Does autogenous bone graft work? A logistic regression analysis of data from 159 papers in the foot and ankle literature.

Background: While autogenous cancellous iliac crest bone graft is the gold standard for foot and ankle surgery, it lacks Level I evidence. Although one third of all graft cases performed in the United States today rely on allograft, some surgeons believe no graft is necessary. We hypothesized that a systematic review of the foot and ankle literature would reveal that (1) autogenous bone graft during foot and ankle arthrodesis would demonstrate healing rates that were superior to the use of either using allograft or no bone graft at all, and (2) these differences would be even more dramatic in patients having risk factors that impair bone healing.

Preliminary efficacy of group medical nutrition therapy and motivational interviewing among obese African American women with type 2 diabetes: a pilot study.

Objective: To assess the efficacy and acceptability of a group medical nutritional therapy (MNT) intervention, using motivational interviewing (MI). RESEARCH DESIGN & METHOD: African American (AA) women with type 2 diabetes (T2D) participated in five, certified diabetes educator/dietitian-facilitated intervention sessions targeting carbohydrate, fat, and fruit/vegetable intake and management. Motivation-based activities centered on exploration of dietary ambivalence and the relationships between diet and personal strengths.

Estimating time-varying effects for overdispersed recurrent events data with treatment switching.

In the analysis of multivariate event times, frailty models assuming time-independent regression coefficients are often considered, mainly due to their mathematical convenience. In practice, regression coefficients are often time dependent and the temporal effects are of clinical interest. Motivated by a phase III clinical trial in multiple sclerosis, we develop a semiparametric frailty modelling approach to estimate time-varying effects for overdispersed recurrent events data with treatment switching.

Estimating the probability of IQ impairment from blood phenylalanine for phenylketonuria patients: a hierarchical meta-analysis.

Though the control of blood phenylalanine (Phe) levels is essential for minimizing impairment in individuals with phenylketonuria (PKU), the empirical basis for the selection of specific blood Phe levels as targets has not been evaluated. We evaluated the current evidence that particular Phe levels are optimal for minimizing or avoiding cognitive impairment in individuals with PKU. This work uses meta-estimates of blood Phe-IQ correlation to predict the probability of low IQ for a range of Phe levels.

Outcome-dependent sampling for longitudinal binary response data based on a time-varying auxiliary variable.

Outcome-dependent sampling (ODS) study designs are commonly implemented with rare diseases or when prospective studies are infeasible. In longitudinal data settings, when a repeatedly measured binary response is rare, an ODS design can be highly efficient for maximizing statistical information subject to resource limitations that prohibit covariate ascertainment of all observations. This manuscript details an ODS design where individual observations are sampled with probabilities determined by an inexpensive, time-varying auxiliary variable that is related but is not equal to the response.

Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders.

Propensity scores are widely used in cohort studies to improve performance of regression models when considering large numbers of covariates. Another type of summary score, the disease risk score (DRS), which estimates disease probability conditional on nonexposure, has also been suggested. However, little is known about how it compares with propensity scores.

Assessing variance components in multilevel linear models using approximate Bayes factors: A case study of ethnic disparities in birthweight.

Racial/ethnic disparities in birthweight are a large source of differential morbidity and mortality worldwide and have remained largely unexplained in epidemiologic models. We assess the impact of maternal ancestry and census tract residence on infant birth weights in New York City and the modifying effects of race and nativity by incorporating random effects in a multilevel linear model. Evaluating the significance of these predictors involves the test of whether the variances of the random effects are equal to zero.

Outcome-dependent sampling from existing cohorts with longitudinal binary response data: study planning and analysis.

When novel scientific questions arise after longitudinal binary data have been collected, the subsequent selection of subjects from the cohort for whom further detailed assessment will be undertaken is often necessary to efficiently collect new information. Key examples of additional data collection include retrospective questionnaire data, novel data linkage, or evaluation of stored biological specimens. In such cases, all data required for the new analyses are available except for the new target predictor or exposure.

A robust method for comparing two treatments in a confirmatory clinical trial via multivariate time-to-event methods that jointly incorporate information from longitudinal and time-to-event data.

We consider regulatory clinical trials that require a prespecified method for the comparison of two treatments for chronic diseases (e.g. Chronic Obstructive Pulmonary Disease) in which patients suffer deterioration in a longitudinal process until death occurs.

Longitudinal studies of binary response data following case-control and stratified case-control sampling: design and analysis.

We discuss design and analysis of longitudinal studies after case-control sampling, wherein interest is in the relationship between a longitudinal binary response that is related to the sampling (case-control) variable, and a set of covariates. We propose a semiparametric modeling framework based on a marginal longitudinal binary response model and an ancillary model for subjects' case-control status. In this approach, the analyst must posit the population prevalence of being a case, which is then used to compute an offset term in the ancillary model.

Gamma frailty transformation models for multivariate survival times.

We propose a class of transformation models for multivariate failure times. The class of transformation models generalize the usual gamma frailty model and yields a marginally linear transformation model for each failure time. Nonparametric maximum likelihood estimation is used for inference.

A Bayesian latent variable mixture model for longitudinal fetal growth.

Fetal growth restriction is a leading cause of perinatal morbidity and mortality that could be reduced if high-risk infants are identified early in pregnancy. We propose a Bayesian model for aggregating 18 longitudinal ultrasound measurements of fetal size and blood flow into three underlying, continuous latent factors. Our procedure is more flexible than typical latent variable methods in that we relax the normality assumptions by allowing the latent factors to follow finite mixture distributions.

Testing random effects in the linear mixed model using approximate bayes factors.

Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors.

Regression analysis of longitudinal binary data with time-dependent environmental covariates: bias and efficiency.

Generalized estimating equations (Liang and Zeger, 1986) is a widely used, moment-based procedure to estimate marginal regression parameters. However, a subtle and often overlooked point is that valid inference requires the mean for the response at time t to be expressed properly as a function of the complete past, present, and future values of any time-varying covariate. For example, with environmental exposures it may be necessary to express the response as a function of multiple lagged values of the covariate series.

Model-checking techniques for stratified case-control studies.

We present graphical and numerical methods for assessing the adequacy of the logistic regression model for stratified case-control data. The proposed methods are derived from the cumulative sum of residuals over the covariate or linear predictor. Under the assumed model, the cumulative residual process converges weakly to a zero-mean Gaussian process whose distribution can be approximated via Monte Carlo simulation.