Predicted 25-hydroxyvitamin D over the adult life and the risk of ovarian cancer.

The evidence from previous studies of serum 25-hydroxyvitamin D [25(OH)D] and ovarian cancer risk are not conclusive. However, 25(OH)D was generally only measured in late adulthood, which may not capture the etiologically relevant exposure periods. We investigated predicted 25(OH)D over the adult lifetime in relation to ovarian cancer risk in a population-based case-control study conducted from 2011 to 2016 in Montreal, Canada (490 cases, 896 controls). Predicted 25(OH)D was computed using previously validated regression models. Unconditional multivariable logistic regression models were used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (CI) for average predicted 25(OH)D over the adult life and risk. In addition, the relative importance of different periods of past 25(OH)D exposure was explored using a weighted cumulative exposure (WCE) model. For each 20 nmol/L increase in average predicted 25(OH)D over the adult life, the aOR (95% CI) was 0.73 (0.55-0.96). In WCE analyses, the inverse association was strongest for exposures 5 to 20 years and 35 to 55 years prior to diagnosis, with aORs (95% CIs) of 0.82 (0.69-0.94) and 0.79 (0.66-1.02), respectively, for each 20 nmol/L increase in predicted 25(OH)D. These results support an inverse association between 25(OH)D in adulthood and ovarian cancer risk.

Importance of accounting for timing of time-varying exposures in association studies: Hydrochlorothiazide and non-melanoma skin cancer.

PURPOSE: Hydrochlorothiazide (HCTZ), a widely prescribed antihypertensive drug with photosensitising properties, has been linked with non-melanoma skin cancer (NMSC) risk. However, previous analyses did not fully explore if and how the impact of past HCTZ exposures accumulates with prolonged use and/or depends on time elapsed since exposures. Therefore, we used different models to more comprehensively assess how NMSC risk vary with HCTZ exposure, and explore how the results may depend on modeling strategies. METHODS: We used different parametric models with alternative time-varying exposure metrics, and the flexible weighted cumulative exposure model (WCE) to estimate associations between HCTZ exposures and NMSC risk in a population-based cohort of HCTZ users over 65 years old, in the province of Ontario, Canada. RESULTS: Among 3844 HCTZ users, 273 developed NMSC during up to 8 years of follow-up. In parametric models, based on all exposures, increased duration of past HCTZ use was associated with an increase of NMSC risk but cumulative dose showed no systematic association. Yet, WCE results suggested that only exposures taken 2.5-4 years in the past were associated with the current NMSC hazard. This finding led us to re-define the parametric models, which also confirmed that any HCTZ dose taken outside this time-window were not systematically associated with NMSC incidence. CONCLUSIONS: Our analyses illustrate how flexible modeling may yield new insights into complex temporal relationships between a time-varying drug exposure and risks of adverse events. Duration and recency of antihypertensive agents exposures must be taken into account in evaluating risk and benefits.

Preserving data privacy when using multi-site data to estimate individualized treatment rules.

Precision medicine is a rapidly expanding area of health research wherein patient level information is used to inform treatment decisions. A statistical framework helps to formalize the individualization of treatment decisions that characterize personalized management plans. Numerous methods have been proposed to estimate individualized treatment rules that optimize expected patient outcomes, many of which have desirable properties such as robustness to model misspecification. However, while individual data are essential in this context, there may be concerns about data confidentiality, particularly in multi-center studies where data are shared externally. To address this issue, we compared two approaches to privacy preservation: (i) data pooling, which is a covariate microaggregation technique and (ii) distributed regression. These approaches were combined with the doubly robust yet user-friendly method of dynamic weighted ordinary least squares to estimate individualized treatment rules. In simulations, we extensively evaluated the performance of the methods in estimating the parameters of the decision rule under different assumptions. The results demonstrate that double robustness is not maintained in data pooling setting and that this can result in bias, whereas the distributed regression provides good performance. We illustrate the methods via an analysis of optimal Warfarin dosing using data from the International Warfarin Consortium.

Adapting SIMEX to correct for bias due to interval-censored outcomes in survival analysis with time-varying exposure.

