Systematic investigations into the structure of measurement error of physical activity questionnaires are lacking. We propose a measurement error model for a physical activity questionnaire that uses physical activity level (the ratio of total energy expenditure to basal energy expenditure) to relate questionnaire-based reports of physical activity level to true physical activity levels. The 1999–2006 National Health and Nutrition Examination Survey physical activity questionnaire was administered to 433 participants aged 40–69 years in the Observing Protein and Energy Nutrition (OPEN) Study (Maryland, 1999–2000). Valid estimates of participants’ total energy expenditure were also available from doubly labeled water, and basal energy expenditure was estimated from an equation; the ratio of those measures estimated true physical activity level (“truth”). We present a measurement error model that accommodates the mixture of errors that arise from assuming a classical measurement error model for doubly labeled water and a Berkson error model for the equation used to estimate basal energy expenditure. The method was then applied to the OPEN Study. Correlations between the questionnaire-based physical activity level and truth were modest (r = 0.32–0.41); attenuation factors (0.43–0.73) indicate that the use of questionnaire-based physical activity level would lead to attenuated estimates of effect size. Results suggest that sample sizes for estimating relationships between physical activity level and disease should be inflated, and that regression calibration can be used to provide measurement error–adjusted estimates of relationships between physical activity and disease.
Berkson model; bias; energy metabolism; measurement error model; models, statistical; motor activity; self-assessment
It is of interest to estimate the distribution of usual nutrient intake for a population from repeat 24-h dietary recall assessments. A mixed effects model and quantile estimation procedure, developed at the National Cancer Institute (NCI), may be used for this purpose. The model incorporates a Box–Cox parameter and covariates to estimate usual daily intake of nutrients; model parameters are estimated via quasi-Newton optimization of a likelihood approximated by the adaptive Gaussian quadrature. The parameter estimates are used in a Monte Carlo approach to generate empirical quantiles; standard errors are estimated by bootstrap. The NCI method is illustrated and compared with current estimation methods, including the individual mean and the semi-parametric method developed at the Iowa State University (ISU), using data from a random sample and computer simulations. Both the NCI and ISU methods for nutrients are superior to the distribution of individual means. For simple (no covariate) models, quantile estimates are similar between the NCI and ISU methods. The bootstrap approach used by the NCI method to estimate standard errors of quantiles appears preferable to Taylor linearization. One major advantage of the NCI method is its ability to provide estimates for subpopulations through the incorporation of covariates into the model. The NCI method may be used for estimating the distribution of usual nutrient intake for populations and subpopulations as part of a unified framework of estimation of usual intake of dietary constituents.
statistical distributions; diet surveys; nutrition assessment; mixed-effects model; nutrients; percentiles
To study the implications of implementing the International Association of Diabetes in Pregnancy Study Group (IADPSG) recommendations for screening and diagnosis of gestational diabetes mellitus (GDM) in Israel and explore alternative methods for identifying women at risk for adverse pregnancy outcomes.
RESEARCH DESIGN AND METHODS
We analyzed data of the Israeli Hyperglycemia and Adverse Pregnancy Outcomes study participants (N = 3,345). Adverse outcome rates were calculated and compared for women who were positive according to 1) IADPSG criteria, 2) IADPSG criteria with risk stratification, or 3) screening with BMI or fasting plasma glucose (FPG).
Adopting IADPSG recommendations would increase GDM diagnosis by ∼50%. One-third of IADPSG-positive women were at low risk for adverse outcomes and could be managed less intensively. FPG ≥89 mg/dL or BMI ≥33.5 kg/m2 at 28–32 weeks of gestation detected proportions of adverse outcomes similar to IADPSG criteria.
Implementing IADPSG recommendations will substantially increase GDM diagnosis. Risk stratification in IADPSG-positive women may reduce over-treatment. Screening with FPG or BMI may be a practical alternative.
