students in the comparison schools received a drug education unit as part of the health curriculum. All students were pretested during the 6th grade prior to delivery of the programs, posttested shortly after completion, and resurveyed each subsequent year through the 10th grade. Three-stage mixed effects regression models were used to analyze these data. RESULTS: No significant differences were observed between intervention and comparison schools with respect to cigarette, alcohol, or marijuana use during the 7th grade, approximately 1 year after completion of the program, or over the full 5-year measurement interval. Significant intervention effects in the hypothesized direction were observed during the 7th grade for measures of students' general and specific attitudes toward drugs, the capability to resist peer pressure, and estimated level of drug use by peers. Over the full measurement interval, however, average trajectories of change for these outcomes were similar in the intervention and comparison conditions. CONCLUSIONS: The findings of this 5-year prospective study are largely consonant with the results obtained from prior short-term evaluations of the DARE curriculum, which have reported limited effects of the program upon drug use, greater efficacy with respect to attitudes, social skills, and knowledge, but a general tendency for curriculum effects to decay over time. The results of this study underscore the need for more robust prevention programming targeted specifically at risk factors, the inclusion of booster sessions to sustain positive effects, and greater attention to interrelationships between developmental processes in adolescent substance use, individual level characteristics, and social context.

Child↗
PubMedDate not supplied

Aerobic exercise and bone density at the hip in postmenopausal women: a meta-analysis.

BACKGROUND: The effects of aerobic exercise on bone density at the hip in postmenopausal women in the absence of estrogen replacement therapy are not currently known. The purpose of this study was to examine the effects of aerobic exercise on bone density at the hip in postmenopausal women. METHODS: Using the meta-analytic approach, studies dealing with the effects of aerobic exercise on bone density at the hip in postmenopausal women were searched for using computerized literature searches (MEDLINE, January 1978 to December 1995) as well as cross-referencing from retrieved review articles and original investigations. RESULTS: A total of 18 effect sizes were derived from six studies. Using a fixed-effects model and bootstrap resampling (5,000 iterations) overall changes in bone density at the hip yielded an average effect size of 0.43 (95% CI = 0.04 to 0.81), equivalent to an overall change of approximately 2.42% (exercise = 2.13%; nonexercise = -0.29%). Statistically significant differences were observed when effect sizes were partitioned by country in which studies were conducted (United States, mean = 1.03, 95% CI = 0.48 to 1.68; other countries, mean = 0.18, 95% CI = -0.27 to 0.54; Qb = 5.44, P = 0.04) and calcium intake (> or =1,000 mg/day, mean = 0.83, 95% CI = 0.49 to 1.23; <1,000 mg/day = -0.23, 95% CI = -0.85 to 0.21; Qb = 10.64, P = 0.002). CONCLUSIONS: The overall results of this study suggest that site-specific aerobic exercise has a moderately positive effect on bone density at the hip in postmenopausal women. However, a need exists for additional, well-designed studies before a final recommendation can be made regarding the efficacy of aerobic exercise as a nonpharmacologic intervention for optimizing bone density at the hip in postmenopausal women.

Absorptiometry, Photon↗
PubMedDate not supplied

Smoking as a risk factor for injury death: a meta-analysis of cohort studies.

BACKGROUND: Injury and tobacco effects represent one-quarter of the global burden of disease. Understanding the causes of injury and the effects of smoking may help reduce those burdens. Some smokers have high risks of injury. We provide an initial meta-analysis of cohort associations between smoking and fatal injury. METHODS: Three authors independently searched MEDLINE, and bibliographies of the pertinent studies found, for cigarette smoker-specific injury death data which allowed estimation of an appropriate relative risk (RR) and 95% confidence interval (CI). Relative risks and dose response were summarized by fixed effects and Poisson modeling, respectively. RESULTS: Six studies covering 10 pertinent cohorts were located. Associations between smoking and injury death have been significant after adjustment or, in effect, stratification for age, race, sex, country, and, respectively, alcohol, marriage, education, and body mass; job and time period; job, alcohol, and exercise; etc. Summary dose-response trends were significantly positive (P < 0.00005). Cigarette smoking predicted summary injury death crude RRs of 1.61 (CI 1.44-1.81) vs never smokers and 1.39 (CI 1.25-1.55) vs ex-smokers. CONCLUSIONS: Smoking has significant, consistent, dose-response, often strong and independent, prospective associations with injury death, internationally.

