Risk factors for abnormal glucose metabolism during antipsychotic treatment: A prospective cohort study

Antipsychotic medications increase the risk of abnormal glucose metabolism. However, in clinical practice, it is difficult to predict this risk because it is affected by medication-related and background factors. This study aimed to identify the risk factors for abnormal glucose metabolism during antipsychotic treatment. We conducted a multicenter, prospective, cohort study in patients with schizophrenia, schizoaffective disorder, or bipolar disorder. Of these patients, those with prediabetes or possible diabetes were excluded. Finally, 706 patients were included in the analysis. The hazard ratio (HR) for each factor was calculated for events of progression to hyperglycemia using time-dependent Cox regression analysis stratified according to facility type and adjusted for available background and drug-related factors. Treatments with olanzapine (HR = 2.06, 95% confidence interval [CI] = 1.05-4.05), clozapine (HR = 4.25, 95% CI = 1.56-11.60), and chlorpromazine (HR = 4.48, 95% CI = 1.21-16.57), overweight and obesity (HR = 1.57, 95% CI = 1.02-2.41), and hypertriglyceridemia (HR = 1.72, 95% CI = 1.02-2.88) were associated with a significantly higher occurrence of hyperglycemic progression. The number and daily dose of antipsychotics were not associated with their occurrence. Our study demonstrated that more careful monitoring is necessary during olanzapine, clozapine, and chlorpromazine treatment because of the higher occurrence of abnormalities in glucose metabolism. Furthermore, patients with obesity or hypertriglyceridemia warrant monitoring for the occurrence of abnormal glucose metabolism, regardless of the type of antipsychotic medication.

causalCmprsk: An R package for nonparametric and Cox-based estimation of average treatment effects in competing risks data

BACKGROUND AND OBJECTIVE

Competing risks data arise in both observational and experimental clinical studies with time-to-event outcomes, when each patient might follow one of the multiple mutually exclusive competing paths. Ignoring competing risks in the analysis can result in biased conclusions. In addition, possible confounding bias of the treatment-outcome relationship has to be addressed, when estimating treatment effects from observational data. In order to provide tools for estimation of average treatment effects on time-to-event outcomes in the presence of competing risks, we developed the R package causalCmprsk. We illustrate the package functionality in the estimation of effects of a right heart catheterization procedure on discharge and in-hospital death from observational data.

METHODS

The causalCmprsk package implements an inverse probability weighting estimation approach, aiming to emulate baseline randomization and alleviate possible treatment selection bias. The package allows for different types of weights, representing different target populations. causalCmprsk builds on existing methods from survival analysis and adapts them to the causal analysis in non-parametric and semi-parametric frameworks.

RESULTS

The causalCmprsk package has two main functions: fit.cox assumes a semiparametric structural Cox proportional hazards model for the counterfactual cause-specific hazards, while fit.nonpar does not impose any structural assumptions. In both frameworks, causalCmprsk implements estimators of (i) absolute risks for each treatment arm, e.g., cumulative hazards or cumulative incidence functions, and (ii) relative treatment effects, e.g., hazard ratios, or restricted mean time differences. The latter treatment effect measure translates the treatment effect from probability into more intuitive time domain and allows the user to quantify, for example, by how many days or months the treatment accelerates the recovery or postpones illness or death.

CONCLUSIONS

The causalCmprsk package provides a convenient and useful tool for causal analysis of competing risks data. It allows the user to distinguish between different causes of the end of follow-up and provides several time-varying measures of treatment effects. The package is accompanied by a vignette that contains more details, examples and code, making the package accessible even for non-expert users.

Consistent and robust inference in hazard probability and odds models with discrete-time survival data

For discrete-time survival data, conditional likelihood inference in Cox's hazard odds model is theoretically desirable but exact calculation is numerical intractable with a moderate to large number of tied events. Unconditional maximum likelihood estimation over both regression coefficients and baseline hazard probabilities can be problematic with a large number of time intervals. We develop new methods and theory using numerically simple estimating functions, along with model-based and model-robust variance estimation, in hazard probability and odds models. For the probability hazard model, we derive as a consistent estimator the Breslow-Peto estimator, previously known as an approximation to the conditional likelihood estimator in the hazard odds model. For the hazard odds model, we propose a weighted Mantel-Haenszel estimator, which satisfies conditional unbiasedness given the numbers of events in addition to the risk sets and covariates, similarly to the conditional likelihood estimator. Our methods are expected to perform satisfactorily in a broad range of settings, with small or large numbers of tied events corresponding to a large or small number of time intervals. The methods are implemented in the R package dSurvival.

A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model

The proportional hazards (PH) model is, arguably, the most popular model for the analysis of lifetime data arising from epidemiological studies, among many others. In such applications, analysts may be faced with censored outcomes and/or studies which institute enrollment criterion leading to left truncation. Censored outcomes arise when the event of interest is not observed but rather is known relevant to an observation time(s). Left truncated data occur in studies that exclude participants who have experienced the event prior to being enrolled in the study. If not accounted for, both of these features can lead to inaccurate inferences about the population under study. Thus, to overcome this challenge, herein we propose a novel unified PH model that can be used to accommodate both of these features. In particular, our approach can seamlessly analyze exactly observed failure times along with interval-censored observations, while aptly accounting for left truncation. To facilitate model fitting, an expectation-maximization algorithm is developed through the introduction of carefully structured latent random variables. To provide modeling flexibility, a monotone spline representation is used to approximate the cumulative baseline hazard function. The performance of our methodology is evaluated through a simulation study and is further illustrated through the analysis of two motivating data sets; one that involves child mortality in Nigeria and the other prostate cancer.

TG/HDL-C ratio as a predictor of stroke in the population with healthy BMI: The Jichi Medical School Cohort Study

BACKGROUND AND AIMS

The triglycerides-to-high-density lipoprotein cholesterol ratio (TG/HDL-C) is a predictor of metabolic syndrome and cardiovascular disease onset. However, the relationship between TG/HDL-C and stroke has not been established. This study examined whether TG/HDL-C helps in predicting stroke onset; this was compared between the whole population and healthy body mass index (BMI) population.

METHODS AND RESULTS

The Jichi Medical School Cohort Study is a prospective cohort study involving baseline data collected in 12 Japanese districts between April 1992 and July 1995. We used data from 11,699 participants; participants with a healthy BMI (20.0-24.9 kg/m²) were grouped into sex-specific TG/HDL-C quartiles. Using the first quartile groups as references, the hazard ratios (HRs) and 95% confidence intervals (CIs) of the Cox proportional hazards model were calculated. During the mean 10.8 years of follow-up, 419 new stroke events were recorded. The multivariable-adjusted HRs (95% CIs) in the fourth quartile of the whole population were 1.28 (0.94-1.75), 1.78 (0.91-3.48), 1.20 (0.82-1.77), and 1.13 (0.50-2.54), as compared to those in the fourth quartile of the healthy BMI population, which were 1.87 (1.24-2.83), 3.06 (1.21-7.74), 1.79 (1.05-3.05), and 1.29 (0.49-3.41) for all patients with all stroke, intracerebral hemorrhage, cerebral infarction, and subarachnoid hemorrhage, respectively.