We examined the impact of environmental, person, and stimulus characteristics on pleasure in persons with dementia. Study participants were 193 residents of 7 Maryland nursing homes who were presented with 25 stimuli from these categories: live human social stimuli, simulated social stimuli, inanimate social stimuli, a reading stimulus, manipulative stimuli, a music stimulus, task and work-related stimuli, and two different self-identity stimuli. Systematic observations of pleasure in the natural environment were conducted using Lawton's Modified Behavior Stream. Analysis showed that pleasure is related to stimulus category, personal attributes and environmental conditions. In the multivariate analyses, all types of social stimuli (live and simulated, human and nonhuman), self-identity stimuli, and music were related to significantly higher levels of pleasure than the control condition. Females and persons with higher ADL and communication functional status exhibited more pleasure. Pleasure was most likely to occur in environments with moderate noise levels. These results demonstrate that these nursing home residents are indeed capable of showing pleasure. Caregivers of nursing home residents with dementia should incorporate social, self-identity, and music stimuli into their residents' care plans so that eliciting pleasure from each resident becomes the norm rather than a random occurrence.
nursing home residents with dementia; pleasure; environment; personal characteristics; nonpharmacological intervention
With the advent of Internet-based 24-hour recall (24HR) instruments, it is now possible to envision their use in cohort studies investigating the relation between nutrition and disease. Understanding that all dietary assessment instruments are subject to measurement errors and correcting for them under the assumption that the 24HR is unbiased for usual intake, here the authors simultaneously address precision, power, and sample size under the following 3 conditions: 1) 1–12 24HRs; 2) a single calibrated food frequency questionnaire (FFQ); and 3) a combination of 24HR and FFQ data. Using data from the Eating at America’s Table Study (1997–1998), the authors found that 4–6 administrations of the 24HR is optimal for most nutrients and food groups and that combined use of multiple 24HR and FFQ data sometimes provides data superior to use of either method alone, especially for foods that are not regularly consumed. For all food groups but the most rarely consumed, use of 2–4 recalls alone, with or without additional FFQ data, was superior to use of FFQ data alone. Thus, if self-administered automated 24HRs are to be used in cohort studies, 4–6 administrations of the 24HR should be considered along with administration of an FFQ.
combining dietary instruments; data collection; dietary assessment; energy adjustment; epidemiologic methods; measurement error; nutrient density; nutrient intake
The authors describe a statistical method of combining self-reports and biomarkers that, with adequate control for confounding, will provide nearly unbiased estimates of diet-disease associations and a valid test of the null hypothesis of no association. The method is based on regression calibration. In cases in which the diet-disease association is mediated by the biomarker, the association needs to be estimated as the total dietary effect in a mediation model. However, the hypothesis of no association is best tested through a marginal model that includes as the exposure the regression calibration-estimated intake but not the biomarker. The authors illustrate the method with data from the Carotenoids and Age-Related Eye Disease Study (2001--2004) and show that inclusion of the biomarker in the regression calibration-estimated intake increases the statistical power. This development sheds light on previous analyses of diet-disease associations reported in the literature.
bias (epidemiology); carotenoids; cataract; lutein; measurement error; sample size
Only a minority of smokers develop lung cancer, possibly due to genetic predisposition, including DNA repair deficiencies. To examine whether inter-individual variations in DNA repair activity of N-methylpurine DNA glycosylase (MPG) are associated with lung cancer, we conducted a blinded, population-based, case–control study with 100 lung cancer case patients and 100 matched control subjects and analyzed the data with conditional logistic regression. All statistical tests were two-sided. MPG enzyme activity in peripheral blood mononuclear cells from case patients was higher than in control subjects, results opposite that of 8-oxoguanine DNA glycosylase (OGG1) DNA repair enzyme activity. For lung cancer associated with one standard deviation increase in MPG activity, the adjusted odds ratio was 1.8 (95% confidence interval [CI] = 1.2 to 2.6; P = .006). A combined MPG and OGG1 activities score was more strongly associated with lung cancer risk than either activity alone, with an odds ratio of 2.3 (95% CI = 1.4 to 3.6; P < .001). These results form a basis for a future panel of risk biomarkers for lung cancer risk assessment and prevention.
