Left Truncation Research Articles

Predictors of Ventricular Arrhythmias and Sudden Death in a Québec Cohort With Brugada Syndrome

BackgroundPatients with Brugada syndrome (BrS) are at risk for ventricular arrhythmias (VAs) and sudden death. Identification of high-risk individuals beyond those with syncope or resuscitated sudden death remains a major challenge. MethodsWe assessed the value of clinical, electrophysiological, and electrocardiographic (ECG) features, including depolarization and repolarization metrics, in predicting arrhythmic events and sudden death in consecutive patients with BrS diagnosed between 2002 and 2013 in Quebec, Canada. Qualifying electrocardiograms with the highest type 1 ST-segment elevations were reviewed and analyzed by 2 electrophysiologists who were blinded to clinical history. Survival analyses were adjusted for Firth bias correction and left truncation. ResultsA total of 105 patients, 79.8% of whom were men, were diagnosed with BrS at a mean age of 46.2 ± 13.3 years and were followed for 59.6 ± 16.4 months. Ten (9.5%) had a history of cardiac arrest, 37 (35.2%) had syncope, and 7 (6.7%) experienced 20 arrhythmic events during follow-up, all consisting of appropriate ICD therapy (7 antitachycardia pacing; 13 shocks). In multivariate Cox regression analyses, a spontaneous type 1 electrocardiographic (ECG) pattern (hazard ratio [HR], 10.80; 95% confidence interval [CI], 1.03-113.87; P = 0.0476), maximal T peak-end (Tp-e) duration ≥ 100 ms (HR, 29.73; 95% CI, 1.33-666.37; P = 0.0325), and QRS duration in lead V6 > 110 ms (HR, 15.27; 95% CI, 1.07-217.42; P = 0.0443) were independently associated with VAs or aborted sudden cardiac death. ConclusionsIn a multicentre cohort with BrS from Quebec, Canada, VAs and sudden death were independently associated with standard 12-lead ECG features, including a spontaneous type 1 pattern, depolarization (QRS in lead V6), and repolarization (maximal Tp-e duration) criteria.

Incident Ischemic Heart Disease After Long-Term Occupational Exposure to Fine Particulate Matter: Accounting for 2 Forms of Survivor Bias.

Little is known about the heart disease risks associated with occupational, rather than traffic-related, exposure to particulate matter with aerodynamic diameter of 2.5 µm or less (PM2.5). We examined long-term exposure to PM2.5 in cohorts of aluminum smelters and fabrication workers in the United States who were followed for incident ischemic heart disease from 1998 to 2012, and we addressed 2 forms of survivor bias. Left truncation bias was addressed by restricting analyses to the subcohort hired after the start of follow up. Healthy worker survivor bias, which is characterized by time-varying confounding that is affected by prior exposure, was documented only in the smelters and required the use of marginal structural Cox models. When comparing always-exposed participants above the 10th percentile of annual exposure with those below, the hazard ratios were 1.67 (95% confidence interval (CI): 1.11, 2.52) and 3.95 (95% CI: 0.87, 18.00) in the full and restricted subcohorts of smelter workers, respectively. In the fabrication stratum, hazard ratios based on conditional Cox models were 0.98 (95% CI: 0.94, 1.02) and 1.17 (95% CI: 1.00, 1.37) per 1 mg/m3-year in the full and restricted subcohorts, respectively. Long-term exposure to occupational PM2.5 was associated with a higher risk of ischemic heart disease among aluminum manufacturing workers, particularly in smelters, after adjustment for survivor bias.

Bayesian joint modeling for assessing the progression of chronic kidney disease in children.

Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation. The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed through longitudinal measurements of glomerular filtration rate.

Methods for testing the Markov condition in the illness-death model: a comparative study.

Markov three-state progressive and illness-death models are often used in biomedicine for describing survival data when an intermediate event of interest may be observed during the follow-up. However, the usual estimators for Markov models (e.g., Aalen-Johansen transition probabilities) may be systematically biased in non-Markovian situations. On the other hand, despite non-Markovian estimators for transition probabilities and related curves are available, including the Markov information in the construction of the estimators allows for variance reduction. Therefore, testing for the Markov condition is a relevant issue in practice. In this paper, we discuss several characterizations of the Markov condition, with special focus on its equivalence with the quasi-independence between left truncation and survival times in standard survival analysis. New methods for testing the Markovianity of an illness-death model are proposed and compared with existing ones by means of an intensive simulation study. We illustrate our findings through the analysis of a data set from stem cell transplant in leukemia. Copyright © 2016 John Wiley & Sons, Ltd.

A new flexible dependence measure for semi-competing risks.