Many clinical and epidemiological applications of survival analysis focus on interval-censored events that can be ascertained only at discrete times of clinic visits. This implies that the values of time-varying covariates are not correctly aligned with the true, unknown event times, inducing a bias in the estimated associations. To address this issue, we adapted the simulation-extrapolation (SIMEX) methodology, based on assessing how the estimates change with the artificially increased time between clinic visits. We propose diagnostics to choose the extrapolating function. In simulations, the SIMEX-corrected estimates reduced considerably the bias to the null and generally yielded a better bias/variance trade-off than conventional estimates. In a real-life pharmacoepidemiological application, the proposed method increased by 27% the excess hazard of the estimated association between a time-varying exposure, representing the 2-year cumulative duration of past use of a hypertensive medication, and the hazard of nonmelanoma skin cancer (interval-censored events). These simulation-based and real-life results suggest that the proposed SIMEX-based correction may help improve the accuracy of estimated associations between time-varying exposures and the hazard of interval-censored events in large cohort studies where the events are recorded only at relatively sparse times of clinic visits/assessments. However, these advantages may be less certain for smaller studies and/or weak associations.

Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes.

Estimating individualized treatment rules-particularly in the context of right-censored outcomes-is challenging because the treatment effect heterogeneity of interest is often small, thus difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centres, data privacy may be of concern with participating data centres reluctant to share individual-level data. In this case study on the treatment of depression, we demonstrate an application of distributed regression for privacy protection used in combination with dynamic weighted survival modelling (DWSurv) to estimate an optimal individualized treatment rule whilst obscuring individual-level data. In simulations, we demonstrate the flexibility of this approach to address local treatment practices that may affect confounding, and show that DWSurv retains its double robustness even when performed through a (weighted) distributed regression approach. The work is motivated by, and illustrated with, an analysis of treatment for unipolar depression using the United Kingdom's Clinical Practice Research Datalink.

Flexible Modeling of the Association Between Cumulative Exposure to Low-Dose Ionizing Radiation From Cardiac Procedures and Risk of Cancer in Adults With Congenital Heart Disease.

Adults with congenital heart disease are increasingly being exposed to low-dose ionizing radiation (LDIR) from cardiac procedures. In a recent study, Cohen et al. (Circulation. 2018;137(13):1334-1345) reported an association between increased LDIR exposure and cancer incidence but did not explore temporal relationships. Yet, the impact of past exposures probably accumulates over years, and its strength may depend on the amount of time elapsed since exposure. Furthermore, LDIR procedures performed shortly before a cancer diagnosis may have been ordered because of early symptoms of cancer, raising concerns about reversal causality bias. To address these challenges, we combined flexible modeling of cumulative exposures with competing-risks methodology to estimate separate associations of time-varying LDIR exposure with cancer incidence and all-cause mortality. Among 24,833 patients from the Quebec Congenital Heart Disease Database, 602 had incident cancer and 500 died during a follow-up period of up to 15 years (1995-2010). Initial results suggested a strong association of cancer incidence with very recent LDIR exposures, likely reflecting reverse causality bias. When exposure was lagged by 2 years, an increased cumulative LDIR dose from the previous 2-6 years was associated with increased cancer incidence, with a stronger association for women. These results illustrate the importance of accurate modeling of temporal relationships between time-varying exposures and health outcomes.

Performance of two formal tests based on martingales residuals to check the proportional hazard assumption and the functional form of the prognostic factors in flexible parametric excess hazard models.

Net survival, the one that would be observed if the disease under study was the only cause of death, is an important, useful, and increasingly used indicator in public health, especially in population-based studies. Estimates of net survival and effects of prognostic factor can be obtained by excess hazard regression modeling. Whereas various diagnostic tools were developed for overall survival analysis, few methods are available to check the assumptions of excess hazard models. We propose here two formal tests to check the proportional hazard assumption and the validity of the functional form of the covariate effects in the context of flexible parametric excess hazard modeling. These tests were adapted from martingale residual-based tests for parametric modeling of overall survival to allow adding to the model a necessary element for net survival analysis: the population mortality hazard. We studied the size and the power of these tests through an extensive simulation study based on complex but realistic data. The new tests showed sizes close to the nominal values and satisfactory powers. The power of the proportionality test was similar or greater than that of other tests already available in the field of net survival. We illustrate the use of these tests with real data from French cancer registries.

