Prognostic value of lymphocyte-to-monocyte ratio and neutrophil-to-lymphocyte ratio in follicular lymphoma: a retrospective cohort study.

OBJECTIVES: The clinical course and prognosis of follicular lymphoma (FL) are diverse and associated with the patient's immune response. We investigated the lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-lymphocyte ratio (NLR) as prognostic factors in patients with FL, including those receiving radiotherapy. DESIGN: A retrospective cohort study. SETTING: Regional cancer centre in Hong Kong. PARTICIPANTS: 88 patients with histologically proven FL diagnosed between 2000 and 2014. MATERIALS AND METHODS: The best LMR and NLR cut-off values were determined using cross-validated areas under the receiver operating characteristic curves. The extent to which progression-free survival (PFS) and overall survival differed by NLR and LMR cut-off values was assessed using Kaplan-Meier analysis and log-rank tests. A Cox proportional hazards model was fitted to adjust for confounders. RESULTS: The best cut-off values for LMR and NLR were 3.20 and 2.18, respectively. The 5-year PFS was 73.6%. After multivariate adjustment, high LMR (>3.20) at diagnosis was associated with superior PFS, with a HR of 0.31 (95% CI 0.13 to 0.71), whereas high NLR at relapse was associated with poorer postprogression survival (HR 1.24, 95% CI 1.04 to 1.49). CONCLUSIONS: Baseline LMR and NLR at relapse were shown to be independent prognostic factors in FL. LMR and NLR are cheap and widely available biomarkers that could be used in combination with the Follicular Lymphoma International Prognostic Index by clinicians to better predict prognosis

Is cancer-related death associated with circadian rhythm?

We investigated the temporal pattern of death in cancer patients using a large sample size and robust statistical methods to account for chronobiological periodicity.We did not detect a circadian pattern of cancer death. The present study evaluated the temporal pattern of death among cancer patients using trigonometric functions and with time modeled in a circular scale.To conclude, we found no evidence of a chronobiological circadian pattern in death among cancer patients by using robust statistical methods and data from a large population in a hospital setting. Increased understanding of the temporal pattern of deaths may yield important insights toward understanding external factors associated with cancer death

Deconstructing the smoking-preeclampsia paradox through a counterfactual framework.

Although smoking during pregnancy may lead to many adverse outcomes, numerous studies have reported a paradoxical inverse association between maternal cigarette smoking during pregnancy and preeclampsia. Using a counterfactual framework we aimed to explore the structure of this paradox as being a consequence of selection bias. Using a case-control study nested in the Icelandic Birth Registry (1309 women), we show how this selection bias can be explored and corrected for. Cases were defined as any case of pregnancy induced hypertension or preeclampsia occurring after 20 weeks' gestation and controls as normotensive mothers who gave birth in the same year. First, we used directed acyclic graphs to illustrate the common bias structure. Second, we used classical logistic regression and mediation analytic methods for dichotomous outcomes to explore the structure of the bias. Lastly, we performed both deterministic and probabilistic sensitivity analysis to estimate the amount of bias due to an uncontrolled confounder and corrected for it. The biased effect of smoking was estimated to reduce the odds of preeclampsia by 28 % (OR 0.72, 95 %CI 0.52, 0.99) and after stratification by gestational age at delivery ( 1, revealing the structure of the paradox. The bias-adjusted estimation of the smoking effect on preeclampsia showed an OR of 1.22 (95 %CI 0.41, 6.53). The smoking-preeclampsia paradox appears to be an example of (1) selection bias most likely caused by studying cases prevalent at birth rather than all incident cases from conception in a pregnancy cohort, (2) omitting important confounders associated with both smoking and preeclampsia (preventing the outcome to develop) and (3) controlling for a collider (gestation weeks at delivery). Future studies need to consider these aspects when studying and interpreting the association between smoking and pregnancy outcomes

Association of socioeconomic deprivation with life expectancy and all-cause mortality in Spain, 2011-2013