Engagement refers to the act of being occupied or involved with an external stimulus. In dementia, engagement is the antithesis of apathy.
The Comprehensive Process Model of Engagement was examined, in which environmental, person, and stimulus characteristics impact the level of engagement of persons with dementia.
Participants were 193 residents of 7 Maryland nursing homes. All participants had a diagnosis of dementia. Stimulus engagement was assessed via the Observational Measure of Engagement. Engagement was measured by duration, attention, and attitude to the stimulus. 25 stimuli were presented, which were categorized as live human social stimuli, simulated social stimuli, inanimate social stimuli, a reading stimulus, manipulative stimuli, a music stimulus, task and work-related stimuli, and two different self-identity stimuli.
All stimuli elicited significantly greater engagement in comparison to the control stimulus. In the multivariate model, music significantly increased engagement duration, while all other stimuli significantly increased duration, attention, and attitude. Significant environmental variables in the multivariate model that increased engagement were: use of the long introduction with modeling (relative to minimal introduction), any level of sound (most especially moderate sound), and the presence of between 2 to 24 people in the room. Significant personal attributes included MMSE scores, ADL performance and clarity of speech, which were positively associated with higher engagement scores.
Results are consistent with the Comprehensive Process Model of Engagement. Person attributes, environmental factors, and stimulus characteristics all contribute to the level and nature of engagement, with a secondary finding being that exposure to any stimulus elicits engagement in persons with dementia.
nursing home residents; dementia; engagement; environment; personal characteristics; stimuli
Both diabetes and glucose-lowering medications have been associated with an increased risk of cancer incidence. This study will compare cancer incidence rates in individuals with and without diabetes; and will investigate, in individuals with diabetes, an association between glucose control and cancer incidence; and between the use of specific glucose-lowering medications, as well as no drug exposure, and cancer incidence.
This is a population based historical cohort study of all individuals aged 21 years or older (about 2,300,000) who were insured by Clalit Health Services, the largest health maintenance organization in Israel during a ten-year study period. Four study groups will be established according to the status of diabetes and cancer at study entry, Jan 1, 2002: cancer free, diabetes free; cancer free, diabetes prevalent; cancer prevalent, diabetes free; and cancer prevalent, diabetes prevalent. Individuals without diabetes at study entry will be followed for diabetes incidence, and all four groups will be followed for specific cancer incidence, including second primary neoplasms. Glucose control will be assessed by HbA1c and by fasting plasma glucose levels. Time dependent regression models for cancer incidence will account for glucose-lowering medications as they are added and changed over the follow-up period. A large number of demographic and clinical variables will be considered, including: age, gender, BMI, smoking status, concomitant medications, glucose control (assessed by HbA1c and by fasting plasma glucose) and cancer screening tests.
Strengths of this study include the large population; high quality comprehensive data; comparison to individuals without diabetes, and to those with diabetes but not treated with glucose-lowering medications; and the extensive range of variables available for analysis. The great increases in diabetes prevalence and in treatment options render this study particularly relevant and timely. The Israeli national healthcare system, characterized by high standard and uniform healthcare, offers an advantageous environment for its conduct.