CONCLUSION

Increased TG/HDL-C correlated with a significant increase in stroke risk only in the healthy BMI population and not the whole population. Furthermore, it was primarily associated with increased intracerebral hemorrhage and cerebral infarction risk.

Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths

BACKGROUND

Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone.

METHODS

We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified non-linear mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.

RESULTS

An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations.

CONCLUSION

Symbolic regression is a way to find transformations of covariates from patients' medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.

Medicine before and after David Cox

Herein we recount the legacy of Sir David Roxbee Cox (15 July 1924 - 18 January 2022) from the perspective of practicing clinicians. His-pioneering work in developing the logistic and Cox proportional hazard regression models revolutionized the analysis and interpretation of categorical and time-to-event survival outcomes in modern medicine. This legacy is an inspiration for all those who follow on Sir David Cox's path.

Stroke Risk Due to Smoking Characterized by Sex Differences in Japan: The Jichi Medical School Cohort Study

OBJECTIVES

Smoking is a risk factor for stroke. The relationship between smoking and the risk of different subtypes of stroke has not been fully elucidated. We investigated the relationship between smoking and the incidence of stroke in the Japanese population.

MATERIALS AND METHODS

This prospective, population-based cohort study included 11,324 participants (4447 men; 6877 women) from 12 districts in Japan, between April 1992 and July 1995. Participants were stratified according to smoking status (non-smoker [never smoked]/ex-smoker/current smoker). Male current smokers were further stratified according to the number of cigarettes smoked per day (1-14, 15-29, or ≥ 30). The non-smoking group was used as a reference. Cox proportional hazards analysis was used to determine the risk of stroke due to smoking.

RESULTS

Four hundred and seventeen new stroke events (212 men; 205 women) were recorded during a mean follow-up of 10.7 years, including 95 intracerebral hemorrhages (48 men; 47 women), 267 cerebral infarctions (152 men; 115 women), and 54 subarachnoid hemorrhages (12 men; 42 women). In multivariable analysis, the hazard ratios (95% confidence intervals) for male current smokers (≥ 30 cigarettes/day) were 1.89 (1.08-3.31) and 3.41 (1.22-9.57) for all strokes and intracerebral hemorrhages, respectively; those for female current smokers were 2.78 (1.62-4.74), 3.14 (1.51-6.54), and 4.03 (1.64-9.93) for all strokes, cerebral infarctions, and subarachnoid hemorrhages, respectively.

CONCLUSIONS

Smoking ≥ 30 cigarettes/day is a risk factor for stroke, especially intracerebral hemorrhage in men. Furthermore, smoking increases the risk of cerebral infarction and subarachnoid hemorrhage in women.

Thymectomy and Risk of Generalization in Patients with Ocular Myasthenia Gravis: A Multicenter Retrospective Cohort Study

This study aims to investigate the association between thymectomy and the risk of generalization in patients with ocular myasthenia gravis (MG). Data on patients with ocular MG from seven neurological centers in China were retrospectively reviewed. Ocular MG naïve to immunotherapy was categorized according to whether thymectomy was performed (thymectomized group vs. nonsurgical group). Patients in the thymectomized group all underwent surgery within 2 years since ocular symptom onset. The main outcome measure was the generalization. The follow-up period was defined from the date of ocular symptom onset to the date of generalization confirmation, immunotherapy initiation, or last follow-up (defined as 60 months). Of 519 eligible patients (mean [SD] age, 48.7 [15.2] years, 46.6% women), 31 (23.7%) of 131 generalized in the thymectomized group and 122 (31.4%) of 388 did in the nonsurgical group during a median follow-up of 19 months (IQR 8.0-50.0). Thymectomy was independently associated with reduced generalization risk (adjusted HR 0.41, 95% CI 0.25-0.66, P < 0.001). Multivariable stratified analysis also verified this association across the subgroups. Kaplan-Meier curves showed that the 5-year cumulative rate was significantly lower in the thymectomized group than in the nonsurgical group. To conclude, thymectomy may be considered effective in modifying the progression from ocular to generalized MG irrespective of thymoma.

Polymorphisms in glia maturation factor β gene are markers of cellulose ether effectiveness in prion-infected mice

Anti-prion effects of cellulose ether (CE) are reported in rodents, but the molecular mechanism is fully unknown. Here, we investigated the genetic background of CE effectiveness by proteomic and genetic analysis in mice. Proteomic analysis in the two mouse lines showing a dramatic difference in CE effectiveness revealed a distinct polymorphism in the glia maturation factor β gene. This polymorphism was significantly associated with the CE effectiveness in various prion-infected mouse lines. Sequencing of this gene and its vicinity genes also revealed several other polymorphisms that were significantly related to the CE effectiveness. These polymorphisms are useful as genetic markers for finding more suitable mouse lines and exploring the genetic factors of CE effectiveness.

Mitigation of biases in estimating hazard ratios under non-sensitive and non-specific observation of outcomes-applications to influenza vaccine effectiveness

Background

Non-sensitive and non-specific observation of outcomes in time-to-event data affects event counts as well as the risk sets, thus, biasing the estimation of hazard ratios. We investigate how imperfect observation of incident events affects the estimation of vaccine effectiveness based on hazard ratios.

Methods

Imperfect time-to-event data contain two classes of events: a portion of the true events of interest; and false-positive events mistakenly recorded as events of interest. We develop an estimation method utilising a weighted partial likelihood and probabilistic deletion of false-positive events and assuming the sensitivity and the false-positive rate are known. The performance of the method is evaluated using simulated and Finnish register data.

Results

The novel method enables unbiased semiparametric estimation of hazard ratios from imperfect time-to-event data. False-positive rates that are small can be approximated to be zero without inducing bias. The method is robust to misspecification of the sensitivity as long as the ratio of the sensitivity in the vaccinated and the unvaccinated is specified correctly and the cumulative risk of the true event is small.

Conclusions

The weighted partial likelihood can be used to adjust for outcome measurement errors in the estimation of hazard ratios and effectiveness but requires specifying the sensitivity and the false-positive rate. In absence of exact information about these parameters, the method works as a tool for assessing the potential magnitude of bias given a range of likely parameter values.

Concordance probability as a meaningful contrast across disparate survival times

The performance of time-to-event models is frequently assessed in part by estimating the concordance probability, which evaluates the probabilistic pairwise ordering of the model-based risk scores and survival times. The standard definition of this probability conditions on any survival time pair ordering, irrespective of whether the times are meaningfully separated. Inclusion of survival times that would be deemed clinically similar attenuates the concordance and moves the estimate away from the contrast-of-interest: comparing the risk scores between individuals with disparate survival times. In this manuscript, we propose a concordance definition and corresponding method to estimate the probability conditional on survival times being separated by at least a minimum difference. The proposed estimate requires direct input from the analyst to identify a separable survival region and, in doing so, is analogous to the clinically defined subgroups used for binary outcome area under the curve estimates. The method is illustrated in two cancer examples: a prognostic score in clear cell renal cell carcinoma and two biomarkers in metastatic prostate cancer.

Validation of a Questionnaire-Suitable Comorbidity Index in Patients Undergoing Radical Cystectomy

OBJECTIVE

To investigate the capability of a modified self-administrable comorbidity index recommended in the standard sets for neoplastic diseases published by the International Consortium for Health Outcomes Measurement (ICHOM) to predict 90-day and long-term mortality after radical cystectomy.

