Epidemiology of perinatal mortality: Birthweight, crown -heel length, gestational age, and weight -for -height

Studies suggest that slim infants (low weight-for-height) experienced higher mortality rates than average or high weight-for-height infants (Miller and Hassanein, 1973; Hoffman, Meirik, and Bakketeig, 1984). In this study, the 1980 National Natality Survey and the National Fetal Mortality Survey were used to examine the association of weight, height and perinatal mortality. All singleton births to white married mothers, between 18 and 34 years of age and of parity less than 4, for whom both mother's and hospital questionnaires were completed in those two surveys (3796 live births and 2043 fetal deaths) were selected for analysis. Overall, low weight and height infants had excess mortality rates. However, after adjustment for low birthweight and preterm birth status, low weight and height infants had only slightly higher mortality rates than their medium or high weight and height counterparts. The current study consists of relatively well-educated white married mothers of optimal reproductive age and low parity. Therefore, lower than expected mortality rates for slim infants may be attributed to these favorable demographic factors in this sample as compared with previous studies, or because of advances in perinatal medicine, slim infants may be prevented from achieving the high mortality seen in earlier studies. ^

Age -period -cohort analysis: An extension of Cox's lifetable regression with time -dependent covariates

It is well known that an identification problem exists in the analysis of age-period-cohort data because of the relationship among the three factors (date of birth + age at death = date of death). There are numerous suggestions about how to analyze the data. No one solution has been satisfactory. The purpose of this study is to provide another analytic method by extending the Cox's lifetable regression model with time-dependent covariates. The new approach contains the following features: (1) It is based on the conditional maximum likelihood procedure using a proportional hazard function described by Cox (1972), treating the age factor as the underlying hazard to estimate the parameters for the cohort and period factors. (2) The model is flexible so that both the cohort and period factors can be treated as dummy or continuous variables, and the parameter estimations can be obtained for numerous combinations of variables as in a regression analysis. (3) The model is applicable even when the time period is unequally spaced.^ Two specific models are considered to illustrate the new approach and applied to the U.S. prostate cancer data. We find that there are significant differences between all cohorts and there is a significant period effect for both whites and nonwhites. The underlying hazard increases exponentially with age indicating that old people have much higher risk than young people. A log transformation of relative risk shows that the prostate cancer risk declined in recent cohorts for both models. However, prostate cancer risk declined 5 cohorts (25 years) earlier for whites than for nonwhites under the period factor model (0 0 0 1 1 1 1). These latter results are similar to the previous study by Holford (1983).^ The new approach offers a general method to analyze the age-period-cohort data without using any arbitrary constraint in the model. ^

The number and timing of time -dependent covariate measurements for the Cox regression model

The problem of analyzing data with updated measurements in the time-dependent proportional hazards model arises frequently in practice. One available option is to reduce the number of intervals (or updated measurements) to be included in the Cox regression model. We empirically investigated the bias of the estimator of the time-dependent covariate while varying the effect of failure rate, sample size, true values of the parameters and the number of intervals. We also evaluated how often a time-dependent covariate needs to be collected and assessed the effect of sample size and failure rate on the power of testing a time-dependent effect.^ A time-dependent proportional hazards model with two binary covariates was considered. The time axis was partitioned into k intervals. The baseline hazard was assumed to be 1 so that the failure times were exponentially distributed in the ith interval. A type II censoring model was adopted to characterize the failure rate. The factors of interest were sample size (500, 1000), type II censoring with failure rates of 0.05, 0.10, and 0.20, and three values for each of the non-time-dependent and time-dependent covariates (1/4,1/2,3/4).^ The mean of the bias of the estimator of the coefficient of the time-dependent covariate decreased as sample size and number of intervals increased whereas the mean of the bias increased as failure rate and true values of the covariates increased. The mean of the bias of the estimator of the coefficient was smallest when all of the updated measurements were used in the model compared with two models that used selected measurements of the time-dependent covariate. For the model that included all the measurements, the coverage rates of the estimator of the coefficient of the time-dependent covariate was in most cases 90% or more except when the failure rate was high (0.20). The power associated with testing a time-dependent effect was highest when all of the measurements of the time-dependent covariate were used. An example from the Systolic Hypertension in the Elderly Program Cooperative Research Group is presented. ^

Genome instability quantified in constitutive tissues as well as putatively stable tumor tissue in hereditary non -polyposis colon cancer patients by small pool PCR

Microsatellite instability (MSI) is a hallmark of the mutator phenotype associated with Hereditary Non-Polyposis Colon Cancer (HNPCC). The MSI-High (MSI-H) HNPCC population has been well characterized, but the microsatellite low and stable (MSI-L/MSS) HNPCC population is much less understood. We hypothesize there are significant levels of MSI in HNPCC DNA classified as MSI-L/MSS, but no single variant allele makes up a sufficient population in the tumor DNA to be detected by standard analysis. Finding variants would suggest there is a mutator phenotype for the MSI-L/MSS HNPCC population that is distinct from the MSI-H HNPCC populations. This study quantified and compared MSI in HNPCC patients previously shown to be MSI-H, MSI-L/MSS and an MSI-H older, sporadic colorectal cancer patient. Small-pool Polymerase Chain Reactions (SP-PCRs) were conducted where the DNAs from each sample and controls are diluted into multiple pools, each containing approximately single genome equivalents. At least 100 alleles/sample were studied at six microsatellite loci. Mutant fragments were identified, quantified, and compared using Poisson statistics. Most of the variants were small deletions or insertions, with more mutants being deletions, as has been previously described in yeast and transgenic mice. SP-PCR, where most of the pools contained only 3 or less fragments, enabled identification of variants too infrequent to be detected by large pool PCR. Mutant fragments in positive control MSI-H tumor samples ranged from 0.26 to 0.68 in at least 4 of the 6 loci tested and were consistent with their MSI-H status. In the so called MSS tumors and constitutive tissues (normal colon tissue, and PBLs) of all the HNPCC patients, low, but significant levels of MSI were seen in at least two of the loci studied. This phenomenon was not seen in the sporadic MSI constitutive tissues nor the normal controls and suggests haploinsufficiency, gain-of-function, or a dominant/negative basis of the instability in HNPCC patients carrying germline mutations for tumor suppressor genes. A different frequency and spectrum of mutant fragments suggests a different genetic basis (other than a major mutation in MLH1 or MSH2) for disease in MSI-L and MSS HNPCC patients. ^

Analysis of interval -censored events in hypertension studies: Evaluation of treatment of coronary heart disease

Many statistical studies feature data with both exact-time and interval-censored events. While a number of methods currently exist to handle interval-censored events and multivariate exact-time events separately, few techniques exist to deal with their combination. This thesis develops a theoretical framework for analyzing a multivariate endpoint comprised of a single interval-censored event plus an arbitrary number of exact-time events. The approach fuses the exact-time events, modeled using the marginal method of Wei, Lin, and Weissfeld, with a piecewise-exponential interval-censored component. The resulting model incorporates more of the information in the data and also removes some of the biases associated with the exclusion of interval-censored events. A simulation study demonstrates that our approach produces reliable estimates for the model parameters and their variance-covariance matrix. As a real-world data example, we apply this technique to the Systolic Hypertension in the Elderly Program (SHEP) clinical trial, which features three correlated events: clinical non-fatal myocardial infarction, fatal myocardial infarction (two exact-time events), and silent myocardial infarction (one interval-censored event). ^