Ifosfamide plus doxorubicin produced a significantly higher regression rate (P = .03) than did doxorubicin alone; however, this was achieved at a level of myelosuppression significantly more intense than that produced by the single agent or by the three-drug combination. Mitomycin, doxorubicin, and cisplatin also appeared to be more active than the single agent; however, at a myelosuppression level similar to that of doxorubicin alone, this trend (P = .07) did not attain the usual level for significance. No significant survival differences were observed.

Arsenic is a known carcinogen, but data are especially lacking on the health effects of low-level exposure, and on the health significance of methylation ability. We conducted a case-control study (76 cases and 224 controls from 1996 to 1999) in southwestern Taiwan to explore the association among primary and secondary arsenic methylation index (PMI and SMI, respectively), cumulative arsenic exposure (CAE), and the risk of skin cancer. As compared with the controls, the skin cancer group reported more sun exposure (P = 0.02) and had a lower BMI (P = 0.03), as well as lower education level (P = 0.01). Skin cancer patients and controls were similar with regard to age, gender, smoking and alcohol consumption. Given a low SMI (< or = 5), CAE > 15 mg/L-year was associated with an increased risk of skin cancer (OR, 7.48; 95% CI, 1.65-33.99) compared to a CAE < or = 2 mg/L-year. Given the same level of PMI, SMI, and CAE, men had a higher risk of skin cancer (OR, 4.04; 95% CI, 1.46-11.22) when compared to women. Subjects with low SMI and high CAE have a substantially increased risk of skin cancer. Males in all strata of arsenic exposure and methylation ability had a higher risk of skin cancer than women.

Sperm DNA damage was associated with MEP and with MEHP after adjusting for DEHP oxidative metabolites, which may serve as phenotypic markers of DEHP metabolism to 'less toxic' metabolites. The urinary levels of phthalate metabolites among these men were similar to those reported for the US general population, suggesting that exposure to some phthalates may affect the population distribution of sperm DNA damage.

BackgroundPrenatal exposure to mercury has been associated with adverse childhood neurologic outcomes in epidemiologic studies. Dose–response information for this relationship is useful for estimating benefits of reduced mercury exposure.ObjectivesWe estimated a dose–response relationship between maternal mercury body burden and subsequent childhood decrements in intelligence quotient (IQ), using a Bayesian hierarchical model to integrate data from three epidemiologic studies.MethodsInputs to the model consist of dose–response coefficients from studies conducted in the Faroe Islands, New Zealand, and the Seychelles Islands. IQ coefficients were available from previous work for the latter two studies, and a coefficient for the Faroe Islands study was estimated from three IQ subtests. Other tests of cognition/achievement were included in the hierarchical model to obtain more accurate estimates of study-to-study and end point–to–end point variability.ResultsWe find a central estimate of −0.18 IQ points (95% confidence interval, −0.378 to −0.009) for each parts per million increase of maternal hair mercury, similar to the estimates for both the Faroe Islands and Seychelles studies, and lower in magnitude than the estimate for the New Zealand study. Sensitivity analyses produce similar results, with the IQ coefficient central estimate ranging from −0.13 to −0.25.ConclusionsIQ is a useful end point for estimating neurodevelopmental effects, but may not fully represent cognitive deficits associated with mercury exposure, and does not represent deficits related to attention and motor skills. Nevertheless, the integrated IQ coefficient provides a more robust description of the dose–response relationship for prenatal mercury exposure and cognitive functioning than results of any single study.

We propose a latent variable model for mixed discrete and continuous outcomes. The model accommodates any mixture of outcomes from an exponential family and allows for arbitrary covariate effects, as well as direct modelling of covariates on the latent variable. An EM algorithm is proposed for parameter estimation and estimates of the latent variables are produced as a by-product of the analysis. A generalized likelihood ratio test can be used to test the signi®cance of covariates affecting the latent outcomes. This method is applied to birth defects data, where the outcomes of interest are continuous measures of size and binary indicators of minor physical anomalies. Infants who were exposed in utero to anticonvulsant medications are compared with controls.

A distinctive pattern of physical abnormalities in infants of mothers with epilepsy is associated with the use of anticonvulsant drugs during pregnancy, rather than with epilepsy itself.

Early endotoxin exposure may be a protective factor against atopy but a risk factor for wheeze in high-risk children.

In standard time-to-event or survival analysis, occurrence times of the event of interest are observed exactly or are right-censored, meaning that it is only known that the event occurred after the last observation time. There are numerous methods available for estimating the survival curve and for testing and estimation of the effects of covariates in this context. In some situations, however, the times of the events of interest may only be known to have occurred within an interval of time. In clinical trials, for example, patients are often seen at pre-scheduled visits but the event of interest may occur in between visits. These data are interval-censored. Owing to the lack of well-known statistical methodology and available software, a common ad hoc approach is to assume that the event occurred at the end (or beginning or midpoint) of each interval, and then apply methods for standard time-to-event data. However, this approach can lead to invalid inferences, and in particular will tend to underestimate the standard errors of the estimated parameters. The purpose of this tutorial is to illustrate and compare available methods which correctly treat the data as being interval-censored. It is not meant to be a full review of all existing methods, but only those which are available in standard statistical software, or which can be easily programmed. All approaches will be illustrated on two data sets and compared with methods which ignore the interval-censored nature of the data. We hope this tutorial will allow those familiar with the application of standard survival analysis techniques the option of applying appropriate methods when presented with interval-censored data.