METHODS

A single-center series of 1,337 consecutive patients who underwent radical cystectomy for muscle-invasive or high-risk non-muscle-invasive urothelial or undifferentiated bladder cancer were stratified by the modified self-administrable comorbidity index and Charlson score, respectively. Multivariate logit models (for 90-day mortality) and proportional-hazards models (for overall and non-bladder cancer mortality) were used for statistical workup.

RESULTS

Considering 90-day mortality, both comorbidity indexes contributed independent information when analyzed together with age (p < 0.0001). The Charlson score performed slightly better (area under the curve [AUC] 0.74 vs. 0.72 for the ICHOM-recommended comorbidity index). Considering 5-year overall mortality in 727 patients with complete observation, the performance of both measures was similar (AUC 0.63 vs. 0.62, including age AUC 0.66 for both indexes). With 6-sided stratifications, the modified self-administrable comorbidity index separated the risk groups slightly better (p values for directly neighboring curves: 0.0068-0.1043 vs. 0.0001-0.8100).

CONCLUSION

The ICHOM-recommended modified self-administrable comorbidity index is capable of predicting 90-day mortality and long-term non-bladder cancer mortality after radical cystectomy similarly to the commonly used Charlson score.

Regression Analysis of Case-cohort Studies in the Presence of Dependent Interval Censoring

The case-cohort design is widely used as a means of reducing the cost in large cohort studies, especially when the disease rate is low and covariate measurements may be expensive, and has been discussed by many authors. In this paper, we discuss regression analysis of case-cohort studies that produce interval-censored failure time with dependent censoring, a situation for which there does not seem to exist an established approach. For inference, a sieve inverse probability weighting estimation procedure is developed with the use of Bernstein polynomials to approximate the unknown baseline cumulative hazard functions. The proposed estimators are shown to be consistent and the asymptotic normality of the resulting regression parameter estimators are established. A simulation study is conducted to assess the finite sample properties of the proposed approach and indicates that it works well in practical situations. The proposed method is applied to an HIV/AIDS case-cohort study that motivated this investigation.

Prognosis after autologous peripheral blood stem cell transplantation for osteonecrosis of the femoral head in the pre-collapse stage: a retrospective cohort study

OBJECTIVES

Autologous peripheral blood stem cell (auto-PBSC) transplantation is an effective therapeutic for the osteonecrosis of the femoral head (ONFH) but without prognosis estimation. This study mainly aimed to (1) determine whether auto-PBSC transplantation is a promising option, (2) assess the risk of hip-preservation failure, (3) achieve a predictive model of femoral head survival after the intervention, and (4) eventually identify clinical indications for auto-PBSC transplantation in future.

METHODS

After reviewing the in-patient database of the First Affiliated Hospital of Zhejiang Chinese Medicine University from June 2012 to June 2014, 37 eligible patients with Association Research Circulation Osseous stage I or II ONFH who were receiving intra-arterial infusion of auto-PBSCs were recruited. A case form was designed to retrieve relevant data. Hip-preservation failure was defined as the endpoint. All participants were stratified by the categorical risk of collapse, which was statistically tested through log-rank analysis. All significant factors were evaluated using Cox proportional hazards regression model, and a predictive nomogram plot was generated.

RESULTS

In total, 47 hips were followed up for 53.96 ± 21.09 months; the median survival time was 60.18 months. Among the predictors, body mass index (BMI; P = 0.0015) and Harris hip score (HHS; P < 0.0001) independently affected femoral head survival. Patients with BMI ≥ 24 kg/m² exhibited a 2.58 times higher risk of hip-preservation failure [95% confidence interval (CI), 1.32-5.45] than those with BMI < 24 kg/m², whereas those with HHS ≥ 70 exhibited a 0.19 times lower risk (95% CI, 0.09-0.38) than those with HHS < 70. Hazard ratios associated with age (P = 0.042), BMI (P = 0.012), HHS (P = 0.022), and necrotic volume (P = 0.000) were 1.038 (95% CI, 1.001-1.075), 1.379 (95% CI, 1.072-1.773), 0.961 (95% CI, 0.928-0.994), and 1.258 (95% CI, 1.120-1.412), respectively. A nomogram plot (score test P = 0.000; C-index = 0.8863) was available for the orthopedic doctor to predict hip survival probability.

CONCLUSIONS

The results suggest that intra-arterial infusion of auto-PBSCs prolongs femoral head survival. Age, BMI, HHS, and necrotic volume can influence the efficacy of this intervention. This study was approved by ethics committee of the trial center, number 2019-KL-075-01.

Semiparametric inference for a two-stage outcome-dependent sampling design with interval-censored failure time data

We propose a two-stage outcome-dependent sampling design and inference procedure for studies that concern interval-censored failure time outcomes. This design enhances the study efficiency by allowing the selection probabilities of the second-stage sample, for which the expensive exposure variable is ascertained, to depend on the first-stage observed interval-censored failure time outcomes. In particular, the second-stage sample is enriched by selectively including subjects who are known or observed to experience the failure at an early or late time. We develop a sieve semiparametric maximum pseudo likelihood procedure that makes use of all available data from the proposed two-stage design. The resulting regression parameter estimator is shown to be consistent and asymptotically normal, and a consistent estimator for its asymptotic variance is derived. Simulation results demonstrate that the proposed design and inference procedure performs well in practical situations and is more efficient than the existing designs and methods. An application to a phase 3 HIV vaccine trial is provided.

An extended proportional hazards model for interval-censored data subject to instantaneous failures

The proportional hazards (PH) model is arguably one of the most popular models used to analyze time to event data arising from clinical trials and longitudinal studies. In many such studies, the event time is not directly observed but is known relative to periodic examination times; i.e., practitioners observe either current status or interval-censored data. The analysis of data of this structure is often fraught with many difficulties since the event time of interest is unobserved. Further exacerbating this issue, in some such studies the observed data also consists of instantaneous failures; i.e., the event times for several study units coincide exactly with the time at which the study begins. In light of these difficulties, this work focuses on developing a mixture model, under the PH assumptions, which can be used to analyze interval-censored data subject to instantaneous failures. To allow for modeling flexibility, two methods of estimating the unknown cumulative baseline hazard function are proposed; a fully parametric and a monotone spline representation are considered. Through a novel data augmentation procedure involving latent Poisson random variables, an expectation-maximization (EM) algorithm is developed to complete model fitting. The resulting EM algorithm is easy to implement and is computationally efficient. Moreover, through extensive simulation studies the proposed approach is shown to provide both reliable estimation and inference. The motivation for this work arises from a randomized clinical trial aimed at assessing the effectiveness of a new peanut allergen treatment in attaining sustained unresponsiveness in children.

Socioeconomic Status-Related Parameters as Predictors of Competing (Non-Bladder Cancer) Mortality after Radical Cystectomy

OBJECTIVE

To investigate the impact of socioeconomic status-related parameters on competing (non-bladder cancer) mortality after radical cystectomy.

