The population attributable fraction (PAF) is a useful measure for quantifying the impact of exposure to certain risk factors on a particular outcome at the population level. Recently, new model-based methods for the estimation of PAF and... more
The population attributable fraction (PAF) is a useful measure for quantifying the impact of exposure to certain risk factors on a particular outcome at the population level. Recently, new model-based methods for the estimation of PAF and its confidence interval for different types of outcomes in a cohort study design have been proposed. In this paper, we introduce SAS macros
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Virtually no comparisons of different psychotherapies with long follow-up times have been carried out until now. The Helsinki Psychotherapy Study is a randomized clinical trial, where patients were monitored for 12 months after the onset... more
Virtually no comparisons of different psychotherapies with long follow-up times have been carried out until now. The Helsinki Psychotherapy Study is a randomized clinical trial, where patients were monitored for 12 months after the onset of study treatments, of which each lasted approximately 6 months. The patients' psychiatric status was measured at five pre-determined time points during the follow-up period. In general, the analyses of trials are complicated in cases where compliance with the given treatment is incomplete or the drop-out from the follow-up is non-ignorable. In the present study, the quality of the treatment deviated from the protocol for some patients and some patients took auxiliary treatments which had similar effects to the study treatment during the study treatment or follow-up period. This might have resulted in standard intention-to-treat analyses providing excessively conservative or liberal conclusions. Non-compliance may have been non-ignorable in some cases, so subject-specific latent factors may have influenced the outcome both directly and indirectly via compliance behaviour. The most and least healthy patients are the most likely to dropout from the follow-up a priori, so the missing data process is informative. The missing data can partly be augmented with surrogate information collected during interviews with patients who dropped out. A Bayesian hierarchical as-treated model, which uses random-effects-based selection models to account for non-ignorable missing data and non-compliance, was compared with different mixed effects models.
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We developed a test statistic based on an approach of Whittemore et al. (1987) to detect space-time clustering for non-infectious diseases. We extended the spatial test of Whittemore et al. by deriving conditional probabilities for... more
We developed a test statistic based on an approach of Whittemore et al. (1987) to detect space-time clustering for non-infectious diseases. We extended the spatial test of Whittemore et al. by deriving conditional probabilities for Poisson distributed random variables. To combine spatial and time distances we defined a distance matrix D, where dij is the distance between the ith and jth cell in a three-dimensional space-time grid. Spatial and temporal components are controlled by a weight. By altering the weight, both marginal tests and the intermediate test can be reached. Allowing a continuum from a pure spatial to a pure temporal test, the best result will be gained by trying different weights, because the occurrence of a disease might show some temporal and some spatial tendency to cluster. We examined the behaviour of the test statistic by simulating different distributions for cases and the population. The test was applied to the incidence data of insulin-dependent diabetes mellitus in Finland. This test could be used in the analysis of data which are localized according to map co-ordinates, by addresses or postcodes. This information is important when using the Geographical Information System (GIS) technology to compute the pairwise distances needed for the proposed test.
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The population attributable fraction (PAF) is a useful measure for describing the expected change in an outcome if its risk factors are modified. Cohort studies allow researchers to assess the predictive value of the risk factor... more
The population attributable fraction (PAF) is a useful measure for describing the expected change in an outcome if its risk factors are modified. Cohort studies allow researchers to assess the predictive value of the risk factor modification on the incidence of the outcome during a certain follow-up. Estimation of PAF for both mortality and morbidity in cohort studies with censored survival data has been developed in the recent years. So far, however, censoring due to death in the estimation of PAF for morbidity has been ignored, resulting in estimation of a quantity which is not relevant in practice as some people are likely to die during the follow-up. The risk factors related to the disease incidence may also be related to mortality, and modification of these risk factors is likely to delay the occurrence of both events. Thus, censoring due to death and the impact of risk factor modification must be considered when estimating PAF for disease incidence. We consider both and introduce two measures of disease burden: PAF for the incidence of disease during lifetime and PAF for the prevalence of disease in the population at a certain time. We demonstrate how consideration of censoring due to death changes the estimated PAF for disease incidence and its confidence interval. This underlines the importance of choosing a correct PAF measure depending on the outcome of interest and the risk factors of interest to obtain accurate and interpretable results.
Research Interests: Statistics, Biostatistics, Risk assessment, Finland, Population, and 21 moreHumans, Computer Simulation, Smoking, Female, Alcohol Drinking, Male, Confidence intervals, Cohort Study, Exercise, Incidence, Body Mass Index, Risk factors, Aged, Prevalence, Middle Aged, Adult, Public health systems and services research, Risk Factors, Risk Assessment, Type 2 Diabetes Mellitus, and Cohort Studies
Research Interests: Health Behavior, Epidemiology, Life Style, Vitamin D, Public Health, and 29 moreCardiovascular disease, Metabolic syndrome, Finland, Overweight, Population, Humans, Blood Pressure, Female, Male, Cohort Study, Body Mass Index, Risk factors, Metaanalysis, Pooling, Meta Analysis, Alcohol Consumption, Aged, Middle Aged, Risk Factor, Questionnaires, Adult, Public health systems and services research, Risk Factors, Low Risk, Type 2 Diabetes Mellitus, Proportional Hazards Models, Confidence Interval, Cohort Studies, and Relative Risk
Quantification of the impact of exposure to modifiable risk factors on a particular outcome at the population level is a fundamental public health issue. In cohort studies, the population attributable fraction (PAF) is used to assess the... more
Quantification of the impact of exposure to modifiable risk factors on a particular outcome at the population level is a fundamental public health issue. In cohort studies, the population attributable fraction (PAF) is used to assess the proportion of the outcome that is attributable to exposure to certain risk factors in a given population during a certain time interval. This is done by combining information about the prevalence of the risk factor in the population with estimates of the strength of the association between the risk factor and the outcome. In case of mortality, the PAF demonstrates what proportion of mortality can be delayed during the given follow-up time. However, literature on carrying out model-based estimation of PAF and its variance in cohort studies while properly taking follow-up time into account is still scarce. In this article, the authors present formulas for estimation of PAF, its variance, and its confidence interval using the piecewise constant hazards model and apply a SAS macro created for the estimation of PAF (SAS Institute Inc., Cary, North Carolina) to estimate the mortality attributable to some common risk factors.