Historical prospective; Incidence; Population based; Time dependent analysis
We consider a Bayesian analysis using WinBUGS to estimate the distribution of usual intake for episodically consumed foods and energy (calories). The model uses measures of nutrition and energy intakes via a food frequency questionnaire (FFQ) along with repeated 24 hour recalls and adjusting covariates. In order to estimate the usual intake of the food, we phrase usual intake in terms of person-specific random effects, along with day-to-day variability in food and energy consumption. Three levels are incorporated in the model. The first level incorporates information about whether an individual in fact reported consumption of a particular food item. The second level incorporates the amount of intake from those individuals who reported consumption of the food, and the third level incorporates the energy intake. Estimates of posterior means of parameters and distributions of usual intakes are obtained by using Markov chain Monte Carlo calculations. This R function reports to users point estimates and credible intervals for parameters in the model, samples from their posterior distribution, samples from the distribution of usual intake and usual energy intake, trace plots of parameters and summary statistics of usual intake, usual energy intake and energy adjusted usual intake.
excess zero models; MCMC; nonlinear mixed models; R; R2WinBUGS; zero-inflation
Dietary measurement error creates serious challenges to reliably discovering new diet–disease associations in nutritional cohort studies. Such error causes substantial underestimation of relative risks and reduction of statistical power for detecting associations. On the basis of data from the Observing Protein and Energy Nutrition Study, we recommend the following approaches to deal with these problems. Regarding data analysis of cohort studies using food-frequency questionnaires, we recommend 1) using energy adjustment for relative risk estimation; 2) reporting estimates adjusted for measurement error along with the usual relative risk estimates, whenever possible (this requires data from a relevant, preferably internal, validation study in which participants report intakes using both the main instrument and a more detailed reference instrument such as a 24-hour recall or multiple-day food record); 3) performing statistical adjustment of relative risks, based on such validation data, if they exist, using univariate (only for energy-adjusted intakes such as densities or residuals) or multivariate regression calibration. We note that whereas unadjusted relative risk estimates are biased toward the null value, statistical significance tests of unadjusted relative risk estimates are approximately valid. Regarding study design, we recommend increasing the sample size to remedy loss of power; however, it is important to understand that this will often be an incomplete solution because the attenuated signal may be too small to distinguish from unmeasured confounding in the model relating disease to reported intake. Future work should be devoted to alleviating the problem of signal attenuation, possibly through the use of improved self-report instruments or by combining dietary biomarkers with self-report instruments.
A retrospective analysis was conducted to examine factors affecting early mortality following myeloablative, single-unit cord blood transplantation (CBT) for hematological malignancies in adolescents and adults. Data were collected from the three main CBT registries pooling 514 records of unrelated, single, unmanipulated, first myeloablative allogeneic CBTs conducted in North America or Europe from 1995 to 2005, with an HLA match ≥4/6 loci, in patients aged 12 to 55. Overall 100-, 180- day and 1-year survival (Kaplan-Meier method) were 56%, 46% and 37%, respectively, with no significant heterogeneity across registries. Multivariate analysis showed cell dose < 2.5×107/Kg (Odds Ratio [OR] 2.76, p<0.0001), older age (p=0.002), advanced disease (p=0.02), positive CMV sero-status (OR 1.37 p=0.11), female gender (OR 1.43, p=0.07) and limited CBT center experience (<10 records contributed, OR 2.08, p=0.0003) to be associated with higher 100-day mortality. A multivariate model predictive of 1-year mortality included similar prognostic factors except female gender. Transplant year did not appear as a significant independent predictor. This is the first analysis to pool records from three major CBT registries in the US and Europe. Despite some differences in practice patterns, survival was remarkably homogeneous. The resulting model may contribute to better understanding factors affecting CBT outcomes.
Cord blood transplantation; registry; leukemia; mortality; leukemia
To develop a method to validate an FFQ for reported intake of episodically consumed foods when the reference instrument measures short-term intake, and to apply the method in a large prospective cohort.
The FFQ was evaluated in a sub-study of cohort participants who, in addition to the questionnaire, were asked to complete two non-consecutive 24 h dietary recalls (24HR). FFQ-reported intakes of twenty-nine food groups were analysed using a two-part measurement error model that allows for nonconsumption on a given day, using 24HR as a reference instrument under the assumption that 24HR is unbiased for true intake at the individual level.
