@article {913, title = {Body mass index is not a good predictor of bone density: results from WHI, CHS, and EPIDOS.}, journal = {J Clin Densitom}, volume = {9}, year = {2006}, month = {2006 Jul-Sep}, pages = {329-34}, abstract = {

Body mass index (BMI) is often used to predict bone mineral density (BMD). This may be flawed. Large epidemiologic studies with BMI and BMD data were analyzed. Weight alone is a better predictor of BMD than BMI. Thus, when selecting individuals for dual-energy X-ray absorptiometry, weight should be used instead of BMI. Low body mass index (BMI) is frequently suggested as one of the factors that indicates the need for bone mineral density (BMD) screening for osteoporosis. The inclusion of the height-squared term in the denominator of this predictive factor is taken on faith or from other data, but it may not be reasonable in this case. We used data from three large epidemiologic studies to test the BMI, height, and weight as predictors of BMD: (1) the Women{\textquoteright}s Health Initiative (WHI) with 11,390 women; (2) the Cardiovascular Health Study (CHS) with 1,578 men and women; (3) and EPIDOS with 7,598 women. Dual-energy X-ray absorptiometry data on one or more BMD sites, the total hip, the femoral neck, and the lumbar spine from the three studies, as well as height and weight were examined. Correlation coefficients for BMI and weight with BMD were compared. Log transformed models were evaluated to compare the strengths of the models. The result of weight alone was a much better predictor of BMD for all sites in the three studies than BMI. Taller participants had larger BMDs than would have been predicted by BMI. In conclusion, BMIs should not be used to select individuals for BMD screening. A regression model using weight alone or weight and height is a better predictor of BMD in all three populations.

}, keywords = {Absorptiometry, Photon, Aged, Aged, 80 and over, Biometry, Body Height, Body Mass Index, Body Weight, Bone Density, Female, France, Humans, Male, United States}, issn = {1094-6950}, doi = {10.1016/j.jocd.2006.02.005}, author = {Robbins, John and Schott, Anne-Marie and Azari, Rahman and Kronmal, Richard} } @article {849, title = {The sensitivity and specificity of markers for event times.}, journal = {Biostatistics}, volume = {7}, year = {2006}, month = {2006 Apr}, pages = {182-97}, abstract = {

The statistical literature on assessing the accuracy of risk factors or disease markers as diagnostic tests deals almost exclusively with settings where the test, Y, is measured concurrently with disease status D. In practice, however, disease status may vary over time and there is often a time lag between when the marker is measured and the occurrence of disease. One example concerns the Framingham risk score (FR-score) as a marker for the future risk of cardiovascular events, events that occur after the score is ascertained. To evaluate such a marker, one needs to take the time lag into account since the predictive accuracy may be higher when the marker is measured closer to the time of disease occurrence. We therefore consider inference for sensitivity and specificity functions that are defined as functions of time. Semiparametric regression models are proposed. Data from a cohort study are used to estimate model parameters. One issue that arises in practice is that event times may be censored. In this research, we extend in several respects the work by Leisenring et al. (1997) that dealt only with parametric models for binary tests and uncensored data. We propose semiparametric models that accommodate continuous tests and censoring. Asymptotic distribution theory for parameter estimates is developed and procedures for making statistical inference are evaluated with simulation studies. We illustrate our methods with data from the Cardiovascular Health Study, relating the FR-score measured at enrollment to subsequent risk of cardiovascular events.

}, keywords = {Aged, Aged, 80 and over, Biomarkers, Biometry, Cardiovascular Diseases, Female, Humans, Male, Models, Statistical, Risk Factors, ROC Curve, Sensitivity and Specificity, Time Factors}, issn = {1465-4644}, doi = {10.1093/biostatistics/kxi047}, author = {Cai, Tianxi and Pepe, Margaret Sullivan and Zheng, Yingye and Lumley, Thomas and Jenny, Nancy Swords} } @article {981, title = {Age-specific prevalence and years of healthy life in a system with three health states.}, journal = {Stat Med}, volume = {27}, year = {2008}, month = {2008 Apr 30}, pages = {1371-86}, abstract = {

Consider a 3-state system with one absorbing state, such as Healthy, Sick, and Dead. Over time, the prevalence of the Healthy state will approach an {\textquoteright}equilibrium{\textquoteright} value that is independent of the initial conditions. We derived this equilibrium prevalence (Prev:Equil) as a function of the local transition probabilities. We then used Prev:Equil to estimate the expected number of years spent in the healthy state over time. This estimate is similar to the one calculated by multi-state life table methods, and has the advantage of having an associated standard error. In longitudinal data for older adults, the standard error was accurate when a valid survival table was known from other sources, or when the available data set was sufficient to estimate survival accurately. Performance was better with fewer waves of data. If validated in other situations, these estimates of prevalence and years of healthy life (YHL) and their standard errors may be useful when the goal is to compare YHL for different populations.

}, keywords = {Age Factors, Biometry, Health Status, Humans, Life Tables, Longitudinal Studies, Middle Aged, Models, Statistical, Probability, Quality-Adjusted Life Years}, issn = {0277-6715}, doi = {10.1002/sim.3056}, author = {Diehr, Paula and Yanez, David and Derleth, Ann and Newman, Anne B} }