PATIENTS AND METHODS

A total of 1,268 consecutive patients who underwent radical cystectomy for urothelial or undifferentiated bladder cancer at our institution between 1993 and 2016 with a mean age of 69 years (median 70 years) were studied. The mean -follow-up of the censored patients was 7.2 years (median 5.7 years). Proportional hazard models for competing risk were used to identify predictors of non-bladder cancer (competing) mortality. The following parameters were included into multivariate analyses: age, American Society of Anesthesiologists physical status classification, Charlson score, gender, level of education, smoking status, marital status, local tumour stage, lymph node status, adjuvant and neoadjuvant chemotherapy.

RESULTS

Besides age and both comorbidity classifications, the socioeconomic status-related parameters gender (female versus male, hazard ratio [HR] 0.58, 95% CI 0.40-0.84, p = 0.0042), level of education (university degree or master craftsman versus others, HR 0.76, 95% CI 0.56-0.1.03, p = 0.0801), smoking status (current smoking versus others, HR 1.47, 95% CI 1.10-1.96, p = 0.0085) and marital status (married versus others, HR 0.68, 95% CI 0.50-0.92, p = 0.0133) were independent predictors of competing mortality after radical cystectomy. If considered in combination (multiplication of HRs), the prognostic impact of socioeconomic parameters superseded that of the investigated comorbidity classifications.

CONCLUSION

Socioeconomic status-related parameters may provide important information on the long-term competing mortality risk after radical cystectomy supplementary to chronological age and comorbidity.

Instrumental variable approach for estimating a causal hazard ratio: application to the effect of postmastectomy radiotherapy on breast cancer patients

The use of postmastectomy radiotherapy (PMRT) on women with AJCC (American Joint Committee on Cancer) pT1-2pN1 breast cancer is controversial in practice. Huo et al. (2015) found that PMRT was associated with longer survival among a high-risk subgroup of AJCC pT1-2pN1 patients using a Cox model on data from the National Cancer Database. To address unmeasured confounding in this observational study, we consider the variation among facilities in the use of PMRT as an instrumental variable (IV). Recently, there has been widespread use of the two-stage residual inclusion (2SRI) method offered by Terza et al. (2008) for nonlinear models, and 2SRI has been the method of choice for analyzing proportional hazards model using IV in clinical studies. However, the causal parameter using 2SRI is only identified under a homogeneity assumption that goes beyond the standard assumptions of IV, and Wan et al. (2015) demonstrated that under standard IV assumptions, 2SRI could fail to consistently estimate the causal hazard ratio for compliers. In this paper, following Yu et al. (2015), we apply a model-based IV approach (Imbens and Rubin, 1997; Hirano et al., 2000) which allows consistent estimation of the causal hazard ratio for survival outcomes with a proportional hazards model specification under standard IV assumptions while flexibly incorporating the restrictions imposed by IV assumptions. Simulation studies show that when there is unmeasured confounding, both 2SRI and the standard Cox regression could provide biased estimates of the causal hazard ratio among compliers, while this model-based IV approach provides consistent estimates. We apply this IV method to the breast cancer study and our IV analysis did not find strong evidence to support the benefit of PMRT on survival among the targeted patients. In addition, we develop sensitivity analysis approaches to assess the sensitivity of causal conclusions to violations of the exclusion restrictions assumption for IV.

Remaining Systemic Treatment Options: A Valuable Predictor of Survival and Functional Outcomes after Surgical Treatment for Spinal Metastasis

Objectives

To evaluate survival and functional outcomes in surgically‐treated spinal metastasis patients and to identify the prognostic value of the remaining options for systemic treatment.

Methods

The current study reviewed 100 consecutive patients who received surgery for spinal metastasis in a single center from March 2012 to June 2016. The decision for surgery had been made in a weekly multidisciplinary tumor board after considering multiple factors. Among these factors, those associated with the functional outcome were identified using crosstab and logistic regression analyses. Survival analysis applying the Kaplan–Meier curve and the Cox proportional hazards model was used to identify factors associated with improved survival.

Results

Of the 100 patients, there were 62 men and 38 women, with a mean age of 60.4 years at the time of surgery. The median postoperative survival of the whole cohort was 16.2 months (95% confidence interval: 10.1–22.3). When patients were stratified by the functional outcome, a significantly large proportion of patients with good functional outcome (Eastern Cooperative Oncology Group performance status better than 3) had an available option for systemic treatment at the time of surgery (P < 0.001, Pearson χ²‐test). Logistic regression analysis found that the presence of remaining options for systemic treatment at the time of decision‐making for surgery was associated with improved postoperative functional performance status (P = 0.004, odds ratio = 7.59). Survival analysis also found that the availability of remaining options for systemic treatment was associated with improved survival (P = 0.001, hazard ratio = 0.22). This finding was statistically more significant in a group of patients with a low revised Tokuhashi score of 0 to 8 (P < 0.001) when compared to the group of patients with a high revised Tokuhashi score of 9 to 15 (P = 0.082).

Conclusions

Availability of remaining options for systemic treatment is an important factor to consider when deciding on surgical treatment for spinal metastasis.

Which comorbidity classification is best suited to identify patients at risk for 90-day and long-term non-bladder cancer mortality after radical cystectomy?

PURPOSE

There is no consensus on the best comorbidity measure in candidates for radical cystectomy. The aim of this study was to identify tool best suited to identify patients at risk for 90-day or premature long-term non-bladder cancer mortality.

METHODS

We studied 1268 patients who underwent radical cystectomy to identify patients at risk for 90-day and later-than-90-day mortality, respectively. Six classifications were investigated as possible predictors of both types of mortality. Multivariable models including age as continuous variable and each classification separately were calculated. A heuristic ranking was based on the evaluation of the hazard ratios, p values, Akaike's information criteria, and concerning the logit models also the areas under the curve.

RESULTS

The median follow-up was 5.7 years. Within 90 days after surgery, the mortality rate was 4.2%. The greatest independent contribution concerning the prediction of 90-day mortality was seen with the American Society of Anesthesiologists (ASA) physical status classification (classes 3-4 versus 1-2: hazard ratio 7.98, 95% confidence interval 3.54-18.01, p < 0.0001). In the longer term, countable diseases (Canadian Cardiovascular Society classification of angina pectoris, conditions contributing the Charlson score) were of greater importance. The results of heuristic ranking were confirmed by multivariate analyses including age and all classifications together.

CONCLUSIONS

Besides to chronological age, clinicians should pay particular attention to the ASA classification to identify patients at risk for 90-day mortality after radical cystectomy, whereas long-term mortality is more determined by countable comorbid diseases.

Semiparametric competing risks regression under interval censoring using the R package intccr

BACKGROUND AND OBJECTIVE

Competing risk data are frequently interval-censored in real-world applications, that is, the exact event time is not precisely observed but is only known to lie between two time points such as clinic visits. This type of data requires special handling because the actual event times are unknown. To deal with this problem we have developed an easy-to-use open-source statistical software.

METHODS

An approach to perform semiparametric regression analysis of the cumulative incidence function with interval-censored competing risks data is the sieve maximum likelihood method based on B-splines. An important feature of this approach is that it does not impose restrictive parametric assumptions. Also, this methodology provides semiparametrically efficient estimates. Implementation of this methodology can be easily performed using our new R package intccr.

RESULTS

The R package intccr performs semiparametric regression analysis of the cumulative incidence function based on interval-censored competing risks data. It supports a large class of models including the proportional odds and the Fine-Gray proportional subdistribution hazards model as special cases. It also provides the estimated cumulative incidence functions for a particular combination of covariate values. The package also provides some data management functionality to handle data sets which are in a long format involving multiple lines of data per subject.