The National Institutes of Health–AARP Diet and Health Study, a cohort of 567 169 participants living in the USA and aged 50–71 years at baseline in 1995.
A sub-study of the cohort consisting of 2055 participants.
Estimated correlations of true and FFQ-reported energy-adjusted intakes were 0·5 or greater for most of the twenty-nine food groups evaluated, and estimated attenuation factors (a measure of bias in estimated diet–disease associations) were 0·4 or greater for most food groups.
The proposed methodology extends the class of foods and nutrients for which an FFQ can be evaluated in studies with short-term reference instruments. Although violations of the assumption that the 24HR is unbiased could be inflating some of the observed correlations and attenuation factors, results suggest that the FFQ is suitable for testing many, but not all, diet–disease hypotheses in a cohort of this size.
Diet; Food; Epidemiological methods; Questionnaires; Validation studies
To investigate the association of polymorphisms within candidate genes which we hypothesized may contribute to stress fracture predisposition, a case-control, cross- sectional study design was employed. Genotyping 268 Single Nucleotide Polymorphisms- SNPs within 17 genes in 385 Israeli young male and female recruits (182 with and 203 without stress fractures). Twenty-five polymorphisms within 9 genes (NR3C1, ANKH, VDR, ROR2, CALCR, IL6, COL1A2, CBG, and LRP4) showed statistically significant differences (p < 0.05) in the distribution between stress fracture cases and non stress fracture controls. Seventeen genetic variants were associated with an increased stress fracture risk, and eight variants with a decreased stress fracture risk. None of the SNP associations remained significant after correcting for multiple comparisons (false discovery rate- FDR). Our findings suggest that genes may be involved in stress fracture pathogenesis. Specifically, the CALCR and the VDR genes are intriguing candidates. The putative involvement of these genes in stress fracture predisposition requires analysis of more cases and controls and sequencing the relevant genomic regions, in order to define the specific gene mutations.
Key pointsUnderstanding the possible contribution of genetic variants to stress fracture pathogenesis.There is a paucity of data on the involvement of polymorphisms in specific genes in active military personnel/athletes which may contribute to stress fractures development.The results from the current study should facilitate a more comprehensive look at the genetic component of stress fractures.
Stress fractures; Bone remodeling; genetic variance; SNPs; inherited predisposition.
There has been great public health interest in estimating usual, i.e., long-term average, intake of episodically consumed dietary components that are not consumed daily by everyone, e.g., fish, red meat and whole grains. Short-term measurements of episodically consumed dietary components have zero-inflated skewed distributions. So-called two-part models have been developed for such data in order to correct for measurement error due to within-person variation and to estimate the distribution of usual intake of the dietary component in the univariate case. However, there is arguably much greater public health interest in the usual intake of an episodically consumed dietary component adjusted for energy (caloric) intake, e.g., ounces of whole grains per 1000 kilo-calories, which reflects usual dietary composition and adjusts for different total amounts of caloric intake. Because of this public health interest, it is important to have models to fit such data, and it is important that the model-fitting methods can be applied to all episodically consumed dietary components.
We have recently developed a nonlinear mixed effects model (Kipnis, et al., 2010), and have fit it by maximum likelihood using nonlinear mixed effects programs and methodology (the SAS NLMIXED procedure). Maximum likelihood fitting of such a nonlinear mixed model is generally slow because of 3-dimensional adaptive Gaussian quadrature, and there are times when the programs either fail to converge or converge to models with a singular covariance matrix. For these reasons, we develop a Monte-Carlo (MCMC) computation of fitting this model, which allows for both frequentist and Bayesian inference. There are technical challenges to developing this solution because one of the covariance matrices in the model is patterned. Our main application is to the National Institutes of Health (NIH)-AARP Diet and Health Study, where we illustrate our methods for modeling the energy-adjusted usual intake of fish and whole grains. We demonstrate numerically that our methods lead to increased speed of computation, converge to reasonable solutions, and have the flexibility to be used in either a frequentist or a Bayesian manner.