Predicting serum vitamin D concentrations based on self-reported lifestyle factors and personal attributes.

Evidence supports the role of vitamin D in various conditions of development and ageing. Serum 25-hydroxyvitamin D (25(OH)D) is the best indicator for current vitamin D status. However, the cost of its measurement can be prohibitive in epidemiological research. We developed and validated multivariable regression models that quantified the relationships between vitamin D determinants, measured through an in-person interview, and serum 25(OH)D concentrations. A total of 200 controls participating in a population-based case-control study in Montreal, Canada, provided a blood specimen and completed an in-person interview on socio-demographic, reproductive, medical and lifestyle characteristics and personal attributes. Serum 25(OH)D concentrations were quantified by liquid chromatography-tandem MS. Multivariable least squares regression was used to build models that predict 25(OH)D concentrations from interview responses. We assessed high-order effects, performed sensitivity analysis using the lasso method and conducted cross-validation of the prediction models. Prediction models were built for users and non-users of vitamin D supplements separately. Among users, alcohol intake, outdoor time, sun protection, dose of supplement use, menopausal status and recent vacation were predictive of 25(OH)D concentrations. Among non-users, BMI, sun sensitivity, season and recent vacation were predictive of 25(OH)D concentrations. In cross-validation, 46-47 % of the variation in 25(OH)D concentrations were explained by these predictors. In the absence of 25(OH)D measures, our study supports that predicted 25(OH)D scores may be used to assign exposure in epidemiological studies that examine vitamin D exposure.

Physical activity and lung cancer risk in men and women.

PURPOSE: Although evidence has accumulated that recreational physical activities (PA) may reduce lung cancer risk, there is little evidence concerning the possible role of a potentially more important source of PA, namely occupational PA. We investigated both recreational and lifetime occupational PA in relation to lung cancer risk in a population-based case-control study in Montreal, Canada (NCASES = 727; NCONTROLS = 1,351). METHODS: Unconditional logistic regression was used to estimate odds ratios (OR), separately for men and women, adjusting for smoking, exposure to occupational carcinogens, and sociodemographic and lifestyle factors. RESULTS: In both sexes, increasing recreational PA was associated with a lower lung cancer risk (ORMEN = 0.66, 95% confidence interval (CI) 0.47-0.92; ORWOMEN = 0.55, 95% CI 0.34-0.88, comparing the highest versus lowest tertiles). For occupational PA, no association was observed among women, while increasing occupational PA was associated with increased risk among men (ORMEN = 1.96, 95% CI 1.27-3.01). ORs were not modified by occupational lung carcinogen exposure, body mass index, and smoking level; results were similar across lung cancer histological types. CONCLUSIONS: Our results support the previous findings for recreational PA and lung cancer risk. Unexpectedly, our findings suggest a positive association for occupational PA; this requires replication and more detailed investigation.

Cancer net survival on registry data: use of the new unbiased Pohar-Perme estimator and magnitude of the bias with the classical methods.

Net survival, the survival which might occur if cancer was the only cause of death, is a major epidemiological indicator required for international or temporal comparisons. Recent findings have shown that all classical methods used for routine estimation of net survival from cancer-registry data, sometimes called "relative-survival methods," provide biased estimates. Meanwhile, an unbiased estimator, the Pohar-Perme estimator (PPE), was recently proposed. Using real data, we investigated the magnitude of the errors made by four "relative-survival" methods (Ederer I, Hakulinen, Ederer II and a univariable regression model) vs. PPE as reference and examined the influence of time of follow-up, cancer prognosis, and age on the errors made. The data concerned seven cancer sites (2,51,316 cases) collected by FRANCIM cancer registries. Net survivals were estimated at 5, 10 and 15 years postdiagnosis. At 5 years, the errors were generally small. At 10 years, in good-prognosis cancers, the errors made in nonstandardized estimates with all classical methods were generally great (+2.7 to +9% points in prostate cancer) and increased in age-class estimations (vs. 5-year ones). At 15 years, in bad- or average-prognosis cancers, the errors were often substantial whatever the nature of the estimation. In good-prognosis cancers, the errors in nonstandardized estimates of all classical methods were great and sometimes very important. With all classical methods, great errors occurred in age-class estimates resulting in errors in age-standardized estimates (+0.4 to +3.2% points in breast cancer). In estimating net survival, cancer registries should abandon all classical methods and adopt the new Pohar-Perme estimator.

