Dataset Information

Calibration and seasonal adjustment for matched case-control studies of vitamin D and cancer.

ABSTRACT: Vitamin D measurements are influenced by seasonal variation and specific assay used. Motivated by multicenter studies of associations of vitamin D with cancer, we formulated an analytic framework for matched case-control data that accounts for seasonal variation and calibrates to a reference assay. Calibration data were obtained from controls sampled within decile strata of the uncalibrated vitamin D values. Seasonal sine-cosine series were fit to control data. Practical findings included the following: (1) failure to adjust for season and calibrate increased variance, bias, and mean square error and (2) analysis of continuous vitamin D requires a variance adjustment for variation in the calibration estimate. An advantage of the continuous linear risk model is that results are independent of the reference date for seasonal adjustment. (3) For categorical risk models, procedures based on categorizing the seasonally adjusted and calibrated vitamin D have near nominal operating characteristics; estimates of log odds ratios are not robust to choice of seasonal reference date, however. Thus, public health recommendations based on categories of vitamin D should also define the time of year to which they refer. This work supports the use of simple methods for calibration and seasonal adjustment and is informing analytic approaches for the multicenter Vitamin D Pooling Project for Breast and Colorectal Cancer. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

SUBMITTER: Gail MH

PROVIDER: S-EPMC4853926 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Calibration and seasonal adjustment for matched case-control studies of vitamin D and cancer.

Gail Mitchell H MH Wu Jincao J Wang Molin M Yaun Shiaw-Shyuan SS Cook Nancy R NR Eliassen A Heather AH McCullough Marjorie L ML Yu Kai K Zeleniuch-Jacquotte Anne A Smith-Warner Stephanie A SA Ziegler Regina G RG Carroll Raymond J RJ

Statistics in medicine 20160105 13

Vitamin D measurements are influenced by seasonal variation and specific assay used. Motivated by multicenter studies of associations of vitamin D with cancer, we formulated an analytic framework for matched case-control data that accounts for seasonal variation and calibrates to a reference assay. Calibration data were obtained from controls sampled within decile strata of the uncalibrated vitamin D values. Seasonal sine-cosine series were fit to control data. Practical findings included the fo ...[more]

PMID: 27133461

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Project description:BackgroundIn epidemiological studies explanatory variables are frequently subject to measurement error. The aim of this paper is to develop a Bayesian method to correct for measurement error in multiple continuous exposures in individually matched case-control studies. This is a topic that has not been widely investigated. The new method is illustrated using data from an individually matched case-control study of the association between thyroid hormone levels during pregnancy and exposure to perfluorinated acids. The objective of the motivating study was to examine the risk of maternal hypothyroxinemia due to exposure to three perfluorinated acids measured on a continuous scale. Results from the proposed method are compared with those obtained from a naive analysis.MethodsUsing a Bayesian approach, the developed method considers a classical measurement error model for the exposures, as well as the conditional logistic regression likelihood as the disease model, together with a random-effect exposure model. Proper and diffuse prior distributions are assigned, and results from a quality control experiment are used to estimate the perfluorinated acids' measurement error variability. As a result, posterior distributions and 95% credible intervals of the odds ratios are computed. A sensitivity analysis of method's performance in this particular application with different measurement error variability was performed.ResultsThe proposed Bayesian method to correct for measurement error is feasible and can be implemented using statistical software. For the study on perfluorinated acids, a comparison of the inferences which are corrected for measurement error to those which ignore it indicates that little adjustment is manifested for the level of measurement error actually exhibited in the exposures. Nevertheless, a sensitivity analysis shows that more substantial adjustments arise if larger measurement errors are assumed.ConclusionsIn individually matched case-control studies, the use of conditional logistic regression likelihood as a disease model in the presence of measurement error in multiple continuous exposures can be justified by having a random-effect exposure model. The proposed method can be successfully implemented in WinBUGS to correct individually matched case-control studies for several mismeasured continuous exposures under a classical measurement error model.

Project description:BackgroundIdentifying breast cancer risk factors is a critical component of preventative strategies for this disease. This study aims to identify modifiable and non-modifiable risk factors of breast cancer in Iranian women.MethodsWe used international databases (PubMed/Medline, Scopus, Web of Knowledge, and Embase) and national databases (SID, Magiran, and ISC) to retrieve relevant studies until November 13, 2022. The odds ratio (OR) with a 95% confidence interval using the random-effect model was used to estimate the pooled effect. The publication bias was assessed by the Egger and Begg test. A sensitivity analysis was conducted to evaluate the effect of each included study on the final measurement.ResultsOf the 30,351 retrieved articles, 24 matched case-control records were included with 12,460 participants (5675 newly diagnosed cases of breast cancer and 6785 control). This meta-analysis showed that of the known modifiable risk factors for breast cancer, obesity (vs normal weight) had the highest risk (OR = 2.17, 95% CI 1.47 to 3.21; I2 = 85.7) followed by age at marriage (25-29 vs < 18 years old) (OR = 2.00, 95% CI 1.53 to 2.61; I2 = 0), second-hand smoking (OR = 1.86, 95% CI 1.58 to 2.19; I2 = 0), smoking (OR = 1.83, 95% CI 1.41 to 2.38; I2 = 18.9), abortion history (OR = 1.44, 95% CI 1.02 to 2.05; I2 = 66.3), oral contraceptive use (OR = 1.35, 95% CI 1.11 to 1.63; I2 = 74.1), age at marriage (18-24 vs < 18 years old) (OR: 1.22, 95% CI 1.02 to 1.47; I2 = 0). Of non-modifiable risk factors, history of radiation exposure (OR = 3.48, 95% CI 2.17 to 5.59; I2 = 0), family history of breast cancer (OR = 2.47, 95% CI 1.83 to 3.33; I2 = 73), and age at menarche (12-13 vs ≥ 14 years old) (OR = 1.67, 95% CI 1.31-2.13; I2 = 25.4) significantly increased the risk of breast cancer.ConclusionsSince most risk factors related to breast cancer incidence are modifiable, promoting healthy lifestyles can play an influential role in preventing breast cancer. In women with younger menarche age, a family history of breast cancer, or a history of radiation exposure, screening at short intervals is recommended.

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Calibration and seasonal adjustment for matched case-control studies of vitamin D and cancer.

Publications

Calibration and seasonal adjustment for matched case-control studies of vitamin D and cancer.

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