Bayesian approach; latent variables; measurement error; mixed effects models; nutritional epidemiology; zero-inflated data
The objective of this paper was to assess the relative impact of different types of stimuli on agitated behaviors of nursing home residents with dementia.
Participants were 111 residents of 7 Maryland nursing homes with a diagnosis of dementia who exhibited agitation.
Different types of stimuli (music, social stimuli, simulated social stimuli, and individualized stimuli based on the person’s self-identity) to prevent behavior problems.
Agitation was directly observed and recorded via the Agitated Behaviors Mapping Instrument.
All stimulus categories were associated with significantly less physical agitation than baseline observations, and all except for manipulative stimuli were associated with significantly less total agitation. Live social stimuli were associated with less agitation than music, self-identity, work, simulated social, and manipulative stimulus categories. Task and reading stimulus categories were each associated with significantly less agitation than work, simulated social, and manipulative stimulus categories. Music and self-identity stimuli were associated with less agitation than simulated social and manipulative stimuli.
Providing stimuli offers a proactive approach to preventing agitation in persons with dementia, with live social stimuli being most successful.
dementia; agitation; prevention; stimuli; nursing home residents
In the United States the preferred method of obtaining dietary intake data is the 24-hour dietary recall, yet the measure of most interest is usual or long-term average daily intake, which is impossible to measure. Thus, usual dietary intake is assessed with considerable measurement error. Also, diet represents numerous foods, nutrients and other components, each of which have distinctive attributes. Sometimes, it is useful to examine intake of these components separately, but increasingly nutritionists are interested in exploring them collectively to capture overall dietary patterns. Consumption of these components varies widely: some are consumed daily by almost everyone on every day, while others are episodically consumed so that 24-hour recall data are zero-inflated. In addition, they are often correlated with each other. Finally, it is often preferable to analyze the amount of a dietary component relative to the amount of energy (calories) in a diet because dietary recommendations often vary with energy level. The quest to understand overall dietary patterns of usual intake has to this point reached a standstill. There are no statistical methods or models available to model such complex multivariate data with its measurement error and zero inflation. This paper proposes the first such model, and it proposes the first workable solution to fit such a model. After describing the model, we use survey-weighted MCMC computations to fit the model, with uncertainty estimation coming from balanced repeated replication.
The methodology is illustrated through an application to estimating the population distribution of the Healthy Eating Index-2005 (HEI-2005), a multi-component dietary quality index involving ratios of interrelated dietary components to energy, among children aged 2-8 in the United States. We pose a number of interesting questions about the HEI-2005 and provide answers that were not previously within the realm of possibility, and we indicate ways that our approach can be used to answer other questions of importance to nutritional science and public health.
Bayesian methods; Dietary assessment; Latent variables; Measurement error; Mixed models; Nutritional epidemiology; Nutritional surveillance; Zero-Inflated Data
A major problem in detecting diet-disease associations in nutritional cohort studies is measurement error in self-reported intakes, which causes loss of statistical power. The authors propose using biomarkers correlated with dietary intake to strengthen analyses of diet-disease hypotheses and to increase statistical power. They consider combining self-reported intakes and biomarker levels using principal components or a sum of ranks and relating the combined measure to disease in conventional regression analyses. They illustrate their method in a study of the inverse association of dietary lutein plus zeaxanthin with nuclear cataracts, using serum lutein plus zeaxanthin as the biomarker, with data from the Carotenoids in Age-Related Eye Disease Study (United States, 2001–2004). This example demonstrates that the combined measure provides higher statistical significance than the dietary measure or the serum measure alone, and it potentially provides sample savings of 8%–53% over analysis with dietary intake alone and of 6%–48% over analysis with serum level alone, depending on the definition of the outcome variable and the choice of confounders entered into the regression model. The authors conclude that combining appropriate biomarkers with dietary data in a cohort can strengthen the investigation of diet-disease associations by increasing the statistical power to detect them.
carotenoids; cataract; lutein; ranks; sample size
Dietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006, Journal of the American Dietetic Association 106, 1575–1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the food’s usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at America’s Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context.