Life tables summarise a population's mortality experience during a time period. Sex- and age-specific life tables are needed to compute various cancer survival measures. However, mortality rates vary according to socioeconomic status. We present sex- and age-specific life tables based on socioeconomic status at the census tract level in Spain during 2011-2013 that will allow estimating cancer relative survival estimates and life expectancy measures by socioeconomic status. Population and mortality data were obtained from the Spanish Statistical Office. Socioeconomic level was measured using the Spanish Deprivation Index by census tract. We produced sex- and age-specific life expectancies at birth by quintiles of deprivation, and life tables by census tract and province. Life expectancy at birth was higher among women than among men. Women and men in the most deprived census tracts in Spain lived 3.2 and 3.8 years less than their counterparts in the least deprived areas. A higher life expectancy in the northern regions of Spain was discovered. Life expectancy was higher in provincial capitals than in rural areas. We found a significant life expectancy gap and geographical variation by sex and socioeconomic status in Spain. The gap was more pronounced among men than among women. Understanding the association between life expectancy and socioeconomic status could help in developing appropriate public health programs. Furthermore, the life tables we produced are needed to estimate cancer specific survival measures by socioeconomic status. Therefore, they are important for cancer control in Spain.Instituto de Salud Carlos III (ISCIII): I18/01593 & CP17/00206-EU/FEDER. Asociación Española Contra el Cáncer (AECC): PROYE20023SÁNC and the Cancer Epidemiological Surveillance Subprogram (VICA) from the CIBER Epidemiología y Salud Pública (CIBERESP) from the Instituto de Salud Carlos III. Te funders had no role in the study design, data collection and analysis, decision to publish, or manuscript preparation.S

Mediating Effects of Diagnostic Route on the Comorbidity Gap in Survival of Patients with Diffuse Large B-Cell or Follicular Lymphoma in England.

Background: Socioeconomic inequalities in survival from non-Hodgkin lymphoma persist. Comorbidities are more prevalent amongst those in more deprived areas and are associated with diagnostic delay (emergency diagnostic route), which is also associated with poorer survival probability. We aimed to describe the effect of comorbidity on the probability of death mediated by diagnostic route (emergency vs. elective route) amongst patients with diffuse large B-cell (DLBCL) or follicular lymphoma (FL). Methods: We linked the English population-based cancer registry and hospital admission records (2005-2013) of patients aged 45-99 years. We decomposed the effect of comorbidity on survival into an indirect effect acting through diagnostic route and a direct effect not mediated by diagnostic route. Furthermore, we estimated the proportion of the comorbidity effect on survival mediated by diagnostic route. Results: For both DLBCL (n = 27,379) and FL (n = 14,043), those with any comorbidity, or living in more deprived areas, were more likely to experience diagnostic delay and poorer survival. The indirect effect of comorbidity on mortality through diagnostic route was highest at 12 months since diagnosis (DLBCL: Odds Ratio 1.10 [95% CI 1.07-1.13], FL: OR 1.09 [95% CI 1.04-1.14]). Within the first 12 months since diagnosis, emergency diagnostic route accounted for 24% (95% CI 17.5-29.5) and 16% (95% CI 6.0-25.6) of the comorbidity effect on mortality, for DLBCL and FL, respectively. Conclusion: Efforts to reduce diagnostic delay (emergency diagnosis) amongst patients with comorbidity would reduce inequalities in DLBCL and FL survival by 24% and 16%, respectively. Further public health programs and interventions are needed to reduce diagnostic delay amongst lymphoma patients with comorbidities

Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation.

