共查询到20条相似文献，搜索用时 0 毫秒
1.
Patrick G. Arbogast D.Y. Lin David S. Siscovick Stephen M. Schwartz 《Biometrical journal. Biometrische Zeitschrift》2002,44(2):227-239
In population‐based case‐control studies, it is of great public‐health importance to estimate the disease incidence rates associated with different levels of risk factors. This estimation is complicated by the fact that in such studies the selection probabilities for the cases and controls are unequal. A further complication arises when the subjects who are selected into the study do not participate (i.e. become nonrespondents) and nonrespondents differ systematically from respondents. In this paper, we show how to account for unequal selection probabilities as well as differential nonresponses in the incidence estimation. We use two logistic models, one relating the disease incidence rate to the risk factors, and one modelling the predictors that affect the nonresponse probability. After estimating the regression parameters in the nonresponse model, we estimate the regression parameters in the disease incidence model by a weighted estimating function that weights a respondent's contribution to the likelihood score function by the inverse of the product of his/her selection probability and his/her model‐predicted response probability. The resulting estimators of the regression parameters and the corresponding estimators of the incidence rates are shown to be consistent and asymptotically normal with easily estimated variances. Simulation results demonstrate that the asymptotic approximations are adequate for practical use and that failure to adjust for nonresponses could result in severe biases. An illustration with data from a cardiovascular study that motivated this work is presented. 相似文献
2.
Disease-mapping models for areal data often have fixed effects to measure the effect of spatially varying covariates and random effects with a conditionally autoregressive (CAR) prior to account for spatial clustering. In such spatial regressions, the objective may be to estimate the fixed effects while accounting for the spatial correlation. But adding the CAR random effects can cause large changes in the posterior mean and variance of fixed effects compared to the nonspatial regression model. This article explores the impact of adding spatial random effects on fixed effect estimates and posterior variance. Diagnostics are proposed to measure posterior variance inflation from collinearity between the fixed effect covariates and the CAR random effects and to measure each region's influence on the change in the fixed effect's estimates by adding the CAR random effects. A new model that alleviates the collinearity between the fixed effect covariates and the CAR random effects is developed and extensions of these methods to point-referenced data models are discussed. 相似文献
3.
Ecological studies aim to analyse the variation of disease risk in relation to exposure variables that are measured at an area unit level. In practice it is rarely possible to use the exposure variables themselves, either because the corresponding data are not available or because the causes of the disease are not fully understood. It is therefore quite common to use crude proxies of the real exposure to the disease in question. These proxies are rarely able to explain the disease variation and hence additional area level random effects are introduced to account for the residual variation. In this paper we investigate the possibility to model the effect of ecological covariates non‐parametrically, with and without additional random effects for the residual spatial variation. We illustrate the issues arising through analyses of simulated and real data on larynx cancer mortality in Germany, during the years of 1986 to 1990, where we use the corresponding lung cancer rates as a proxy for smoking consumption. 相似文献
4.
Hans C. van Houwelingen 《Biometrical journal. Biometrische Zeitschrift》2014,56(6):919-932
This paper reviews and discusses the role of Empirical Bayes methodology in medical statistics in the last 50 years. It gives some background on the origin of the empirical Bayes approach and its link with the famous Stein estimator. The paper describes the application in four important areas in medical statistics: disease mapping, health care monitoring, meta‐analysis, and multiple testing. It ends with a warning that the application of the outcome of an empirical Bayes analysis to the individual “subjects” is a delicate matter that should be handled with prudence and care. 相似文献
5.
We propose two approaches for the spatial analysis of cancer incidence data with additional information on the stage of the disease at time of diagnosis. The two formulations are extensions of commonly used models for multicategorical response data on an ordinal scale. We include spatial and age-group effects in both formulations, which we estimate in a nonparametric smooth way. More specifically, we adopt a fully Bayesian approach based on Gaussian pairwise difference priors where additional smoothing parameters are treated as unknown as well. We argue that the methods are useful in monitoring the effectiveness of mass cancer screening and illustrate this through an application to data on cervical cancer in the former German Democratic Republic. The results suggest that there are large spatial differences in the stage proportions, which indicate spatial variability with respect to the introduction and effectiveness of Pap smear screening programs. 相似文献
6.