Estimating net survival: the importance of allowing for informative censoring.

Net survival, the one that would be observed if cancer were the only cause of death, is the most appropriate indicator to compare cancer mortality between areas or countries. Several parametric and non-parametric methods have been developed to estimate net survival, particularly when the cause of death is unknown. These methods are based either on the relative survival ratio or on the additive excess hazard model, the latter using the general population mortality hazard to estimate the excess mortality hazard (the hazard related to net survival). The present work used simulations to compare estimator abilities to estimate net survival in different settings such as the presence/absence of an age effect on the excess mortality hazard or on the potential time of follow-up, knowing that this covariate has an effect on the general population mortality hazard too. It showed that when age affected the excess mortality hazard, most estimators, including specific survival, were biased. Only two estimators were appropriate to estimate net survival. The first is based on a multivariable excess hazard model that includes age as covariate. The second is non-parametric and is based on the inverse probability weighting. These estimators take differently into account the informative censoring induced by the expected mortality process. The former offers great flexibility whereas the latter requires neither the assumption of a specific distribution nor a model-building strategy. Because of its simplicity and availability in commonly used software, the nonparametric estimator should be considered by cancer registries for population-based studies.

Association of epicardial fat with noncalcified coronary plaque volume and with low attenuation plaque in people with HIV.

OBJECTIVES: People with HIV are exposed to a higher risk of coronary artery disease (CAD) compared with the general population. Epicardial fat may play a unique role in promoting coronary atherosclerosis. We measured epicardial fat in participants living with HIV and controls and investigated its association with coronary plaque volume and low attenuation plaque, a marker of plaque vulnerability. DESIGN: This is a cross-sectional study, nested in the Canadian HIV and Aging Cohort Study, a large prospective cohort actively following participants with HIV and controls. Participants with low/intermediate cardiovascular risk without symptoms/history of CAD were invited to undergo cardiac computed tomography (CT). METHODS: Volume of epicardial fat, coronary plaque and low attenuation component of the plaque were measured. Association between epicardial fat, coronary plaque volume and low attenuation component was tested using adjusted regression analysis. RESULTS: A total of 169 participants with HIV and 81 controls underwent cardiac CT. Participants with HIV had a greater epicardial fat volume compared with controls (P = 0.019). In participants with HIV, epicardial fat volume was positively associated with duration of nonnucleoside reverse transcriptase inhibitors (NNRTI) (ß=2.19, P = 0.004). After adjustment for cardiovascular risk factors, epicardial fat volume was positively associated to noncalcified plaque volume [odds ratio (OR) = 1.09, P = 0.028] and to the low-attenuation plaque component portion (ß=0.38, P = 0.026). CONCLUSION: The association of epicardial fat volume to noncalcified plaque volume and to low attenuation component plaque may suggest a potential mechanism by which epicardial fat could be a silent driver of CAD in the HIV population.

Modeling of cumulative effects of time-varying drug exposures on within-subject changes in a continuous outcome.

An accurate assessment of the safety or effectiveness of drugs in pharmaco-epidemiological studies requires defining an etiologically correct time-varying exposure model, which specifies how previous drug use affects the outcome of interest. To address this issue, we develop, and validate in simulations, a new approach for flexible modeling of the cumulative effects of time-varying exposures on repeated measures of a continuous response variable, such as a quantitative surrogate outcome, or a biomarker. Specifically, we extend the linear mixed effects modeling to estimate how past and recent drug exposure affects the way individual values of the outcome change throughout the follow-up. To account for the dosage, duration and timing of past exposures, we rely on a flexible weighted cumulative exposure methodology to model the cumulative effects of past drug use, as the weighted sum of past doses. Weights, modeled with unpenalized cubic regression B-splines, reflect the relative importance of doses taken at different times in the past. In simulations, we evaluate the performance of the model under different assumptions concerning (i) the shape of the weight function, (ii) the sample size, (iii) the number of the longitudinal observations and (iv) the intra-individual variance. Results demonstrate the accuracy of our estimates of the weight function and of the between- and within-patients variances, and good correlation between the observed and predicted longitudinal changes in the outcome. We then apply the proposed method to re-assess the association between time-varying glucocorticoid exposure and weight gain in people living with rheumatoid arthritis.

