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Biomedical subjects

R Dersimonian

Publications and source records attributed to R Dersimonian.

5 recordsLinked to original sources

Serum caffeine and paraxanthine as markers for reported caffeine intake in pregnancy.

PURPOSE: Previous studies of maternal caffeine use and pregnancy outcome have relied on self-reported use. Even if these were perfectly accurate, inter-individual differences in caffeine metabolism result in a relatively weak correlation between caffeine intake and serum concentration. The purpose of this study was to determine whether the serum concentration of caffeine or its primary metabolite, paraxanthine, obtained at an unknown time during working hours, is useful to distinguish between pregnant women who report consuming small and large amounts of caffeine. METHODS: We selected from the Birmingham fetal growth study 60 women with normal pregnancy outcomes who reported consuming < or = 0.8 mg/kg/day of caffeine in a 24-hour dietary recall, 60 who consumed 0.81-2.5 mg/kg/day, 60 who consumed 2.51-5.0 mg/kg/day and 59 who consumed > or = 5.01 mg/kg/day. These women had serum drawn for storage during regular clinic hours on the same day as the recall interview. Caffeine and paraxanthine were measured in the stored serum using high performance liquid chromatography. RESULTS: The weighted kappa coefficient between strata of caffeine intake and quartiles of serum paraxanthine was 0.58 among smokers and 0.53 among nonsmokers, versus 0.44 and 0.51, respectively, for quartiles of serum caffeine. The Pearson correlation coefficient between intake and paraxanthine was 0.50 for smokers and 0.53 for nonsmokers, and 0.37 and 0.51, respectively, for serum caffeine. These values are comparable to the correlation between reported smoking and serum cotinine in pregnancy. CONCLUSIONS: The serum concentrations of paraxanthine, and to a lesser degree, caffeine are useful to distinguish between women with varying levels of caffeine intake.

Biomarkers↗

Problems in interpreting HIV sentinel seroprevalence studies.

Estimating human immunodeficiency virus (HIV) prevalence from sentinel seroprevalence studies is difficult. We characterize these studies and show that most are investigations of incompletely defined (hypothetical) cohorts and are usually based on nonprobability samples. Prevalence in HIV sentinel serosurveys is also time-averaged and vulnerable to several time-dependent sources of bias (e.g., migration, deaths, and changes in incidence). Assumptions must be made that these time-dependent biases did not meaningfully affect the data, and this can be helped by reducing the period of investigation. Furthermore, we show that "reliability" can not be adequately measured by standard error, that "internal validity" is vulnerable to self-selection bias and laboratory problems, and that "generalizability" is limited. We propose that what is needed is a procedure (like formal metaanalysis methods) incorporating information from several separate HIV sentinel seroprevalence studies, in a manner that is reproducible and can take into consideration the differences between studies.

Acquired Immunodeficiency Syndrome↗

Closed-form estimates for missing counts in two-way contingency tables.

One method for analyzing contingency tables with missing observations is to model the missing-data mechanism using log-linear models. Previous methods for obtaining estimates (of missing counts and parameters) have required an iterative algorithm. In many cases, however, one can obtain estimates by use of a simple algebraic formula. We illustrate the method with data on smoking and birth weight.

Algorithms↗

Estimation of risk ratios in case-base studies with competing risks.

The case-base study is a recently developed modification of the case-control design that leads to risk ratio estimates. Currently available methods for the analysis of case-base studies lead to estimates of the unconditional risk ratio which may be misleading of censoring occurs. In this manuscript, we describe an approach for the analysis of case-base studies that yields conditional risk ratio estimates which remain valid in the presence of censoring. The approach we propose for estimating risk ratios and their standard errors involves a non-iterative procedure.

Case-Control Studies↗

Two-process models for discrete-time serial categorical response.

This paper proposes a two-process log-linear model for analysis of polychotomous response data generated on study subjects assessed at successive discrete intervals. Response type at each discrete time may be either a transient response or a cause-specific failure. We view outcome of transient response as fundamentally different from outcome of failure, and, in a competing risk framework, we motivate a separate model for each: one to describe the process for transitions to transient response states and the other to describe the process for transitions to absorbing failure states. We maximize the likelihood for each model separately with use of existing software for iterative proportional fitting.

Aged↗