350 research outputs found
The surprising implications of familial association in disease risk
Background: A wide range of diseases show some degree of clustering in
families; family history is therefore an important aspect for clinicians when
making risk predictions. Familial aggregation is often quantified in terms of a
familial relative risk (FRR), and although at first glance this measure may
seem simple and intuitive as an average risk prediction, its implications are
not straightforward.
Methods: We use two statistical models for the distribution of disease risk
in a population: a dichotomous risk model that gives an intuitive understanding
of the implication of a given FRR, and a continuous risk model that facilitates
a more detailed computation of the inequalities in disease risk. Published
estimates of FRRs are used to produce Lorenz curves and Gini indices that
quantifies the inequalities in risk for a range of diseases.
Results: We demonstrate that even a moderate familial association in disease
risk implies a very large difference in risk between individuals in the
population. We give examples of diseases for which this is likely to be true,
and we further demonstrate the relationship between the point estimates of FRRs
and the distribution of risk in the population.
Conclusions: The variation in risk for several severe diseases may be larger
than the variation in income in many countries. The implications of familial
risk estimates should be recognized by epidemiologists and clinicians.Comment: 17 pages, 5 figure
Does Cox analysis of a randomized survival study yield a causal treatment effect?
The final publication (Aalen, Odd O., Richard J. Cook, and Kjetil Røysland. Does Cox analysis of a randomized survival study yield a causal treatment effect?. Lifetime Data Analysis 21(4) (2015): 579-593. DOI: 10.1007/s10985-015-9335-y) is available at http://link.springer.com/article/10.1007/s10985-015-9335-yStatistical methods for survival analysis play a central role in the assessment
of treatment effects in randomized clinical trials in cardiovascular disease, cancer, and
many other fields. The most common approach to analysis involves fitting a Cox
regression model including a treatment indicator, and basing inference on the large
sample properties of the regression coefficient estimator. Despite the fact that treatment
assignment is randomized, the hazard ratio is not a quantity which admits a causal
interpretation in the case of unmodelled heterogeneity. This problem arises because
the risk sets beyond the first event time are comprised of the subset of individuals who
have not previously failed. The balance in the distribution of potential confounders
between treatment arms is lost by this implicit conditioning, whether or not censoring
is present. Thus while the Cox model may be used as a basis for valid tests of the
null hypotheses of no treatment effect if robust variance estimates are used, modeling
frameworks more compatible with causal reasoning may be preferable in general for
estimation.Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626
Likelihood for generally coarsened observations from multi-state or counting process models
We consider first the mixed discrete-continuous scheme of observation in
multistate models; this is a classical pattern in epidemiology because very
often clinical status is assessed at discrete visit times while times of death
or other events are observed exactly. A heuristic likelihood can be written for
such models, at least for Markov models; however, a formal proof is not easy
and has not been given yet. We present a general class of possibly non-Markov
multistate models which can be represented naturally as multivariate counting
processes. We give a rigorous derivation of the likelihood based on applying
Jacod's formula for the full likelihood and taking conditional expectation for
the observed likelihood. A local description of the likelihood allows us to
extend the result to a more general coarsening observation scheme proposed by
Commenges & G\'egout-Petit. The approach is illustrated by considering models
for dementia, institutionalization and death
Frailty modeling of bimodal age-incidence curves of nasopharyngeal carcinoma in low-risk populations
The incidence of nasopharyngeal carcinoma (NPC) varies widely according to age at diagnosis, geographic location, and ethnic background. On a global scale, NPC incidence is common among specific populations primarily living in southern and eastern Asia and northern Africa, but in most areas, including almost all western countries, it remains a relatively uncommon malignancy. Specific to these low-risk populations is a general observation of possible bimodality in the observed age-incidence curves. We have developed a multiplicative frailty model that allows for the demonstrated points of inflection at ages 15–24 and 65–74. The bimodal frailty model has 2 independent compound Poisson-distributed frailties and gives a significant improvement in fit over a unimodal frailty model. Applying the model to population-based cancer registry data worldwide, 2 biologically relevant estimates are derived, namely the proportion of susceptible individuals and the number of genetic and epigenetic events required for the tumor to develop. The results are critically compared and discussed in the context of existing knowledge of the epidemiology and pathogenesis of NPC
A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models
Multi-state models are increasingly being used to model complex
epidemiological and clinical outcomes over time. It is common to assume that
the models are Markov, but the assumption can often be unrealistic. The Markov
assumption is seldomly checked and violations can lead to biased estimation for
many parameters of interest. As argued by Datta and Satten (2001), the
Aalen-Johansen estimator of occupation probabilities is consistent also in the
non-Markov case. Putter and Spitoni (2018) exploit this fact to construct a
consistent estimator of state transition probabilities, the landmark
Aalen-Johansen estimator, which does not rely on the Markov assumption. A
disadvantage of landmarking is data reduction, leading to a loss of power. This
is problematic for less traveled transitions, and undesirable when such
transitions indeed exhibit Markov behaviour. Using a framework of partially
non-Markov multi-state models we suggest a hybrid landmark Aalen-Johansen
estimator for transition probabilities. The proposed estimator is a compromise
between regular Aalen-Johansen and landmark estimation, using transition
specific landmarking, and can drastically improve statistical power. The
methods are compared in a simulation study and in a real data application
modelling individual transitions between states of sick leave, disability,
education, work and unemployment. In the application, a birth cohort of 184951
Norwegian men are followed for 14 years from the year they turn 21, using data
from national registries
Breast cancer tumor growth estimated through mammography screening data
Introduction
Knowledge of tumor growth is important in the planning and evaluation of screening programs, clinical trials, and epidemiological studies. Studies of tumor growth rates in humans are usually based on small and selected samples. In the present study based on the Norwegian Breast Cancer Screening Program, tumor growth was estimated from a large population using a new estimating procedure/model.
Methods
A likelihood-based estimating procedure was used, where both tumor growth and the screen test sensitivity were modeled as continuously increasing functions of tumor size. The method was applied to cancer incidence and tumor measurement data from 395,188 women aged 50 to 69 years.
Results
Tumor growth varied considerably between subjects, with 5% of tumors taking less than 1.2 months to grow from 10 mm to 20 mm in diameter, and another 5% taking more than 6.3 years. The mean time a tumor needed to grow from 10 mm to 20 mm in diameter was estimated as 1.7 years, increasing with age. The screen test sensitivity was estimated to increase sharply with tumor size, rising from 26% at 5 mm to 91% at 10 mm. Compared with previously used Markov models for tumor progression, the applied model gave considerably higher model fit (85% increased predictive power) and provided estimates directly linked to tumor size.
Conclusion
Screening data with tumor measurements can provide population-based estimates of tumor growth and screen test sensitivity directly linked to tumor size. There is a large variation in breast cancer tumor growth, with faster growth among younger women
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