Exploring different research questions via complex multi-state models when using registry-based repeated prescriptions of antidepressants in women with breast cancer and a matched population comparison group.

BACKGROUND: Multi-state models are used to study several clinically meaningful research questions. Depending on the research question of interest and the information contained in the data, different multi-state structures and modelling choices can be applied. We aim to explore different research questions using a series of multi-state models of increasing complexity when studying repeated prescriptions data, while also evaluating different modelling choices. METHODS: We develop a series of research questions regarding the probability of being under antidepressant medication across time using multi-state models, among Swedish women diagnosed with breast cancer (n = 18,313) and an age-matched population comparison group of cancer-free women (n = 92,454) using a register-based database (Breast Cancer Data Base Sweden 2.0). Research questions were formulated ranging from simple to more composite ones. Depending on the research question, multi-state models were built with structures ranging from simpler ones, like single-event survival analysis and competing risks, up to complex bidirectional and recurrent multi-state structures that take into account the recurring start and stop of medication. We also investigate modelling choices, such as choosing a time-scale for the transition rates and borrowing information across transitions. RESULTS: Each structure has its own utility and answers a specific research question. However, the more complex structures (bidirectional, recurrent) enable accounting for the intermittent nature of prescribed medication data. These structures deliver estimates of the probability of being under medication and total time spent under medication over the follow-up period. Sensitivity analyses over different definitions of the medication cycle and different choices of timescale when modelling the transition intensity rates show that the estimates of total probabilities of being in a medication cycle over follow-up derived from the complex structures are quite stable. CONCLUSIONS: Each research question requires the definition of an appropriate multi-state structure, with more composite ones requiring such an increase in the complexity of the multi-state structure. When a research question is related with an outcome of interest that repeatedly changes over time, such as the medication status based on prescribed medication, the use of novel multi-state models of adequate complexity coupled with sensible modelling choices can successfully address composite, more realistic research questions.

On the choice of timescale for other cause mortality in a competing risk setting using flexible parametric survival models.

In competing risks settings where the events are death due to cancer and death due to other causes, it is common practice to use time since diagnosis as the timescale for all competing events. However, attained age has been proposed as a more natural choice of timescale for modeling other cause mortality. We examine the choice of using time since diagnosis versus attained age as the timescale when modeling other cause mortality, assuming that the hazard rate is a function of attained age, and how this choice can influence the cumulative incidence functions ( C I F $CIF$ s) derived using flexible parametric survival models. An initial analysis on the colon cancer data from the population-based Swedish Cancer Register indicates such an influence. A simulation study is conducted in order to assess the impact of the choice of timescale for other cause mortality on the bias of the estimated C I F s $CIFs$ and how different factors may influence the bias. We also use regression standardization methods in order to obtain marginal C I F $CIF$ estimates. Using time since diagnosis as the timescale for all competing events leads to a low degree of bias in C I F $CIF$ for cancer mortality ( C I F 1 $CIF_{1}$ ) under all approaches. It also leads to a low degree of bias in C I F $CIF$ for other cause mortality ( C I F 2 $CIF_{2}$ ), provided that the effect of age at diagnosis is included in the model with sufficient flexibility, with higher bias under scenarios where a covariate has a time-varying effect on the hazard rate for other cause mortality on the attained age scale.

Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus.

BACKGROUND: Multi-state models are used in complex disease pathways to describe a process where an individual moves from one state to the next, taking into account competing states during each transition. In a multi-state setting, there are various measures to be estimated that are of great epidemiological importance. However, increased complexity of the multi-state setting and predictions over time for individuals with different covariate patterns may lead to increased difficulty in communicating the estimated measures. The need for easy and meaningful communication of the analysis results motivated the development of a web tool to address these issues. RESULTS: MSMplus is a publicly available web tool, developed via the Shiny R package, with the aim of enhancing the understanding of multi-state model analyses results. The results from any multi-state model analysis are uploaded to the application in a pre-specified format. Through a variety of user-tailored interactive graphs, the application contributes to an improvement in communication, reporting and interpretation of multi-state analysis results as well as comparison between different approaches. The predicted measures that can be supported by MSMplus include, among others, the transition probabilities, the transition intensity rates, the length of stay in each state, the probability of ever visiting a state and user defined measures. Representation of differences, ratios and confidence intervals of the aforementioned measures are also supported. MSMplus is a useful tool that enhances communication and understanding of multi-state model analyses results. CONCLUSIONS: Further use and development of web tools should be encouraged in the future as a means to communicate scientific research.