CONCLUSIONS

The R package intccr provides a convenient and flexible software for the analysis of the cumulative incidence function based on interval-censored competing risks data.

Model diagnostics for the proportional hazards model with length-biased data

Length-biased data are frequently encountered in prevalent cohort studies. Many statistical methods have been developed to estimate the covariate effects on the survival outcomes arising from such data while properly adjusting for length-biased sampling. Among them, regression methods based on the proportional hazards model have been widely adopted. However, little work has focused on checking the proportional hazards model assumptions with length-biased data, which is essential to ensure the validity of inference. In this article, we propose a statistical tool for testing the assumed functional form of covariates and the proportional hazards assumption graphically and analytically under the setting of length-biased sampling, through a general class of multiparameter stochastic processes. The finite sample performance is examined through simulation studies, and the proposed methods are illustrated with the data from a cohort study of dementia in Canada.

A partially linear proportional hazards model for current status data

For analyzing current status data, a flexible partially linear proportional hazards model is proposed. Modeling flexibility is attained through using monotone splines to approximate the baseline cumulative hazard function, as well as B-splines to accommodate nonlinear covariate effects. To facilitate model fitting, a computationally efficient and easy to implement expectation-maximization algorithm is developed through a two-stage data augmentation process involving carefully structured latent Poisson random variables. Asymptotic normality and the efficiency of the spline estimator of the regression coefficients are established, and the spline estimators of the nonparametric components are shown to possess the optimal rate of convergence under suitable regularity conditions. The finite-sample performance of the proposed approach is evaluated through Monte Carlo simulation and it is further illustrated using uterine fibroid data arising from a prospective cohort study on early pregnancy.

A semiparametric regression cure model for doubly censored data

This paper discusses regression analysis of doubly censored failure time data when there may exist a cured subgroup. By doubly censored data, we mean that the failure time of interest denotes the elapsed time between two related events and the observations on both event times can suffer censoring (Sun in The statistical analysis of interval-censored failure time data. Springer, New York, 2006). One typical example of such data is given by an acquired immune deficiency syndrome cohort study. Although many methods have been developed for their analysis (De Gruttola and Lagakos in Biometrics 45:1-12, 1989; Sun et al. in Biometrics 55:909-914, 1999; 60:637-643, 2004; Pan in Biometrics 57:1245-1250, 2001), it does not seem to exist an established method for the situation with a cured subgroup. This paper discusses this later problem and presents a sieve approximation maximum likelihood approach. In addition, the asymptotic properties of the resulting estimators are established and an extensive simulation study indicates that the method seems to work well for practical situations. An application is also provided.

Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations

In comparing two treatments with the event time observations, the hazard ratio (HR) estimate is routinely used to quantify the treatment difference. However, this model dependent estimate may be difficult to interpret clinically especially when the proportional hazards (PH) assumption is violated. An alternative estimation procedure for treatment efficacy based on the restricted means survival time or t-year mean survival time (t-MST) has been discussed extensively in the statistical and clinical literature. On the other hand, a statistical test via the HR or its asymptotically equivalent counterpart, the logrank test, is asymptotically distribution-free. In this article, we assess the relative efficiency of the hazard ratio and t-MST tests with respect to the statistical power under various PH and non-PH models theoretically and empirically. When the PH assumption is valid, the t-MST test performs almost as well as the HR test. For non-PH models, the t-MST test can substantially outperform its HR counterpart. On the other hand, the HR test can be powerful when the true difference of two survival functions is quite large at end but not the beginning of the study. Unfortunately, for this case, the HR estimate may not have a simple clinical interpretation for the treatment effect due to the violation of the PH assumption.

Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data

Left-truncated data often arise in epidemiology and individual follow-up studies due to a biased sampling plan since subjects with shorter survival times tend to be excluded from the sample. Moreover, the survival time of recruited subjects are often subject to right censoring. In this article, a general class of semiparametric transformation models that include proportional hazards model and proportional odds model as special cases is studied for the analysis of left-truncated and right-censored data. We propose a conditional likelihood approach and develop the conditional maximum likelihood estimators (cMLE) for the regression parameters and cumulative hazard function of these models. The derived score equations for regression parameter and infinite-dimensional function suggest an iterative algorithm for cMLE. The cMLE is shown to be consistent and asymptotically normal. The limiting variances for the estimators can be consistently estimated using the inverse of negative Hessian matrix. Intensive simulation studies are conducted to investigate the performance of the cMLE. An application to the Channing House data is given to illustrate the methodology.

Cox regression with dependent error in covariates

Many survival studies have error-contaminated covariates due to the lack of a gold standard of measurement. Furthermore, the error distribution can depend on the true covariates but the structure may be difficult to characterize; heteroscedasticity is a common manifestation. We suggest a novel dependent measurement error model with minimal assumptions on the dependence structure, and propose a new functional modeling method for Cox regression when an instrumental variable is available. This proposal accommodates much more general error contamination than existing approaches including nonparametric correction methods of Huang and Wang (2000, Journal of the American Statistical Association 95, 1209-1219; 2006, Statistica Sinica 16, 861-881). The estimated regression coefficients are consistent and asymptotically normal, and a consistent variance estimate is provided for inference. Simulations demonstrate that the procedure performs well even under substantial error contamination. Illustration with a clinical study is provided.

Methods for Contrasting Gap Time Hazard Functions: Application to Repeat Liver Transplantation

In studies featuring a sequence of ordered events, gap times between successive events are often of interest. Despite the rich literature in this area, very few methods for comparing gap times have been developed. We propose methods for estimating a hazard ratio connecting the first and second gap times. Specifically, a two-stage procedure is developed based on estimating equations. At the first stage, a proportional hazards model is fitted for the first gap time. Weighted estimating equations are then solved at the second stage to estimate the hazard ratio between the first and second gap times. The proposed estimator has a closed form and, being analogous to a standardized mortality ratio, is easy to interpret. Large sample properties of the proposed estimators are derived, with simulation studies used to evaluate finite sample characteristics. Extension of the approach to accommodate a piecewise constant hazard ratio is considered. The proposed methods are applied to contrast repeat (second) versus primary (first) liver transplants with respect to graft failure, based on national registry data.

Statistical Methods for Conditional Survival Analysis

We investigate the survival distribution of the patients who have survived over a certain time period. This is called a conditional survival distribution. In this paper, we show that one-sample estimation, two-sample comparison and regression analysis of conditional survival distributions can be conducted using the regular methods for unconditional survival distributions that are provided by the standard statistical software, such as SAS and SPSS. We conduct extensive simulations to evaluate the finite sample property of these conditional survival analysis methods. We illustrate these methods with real clinical data.

Testing and Confidence Intervals for High Dimensional Proportional Hazards Model

This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models. Motivated by the geometric projection principle, we propose new decorrelated score, Wald and partial likelihood ratio statistics. Without assuming model selection consistency, we prove the asymptotic normality of these test statistics, establish their semiparametric optimality. We also develop new procedures for constructing pointwise confidence intervals for the baseline hazard function and baseline survival function. Thorough numerical results are provided to back up our theory.

Improving efficiency of parameter estimation in case-cohort studies with multivariate failure time data

The case-cohort study design is an effective way to reduce cost of assembling and measuring expensive covariates in large cohort studies. Recently, several weighted estimators were proposed for the case-cohort design when multiple diseases are of interest. However, these existing weighted estimators do not make effective use of the covariate information available in the whole cohort. Furthermore, the auxiliary information for the expensive covariates, which may be available in the studies, cannot be incorporated directly. In this article, we propose a class of updated-estimators. We show that, by making effective use of the whole cohort information, the proposed updated-estimators are guaranteed to be more efficient than the existing weighted estimators asymptotically. Furthermore, they are flexible to incorporate the auxiliary information whenever available. The advantages of the proposed updated-estimators are demonstrated in simulation studies and a real data analysis.

Quantitative Analysis of the Cellular Microenvironment of Glioblastoma to Develop Predictive Statistical Models of Overall Survival

Glioblastomas, the most common primary malignant brain tumors, have a distinct tissue microenvironment. Although non-neoplastic cells contribute to glioblastoma progression, very few quantitative studies have shown the effect of tumor microenvironmental influences on patient survival. We examined relationships of the cellular microenvironment, including astrocytes, microglia, oligodendrocytes, and blood vessels, to survival in glioblastoma patients. Using histological staining and quantitative image analyses, we examined the tumor-associated parenchyma of 33 patients and developed statistical models to predict patient outcomes based on the cellular picture of the tumor parenchyma. We found that blood vessel density correlated with poorer prognosis. To examine the role of adjacent parenchymal versus higher tumor cell density bulk parenchymal tissue, we examined the glial components in these highly variable regions. Comparison of bulk and adjacent astrocytes and microglia in tissue yielded the strongest prediction of survival, with high levels of adjacent astrocytes predicted poor prognosis and high levels of microglia correlated with a better prognosis. These results indicate that parenchymal components predict survival in glioblastoma patients and in particular that the balance between reactive glial populations is important for patient prognosis.

On hazard ratio estimators by proportional hazards models in matched-pair cohort studies

Background

In matched-pair cohort studies with censored events, the hazard ratio (HR) may be of main interest. However, it is lesser known in epidemiologic literature that the partial maximum likelihood estimator of a common HR conditional on matched pairs is written in a simple form, namely, the ratio of the numbers of two pair-types. Moreover, because HR is a noncollapsible measure and its constancy across matched pairs is a restrictive assumption, marginal HR as “average” HR may be targeted more than conditional HR in analysis.

Methods

Based on its simple expression, we provided an alternative interpretation of the common HR estimator as the odds of the matched-pair analog of C-statistic for censored time-to-event data. Through simulations assuming proportional hazards within matched pairs, the influence of various censoring patterns on the marginal and common HR estimators of unstratified and stratified proportional hazards models, respectively, was evaluated. The methods were applied to a real propensity-score matched dataset from the Rotterdam tumor bank of primary breast cancer.

Results

We showed that stratified models unbiasedly estimated a common HR under the proportional hazards within matched pairs. However, the marginal HR estimator with robust variance estimator lacks interpretation as an “average” marginal HR even if censoring is unconditionally independent to event, unless no censoring occurs or no exposure effect is present. Furthermore, the exposure-dependent censoring biased the marginal HR estimator away from both conditional HR and an “average” marginal HR irrespective of whether exposure effect is present. From the matched Rotterdam dataset, we estimated HR for relapse-free survival of absence versus presence of chemotherapy; estimates (95% confidence interval) were 1.47 (1.18–1.83) for common HR and 1.33 (1.13–1.57) for marginal HR.

Conclusion

The simple expression of the common HR estimator would be a useful summary of exposure effect, which is less sensitive to censoring patterns than the marginal HR estimator. The common and the marginal HR estimators, both relying on distinct assumptions and interpretations, are complementary alternatives for each other.

Electronic supplementary material

The online version of this article (doi:10.1186/s12982-017-0060-8) contains supplementary material, which is available to authorized users.

Social media engagement analysis of U.S. Federal health agencies on Facebook

BACKGROUND

It is becoming increasingly common for individuals and organizations to use social media platforms such as Facebook. These are being used for a wide variety of purposes including disseminating, discussing and seeking health related information. U.S. Federal health agencies are leveraging these platforms to 'engage' social media users to read, spread, promote and encourage health related discussions. However, different agencies and their communications get varying levels of engagement. In this study we use statistical models to identify factors that associate with engagement.

METHODS

We analyze over 45,000 Facebook posts from 72 Facebook accounts belonging to 24 health agencies. Account usage, user activity, sentiment and content of these posts are studied. We use the hurdle regression model to identify factors associated with the level of engagement and Cox proportional hazards model to identify factors associated with duration of engagement.

RESULTS

In our analysis we find that agencies and accounts vary widely in their usage of social media and activity they generate. Statistical analysis shows, for instance, that Facebook posts with more visual cues such as photos or videos or those which express positive sentiment generate more engagement. We further find that posts on certain topics such as occupation or organizations negatively affect the duration of engagement.

CONCLUSIONS

We present the first comprehensive analyses of engagement with U.S. Federal health agencies on Facebook. In addition, we briefly compare and contrast findings from this study to our earlier study with similar focus but on Twitter to show the robustness of our methods.

Proportional hazards model with a change point for clustered event data

In many epidemiology studies, family data with survival endpoints are collected to investigate the association between risk factors and disease incidence. Sometimes the risk of the disease may change when a certain risk factor exceeds a certain threshold. Finding this threshold value could be important for disease risk prediction and diseases prevention. In this work, we propose a change-point proportional hazards model for clustered event data. The model incorporates the unknown threshold of a continuous variable as a change point in the regression. The marginal pseudo-partial likelihood functions are maximized for estimating the regression coefficients and the unknown change point. We develop a supremum test based on robust score statistics to test the existence of the change point. The inference for the change point is based on the m out of n bootstrap. We establish the consistency and asymptotic distributions of the proposed estimators. The finite-sample performance of the proposed method is demonstrated via extensive simulation studies. Finally, the Strong Heart Family Study dataset is analyzed to illustrate the methods.

Case-cohort studies with interval-censored failure time data

The case-cohort design has been widely used as a means of cost reduction in assembling or measuring expensive covariates in large cohort studies. The existing literature on the case-cohort design is mainly focused on right-censored data. In practice, however, the failure time is often subject to interval-censoring; it is known only to fall within some random time interval. In this paper, we consider the case-cohort study design for interval-censored failure time and develop a sieve semiparametric likelihood approach for analyzing data from this design under the proportional hazards model. We construct the likelihood function using inverse probability weighting and build the sieves with Bernstein polynomials. The consistency and asymptotic normality of the resulting regression parameter estimator are established and a weighted bootstrap procedure is considered for variance estimation. Simulations show that the proposed method works well for practical situations, and an application to real data is provided.