Adult↗
PubMed1990

Hospital stay length as an effect modifier of other risk factors for nosocomial infection.

This paper addresses the problem of hospital stay length as a risk factor for nosocomial infection and as a modifier of the effect of other risk factors for hospital infection. Patients were selected form two cross-sectional studies done in two different seasons of 1986. Risk of infection rose fairly steadily as hospital stay length increased (correlation coefficient: 0.83, p less than 0.01). Several risk factors (operation, underlying disease, and age) were analyzed on the basis of 1) raw data and 2) data stratified by length of stay. The results showed that hospital stay length is a strong modifier of the remaining risk factors, generally reducing, their effect on the development of hospital infection as length of stay increases.

Adolescent↗
PubMed1990

Intra and inter-person sources of variability in fat intake in a feeding trial of 14 men.

An impediment to analyzing the effect of nutritional factors on biologic processes or health status in human populations arises from the relatively small dietary differences that exist between individuals in relation to large periodic fluctuations in dietary intake and the imprecision with which diet is normally assessed. We report here on characteristics of dietary variability in a group of 14 young men who successfully completed an intervention study specifically designed to create large differences in fat intake between baseline and two dietary intervention-periods each lasting two months (during which safflower and coconut oil supplements were given). We found that in the second supplemental phase of the intervention inter-person sources of variability were greatly increased over the low-fat baseline values. For proportion of calories as fat it increased to 64.2% of total variance from 21.6% without supplementation; for saturated fatty acids, 47.3% from 17.7%; for polyunsaturated fatty acids, 62.4% from 22.8%; and for the P:S ratio, 71.5% from 21.6%. During the first intervention phase we observed only moderate changes. Reasons for the intervention phase differences in effect, implications for feeding trials designed to look at dietary fat effects, and the need for future studies aimed at clarifying these results are discussed.

Adult↗
PubMed1993

Teachers' ratings of disruptive behaviors: the influence of halo effects.

This study evaluated the accuracy of teachers' ratings and examined whether these ratings are influenced by halo effects. One hundred thirty-nine elementary school teachers viewed videotapes of what they believed were children in regular fourth-grade classrooms. In fact, the children were actors who followed prepared scripts that depicted a child engaging in behaviors characteristic of an attention-deficit hyperactivity disorder (ADHD), an oppositional defiant disorder or a normal youngster. The findings provide support for a bias that was unidirectional in nature. Specifically, teachers rated hyperactive behaviors accurately when the child behaved like an ADHD youngster. However, ratings of hyperactivity and of ADHD symptomatic behaviors were spuriously inflated when behaviors associated with oppositional defiant disorder occurred. In contrast, teachers rated oppositional and conduct problem behaviors accurately, regardless of the presence of hyperactive behaviors. The implications of these findings regarding diagnostic practices and rating scale formats are discussed.

Adult↗
PubMed1991

Theory-based screening for prevention: focusing on mediating processes in children of divorce.

Prevention programs in mental health theoretically can benefit from selecting participants who have a greater likelihood of developing psychological problems because of their exposure to the putative mediators targeted for change in an intervention. Screening on mediators may increase statistical power to detect program effects, enhance the cost-effectiveness of intervention trials, and decrease the possibility of iatrogenic effects. The circumstances that optimize the strategy of screening on the basis of mediating variables are discussed, and data are presented to illustrate the development of a mediational selection strategy to identify families who might best benefit from a preventive intervention for children of divorce. In addition, we present evidence that adjustment problems for children experiencing a divorce, as with most mental health problems, are not the result of one specific factor, but are jointly determined by several mediating processes that occur subsequent to the divorce. The mediational selection strategy developed illustrates the utility of measuring a set of mediational processes central to conferring risk for mental health problems to children of divorce.

Adolescent↗
PubMed1993

Charting the process of change: a primer on survival analysis.

Survival analysis is a powerful and useful technique for understanding qualitative change. This article provides a practical, nontechnical introduction to the use of survival analysis for social scientists. Important issues in using survival analysis are discussed, including research design, data preparation and management, and data analysis. Attendance data from a self-help organization are used to illustrate common survival analysis tasks such as describing the overall survival and hazard functions, examining covariate effects, and modeling the form of the hazard function over time. An appendix that discusses the strengths and weaknesses of current survival analysis computer programs is included.

Data Collection↗