Long-Term Trends (1994-2011) and Predictors of Total Alcohol and Alcoholic Beverages Consumption: The EPIC Greece Cohort.

The aim of this study was to evaluate the longitudinal changes in alcohol consumption (total alcohol and types of alcoholic beverages) of the Greek EPIC cohort participants (28,572) during a 17-year period (1994-2011), with alcohol information being recorded repeatedly over time. Descriptive statistics were used to show crude trends in drinking behavior. Mixed-effects models were used to study the consumption of total alcohol, wine, beer and spirits/other alcoholic beverages in relation to birth cohort, socio-demographic, lifestyle and health factors. We observed a decreasing trend of alcohol intake as age increased, consistent for total alcohol consumption and the three types of beverages. Older birth cohorts had lower initial total alcohol consumption (8 vs. 10 g/day) and steeper decline in wine, spirits/other alcoholic beverages and total alcohol consumption compared to younger cohorts. Higher education and smoking at baseline had a positive association with longitudinal total alcohol consumption, up to +30% (vs. low education) and more than +25% (vs. non-smoking) respectively, whereas female gender, obesity, history of heart attack, diabetes, peptic ulcer and high blood pressure at baseline had a negative association of -85%, -25%, -16%, -37%, -22% and -24% respectively. Alcohol consumption changed over age with different trends among the studied subgroups and types of alcohol, suggesting targeted monitoring of alcohol consumption.

Changes in the Dietary Habits of the Greek EPIC Cohort Participants during a 14-Year Follow-Up Period (1997-2011).

The aim of this study is to evaluate the changes in the nutritional behavior of the Greek EPIC (European Prospective Investigation into Cancer and Nutrition) cohort participants regarding the consumption of basic food groups, during a 14-year period (1997-2011). In the Greek segment of the EPIC cohort study (EPIC-Greece), the changes in dietary habits of 23,505 participants regarding several food items/groups (vegetables, legumes, fruits, nuts, dairy, cereal, meat, fish/seafood, olive oil) were recorded repeatedly over time and compared to the baseline assessment (1994-1997), using a short, qualitative, follow-up questionnaire. Descriptive statistics were used to study the trends in nutritional behavior over time and ordinal logistic regression models to study the associations between the ordered responses of the questionnaire and sociodemographic and health factors. More participants reported an increase rather than a decrease in the consumption of vegetables, fruits, fish/seafood, whilst the inverse was observed for dairy products, nuts, cereals, and meat. No prevailing trend was noted for legumes and olive oil. Factors such as being female and having high education relate to more positive (healthy) changes in nutritional behavior. There seems to be primarily a change to a more healthy nutritional behavior of the EPIC-Greece participants over the follow-up period, with different participant subgroups presenting different degrees of nutritional changes.

Maternal diet and breastfeeding duration of infants after NICU hospitalization in Greece: a cohort study.

OBJECTIVES: To investigate the role of maternal diet, personal characteristics and willingness to breastfeed on breastfeeding duration of hospitalized neonates as well as to evaluate the mothers' dietetic intake based on the national recommendations. METHODS: A sample of 161 pregnant women from Athens, Greece was followed up during pregnancy, labor and the first 40 weeks of lactation. The participants attended breastfeeding classes and were interviewed regarding their nutritional habits, personal characteristics and breastfeeding intention. A multivariable logistic regression, adjusted for maternal age, smoking, weeks of gestation, body mass index, mode of delivery was conducted in order to estimate the adjusted odds ratios of breastfeeding for at least 6 months for consuming additional serves of fruit or vegetables from the recommended by the national guidelines. RESULTS: The adjusted odds ratios for breastfeeding at 6 months was 2.15 (p = 0.05) for women consumed ≥3.5 servings of fruits/day. Moreover, the participants reported low conformity with the national dietetic guidelines. CONCLUSIONS: Mothers who consumed the recommended by the NDG fruit servings/day breast-fed their hospitalized newborns for a longer period. Despite the fact that our participants were highly motivated and willing to breast-feed, we argue that this relationship is highly unlikely to be biological.