Sinusoidal cox regression-a rare cancer example.

Evidence of an association between survival time and date of birth would suggest an etiologic role for a seasonally variable environmental exposure occurring within a narrow perinatal time period. Risk factors that may exhibit seasonal epidemicity include diet, infectious agents, allergens, and antihistamine use. Typically data has been analyzed by simply categorizing births into months or seasons of the year and performing multiple pairwise comparisons. This paper presents a statistically robust alternative, based upon a trigonometric Cox regression model, to analyze the cyclic nature of birth dates related to patient survival. Disease birth-date results are presented using a sinusoidal plot with peak date(s) of relative risk and a single P value that indicates whether an overall statistically significant seasonal association is present. Advantages of this derivative-free method include ease of use, increased power to detect statistically significant associations, and the ability to avoid arbitrary, subjective demarcation of seasons.

Computing differential sample size for case-control studies of gene-environment interaction.

The rates for diseases such as cancer, cardiovascular disease, and diabetes are known to differ by ethnic/racial groups. However, neither genetic nor environmental factors fully explain the observed differences. Failure to account for genetic expression in the absence or presence of an environmental factor, and vice-versa, may lead to erroneous conclusions regarding the importance of these factors in disease etiology. We present a novel method for computing sample size for case-control studies involving the interaction of genetic and environmental factors. The method is based on an indirect estimate of the odds ratio for gene-environment interaction given only the odds ratio for environmental exposure and population genotype frequency. A table is presented providing sample sizes required for detecting a minimum odds ratio for gene-environment interaction given varying genotype frequencies and environmental exposure odds ratio values. Sample size increases proportionately with genotype frequency for a given environment exposure odds ratio.

A method to model season of birth as a surrogate environmental risk factor for disease.

Environmental exposures, including some that vary seasonally, may play a role in the development of many types of childhood diseases such as cancer. Those observed in children are unique in that the relevant period of exposure is inherently limited or perhaps even specific to a very short window during prenatal development or early infancy. As such, researchers have investigated whether specific childhood cancers are associated with season of birth. Typically a basic method for analysis has been used, for example categorization of births into one of four seasons, followed by simple comparisons between categories such as via logistic regression, to obtain odds ratios (ORs), confidence intervals (CIs) and p-values. In this paper we present an alternative method, based upon an iterative trigonometric logistic regression model used to analyze the cyclic nature of birth dates related to disease occurrence. Disease birth-date results are presented using a sinusoidal graph with a peak date of relative risk and a single p-value that tests whether an overall seasonal association is present. An OR and CI comparing children born in the 3-month period around the peak to the symmetrically opposite 3-month period also can be obtained. Advantages of this derivative-free method include ease of use, increased statistical power to detect associations, and the ability to avoid potentially arbitrary, subjective demarcation of seasons.

A method to compute multiplicity corrected confidence intervals for odds ratios and other relative effect estimates.

Epidemiological studies commonly test multiple null hypotheses. In some situations it may be appropriate to account for multiplicity using statistical methodology rather than simply interpreting results with greater caution as the number of comparisons increases. Given the one-to-one relationship that exists between confidence intervals and hypothesis tests, we derive a method based upon the Hochberg step-up procedure to obtain multiplicity corrected confidence intervals (CI) for odds ratios (OR) and by analogy for other relative effect estimates. In contrast to previously published methods that explicitly assume knowledge of P values, this method only requires that relative effect estimates and corresponding CI be known for each comparison to obtain multiplicity corrected CI.

Use of provider delivered complementary and alternative therapies in Hawai'i: results of the Hawai'i Health Survey.

