Additive and multiplicative hazards modeling for recurrent event data analysis

<p>Abstract</p> <p>Background</p> <p>Sequentially ordered multivariate failure time or recurrent event duration data are commonly observed in biomedical longitudinal studies. In general, standard hazard regression methods cannot be applied because of correlation between recurrent failure times within a subject and induced dependent censoring. Multiplicative and additive hazards models provide the two principal frameworks for studying the association between risk factors and recurrent event durations for the analysis of multivariate failure time data.</p> <p>Methods</p> <p>Using emergency department visits data, we illustrated and compared the additive and multiplicative hazards models for analysis of recurrent event durations under (i) a varying baseline with a common coefficient effect and (ii) a varying baseline with an order-specific coefficient effect.</p> <p>Results</p> <p>The analysis showed that both additive and multiplicative hazards models, with varying baseline and common coefficient effects, gave similar results with regard to covariates selected to remain in the model of our real dataset. The confidence intervals of the multiplicative hazards model were wider than the additive hazards model for each of the recurrent events. In addition, in both models, the confidence interval gets wider as the revisit order increased because the risk set decreased as the order of visit increased.</p> <p>Conclusions</p> <p>Due to the frequency of multiple failure times or recurrent event duration data in clinical and epidemiologic studies, the multiplicative and additive hazards models are widely applicable and present different information. Hence, it seems desirable to use them, not as alternatives to each other, but together as complementary methods, to provide a more comprehensive understanding of data.</p

Resuming Work After Cancer: A Prospective Study of Occupational Register Data

Introduction Long-term employment rates have been studied in cancer survivors, but little is known about the return to work of cancer patients. This study investigated return to work (RTW) within 2 years after the diagnosis of different types of cancer. Methods This prospective study investigated the associations of demographics (age, gender, socioeconomic status, and residential region) and occupational factors (occupation, duration of employment, and company size) of employees absent from work due to cancer with the time to partial RTW, defined as working at least 50% of the earnings before sickness absence. Likewise, the associations of demographics and occupational factors with full RTW at equal earnings as before sickness absence were investigated. Results The cohort included 5,234 employees who had been absent from work due to cancer between January 2004 and December 2006. The time to partial RTW was shortest among employees with skin cancer (median 55 days) and longest among employees with lung cancer (median 377 days). There were no significant associations between RTW and demographics. With regard to the occupational factors, employees in high occupational classes started working earlier than those in low occupational classes, but the time to full RTW did not differ significantly across occupational classes. Employees working in large companies returned to work earlier than those working in small companies. Conclusion RTW after different types of cancer depended on occupational factors rather than demographics

Separate and combined analysis of successive dependent outcomes after breast-conservation surgery: recurrence, metastases, second cancer and death

<p>Abstract</p> <p>Background</p> <p>In the setting of recurrent events, research studies commonly count only the first occurrence of an outcome in a subject. However this approach does not correctly reflect the natural history of the disease. The objective is to jointly identify prognostic factors associated with locoregional recurrences (LRR), contralateral breast cancer, distant metastases (DM), other primary cancer than breast and breast cancer death and to evaluate the correlation between these events.</p> <p>Methods</p> <p>Patients (n = 919) with a primary invasive breast cancer and treated in a cancer center in South-Western France with breast-conserving surgery from 1990 to 1994 and followed up to January 2006 were included. Several types of non-independent events could be observed for the same patient: a LRR, a contralateral breast cancer, DM, other primary cancer than breast and breast cancer death. Data were analyzed separately and together using a random-effects survival model.</p> <p>Results</p> <p>LRR represent the most frequent type of first failure (14.6%). The risk of any event is higher for young women (less than 40 years old) and in the first 10 years of follow-up after the surgery. In the combined analysis histological tumor size, grade, number of positive nodes, progesterone receptor status and treatment combination are prognostic factors of any event. The results show a significant dependence between these events with a successively increasing risk of a new event after the first and second event. The risk of developing a new failure is greatly increased (RR = 4.25; 95%CI: 2.51-7.21) after developing a LRR, but also after developing DM (RR = 3.94; 95%CI: 2.23-6.96) as compared to patients who did not develop a first event.</p> <p>Conclusion</p> <p>We illustrated that the random effects survival model is a more satisfactory method to evaluate the natural history of a disease with multiple type of events.</p

The diffusion of financial supervisory governance ideas

Who is watching the financial services industry? Since 1980, there have been multiple waves of thought about whether the ministry of finance, the central bank, a specialized regulator or some combination of these should have supervisory authority. These waves have been associated with the convergence of actual practices. How much and through what channels did internationally promoted ideas about supervisory 'best practice' influence institutional design choices? I use a new dataset of 83 countries and jurisdictions between the 1980s and 2007 to examine the diffusion of supervisory ideas. With this data, I employ Cox Proportional Hazard and Competing Risks Event History Analyses to evaluate the possible causal roles best practice policy ideas might have played. I find that banking crises and certain peer groups can encourage policy convergence on heavily promoted ideas

Modelling survival : exposure pattern, species sensitivity and uncertainty

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans