Impact of chronic diseases on effect of breast cancer screening.

BACKGROUND: Although breast cancer screening reduces breast cancer mortality at the population level, subgroups of women may benefit differently. We investigated the impact of health status on the effect of breast cancer screening. METHODS: The study included 181 299 women invited in two population-based screening programs in Denmark and 1 526 446 control subjects, followed from April 1981 to December 2014. Poisson regressions were used to compare the observed breast cancer mortality rate in women invited to screening with the expected rate in the absence of screening among women with and without chronic diseases. Chronic diseases were defined as any diagnosis in the Charlson Comorbidity Index during 4 years before the first invitation to screening. RESULTS: Almost 10% of women had chronic diseases before first invitation to screening. Whereas we observed a reduction in breast cancer mortality following invitation to screening of 28% (95% CI, 20% to 35%) among women without chronic diseases, only a 7% (95% CI, -39% to 37%) reduction was seen for women with chronic diseases (P-value for interaction = .22). For participants, the reduction, corrected for selection bias, was 35% (95% CI 16% to 49%) for women without, and 4% (95% CI -146% to 62%) for women with chronic diseases (P-value for interaction = .43). CONCLUSION: Our data indicate a marginal effect of mammography screening on breast cancer mortality in women with chronic diseases. If our results are confirmed in other populations, the presence of chronic diseases will be an important factor to take into consideration in personalized screening.

New drug candidates for bipolar disorder-A nation-wide population-based study.

OBJECTIVE: Drug repurposing is an increasingly promising idea in many fields of medicine. We systematically used Danish nation-wide population-based registers to investigate whether continued use of non-aspirin non-steroidal anti-inflammatory drugs (NSAIDs), low-dose aspirin, high-dose aspirin, statins, allopurinol, and angiotensin agents decrease the rate of incident mania/bipolar disorder. METHODS: A nation-wide population-based longitudinal study using Poisson regression analyses including all persons in Denmark who purchased the exposure medication of interest and a random sample of 30% of the Danish population. The follow-up period comprised a 10 years period from 2005 to 2015. Two different outcome measures were included, (1) a diagnosis of mania/bipolar disorder at a psychiatric hospital contact as inpatient or outpatient and (2) a combined measure of a diagnosis of mania/bipolar disorder or initiation of lithium use. RESULTS: A total of 1,605,365 subjects were exposed to one of the six drugs of interest during the exposure period from 2005 to 2015, median age 57 years [quartiles: 43;69], and female proportion of 53.1%. Continued use of low-dose aspirin, statins, and angiotensin agents were associated with decreased rates of incident mania/bipolar disorder on both outcome measures. Continued uses of non-aspirin NSAIDs as well as high-dose aspirin were associated with an increased rate of incident bipolar disorder. There were no statistically significant associations for allopurinol. CONCLUSIONS: The study supports the potential of agents acting on inflammation and the stress response system in bipolar disorder and illustrates that population-based registers can be used to systematically identify drugs with repurposing potentials.

Causal inference in survival analysis using pseudo-observations.

Causal inference for non-censored response variables, such as binary or quantitative outcomes, is often based on either (1) direct standardization ('G-formula') or (2) inverse probability of treatment assignment weights ('propensity score'). To do causal inference in survival analysis, one needs to address right-censoring, and often, special techniques are required for that purpose. We will show how censoring can be dealt with 'once and for all' by means of so-called pseudo-observations when doing causal inference in survival analysis. The pseudo-observations can be used as a replacement of the outcomes without censoring when applying 'standard' causal inference methods, such as (1) or (2) earlier. We study this idea for estimating the average causal effect of a binary treatment on the survival probability, the restricted mean lifetime, and the cumulative incidence in a competing risks situation. The methods will be illustrated in a small simulation study and via a study of patients with acute myeloid leukemia who received either myeloablative or non-myeloablative conditioning before allogeneic hematopoetic cell transplantation. We will estimate the average causal effect of the conditioning regime on outcomes such as the 3-year overall survival probability and the 3-year risk of chronic graft-versus-host disease. Copyright © 2017 John Wiley & Sons, Ltd.

The Intracranial Distribution of Gliomas in Relation to Exposure From Mobile Phones: Analyses From the INTERPHONE Study.

When investigating the association between brain tumors and use of mobile telephones, accurate data on tumor position are essential, due to the highly localized absorption of energy in the human brain from the radio-frequency fields emitted. We used a point process model to investigate this association using information that included tumor localization data from the INTERPHONE Study (Australia, Canada, Denmark, Finland, France, Germany, Israel, Italy, Japan, New Zealand, Norway, Sweden, and the United Kingdom). Our main analysis included 792 regular mobile phone users diagnosed with a glioma between 2000 and 2004. Similar to earlier results, we found a statistically significant association between the intracranial distribution of gliomas and the self-reported location of the phone. When we accounted for the preferred side of the head not being exclusively used for all mobile phone calls, the results were similar. The association was independent of the cumulative call time and cumulative number of calls. However, our model used reported side of mobile phone use, which is potentially influenced by recall bias. The point process method provides an alternative to previously used epidemiologic research designs when one is including localization in the investigation of brain tumors and mobile phone use.

A three-dimensional point process model for the spatial distribution of disease occurrence in relation to an exposure source.

We study methods for how to include the spatial distribution of tumours when investigating the relation between brain tumours and the exposure from radio frequency electromagnetic fields caused by mobile phone use. Our suggested point process model is adapted from studies investigating spatial aggregation of a disease around a source of potential hazard in environmental epidemiology, where now the source is the preferred ear of each phone user. In this context, the spatial distribution is a distribution over a sample of patients rather than over multiple disease cases within one geographical area. We show how the distance relation between tumour and phone can be modelled nonparametrically and, with various parametric functions, how covariates can be included in the model and how to test for the effect of distance. To illustrate the models, we apply them to a subset of the data from the Interphone Study, a large multinational case-control study on the association between brain tumours and mobile phone use.

Comparing predictions among competing risks models with time-dependent covariates.

Prediction of cumulative incidences is often a primary goal in clinical studies with several endpoints. We compare predictions among competing risks models with time-dependent covariates. For a series of landmark time points, we study the predictive accuracy of a multi-state regression model, where the time-dependent covariate represents an intermediate state, and two alternative landmark approaches. At each landmark time point, the prediction performance is measured as the t-year expected Brier score where pseudovalues are constructed in order to deal with right-censored event times. We apply the methods to data from a bone marrow transplant study where graft versus host disease is considered a time-dependent covariate for predicting relapse and death in remission.

Absolute risk regression for competing risks: interpretation, link functions, and prediction.

In survival analysis with competing risks, the transformation model allows different functions between the outcome and explanatory variables. However, the model's prediction accuracy and the interpretation of parameters may be sensitive to the choice of link function. We review the practical implications of different link functions for regression of the absolute risk (or cumulative incidence) of an event. Specifically, we consider models in which the regression coefficients ß have the following interpretation: The probability of dying from cause D during the next t years changes with a factor exp(ß) for a one unit change of the corresponding predictor variable, given fixed values for the other predictor variables. The models have a direct interpretation for the predictive ability of the risk factors. We propose some tools to justify the models in comparison with traditional approaches that combine a series of cause-specific Cox regression models or use the Fine-Gray model. We illustrate the methods with the use of bone marrow transplant data.

Single-institution long-term outcomes for patients receiving nonmyeloablative conditioning hematopoeitic cell transplantation for chronic lymphocytic leukemia and follicular lymphoma.

Non-myeloablative conditioning hematopoietic cell transplantation (NMC-HCT) has improved the treatment of chronic lymphocytic leukemia (CLL) and follicular lymphoma (FL). In a cohort of 85 patients (45 with CLL and 40 with FL), we observed 5-yr overall survival (OS) and progression-free survival (PFS) of 53% and 38% in the CLL group and 81% and 76% in the FL group. In the both the CLL group and the FL group, a strong trend toward better OS and PFS was observed among patients in complete remission (CR) at HCT. Within the FL group, sixteen patients had at one or more time points in their disease history had transformed FL. In contrast to the poor survival found in patients with transformed FL in previous studies, the 5-yr OS was almost identical in patients with transformed and non-transformed FL, 83% and 78%, respectively. In conclusion, our study supports that NMC-HCT is a safe and efficacious treatment that can provide long-term survival in elderly, heavily pretreated patients with FL and CLL. Especially patients with FL, and also transformed FL, seemed to have a great benefit of NMC-HCT, and CR at the time of HCT was an important prognostic factor.

Multi-state models for the analysis of time-to-event data.

The experience of a patient in a survival study may be modelled as a process with two states and one possible transition from an "alive" state to a "dead" state. In some studies, however, the "alive" state may be partitioned into two or more intermediate (transient) states, each of which corresponding to a particular stage of the illness. In such studies, multi-state models can be used to model the movement of patients among the various states. In these models issues, of interest include the estimation of progression rates, assessing the effects of individual risk factors, survival rates or prognostic forecasting. In this article, we review modelling approaches for multi-state models, and we focus on the estimation of quantities such as the transition probabilities and survival probabilities. Differences between these approaches are discussed, focussing on possible advantages and disadvantages for each method. We also review the existing software currently available to fit the various models and present new software developed in the form of an R library to analyse such models. Different approaches and software are illustrated using data from the Stanford heart transplant study and data from a study on breast cancer conducted in Galicia, Spain.

Preoperative endoscopic stent placement before pancreaticoduodenectomy: a meta-analysis of the effect on morbidity and mortality.

BACKGROUND: Pancreaticoduodenectomy is the only potentially curative treatment for peripapillary pancreatic tumors. However, postoperative morbidity and mortality are high, and different approaches have been tried to improve results, such as preoperative biliary drainage in patients with jaundice. This meta-analysis investigated the effect on postoperative outcome of preoperative biliary drainage by endoscopic biliary stent placement in patients who are jaundiced and who have peripapillary pancreatic tumors. METHODS: A Medline search for the period 1985 to 2001 was performed. Eight retrospective studies and 2 prospective randomized controlled trials were included. Selection criteria for the primary analysis were as follows: patients with peripapillary pancreatic cancer, endoscopic stent placement versus no stent, radical surgery, and assessment of postoperative morbidity and mortality. A secondary analysis included both radical and palliative surgery. RESULTS: In the primary analysis, 337 patients underwent preoperative endoscopic biliary stent placement, and 412 patients had no endoscopic biliary stent placement (controls). The overall odds ratio for postoperative complications (stent vs. no stent) is estimated as 0.79: 95% CI [0.36, 1.73] and the estimated odds ratio for postoperative mortality is 0.81: 95% CI [0.33, 1.99]. In the secondary analysis, 1008 patients underwent preoperative EBS versus 720 control patients. The odds ratio for postoperative complications in this analysis was 0.93: 95% CI [0.65, 1.33] and for postoperative mortality is 1.12: 95% CI [0.62, 2.01]. CONCLUSION: No evidence was found of either a positive or adverse effect of preoperative endoscopic biliary stent placement on the outcome of surgery in patients with pancreatic cancer.