Background stratified Poisson regression analysis of cohort data

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as ‘nuisance’ variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this ‘conditional’ regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models

Endometrial carcinoma risk among women diagnosed with endometrial hyperplasia: the 34-year experience in a large health plan

Classifying endometrial hyperplasia (EH) according to the severity of glandular crowding (simple hyperplasia (SH) vs complex hyperplasia (CH)) and nuclear atypia (simple atypical hyperplasia (SAH) vs complex atypical hyperplasia (CAH)) should predict subsequent endometrial carcinoma risk, but data on progression are lacking. Our nested case–control study of EH progression included 138 cases, who were diagnosed with EH and then with carcinoma (1970–2003) at least 1 year (median, 6.5 years) later, and 241 controls, who were individually matched on age, date, and follow-up duration and counter-matched on EH classification. After centralised pathology panel and medical record review, we generated rate ratios (RRs) and 95% confidence intervals (CIs), adjusted for treatment and repeat biopsies. With disordered proliferative endometrium (DPEM) as the referent, AH significantly increased carcinoma risk (RR=14, 95% CI, 5–38). Risk was highest 1–5 years after AH (RR=48, 95% CI, 8–294), but remained elevated 5 or more years after AH (RR=3.5, 95% CI, 1.0–9.6). Progression risks for SH (RR=2.0, 95% CI, 0.9–4.5) and CH (RR=2.8, 95% CI, 1.0–7.9) were substantially lower and only slightly higher than the progression risk for DPEM. The higher progression risks for AH could foster management guidelines based on markedly different progression risks for atypical vs non-atypical EH

Lagging Exposure Information in Cumulative Exposure-Response Analyses

Lagging exposure information is often undertaken to allow for a latency period in cumulative exposure-disease analyses. The authors first consider bias and confidence interval coverage when using the standard approaches of fitting models under several lag assumptions and selecting the lag that maximizes either the effect estimate or model goodness of fit. Next, they consider bias that occurs when the assumption that the latency period is a fixed constant does not hold. Expressions were derived for bias due to misspecification of lag assumptions, and simulations were conducted. Finally, the authors describe a method for joint estimation of parameters describing an exposure-response association and the latency distribution. Analyses of associations between cumulative asbestos exposure and lung cancer mortality among textile workers illustrate this approach. Selecting the lag that maximizes the effect estimate may lead to bias away from the null; selecting the lag that maximizes model goodness of fit may lead to confidence intervals that are too narrow. These problems tend to increase as the within-person exposure variation diminishes. Lagging exposure assignment by a constant will lead to bias toward the null if the distribution of latency periods is not a fixed constant. Direct estimation of latency periods can minimize bias and improve confidence interval coverage

Crude incidence in two-phase designs in the presence of competing risks.

BackgroundIn many studies, some information might not be available for the whole cohort, some covariates, or even the outcome, might be ascertained in selected subsamples. These studies are part of a broad category termed two-phase studies. Common examples include the nested case-control and the case-cohort designs. For two-phase studies, appropriate weighted survival estimates have been derived; however, no estimator of cumulative incidence accounting for competing events has been proposed. This is relevant in the presence of multiple types of events, where estimation of event type specific quantities are needed for evaluating outcome.MethodsWe develop a non parametric estimator of the cumulative incidence function of events accounting for possible competing events. It handles a general sampling design by weights derived from the sampling probabilities. The variance is derived from the influence function of the subdistribution hazard.ResultsThe proposed method shows good performance in simulations. It is applied to estimate the crude incidence of relapse in childhood acute lymphoblastic leukemia in groups defined by a genotype not available for everyone in a cohort of nearly 2000 patients, where death due to toxicity acted as a competing event. In a second example the aim was to estimate engagement in care of a cohort of HIV patients in resource limited setting, where for some patients the outcome itself was missing due to lost to follow-up. A sampling based approach was used to identify outcome in a subsample of lost patients and to obtain a valid estimate of connection to care.ConclusionsA valid estimator for cumulative incidence of events accounting for competing risks under a general sampling design from an infinite target population is derived

Verwendung von stoffwechselparametern zur beurteilung von haltungssystemen beim rind

International audienc

Markers for early detection of cancer: Statistical guidelines for nested case-control studies

BACKGROUND: Recently many long-term prospective studies have involved serial collection and storage of blood or tissue specimens. This has spurred nested case-control studies that involve testing some specimens for various markers that might predict cancer. Until now there has been little guidance in statistical design and analysis of these studies. METHODS: To develop statistical guidelines, we considered the purpose, the types of biases, and the opportunities for extracting additional information. RESULTS: The following guidelines: (1) For the clearest interpretation, statistics should be based on false and true positive rates – not odds ratios or relative risks (2) To avoid overdiagnosis bias, cases should be diagnosed as a result of symptoms rather than on screening. (3) To minimize selection bias, the spectrum of control conditions should be the same in study and target screening populations. (4) To extract additional information, criteria for a positive test should be based on combinations of individual markers and changes in marker levels over time. (5) To avoid overfitting, the criteria for a positive marker combination developed in a training sample should be evaluated in a random test sample from the same study and, if possible, a validation sample from another study. (6) To identify biomarkers with true and false positive rates similar to mammography, the training, test, and validation samples should each include at least 110 randomly selected subjects without cancer and 70 subjects with cancer. CONCLUSION: These guidelines ensure good practice in the design and analysis of nested case-control studies of early detection biomarkers

PRM38 Estimation of a Markov Chain for Crohn's Disease and Classification of Patients Into Disease Phenotypes, in Eight Countries Using Individual Longitudinal Data Aggregated Over Time

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