Summary Mapping disease risk often involves working with data that have been spatially aggregated to census regions or postal regions, either for administrative reasons or confidentiality. When studying rare diseases, data must be collected over a long time period in order to accumulate a meaningful number of cases. These long time periods can result in spatial boundaries of the census regions changing over time, as is the case with the motivating example of exploring the spatial structure of mesothelioma lung cancer risk in Lambton County and Middlesex County of southwestern Ontario, Canada. This article presents a local‐EM kernel smoothing algorithm that allows for the combining of data from different spatial maps, being capable of modeling risk for spatially aggregated data with time‐varying boundaries. Inference and uncertainty estimates are carried out with parametric bootstrap procedures, and cross‐validation is used for bandwidth selection. Results for the lung cancer study are shown and discussed. 相似文献
7.
Bayesian hierarchical models usually model the risk surface on the same arbitrary geographical units for all data sources. Poisson/gamma random field models overcome this restriction as the underlying risk surface can be specified independently to the resolution of the data. Moreover, covariates may be considered as either excess or relative risk factors. We compare the performance of the Poisson/gamma random field model to the Markov random field (MRF)‐based ecologic regression model and the Bayesian Detection of Clusters and Discontinuities (BDCD) model, in both a simulation study and a real data example. We find the BDCD model to have advantages in situations dominated by abruptly changing risk while the Poisson/gamma random field model convinces by its flexibility in the estimation of random field structures and by its flexibility incorporating covariates. The MRF‐based ecologic regression model is inferior. WinBUGS code for Poisson/gamma random field models is provided. 相似文献
8.
IntroductionThe association between socioeconomic status and cancer prognosis has been demonstrated in several countries. Despite the existence of indirect evidence of this phenomenon in Brazil, few studies in this regard are available.ObjectivesThe objective of the present study is to analyse socioeconomic related survival gaps for patients diagnosed with breast, cervical, lung, prostate, and colorectal cancer in the cities of Aracaju (SE) and Curitiba (PR).MethodsUsing population-based data, we estimated net survival by tumour site, year of diagnosis, socioeconomic status and local of residence. Net survival estimation was done with multilevel parametric model allowing flexible spline functions do estimate excess mortality hazards.Results28,005 cases were included in survival analysis. Five-year net survival showed positive association with SES. Intermunicipal survival gaps favouring Aracaju where prominent for breast (reaching 16,1% in 5 years)ObjectivesStudy the impact of socioeconomic factors on cancer survival in two Brazilian capitals. Methods: Survival analysis using population-based cancer data including patients diagnosed with breast, lung, prostate, cervical and colorectal cancer between 1996 and 2012 in Aracaju and Curitiba. Outcomes were excessive mortality hazard (EMH) and 5- and 8-years net survival (NS). The association of race/skin color and socioeconomic level (SES) with EMH and net survival were analyzed using a multilevel regression model with flexible splines.Results28,005 cases were included, 6636 from Aracaju and 21,369 from Curitiba. NS for all diseases studied increased more prominently for Curitiba population. We observed NS gap between the populations of Aracaju and Curitiba that increased or remained stable during the study period, with emphasis on the growth of the difference in NS of lung and colon cancer (among men). Only for cervical cancer and prostate cancer there was a reduction in the intermunicipal gaps. 5-year NS for breast cancer in Aracaju ranged from 55.2% to 73.4% according to SES. In Curitiba this variation was from 66.5% to 83.8%.ConclusionThe results of the present study suggests widening of socioeconomic and regional inequalities in the survival of patients with colorectal, breast, cervical, lung and prostate cancers in Brazil during the 1990 s and 2000 s 相似文献
9.