Group-Based vs Individual Pelvic Floor Muscle Training to Treat Urinary Incontinence in Older Women: A Randomized Clinical Trial.

Importance: Urinary incontinence is one of the most prevalent health concerns experienced by older women (aged ≥60 years). Individual pelvic floor muscle training (PFMT) is the recommended first-line treatment for stress or mixed urinary incontinence in women, but human and financial resources limit its delivery. Whether group-based PFMT performs as well as individual PFMT in this population remains unclear. Objective: To assess the efficacy of group-based PFMT relative to individual PFMT for urinary incontinence in older women. Design, Setting, and Participants: The Group Rehabilitation or Individual Physiotherapy (GROUP) study is a single-blind, randomized, noninferiority trial conducted in 2 Canadian research centers, from July 1, 2012, to June 2, 2018. A total of 362 community-dwelling women aged 60 years or older with symptoms of stress or mixed urinary incontinence were enrolled. Interventions: After an individual session conducted to learn how to contract pelvic floor muscles, participants completed 12-week PFMT as part of a group of 8 women (n = 178) or in individual sessions (n = 184). Main Outcomes and Measures: The primary outcome measure was the percentage reduction in urinary incontinence episodes at 1 year, as reported in a 7-day bladder diary and relative to pretreatment baseline. Secondary outcomes included lower urinary tract-related signs, symptoms, and quality of life immediately following treatment and at 1 year. Per-protocol analysis was used. Results: Among 362 women who were randomized (mean [SD] age, 67.9 [5.8] years), 319 women (88%) completed the 1-year follow-up and were included in the per-protocol analysis. Median percentage reduction in urinary incontinence episodes was 70% (95% CI, 44%-89%) in individual PFMT compared with 74% (95% CI, 46%-86%) in group-based PFMT. The upper boundary of the 95% CI for the difference in the percentage reduction in urinary incontinence episodes at 1 year was lower than the prespecified margin for noninferiority of 10% (difference, 4%; 95% CI, -10% to 7%; P = .58), confirming noninferiority. Individual PFMT and group-based PFMT had similar effectiveness for all secondary outcomes at 1 year. Adverse events were minor and uncommon. Conclusions and Relevance: Results of the GROUP study suggest that group-based PFMT is not inferior to the recommended individual PFMT for the treatment of stress and mixed urinary incontinence in older women. Widespread use in clinical practice may help increase continence-care affordability and treatment availability. Trial Registration: ClinicalTrials.gov Identifier: NCT02039830.

Acute cardiovascular health effects in a panel study of personal exposure to traffic-related air pollutants and noise in Toronto, Canada.

Urban populations are often simultaneously exposed to air pollution and environmental noise, which are independently associated with cardiovascular disease. Few studies have examined acute physiologic responses to both air and noise pollution using personal exposure measures. We conducted a repeated measures panel study of air pollution and noise in 46 non-smoking adults in Toronto, Canada. Data were analyzed using linear mixed-effects models and weighted cumulative exposure modeling of recent exposure. We examined acute changes in cardiovascular health effects of personal (ultrafine particles, black carbon) and regional (PM2.5, NO2, O3, Ox) measurements of air pollution and the role of personal noise exposure as a confounder of these associations. We observed adverse changes in subclinical cardiovascular outcomes in response to both air pollution and noise, including changes in endothelial function and heart rate variability (HRV). Our findings show that personal noise exposures can confound associations for air pollutants, particularly with HRV, and that impacts of air pollution and noise on HRV occur soon after exposure. Thus, both noise and air pollution have a measurable impact on cardiovascular physiology. Noise should be considered alongside air pollution in future studies to elucidate the combined impacts of these exposures in urban environments.

