Showing papers by "Timo Hakulinen published in 2007"

Patients with previous cancer should not be excluded in international comparative cancer survival studies.

Abstract: In international comparisons or time trend analyses of cancer survival, it is common practice to restrict analyses to patients with a first cancer, i.e., to exclude patients with previous cancer diagnoses. However, the proportion of cancer patients with known previous cancer depends on the time cancer registries have been running, which results in varying proportions of excluded patients across registries. If prognosis of patients with second cancers differs from prognosis of patients with first cancers, varying exclusions may bias survival comparisons. We empirically evaluate the dependence of proportions of patients recorded as having a first cancer on time since initiation of cancer registration and the impact of excluding patients with known previous cancer on cancer survival estimates using the data of the nationwide Finnish Cancer Registry. Among 20 common cancer sites investigated, the proportion of "first cancers" varied between 97.4 and 99.7% in 1953-1957, the first 5-years of cancer registration, and decreased continuously to levels between 83.9 and 92.7% in 1993-1997. Excluding patients with a previous cancer diagnosis had little impact on estimates of survival of cancer patients diagnosed in 1953-1957, but increased 5-year relative survival estimates among patients diagnosed in 1993-1997 for each of the 20 cancers. The extent of the increase varied by cancer site and age. The increase ranged up to 2.9% points for crude and up to 1.7% points for age adjusted 5-year relative survival. These results caution against exclusion of patients with previous cancer diagnosis in comparative analyses of cancer survival.

Estimating the relative risk of pancreatic cancer associated with exposure agents in job title data in a hierarchical Bayesian meta-analysis

Abstract: Objectives The study demonstrates the application of a hierarchical Bayesian meta-analysis of epidemiologic studies that show an association between pancreatic cancer risk and job titles, using a job-exposure matrix to estimate risks for occupational exposure agents. Methods Altogether 261 studies published from 1969 through 1998 on pancreatic cancer and job titles were identified. When proportional studies are excluded, 77 studies were informative for 9 selected occupational agents. These studies included more than 3799 observed pancreatic cancer cases. Hierarchical Bayesian models were used for job titles (lower-level data) and agents (higher-level data), the latter from a Finnish job-exposure matrix. Non-Bayesian random effects models were applied for job titles to check consistency with the Bayesian results. Results The results suggest that occupational exposures to chlorinated hydrocarbon compounds may increase the risk of pancreatic cancer; the meta-relative risk (MRR) was 2.21 [95% credible interval (CrI) 1.31–3.68]. A suggestive weak excess was found for exposure to insecticides (MRR 1.95, 95% CrI 0.51–7.41). Conclusions Hierarchical models are applicable in meta-analyses when studies addressing the agent(s) under study are lacking or are very few, but several studies address job titles with potential exposure to these agents. Hierarchical meta-analytic models involving durations and intensities of exposure to occupational agents from a job-exposure matrix should be developed.

Maximizing the benefits of model-based period analysis of cancer patient survival.

Abstract: Period analysis has been shown to provide more up-to-date estimates of cancer survival than traditional methods of survival analysis. There is, however, a tradeoff between up-to-dateness and precision of period survival estimates: increasing up-to-dateness by restricting the analysis to a relatively short period, such as the most recent calendar year, goes along with loss of precision. Recently, a model-based approach was proposed, in which more precise period survival estimates for the most recent year can be obtained through modeling of survival trends within a recent 5-year period. We assess possibilities to extend the time window used for modeling to come up with even more precise, but equally accurate and up-to-date estimates of prognosis. Empirical evaluation using data from the Finnish Cancer Registry shows that extension of the time window to about 10 years provides, in most cases, as accurate results as using a 5-year time window (whereas further extension may lead to considerably less accurate results in some cases). Using 10-year time windows for modeling, SEs of survival estimates can be approximately halved compared with conventional period survival estimates for the most recent calendar year. Furthermore, we present a modification of the modeling approach, which allows extension to 10-year time windows to be achieved without the need to include additional cohorts of patients diagnosed longer time ago and which provides similarly accurate survival estimates at comparable levels of precision in most cases. Our analyses indicate opportunities to further maximize benefits of model-based period analysis of cancer survival.

Genetic random effects model for family data with long-term survivors: analysis of diabetic nephropathy in type 1 diabetes.

Abstract: A shared and additive genetic variance component-long-term survivor (LTS) model for familial aggregation studies of complex diseases with variable age-at-onset phenotype and non-susceptible subjects in the study cohort is proposed. LTS has been used from the early 1970s, especially in epidemiological studies of cancer. The LTS model utilizes information on the age at onset (survival) distribution to make inference on partially latent susceptibility. Bayesian modeling with uninformative priors is used and estimates of the posterior distribution of age at onset and susceptibility parameters of interest have been obtained using Bayesian Markov chain Monte Carlo (MCMC) methods with OpenBugs program. A simulation study confirms that we obtain posterior estimates of the model parameters on shared and genetic variance components of age at onset and susceptibility with good coverage rates. Further, we analyze familial aggregation of diabetic nephropathy (DN) in large Finnish cohort of 528 sibships with type 1 diabetes (T1D). According to the variance components estimated a substantial familial variation in the susceptibility to DN exist among families, while time to DN is less influenced by shared familial factors.