ObjectiveTo study the impact of socio-economic status and ethno-racial strata on excess mortality hazard and net survival of women with breast cancer in two Brazilian state capitals.MethodWe conducted a survival analysis with individual data from population-based cancer registries including women with breast cancer diagnosed between 1996 and 2012 in Aracaju and Curitiba. The main outcomes were the excess mortality hazard (EMH) and net survival. The associations of age, year of diagnosis, disease stage, race/skin colour and socioeconomic status (SES) with the excess mortality hazard and net survival were analysed using multi-level spline regression models, modelled as cubic splines with knots at 1 and 5 years of follow-up.ResultsA total of 2045 women in Aracaju and 7872 in Curitiba were included in the analyses. The EMH was higher for women with lower SES and for black and brown women in both municipalities. The greatest difference in excess mortality was seen between the most deprived women and the most affluent women in Curitiba, hazard ratio (HR) 1.93 (95%CI 1.63–2.28). For race/skin colour, the greatest ratio was found in Curitiba (HR 1.35, 95%CI 1.09–1.66) for black women compared with white women. The most important socio-economic difference in net survival was seen in Aracaju. Age-standardised net survival at five years was 55.7% for the most deprived women and 67.2% for the most affluent. Net survival at eight years was 48.3% and 61.0%, respectively. Net survival in Curitiba was higher than in Aracaju in all SES groups.”ConclusionOur findings suggest the presence of contrasting breast cancer survival expectancy in Aracaju and Curitiba, highlighting regional inequalities in access to health care. Lower survival among brown and black women, and those in lower SES groups indicates that early detection, early diagnosis and timely access to treatment must be prioritized to reduce inequalities in outcome among Brazilian women. 相似文献
10.
BackgroundArea-based socioeconomic measures are widely used in health research. In theory, the larger the area used the more individual misclassification is introduced, thus biasing the association between such area level measures and health outcomes. In this study, we examined the socioeconomic disparities in cancer survival using two geographic area-based measures to see if the size of the area matters.MethodsWe used population-based cancer registry data for patients diagnosed with one of 10 major cancers in New South Wales (NSW), Australia during 2004–2008. Patients were assigned index measures of socioeconomic status (SES) based on two area-level units, census Collection District (CD) and Local Government Area (LGA) of their address at diagnosis. Five-year relative survival was estimated using the period approach for patients alive during 2004–2008, for each socioeconomic quintile at each area-level for each cancer. Poisson-regression modelling was used to adjust for socioeconomic quintile, sex, age-group at diagnosis and disease stage at diagnosis. The relative excess risk of death (RER) by socioeconomic quintile derived from this modelling was compared between area-units.ResultsWe found extensive disagreement in SES classification between CD and LGA levels across all socioeconomic quintiles, particularly for more disadvantaged groups. In general, more disadvantaged patients had significantly lower survival than the least disadvantaged group for both CD and LGA classifications. The socioeconomic survival disparities detected by CD classification were larger than those detected by LGA. Adjusted RER estimates by SES were similar for most cancers when measured at both area levels.ConclusionsWe found that classifying patient SES by the widely used Australian geographic unit LGA results in underestimation of survival disparities for several cancers compared to when SES is classified at the geographically smaller CD level. Despite this, our RER of death estimates derived from these survival estimates were generally similar for both CD and LGA level analyses, suggesting that LGAs remain a valuable spatial unit for use in Australian health and social research, though the potential for misclassification must be considered when interpreting research. While data confidentiality concerns increase with the level of geographical precision, the use of smaller area-level health and census data in the future, with appropriate allowance for confidentiality 相似文献
11.
AimThis study was aimed to describe the gastric cancer mortality trend, and to analyze the spatial distribution of gastric cancer mortality in Ecuador, between 2004 and 2015.MethodsData were collected from the National Institute of Statistics and Census (INEC) database. Crude gastric cancer mortality rates, standardized mortality ratios (SMRs) and indirect standardized mortality rates (ISMRs) were calculated per 100,000 persons. For time trend analysis, joinpoint regression was used. The annual percentage rate change (APC) and the average annual percent change (AAPC) was computed for each province. Spatial age-adjusted analysis was used to detect high risk clusters of gastric cancer mortality, from 2010 to 2015, using Kulldorff spatial scan statistics.ResultsIn Ecuador, between 2004 and 2015, gastric cancer caused a total of 19,115 deaths: 10,679 in men and 8436 in women. When crude rates were analyzed, a significant decline was detected (AAPC: −1.8%; p < 0.001). ISMR also decreased, but this change was not statistically significant (APC: −0.53%; p = 0.36). From 2004 to 2007 and from 2008 to 2011 the province with the highest ISMR was Carchi; and, from 2012 to 2015, was Cotopaxi. The most likely high occurrence cluster included Bolívar, Los Ríos, Chimborazo, Tungurahua, and Cotopaxi provinces, with a relative risk of 1.34 (p < 0.001).ConclusionThere is a substantial geographic variation in gastric cancer mortality rates among Ecuadorian provinces. The spatial analysis indicates the presence of high occurrence clusters throughout the Andes Mountains. 相似文献
12.
