| 1. |
- Bellavia, Andrea, et al.
(författare)
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Using Laplace Regression to Model and Predict Percentiles of Age at Death When Age Is the Primary Time Scale
- 2015
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Ingår i: American Journal of Epidemiology. - : OXFORD UNIV PRESS INC. - 0002-9262 .- 1476-6256. ; 182:3, s. 271-277
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Tidskriftsartikel (refereegranskat)abstract
- Increasingly often in epidemiologic research, associations between survival time and predictors of interest are measured by differences between distribution functions rather than hazard functions. For example, differences in percentiles of survival time, expressed in absolute time units (e.g., weeks), may complement the popular risk ratios, which are unitless measures. When analyzing time to an event of interest (e.g., death) in prospective cohort studies, the time scale can be set to start at birth or at study entry. The advantages of one time origin over the other have been thoroughly explored for the estimation of risks but not for the estimation of survival percentiles. In this paper, we analyze the use of different time scales in the estimation of survival percentiles with Laplace regression. Using this regression method, investigators can estimate percentiles of survival time over levels of an exposure of interest while adjusting for potential confounders. Our findings may help to improve modeling strategies and ease interpretation in the estimation of survival percentiles in prospective cohort studies.
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| 2. |
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| 3. |
- Nilsson, Anton, et al.
(författare)
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Proxy Variables and the Generalizability of Study Results
- 2023
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Ingår i: American Journal of Epidemiology. - : Oxford University Press. - 0002-9262 .- 1476-6256. ; 192:3, s. 448-454
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Tidskriftsartikel (refereegranskat)abstract
- When individuals self-select (or are selected) into a study based on factors that influence the outcome, conclusions may not generalize to the full population. To compensate for this, results may be adjusted, for example, by standardization on the set of common causes of participation and outcome. Although such standardization is useful in some contexts, the common causes of participation and outcome may in practice not be fully observed. Instead, the researcher may have access to one or several variables related to the common causes, that is, to proxies for the common causes. This article defines and examines different types of proxy variables and shows how these can be used to obtain generalizable study results. First of all, the researcher may exploit proxies that influence only participation or outcome but which still allow for perfect generalizability by rendering participation and outcome conditionally independent. Further, generalizability can be achieved by leveraging 2 proxies, one of which is allowed to influence participation and one of which is allowed to influence the outcome, even if participation and outcome do not become independent conditional on these. Finally, approximate generalizability may be obtained by exploiting a single proxy that does not itself influence participation or outcome.
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| 4. |
- Törner, Anna, et al.
(författare)
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A method to visualize and adjust for selection bias in prevalent cohort studies
- 2011
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Ingår i: American Journal of Epidemiology. - : Oxford University Press (OUP). - 0002-9262 .- 1476-6256. ; 174:8, s. 969-76
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Tidskriftsartikel (refereegranskat)abstract
- Selection bias and confounding are concerns in cohort studies where the reason for inclusion of subjects in the cohort may be related to the outcome of interest. Selection bias in prevalent cohorts is often corrected by excluding observation time and events during the first time period after inclusion in the cohort. This time period must be chosen carefully-long enough to minimize selection bias but not too long so as to unnecessarily discard observation time and events. A novel method visualizing and estimating selection bias is described and exemplified by using 2 real cohort study examples: a study of hepatitis C virus infection and a study of monoclonal gammopathy of undetermined significance. The method is based on modeling the hazard for the outcome of interest as a function of time since inclusion in the cohort. The events studied were "hospitalizations for kidney-related disease" in the hepatitis C virus cohort and "death" in the monoclonal gammopathy of undetermined significance cohort. Both cohorts show signs of considerable selection bias as evidenced by increased hazard in the time period after inclusion in the cohort. The method was very useful in visualizing selection bias and in determining the initial time period to be excluded from the analyses.
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