Biomedical subjects

Harald Weedon-Fekjaer

Publications and source records attributed to Harald Weedon-Fekjaer.

5 recordsLinked to original sources

PubMed2006

Dynamic analysis of recurrent event data using the additive hazard model.

We propose a method for analysis of recurrent event data using information on previous occurrences of the event as a time-dependent covariate. The focus is on understanding how to analyze the effect of such a dynamic covariate while at the same time ensuring that the effects of treatment and other fixed covariates are unbiasedly estimated. By applying an additive regression model for the intensity of the recurrent events, concepts like direct, indirect and total effects of the fixed covariates may be defined in an analogous way as for traditional path analysis. Theoretical considerations as well as simulations are presented, and a data set on recurrent bladder tumors is used to illustrate the methodology.

Biometry↗

PubMed2006

The demographics of same-sex marriages in Norway and Sweden.

The present study investigates the demographics of same-sex marriages--that is, registered partnerships-in Norway and Sweden. We give an overview of the demographic characteristics of the spouses of these partnerships, study patterns of their divorce risks, and compare the dynamics of same-sex couples with those of heterosexual marriages. We use longitudinal information from the population registers of the two countries that cover all persons in partnerships. Our demographic analyses include information on characteristics such as age, sex, geographic background, experience of previous opposite-sex marriage, parenthood, and educational attainment of the partners involved. The results show that in many respects, the distributions of married populations on these characteristics differ by the sex composition of the couples. Patterns in divorce risks are rather similar in same-sex and opposite-sex marriages, but divorce-risk levels are considerably higher in same-sex marriages. The divorce riskforfemale partnerships is double that for male partnerships.

Adult↗

PubMed2005

Empirical evaluation of prediction intervals for cancer incidence.

BACKGROUND: Prediction intervals can be calculated for predicting cancer incidence on the basis of a statistical model. These intervals include the uncertainty of the parameter estimates and variations in future rates but do not include the uncertainty of assumptions, such as continuation of current trends. In this study we evaluated whether prediction intervals are useful in practice. METHODS: Rates for the period 1993-97 were predicted from cancer incidence rates in the five Nordic countries for the period 1958-87. In a Poisson regression model, 95% prediction intervals were constructed for 200 combinations of 20 cancer types for males and females in the five countries. The coverage level was calculated as the proportion of the prediction intervals that covered the observed number of cases in 1993-97. RESULTS: Overall, 52% (104/200) of the prediction intervals covered the observed numbers. When the prediction intervals were divided into quartiles according to the number of cases in the last observed period, the coverage level was inversely proportional to the frequency (84%, 52%, 46% and 26%). The coverage level varied widely among the five countries, but the difference declined after adjustment for the number of cases in each country. CONCLUSION: The coverage level of prediction intervals strongly depended on the number of cases on which the predictions were based. As the sample size increased, uncertainty about the adequacy of the model dominated, and the coverage level fell far below 95%. Prediction intervals for cancer incidence must therefore be interpreted with caution.

Adolescent↗

PubMed2005

Estimating mean sojourn time and screening test sensitivity in breast cancer mammography screening: new results.

OBJECTIVE: To assess if new screening techniques, increased use of hormone replacement therapy, or the transition from breast cancer screening trials to large scale screening programmes may influence the average time in preclinical screening detectable phase (mean sojourn time [MST]) or screening test sensitivity (STS). SETTING: Screening and interval data for 395,188 women participating in the Norwegian Breast Cancer Screening Programme (NBCSP). METHODS: Weighted non-linear least-square regression estimates using a tree step Markov chain model, and a sensitivity analysis of the possible impact by opportunistic screening between ordinary breast cancer screening rounds. RESULTS: MST was estimated to 6.1 (95% confidence interval [CI] 5.1-7.0) years for women aged 50-59 years, and 7.9 (95% CI 6.0-7.9) years for those aged 60-69 years. Correspondingly, STS was estimated to 58% (95% CI 52-64 %) and 73 % (67-78 %), respectively. Simulations revealed that opportunistic screening may give a moderate estimation bias towards higher MST and lower STS. Assuming a probable 21% higher background incidence, due to increased hormone replacement therapy use, MST estimates decreased to 3.9 and 5.0 years for the two age groups, and STS increased to 75 and 85%. CONCLUSIONS: The new estimates indicate that screening detectable phase is longer than that found in previous mammography trials/programmes, but also that the sensitivity of the screening test is lower. Overall, the NBCSP detects more cancer cases than most previous trials/programmes.

Aged↗

PubMed2004

Dynamic analysis of multivariate failure time data.

We present an approach for analyzing internal dependencies in counting processes. This covers the case with repeated events on each of a number of individuals, and more generally, the situation where several processes are observed for each individual. We define dynamic covariates, i.e., covariates depending on the past of the processes. The statistical analysis is performed mainly by the nonparametric additive approach. This yields a method for analyzing multivariate survival data, which is an alternative to the frailty approach. We present cumulative regression plots, statistical tests, residual plots, and a hat matrix plot for studying outliers. A program in R and S-PLUS for analyzing survival data with the additive regression model is available on the web site http://www.med.uio.no/imb/stat/addreg. The program has been developed to fit the counting process framework.

Biometry↗