We describe models for binary valued data to be used to explain the incidence of disease given the level of a known risk factor. Every individual has an unobservable tolerance of the risk. Risk levels below the individual tolerance do not increase the disease incidence above the background, unexposed rate. We estimate parameters from both the tolerance distribution and the risk function for a large group of mice exposed to very low levels of a known carcinogen. 相似文献
13.
MacNab YC 《Biometrics》2003,59(2):305-315
We present Bayesian hierarchical spatial models for spatially correlated small-area health service outcome and utilization rates, with a particular emphasis on the estimation of both measured and unmeasured or unknown covariate effects. This Bayesian hierarchical model framework enables simultaneous modeling of fixed covariate effects and random residual effects. The random effects are modeled via Bayesian prior specifications reflecting spatial heterogeneity globally and relative homogeneity among neighboring areas. The model inference is implemented using Markov chain Monte Carlo methods. Specifically, a hybrid Markov chain Monte Carlo algorithm (Neal, 1995, Bayesian Learning for Neural Networks; Gustafson, MacNab, and Wen, 2003, Statistics and Computing, to appear) is used for posterior sampling of the random effects. To illustrate relevant problems, methods, and techniques, we present an analysis of regional variation in intraventricular hemorrhage incidence rates among neonatal intensive care unit patients across Canada. 相似文献
14.
Nadine Binder Anne‐Sophie Herrnböck Martin Schumacher 《Biometrical journal. Biometrische Zeitschrift》2017,59(2):251-269
In clinical and epidemiological studies information on the primary outcome of interest, that is, the disease status, is usually collected at a limited number of follow‐up visits. The disease status can often only be retrieved retrospectively in individuals who are alive at follow‐up, but will be missing for those who died before. Right‐censoring the death cases at the last visit (ad‐hoc analysis) yields biased hazard ratio estimates of a potential risk factor, and the bias can be substantial and occur in either direction. In this work, we investigate three different approaches that use the same likelihood contributions derived from an illness‐death multistate model in order to more adequately estimate the hazard ratio by including the death cases into the analysis: a parametric approach, a penalized likelihood approach, and an imputation‐based approach. We investigate to which extent these approaches allow for an unbiased regression analysis by evaluating their performance in simulation studies and on a real data example. In doing so, we use the full cohort with complete illness‐death data as reference and artificially induce missing information due to death by setting discrete follow‐up visits. Compared to an ad‐hoc analysis, all considered approaches provide less biased or even unbiased results, depending on the situation studied. In the real data example, the parametric approach is seen to be too restrictive, whereas the imputation‐based approach could almost reconstruct the original event history information. 相似文献
15.
The aggregate data study design (Prentice and Sheppard, 1995, Biometrika 82, 113-125) estimates individual-level exposure effects by regressing population-based disease rates on covariate data from survey samples in each population group. In this work, we further develop the aggregate data model to allow for residual spatial correlation among disease rates across populations. Geographical variation that is not explained by model predictors and has a spatial component often arises in studies of rare chronic diseases, such as breast cancer. We combine the aggregate and Bayesian disease-mapping models to provide an intuitive approach to the modeling of spatial effects while drawing correct inference regarding the exposure effect. Based on the results of simulation studies, we suggest guidelines for use of the proposed model. 相似文献
16.
BackgroundPeople with metabolic syndrome have an elevated risk of developing colorectal cancer (CRC), and are recommended to undergo cancer screening. This study examined the association between metabolic syndrome and CRC screening participation in Japan.MethodsThis retrospective cohort study was conducted using insurance claims data, health checkup data, and cancer screening data from a Japanese city. The study population comprised persons aged 40–74 years who had undergone health checkups between fiscal years (FY) 2016 and 2019. The exposure was metabolic syndrome risk (high risk, moderate risk, and no risk) as determined during health checkups. The outcome was CRC screening participation. Logistic regression analyses were performed to examine the associations between metabolic syndrome risk and CRC screening participation.ResultsWe analyzed 20,558 people in the FY2016 cohort, 19,065 people in the FY2017 cohort, 17,496 people in the FY2018 cohort, and 15,647 people in the FY2019 cohort. The odds of CRC screening participation were significantly lower in the moderate-risk group (P < 0.05) in all FYs except FY2019 and the high-risk group (P < 0.001) in all FYs when compared with the no-risk group. When analyzed according to age group, older persons aged 65–74 years generally had significantly lower odds of CRC screening participation than persons aged 40–49 years across all metabolic syndrome risk groups.ConclusionThis is the first study from Japan to show that people with metabolic syndrome, especially older persons aged 65–74 years, are less likely to undergo CRC screening than people without metabolic syndrome. These findings indicate a need to develop and implement age-specific measures to increase cancer screening uptake among persons with metabolic syndrome. 相似文献
17.