Joint modeling of longitudinal and survival data with the Cox model and two-phase sampling

A common objective of cohort studies and clinical trials is to assess time-varying longitudinal continuous biomarkers as correlates of the instantaneous hazard of a study endpoint. We consider the setting where the biomarkers are measured in a designed sub-sample (i.e., case-cohort or two-phase sampling design), as is normative for prevention trials. We address this problem via joint models, with underlying biomarker trajectories characterized by a random effects model and their relationship with instantaneous risk characterized by a Cox model. For estimation and inference we extend the conditional score method of Tsiatis and Davidian (Biometrika 88(2):447-458, 2001) to accommodate the two-phase biomarker sampling design using augmented inverse probability weighting with nonparametric kernel regression. We present theoretical properties of the proposed estimators and finite-sample properties derived through simulations, and illustrate the methods with application to the AIDS Clinical Trials Group 175 antiretroviral therapy trial. We discuss how the methods are useful for evaluating a Prentice surrogate endpoint, mediation, and for generating hypotheses about biological mechanisms of treatment efficacy.

On the estimators and tests for the semiparametric hazards regression model

In the accelerated hazards regression model with censored data, estimation of the covariance matrices of the regression parameters is difficult, since it involves the unknown baseline hazard function and its derivative. This paper provides simple but reliable procedures that yield asymptotically normal estimators whose covariance matrices can be easily estimated. A class of weight functions are introduced to result in the estimators whose asymptotic covariance matrices do not involve the derivative of the unknown hazard function. Based on the estimators obtained from different weight functions, some goodness-of-fit tests are constructed to check the adequacy of the accelerated hazards regression model. Numerical simulations show that the estimators and tests perform well. The procedures are illustrated in the real world example of leukemia cancer. For the leukemia cancer data, the issue of interest is a comparison of two groups of patients that had two different kinds of bone marrow transplants. It is found that the difference of the two groups are well described by a time-scale change in hazard functions, i.e., the accelerated hazards model.

On the choice of time scales in competing risks predictions

In the standard analysis of competing risks data, proportional hazards models are fit to the cause-specific hazard functions for all causes on the same time scale. These regression analyses are the foundation for predictions of cause-specific cumulative incidence functions based on combining the estimated cause-specific hazard functions. However, in predictions arising from disease registries, where only subjects with disease enter the database, disease-related mortality may be more naturally modeled on the time since diagnosis time scale while death from other causes may be more naturally modeled on the age time scale. The single time scale methodology may be biased if an incorrect time scale is employed for one of the causes and an alternative methodology is not available. We propose inferences for the cumulative incidence function in which regression models for the cause-specific hazard functions may be specified on different time scales. Using the disease registry data, the analysis of other cause mortality on the age scale requires left truncating the event time at the age of disease diagnosis, complicating the analysis. In addition, standard Martingale theory is not applicable when combining regression models on different time scales. We establish that the covariate conditional predictions are consistent and asymptotically normal using empirical process techniques and propose consistent variance estimators for constructing confidence intervals. Simulation studies show that the proposed two time scales method performs well, outperforming the single time-scale predictions when the time scale is misspecified. The methods are illustrated with stage III colon cancer data obtained from the Surveillance, Epidemiology, and End Results program of National Cancer Institute.

Associations between birth weight and colon and rectal cancer risk in adulthood

BACKGROUND

Birth weight has inconsistent associations with colorectal cancer, possibly due to different anatomic features of the colon versus the rectum. The aim of this study was to investigate the association between birth weight and colon and rectal cancers separately.

METHODS

193,306 children, born from 1936 to 1972, from the Copenhagen School Health Record Register were followed prospectively in Danish health registers. Colon and rectal cancer cases were defined using the International Classification of Disease version 10 (colon: C18.0-18.9, rectal: 19.9 and 20.9). Only cancers classified as adenocarcinomas were included in the analyses. Cox regressions were used to estimate hazard ratios (HR) and 95% confidence intervals (CI). Analyses were stratified by birth cohort and sex.

RESULTS

During 3.8 million person-years of follow-up, 1465 colon and 961 rectal adenocarcinomas were identified. No significant sex differences were observed; therefore combined results are presented. Birth weight was positively associated with colon cancers with a HR of 1.14 (95% CI, 1.04-1.26) per kilogram of birth weight. For rectal cancer a significant association was not observed for birth weights below 3.5kg. Above 3.5kg an inverse association was observed (at 4.5kg, HR=0.77 [95% CI, 0.61-0.96]). Further, the associations between birth weight and colon and rectal cancer differed significantly from each other (p=0.006).

CONCLUSIONS

Birth weight is positively associated with the risk of adult colon cancer, whereas the results for rectal cancer were inverse only above values of 3.5kg. The results underline the importance of investigating colon and rectal cancer as two different entities.

Regression analysis of case K interval-censored failure time data in the presence of informative censoring

Interval-censored failure time data occur in many fields such as demography, economics, medical research, and reliability and many inference procedures on them have been developed (Sun, 2006; Chen, Sun, and Peace, 2012). However, most of the existing approaches assume that the mechanism that yields interval censoring is independent of the failure time of interest and it is clear that this may not be true in practice (Zhang et al., 2007; Ma, Hu, and Sun, 2015). In this article, we consider regression analysis of case K interval-censored failure time data when the censoring mechanism may be related to the failure time of interest. For the problem, an estimated sieve maximum-likelihood approach is proposed for the data arising from the proportional hazards frailty model and for estimation, a two-step procedure is presented. In the addition, the asymptotic properties of the proposed estimators of regression parameters are established and an extensive simulation study suggests that the method works well. Finally, we apply the method to a set of real interval-censored data that motivated this study.

Inference for survival prediction under the regularized Cox model

When a moderate number of potential predictors are available and a survival model is fit with regularization to achieve variable selection, providing accurate inference on the predicted survival can be challenging. We investigate inference on the predicted survival estimated after fitting a Cox model under regularization guaranteeing the oracle property. We demonstrate that existing asymptotic formulas for the standard errors of the coefficients tend to underestimate the variability for some coefficients, while typical resampling such as the bootstrap tends to overestimate it; these approaches can both lead to inaccurate variance estimation for predicted survival functions. We propose a two-stage adaptation of a resampling approach that brings the estimated error in line with the truth. In stage 1, we estimate the coefficients in the observed data set and in [Formula: see text] resampled data sets, and allow the resampled coefficient estimates to vote on whether each coefficient should be 0. For those coefficients voted as zero, we set both the point and interval estimates to [Formula: see text] In stage 2, to make inference about coefficients not voted as zero in stage 1, we refit the penalized model in the observed data and in the [Formula: see text] resampled data sets with only variables corresponding to those coefficients. We demonstrate that ensemble voting-based point and interval estimators of the coefficients perform well in finite samples, and prove that the point estimator maintains the oracle property. We extend this approach to derive inference procedures for survival functions and demonstrate that our proposed interval estimation procedures substantially outperform estimators based on asymptotic inference or standard bootstrap. We further illustrate our proposed procedures to predict breast cancer survival in a gene expression study.

The impact of birth weight on cardiovascular disease risk in the Women's Health Initiative

BACKGROUND AND AIMS

Cardiovascular disease (CVD) is among the leading causes of morbidity and mortality worldwide. Traditional risk factors predict 75-80% of an individual's risk of incident CVD. However, the role of early life experiences in future disease risk is gaining attention. The Barker hypothesis proposes fetal origins of adult disease, with consistent evidence demonstrating the deleterious consequences of birth weight outside the normal range. In this study, we investigate the role of birth weight in CVD risk prediction.