Dietary measurement error; Dietary survey; Episodically consumed foods; Excess zero models; Food frequency questionnaire; Fish; Individual usual intake; Mercury; Nonlinear mixed models; Regression calibration; 24-hour recall
In spite of recent advances in post-operative pain relief, pain following orthopedic surgery remains an ongoing challenge for clinicians. We examined whether a well known and frequently prescribed homeopathic preparation could mitigate post-operative pain.
We performed a randomized, double blind, placebo-controlled trial to evaluate the efficacy of the homeopathic preparation Traumeel S® in minimizing post-operative pain and analgesic consumption following surgical correction of hallux valgus. Eighty consecutive patients were randomized to receive either Traumeel tablets or an indistinguishable placebo, and took primary and rescue oral analgesics as needed. Maximum numerical pain scores at rest and consumption of oral analgesics were recorded on day of surgery and for 13 days following surgery.
Traumeel was not found superior to placebo in minimizing pain or analgesic consumption over the 14 days of the trial, however a transient reduction in the daily maximum post-operative pain score favoring the Traumeel arm was observed on the day of surgery, a finding supported by a treatment-time interaction test (p = 0.04).
Traumeel was not superior to placebo in minimizing pain or analgesic consumption over the 14 days of the trial. A transient reduction in the daily maximum post-operative pain score on the day of surgery is of questionable clinical importance.
This study was registered at ClinicalTrials.gov. # NCT00279513
Identifying diet-disease relationships in nutritional cohort studies is plagued by the measurement error in self-reported intakes.
The authors propose using biomarkers known to be correlated with dietary intake, so as to strengthen analyses of diet-disease hypotheses. The authors consider combining self-reported intakes and biomarker levels using principal components, Howe's method, or a joint statistical test of effects in a bivariate model. They compared the statistical power of these methods with that of conventional univariate analyses of self-reported intake or of biomarker level. They used computer simulation of different disease risk models, with input parameters based on data from the literature on the relationship between lutein intake and age-related macular degeneration.
The results showed that if the dietary effect on disease was fully mediated through the biomarker level, then the univariate analysis of the biomarker was the most powerful approach. However, combination methods, particularly principal components and Howe's method, were not greatly inferior in this situation, and were as good as, or better than, univariate biomarker analysis if mediation was only partial or non-existent. In some circumstances sample size requirements were reduced to 20-50% of those required for conventional analyses of self-reported intake.
The authors conclude that (i) including biomarker data in addition to the usual dietary data in a cohort could greatly strengthen the investigation of diet-disease relationships, and (ii) when the extent of mediation through the biomarker is unknown, use of principal components or Howe's method appears a good strategy.
We examine two issues of importance in nutritional epidemiology: the relationship between dietary fat intake and breast cancer, and the comparison of different dietary assessment instruments, in our case the food frequency questionnaire (FFQ) and the multiple-day food record (FR). The data we use come from women participants in the control group of the Dietary Modification component of the Women’s Health Initiative (WHI) Clinical Trial. The difficulty with the analysis of this important data set is that it comes from a truncated sample, namely those women for whom fat intake as measured by the FFQ amounted to 32% or more of total calories. We describe methods that allow estimation of logistic regression parameters in such samples, and also allow comparison of different dietary instruments. Because likelihood approaches that specify the full multivariate distribution can be difficult to implement, we develop approximate methods for both our main problems that are simple to compute and have high efficiency. Application of these approximate methods to the WHI study reveals statistically significant fat and breast cancer relationships when a FR is the instrument used, and demonstrate a marginally significant advantage of the FR over the FFQ in the local power to detect such relationships.