Robert Biedermann 《Global Ecology and Biogeography》2003,12(5):381-387
Aim This paper tests firstly for the existence of a general relationship between body size of terrestrial animals and their incidence across habitat patches of increasing size, and secondly for differences in this relationship between insects and vertebrates. Location The analysis was based on the occupancy pattern of 50 species from 15 different landscapes in a variety of ecosystems ranging from Central European grassland to Asian tropical forest. Methods The area‐occupancy relationship was described by incidence functions that were calculated using logistic regression. A correlation analysis between body size of the species and the patch area referring to the two given points of the incidence function was performed. In order to test for an effect of taxon (insects vs. vertebrates), an analysis of covariance was conducted. Results In all species, the incidence was found to increase with increasing patch area. The macroecological analysis showed a significant relationship between the incidence in habitat patches and the body size of terrestrial animals. The area requirement was found to increase linearly with increasing body size on a log‐log scale. This relationship did not differ significantly between insects and vertebrates. Conclusions The approach highlighted in this paper is to associate incidence functions with body size. The results suggest that body size is a general but rather rough predictor for the area requirements of animals. The relationship seems valid for a wide range of body sizes of terrestrial animals. However, further studies including isolation of habitats as well as additional species traits into the macroecological analysis of incidence functions are needed. 相似文献
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19.
An interesting epidemiological problem is the analysis of geographical variation in rates of disease incidence or mortality. One goal of such an analysis is to detect clusters of elevated (or lowered) risk in order to identify unknown risk factors regarding the disease. We propose a nonparametric Bayesian approach for the detection of such clusters based on Green's (1995, Biometrika 82, 711-732) reversible jump MCMC methodology. The prior model assumes that geographical regions can be combined in clusters with constant relative risk within a cluster. The number of clusters, the location of the clusters, and the risk within each cluster is unknown. This specification can be seen as a change-point problem of variable dimension in irregular, discrete space. We illustrate our method through an analysis of oral cavity cancer mortality rates in Germany and compare the results with those obtained by the commonly used Bayesian disease mapping method of Besag, York, and Mollié (1991, Annals of the Institute of Statistical Mathematics, 43, 1-59). 相似文献
20.
Summary Physical activity has many well‐documented health benefits for cardiovascular fitness and weight control. For pregnant women, the American College of Obstetricians and Gynecologists currently recommends 30 minutes of moderate exercise on most, if not all, days; however, very few pregnant women achieve this level of activity. Traditionally, studies have focused on examining individual or interpersonal factors to identify predictors of physical activity. There is a renewed interest in whether characteristics of the physical environment in which we live and work may also influence physical activity levels. We consider one of the first studies of pregnant women that examines the impact of characteristics of the built environment on physical activity levels. Using a socioecologic framework, we study the associations between physical activity and several factors including personal characteristics, meteorological/air quality variables, and neighborhood characteristics for pregnant women in four counties of North Carolina. We simultaneously analyze six types of physical activity and investigate cross‐dependencies between these activity types. Exploratory analysis suggests that the associations are different in different regions. Therefore, we use a multivariate regression model with spatially varying regression coefficients. This model includes a regression parameter for each covariate at each spatial location. For our data with many predictors, some form of dimension reduction is clearly needed. We introduce a Bayesian variable selection procedure to identify subsets of important variables. Our stochastic search algorithm determines the probabilities that each covariate's effect is null, non‐null but constant across space, and spatially varying. We found that individual‐level covariates had a greater influence on women's activity levels than neighborhood environmental characteristics, and some individual‐level covariates had spatially varying associations with the activity levels of pregnant women. 相似文献