Competing risks modeling of cumulative effects of time-varying drug exposures.

An accurate assessment of drug safety or effectiveness in pharmaco-epidemiology requires defining an etiologically correct time-varying exposure model, which specifies how previous drug use affects the hazard of the event of interest. An additional challenge is to account for the multitude of mutually exclusive events that may be associated with the use of a given drug. To simultaneously address both challenges, we develop, and validate in simulations, a new approach that combines flexible modeling of the cumulative effects of time-varying exposures with competing risks methodology to separate the effects of the same drug exposure on different outcomes. To account for the dosage, duration and timing of past exposures, we rely on a spline-based weighted cumulative exposure modeling. We also propose likelihood ratio tests to test if the cumulative effects of past exposure on the hazards of the competing events are the same or different. Simulation results indicate that the estimated event-specific weight functions are reasonably accurate, and that the proposed tests have acceptable type I error rate and power. In real-life application, the proposed method indicated that recent use of antihypertensive drugs may reduce the risk of stroke but has no effect on the hazard of coronary heart disease events.

Flexible and structured survival model for a simultaneous estimation of non-linear and non-proportional effects and complex interactions between continuous variables: Performance of this multidimensional penalized spline approach in net survival trend analysis.

Cancer survival trend analyses are essential to describe accurately the way medical practices impact patients' survival according to the year of diagnosis. To this end, survival models should be able to account simultaneously for non-linear and non-proportional effects and for complex interactions between continuous variables. However, in the statistical literature, there is no consensus yet on how to build such models that should be flexible but still provide smooth estimates of survival. In this article, we tackle this challenge by smoothing the complex hypersurface (time since diagnosis, age at diagnosis, year of diagnosis, and mortality hazard) using a multidimensional penalized spline built from the tensor product of the marginal bases of time, age, and year. Considering this penalized survival model as a Poisson model, we assess the performance of this approach in estimating the net survival with a comprehensive simulation study that reflects simple and complex realistic survival trends. The bias was generally small and the root mean squared error was good and often similar to that of the true model that generated the data. This parametric approach offers many advantages and interesting prospects (such as forecasting) that make it an attractive and efficient tool for survival trend analyses.

A prospective study of postnatal depressive symptoms and associated risk factors in first-time fathers.

BACKGROUND: Recent studies show that paternal depression negatively impacts children's behavioral and emotional development. This study determined the prevalence of depressed mood in first-time fathers at 2 and 6 months postpartum and identified associated risk factors. METHODS: A prospective cohort study with 622 men who completed sociodemographic and psychosocial questionnaires during their partner's third trimester of pregnancy. Fathers completed measures again at 2 and 6 months postpartum and partners completed the depressed mood measure at all three timepoints. A cutoff of ≥10 for the Edinburgh Postnatal Depression Scale identified depressed mood status. RESULTS: The prevalence of depressive symptoms in fathers was 13.76% at 2 months and 13.60% at 6 months postpartum. Men who were depressed during their partner's pregnancy were 7 times more likely to be depressed at 2 months postpartum. Depressed mood status at both the antenatal and 2 month postpartum assessment was associated with increased risk of depressed mood at 6 months postpartum. Older age, poor sleep quality at study entry, worse couple adjustment, having a partner experiencing antenatal depressive symptoms and elevated parental stress were associated with depressive symptoms at 2 months postpartum. Poor sleep quality, financial stress and a decline in couple adjustment were independently associated to depressive symptoms at 6 months postpartum. LIMITATIONS: This sample was fairly well-educated and predominately middle-class. Depressive symptoms were assessed using a self-report questionnaire. CONCLUSIONS: The psychosocial risk factors identified provide opportunities for early screening and targeted prevention strategies for fathers at risk for depression during the transition to parenthood.

Correction: High expression of HMGA2 independently predicts poor clinical outcomes in acute myeloid leukemia.

Since the publication of the original article the authors noticed the the affiliation details for Paresh Vyas are incorrect. The correct affiliation details for this author are given below.