Biased sampling; Breast cancer; Case–control studies; Comparison of instruments; Measurement error; Misspecified models; Nutritional epidemiology; Truncation; Women’s Health Initiative
Regression calibration (RC) is a popular method for estimating regression coefficients when one or more continuous explanatory variables, X, are measured with an error. In this method, the mismeasured covariate, W, is substituted by the expectation E(X|W), based on the assumption that the error in the measurement of X is non-differential. Using simulations, we compare three versions of RC with two other ‘substitution’ methods, moment reconstruction (MR) and imputation (IM), neither of which rely on the non-differential error assumption. We investigate studies that have an internal calibration sub-study. For RC, we consider (i) the usual version of RC, (ii) RC applied only to the ‘marker’ information in the calibration study, and (iii) an ‘efficient’ version (ERC) in which the estimators (i) and (ii) are combined. Our results show that ERC is preferable when there is non-differential measurement error. Under this condition, there are cases where ERC is less efficient than MR or IM, but they rarely occur in epidemiology. We show that the efficiency gain of usual RC and ERC over the other methods can sometimes be dramatic. The usual version of RC carries similar efficiency gains to ERC over MR and IM, but becomes unstable as measurement error becomes large, leading to bias and poor precision. When differential measurement error does pertain, then MR and IM have considerably less bias than RC, but can have much larger variance. We demonstrate our findings with an analysis of dietary fat intake and mortality in a large cohort study.
differential measurement error; moment reconstruction; multiple imputation; non-differential measurement error; regression calibration
The U.S. Department of Agriculture’s (USDA) Healthy Eating Index-2005 (HEI-2005) is a tool to quantify and evaluate the quality of diet consumed by the US population. It comprises 12 components, expressed as ratios of a food group or nutrient to energy intake. The components are scored on a scale from 0 to M, where M is 5, 10 or 20. Ideally the HEI-2005 is calculated on the basis of the usual dietary intake of an individual. Intake data, collected via a 24-hour recall, are often available for only one day on each individual. In this paper, we examine how best to estimate a population’s mean usual HEI-2005 component and total scores when one day of dietary information is available for a sample of individuals from the population. Three methods are considered: the mean of individual scores, the score of the mean of individual ratios, and the score of the ratio of total food group or nutrient intake to total energy intake, which we call the population ratio. We investigate via computer simulation which method is the least biased. The simulations are based on statistical modeling of the distributions of intakes reported by 738 women participating in the Eating at America’s Table Study. The results show that overall the score of the population ratio is the preferred method. We therefore recommend that the quality of the US population’s diet be assessed and monitored using this method.
We propose a new statistical method that uses information from two 24-hour recalls (24HRs) to estimate usual intake of episodically-consumed foods.
Statistical Analyses Performed
The method developed at the National Cancer Institute (NCI) accommodates the large number of non-consumption days that arise with foods by separating the probability of consumption from the consumption-day amount, using a two-part model. Covariates, such as sex, age, race, or information from a food frequency questionnaire (FFQ), may supplement the information from two or more 24HRs using correlated mixed model regression. The model allows for correlation between the probability of consuming a food on a single day and the consumption-day amount. Percentiles of the distribution of usual intake are computed from the estimated model parameters.
The Eating at America's Table Study (EATS) data are used to illustrate the method to estimate the distribution of usual intake for whole grains and dark green vegetables for men and women and the distribution of usual intakes of whole grains by educational level among men. A simulation study indicates that the NCI method leads to substantial improvement over existing methods for estimating the distribution of usual intake of foods.
The NCI method provides distinct advantages over previously proposed methods by accounting for the correlation between probability of consumption and amount consumed and by incorporating covariate information. Researchers interested in estimating the distribution of usual intakes of foods for a population or subpopulation are advised to work with a statistician and incorporate the NCI method in analyses.
Usual intake; Episodically-consumed foods; statistical methods