BACKGROUND: Provider delivered complementary and alternative medicine (CAM) is used increasingly as a treatment option. Nevertheless, data related to the prevalence of provider delivered CAM (or PDCAM) use in diverse racial and ethnic populations is limited. The purpose of this investigation was to describe the use of provider delivered CAM in Hawaiian, Asian, and other Pacific Island populations in Hawai'i. The investigation was undertaken to test the hypothesis that a significant difference existed in the use of provider delivered CAM in Hawai'i because of the cultural diversity existing within the population. METHODS: The data were collected through the Hawai'i Health Survey (HHS). The HHS was administered by telephone among 5,000 stratified, randomly selected households, representing each of the Hawaiian Islands. Data were collected on all members of sample households. The sample population was statistically adjusted to represent the population of Hawai'i. RESULTS: Several factors emerged that may indicate increased use of provider delivered CAM. Most provider delivered CAM users are more educated, have incomes 200% or more above the poverty line, and reported either good or very good health status. Among respondents with poor health status, 60.4% have used provider delivered CAM. Those with a body mass index indicating that they were overweight also reported a high level of provider delivered CAM use (51.4%). Similar percentages of both women and men use provider delivered CAM, while the youngest and oldest respondents reported the least use of provider delivered CAM. Whites (60.0%) and Koreans (56.6%) reported the highest percentage of use of provider delivered CAM, while African Americans (35.5%) and Filipinos (37. 1%) reported the lowest percentage. The majority of people without health insurance report provider delivered CAM use (53. 7%). The highest portion of people who have usedanyalternative health care service is found among those whose pain severely interferes with normal work (78.3%). CONCLUSIONS The use of provider delivered CAM was found to be significantly greater in Hawaii compared with the mainland. Our results suggest the need for additional investigation of provider delivered CAM use in specific ethnic subpopulations.

Method for indirectly estimating gene-environment effect modification and power given only genotype frequency and odds ratio of environmental exposure.

Both genes and environment are important determinants of disease. In this paper we model gene-environment effect modification on the odds ratio scale OR(GEID) and show how to indirectly estimate the effect and 95% confidence intervals (CI) for the simple case of no main genetic and environmental effects [i.e., OR(GE/D) = OR(GE/D) = 1]. A statistic is presented to test the null hypothesis OR(GE/D) = 1 and to calculate corresponding power, given the odds ratio for environmental exposure OR(E/D) and population genotype frequency (g). Direct extension of the above model provides a mathematical framework for estimating confidence bounds in more complex cases involving partial genetic and/or environmental effects.

Risk of subsequent cancer following invasive or in situ squamous cell skin cancer.

PURPOSE: Determine the risk of subsequent cancer following squamous cell skin cancer. METHODS: Using computerized surgical pathology records and membership data from a health maintenance organization, we retrospectively identified 822 individuals with primary squamous cell skin cancer (SCSC) and 3662 comparison subjects matched for age, sex, race, residence area, and length of membership. Patients were included in the study if they had no prior history of cancer, and received at least one multiphasic health checkup and questionnaire (MHC). Patients were followed for subsequent invasive cancer up to 24 years, with a mean follow-up time of 7.8 years. RESULTS: SCSC patients had a significantly greater risk [adjusted for body mass index (BMI) and education] for subsequent cancer overall (excluding non-melanoma skin cancer) [risk ratio (RR) = 1.4, 95% confidence interval (CI) = 1.2-1.6], and for basal cell skin cancer (RR = 13.8, 95% CI = 8.8-21.9), digestive (RR = 1.6, 95% CI = 1.1-2.4), and genitourinary cancers (RR = 1.5, 95% CI = 1.0-2.0). An increased, but not statistically significant, adjusted risk (RR > or = 1.4) was also observed for lip, oral cavity, and pharynx cancer (RR = 3.9, 95% CI = 0.6-25.0); non-cutaneous squamous cell cancer (RR = 1.9, 95% CI = 0.9-4.4); and respiratory and intrathoracic cancer (RR = 1.4, 95% CI = 0.8-2.6). The addition of alcohol consumption, combined occupational exposure, marital status, and smoking history to the multivariate model did not materially change any significant positive associations with SCSC. CONCLUSIONS: Our results suggest that patients diagnosed with SCSC may be at an increased risk of subsequent cancer at many sites, although several estimated risk estimates were within the limits of chance given no true association.