METHODS AND RESULTS

The Women's Health Initiative (WHI) represents a large national cohort of post-menopausal women with 63,815 participants included in this analysis. Univariable proportional hazards regression analyses evaluated the association of 4 self-reported birth weight categories against 3 CVD outcome definitions, which included indicators of coronary heart disease, ischemic stroke, coronary revascularization, carotid artery disease and peripheral arterial disease. The role of birth weight was also evaluated for prediction of CVD events in the presence of traditional risk factors using 3 existing CVD risk prediction equations: one body mass index (BMI)-based and two laboratory-based models. Low birth weight (LBW) (<6 lbs.) was significantly associated with all CVD outcome definitions in univariable analyses (HR = 1.086, p = 0.009). LBW was a significant covariate in the BMI-based model (HR = 1.128, p < 0.0001) but not in the lipid-based models.

CONCLUSION

LBW (<6 lbs.) is independently associated with CVD outcomes in the WHI cohort. This finding supports the role of the prenatal and postnatal environment in contributing to the development of adult chronic disease.

Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure

We analyse data collected from the administrative datawarehouse of an Italian regional district (Lombardia) concerning patients affected by Chronic Heart Failure. The longitudinal data gathering for each patient hospital readmissions in time, as well as patient-specific covariates, is studied as a realization of non homogeneous Poisson process. Since the aim behind this study is to identify groups of patients behaving similarly in terms of disease progression and then healthcare consumption, we conjectured the time segments between two consecutive hospitalizations to be Weibull distributed in each hidden cluster. Adding a frailty term to take into account the within subjects unknown variability, the corresponding patient-specific hazard functions are reconstructed. Therefore, the comprehensive distribution for each time to event variable is modelled as a Weibull Mixture. We are then able to easily interpret the related hidden groups as healthy, sick, and terminally ill subjects.

Estimating treatment effect in a proportional hazards model in randomized clinical trials with all-or-nothing compliance

We consider methods for estimating the treatment effect and/or the covariate by treatment interaction effect in a randomized clinical trial under noncompliance with time-to-event outcome. As in Cuzick et al. (2007), assuming that the patient population consists of three (possibly latent) subgroups based on treatment preference: the ambivalent group, the insisters, and the refusers, we estimate the effects among the ambivalent group. The parameters have causal interpretations under standard assumptions. The article contains two main contributions. First, we propose a weighted per-protocol (Wtd PP) estimator through incorporating time-varying weights in a proportional hazards model. In the second part of the article, under the model considered in Cuzick et al. (2007), we propose an EM algorithm to maximize a full likelihood (FL) as well as the pseudo likelihood (PL) considered in Cuzick et al. (2007). The E step of the algorithm involves computing the conditional expectation of a linear function of the latent membership, and the main advantage of the EM algorithm is that the risk parameters can be updated by fitting a weighted Cox model using standard software and the baseline hazard can be updated using closed-form solutions. Simulations show that the EM algorithm is computationally much more efficient than directly maximizing the observed likelihood. The main advantage of the Wtd PP approach is that it is more robust to model misspecifications among the insisters and refusers since the outcome model does not impose distributional assumptions among these two groups.

What Cure Models Can Teach us About Genome-Wide Survival Analysis

The aim of logistic regression is to estimate genetic effects on disease risk, while survival analysis aims to determine effects on age of onset. In practice, genetic variants may affect both types of outcomes. A cure survival model analyzes logistic and survival effects simultaneously. The aim of this simulation study is to assess the performance of logistic regression and traditional survival analysis under a cure model and to investigate the benefits of cure survival analysis. We simulated data under a cure model and varied the percentage of subjects at risk for disease (cure fraction), the logistic and survival effect sizes, and the contribution of genetic background risk factors. We then computed the error rates and estimation bias of logistic, Cox proportional hazards (PH), and cure PH analysis, respectively. The power of logistic and Cox PH analysis is sensitive to the cure fraction and background heritability. Our results show that traditional Cox PH analysis may erroneously detect age of onset effects if no such effects are present in the data. In the presence of genetic background risk even the cure model results in biased estimates of both the odds ratio and the hazard ratio. Cure survival analysis takes cure fractions into account and can be used to simultaneously estimate the effect of genetic variants on disease risk and age of onset. Since genome-wide cure survival analysis is not computationally feasible, we recommend this analysis for genetic variants that are significant in a traditional survival analysis.

A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data

The proportional hazards model (PH) is currently the most popular regression model for analyzing time-to-event data. Despite its popularity, the analysis of interval-censored data under the PH model can be challenging using many available techniques. This article presents a new method for analyzing interval-censored data under the PH model. The proposed approach uses a monotone spline representation to approximate the unknown nondecreasing cumulative baseline hazard function. Formulating the PH model in this fashion results in a finite number of parameters to estimate while maintaining substantial modeling flexibility. A novel expectation-maximization (EM) algorithm is developed for finding the maximum likelihood estimates of the parameters. The derivation of the EM algorithm relies on a two-stage data augmentation involving latent Poisson random variables. The resulting algorithm is easy to implement, robust to initialization, enjoys quick convergence, and provides closed-form variance estimates. The performance of the proposed regression methodology is evaluated through a simulation study, and is further illustrated using data from a large population-based randomized trial designed and sponsored by the United States National Cancer Institute.

Power and sample size for randomized phase III survival trials under the Weibull model

Two parametric tests are proposed for designing randomized two-arm phase III survival trials under the Weibull model. The properties of the two parametric tests are compared with the nonparametric log-rank test through simulation studies. Power and sample size formulas of the two parametric tests are derived. The sensitivity of sample size under misspecification of the Weibull shape parameter is also investigated. The study can be designed by planning the study duration and handling nonuniform entry and loss to follow-up under the Weibull model using either the proposed parametric tests or the well-known nonparametric log-rank test.

Decreased Overall and Bladder Cancer-Specific Mortality with Adjuvant Chemotherapy After Radical Cystectomy: Multivariable Competing Risk Analysis

Adding chemotherapy to radical cystectomy (RC) may improve outcome. Neoadjuvant treatment is advocated by guidelines based on meta-analysis data but is severely underused in clinical practice. Adjuvant treatment of patients at risk could be an alternative. We analyzed a sample of 798 patients who underwent RC between 1993 and 2011 for high-risk superficial or muscle-invasive urothelial or undifferentiated bladder cancer, of which 23% received adjuvant cisplatin-based chemotherapy and %5 received neoadjuvant chemotherapy. The use of adjuvant chemotherapy was an independent predictor of decreased overall mortality (hazard ratio [HR]: 0.50; 95% confidence interval [CI], 0.38-0.66; p<0.0001) and bladder cancer-specific mortality (HR: 0.71; 95% CI, 0.52-0.97; p=0.0321), but it was not associated with competing mortality. Similar figures were obtained when analyzing the number of cisplatin-containing cycles administered or when restricting the analysis to patients with lymph node-positive or extravesical but lymph node-negative disease, suggesting a mortality-reducing treatment effect after adjusting for several patient- and tumor-related confounders. Future trials should directly compare the concepts of neoadjuvant and adjuvant application of chemotherapy in candidates for RC.

PATIENT SUMMARY

Adjuvant chemotherapy may decrease overall and bladder cancer-specific mortality after radical cystectomy (RC). Future trials should directly compare the concepts of neoadjuvant and adjuvant application of chemotherapy in candidates for RC.