In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficiency of these estimators. Furthermore, we compare 2 different model selection strategies based on 1) Akaike's Information Criterion and the Bayesian Information Criterion and 2) machine learning algorithms, and we illustrate double-robust estimators' performance in a real-world setting. In simulations with correctly specified models and near-positivity violations, all but the naive estimators had relatively good performance. However, the augmented inverse-probability-of-treatment weighting estimator showed the largest relative bias. Under dual model misspecification and near-positivity violations, all double-robust estimators were biased. Nevertheless, the targeted maximum likelihood estimator showed the best bias-variance trade-off, more precise estimates, and appropriate 95% confidence interval coverage, supporting the use of the data-adaptive model selection strategies based on machine learning algorithms. We applied these methods to estimate adjusted 1-year mortality risk differences in 183,426 lung cancer patients diagnosed after admittance to an emergency department versus persons with a nonemergency cancer diagnosis in England (2006-2013). The adjusted mortality risk (for patients diagnosed with lung cancer after admittance to an emergency department) was 16% higher in men and 18% higher in women, suggesting the importance of interventions targeting early detection of lung cancer signs and symptoms

Sleep-Disordered Breathing and Gestational Diabetes Mellitus: A meta-analysis of 9,795 participants enrolled in epidemiological observational studies

OBJECTIVE Recently, sleep-disordered breathing (SDB) has been reported to be associated with the development of gestational diabetes mellitus (GDM). Accordingly, as this is emergent area of research that has significant clinical relevance, the objective of this meta-analysis is to examine the relationship between SDB with GDM. RESEARCH DESIGN AND METHODS We searched several electronic databases for all of the studies published before January 2013 and reviewed references of published articles. Meta-analytic procedures were used to estimate the unadjusted and BMI-adjusted odds ratios (ORs) using a random effects model. Significant values, weighted effect sizes, and 95% CIs were calculated, and tests of homogeneity of variance were performed. RESULTS Results from nine independent studies with a total of 9,795 pregnant women showed that SDB was significantly associated with an increased risk of GDM. Women with SDB had a more than threefold increased risk of GDM, with a pooled BMI-adjusted OR 3.06 (95% CI 1.89–4.96). CONCLUSIONS These findings demonstrate a significant association between SDB and GDM that is evident even after considered confounding by obesity. This meta-analysis indicates a need to evaluate the role of early recognition and treatment of SDB early during pregnancy

Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research.

BACKGROUND: In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for overdispersion. METHODS: We used a regression-based score test for overdispersion under the relative survival framework and proposed different approaches to correct for overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. RESULTS: All piecewise exponential regression models showed the presence of significant inherent overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. CONCLUSION: We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, overdispersion due to model misspecification and true or inherent overdispersion

Using Longitudinal Targeted Maximum Likelihood Estimation in Complex Settings with Dynamic Interventions

Longitudinal targeted maximum likelihood estimation (LTMLE) has hardly ever been used to estimate dynamic treatment effects in the context of time-dependent confounding affected by prior treatment when faced with long follow-up times, multiple time-varying confounders, and complex associational relationships simultaneously. Reasons for this include the potential computational burden, technical challenges, restricted modeling options for long follow-up times, and limited practical guidance in the literature. However, LTMLE has desirable asymptotic properties, i.e. it is doubly robust, and can yield valid inference when used in conjunction with machine learning. We use a topical and sophisticated question from HIV treatment research to show that LTMLE can be used successfully in complex realistic settings and compare results to competing estimators. Our example illustrates the following practical challenges common to many epidemiological studies 1) long follow-up time (30 months), 2) gradually declining sample size 3) limited support for some intervention rules of interest 4) a high-dimensional set of potential adjustment variables, increasing both the need and the challenge of integrating appropriate machine learning methods. Our analyses, as well as simulations, shed new light on the application of LTMLE in complex and realistic settings: we show that (i) LTMLE can yield stable and good estimates, even when confronted with small samples and limited modeling options; (ii) machine learning utilized with a small set of simple learners (if more complex ones can’t be fitted) can outperform a single, complex model, which is tailored to incorporate prior clinical knowledge; (iii) performance can vary considerably depending on interventions and their support in the data, and therefore critical quality checks should accompany every LTMLE analysis