Semi-competing risks data are often encountered in chronic disease follow-up studies that record both nonterminal events (e.g., disease landmark events) and terminal events (e.g., death). Studying the relationship between the nonterminal event and the terminal event can provide insightful information on disease progression. In this article, we propose a new sensible dependence measure tailored to addressing such an interest. We develop a nonparametric estimator, which is general enough to handle both independent right censoring and left truncation. Our strategy of connecting the new dependence measure with quantile regression enables a natural extension to adjust for covariates with minor additional assumptions imposed. We establish the asymptotic properties of the proposed estimators and develop inferences accordingly. Simulation studies suggest good finite-sample performance of the proposed methods. Our proposals are illustrated via an application to Denmark diabetes registry data.

Trying to Conceive After an Early Pregnancy Loss: An Assessment on How Long Couples Should Wait.

To compare time to pregnancy and live birth among couples with varying intervals of pregnancy loss date to subsequent trying to conceive date. In this secondary analysis of the Effects of Aspirin in Gestation and Reproduction trial, 1,083 women aged 18-40 years with one to two prior early losses and whose last pregnancy outcome was a nonectopic or nonmolar loss were included. Participants were actively followed for up to six menstrual cycles and, for women achieving pregnancy, until pregnancy outcome. We calculated intervals as start of trying to conceive date minus pregnancy loss date. Time to pregnancy was defined as start of trying to conceive until subsequent conception. Discrete Cox models, accounting for left truncation and right censoring, estimated fecundability odds ratios (ORs) adjusting for age, race, body mass index, education, and subfertility. Although intervals were assessed prior to randomization and thus reasoned to have no relation with treatment assignment, additional adjustment for treatment was evaluated given that low-dose aspirin was previously shown to be predictive of time to pregnancy. Couples with a 0-3-month interval (n=765 [76.7%]) compared with a greater than 3-month (n=233 [23.4%]) interval were more likely to achieve live birth (53.2% compared with 36.1%) with a significantly shorter time to pregnancy leading to live birth (median [interquartile range] five cycles [three, eight], adjusted fecundability OR 1.71 [95% confidence interval 1.30-2.25]). Additionally adjusting for low-dose aspirin treatment did not appreciably alter estimates. Our study supports the hypothesis that there is no physiologic evidence for delaying pregnancy attempt after an early loss.

Estimating time-varying exposure-outcome associations using case-control data: logistic and case-cohort analyses.

BackgroundTraditional analyses of standard case-control studies using logistic regression do not allow estimation of time-varying associations between exposures and the outcome. We present two approaches which allow this. The motivation is a study of vaccine efficacy as a function of time since vaccination.MethodsOur first approach is to estimate time-varying exposure-outcome associations by fitting a series of logistic regressions within successive time periods, reusing controls across periods. Our second approach treats the case-control sample as a case-cohort study, with the controls forming the subcohort. In the case-cohort analysis, controls contribute information at all times they are at risk. Extensions allow left truncation, frequency matching and, using the case-cohort analysis, time-varying exposures. Simulations are used to investigate the methods.ResultsThe simulation results show that both methods give correct estimates of time-varying effects of exposures using standard case-control data. Using the logistic approach there are efficiency gains by reusing controls over time and care should be taken over the definition of controls within time periods. However, using the case-cohort analysis there is no ambiguity over the definition of controls.The performance of the two analyses is very similar when controls are used most efficiently under the logistic approach.ConclusionsUsing our methods, case-control studies can be used to estimate time-varying exposure-outcome associations where they may not previously have been considered. The case-cohort analysis has several advantages, including that it allows estimation of time-varying associations as a continuous function of time, while the logistic regression approach is restricted to assuming a step function form for the time-varying association.

Autologous Transplant, and Not ATO Alone, Remains the Preferred Therapy for Relapsed APL: A Report from the CIBMTR, EBMT and Two Specialized Centers

Introduction: Despite its favorable prognosis, 10-20% of APL patients relapse with contemporary therapeutic strategies. At relapse, reinduction therapy is often followed by autologous hematopoietic cell transplantation (auto-HCT). In the last two decades, arsenic trioxide (ATO) has become part of the standard reinduction regimens. Such an approach is often followed successfully by auto-HCT. However, long-term survivors have also been reported after ATO treatment alone without HCT. We retrospectively compared the outcome of auto-HCT, with or without ATO, and ATO-based therapy given alone following relapse in patients with APL.Patients and Methods: Data on APL patients in first relapse were collected from the two largest transplant registries (CIBMTR, EBMT) and two specialty referral centers (Hematology-Oncology & Stem Cell Transplantation Research Center, Shariati Hospital, Tehran and Christian Medical College & Hospital, Vellore, India). The outcome of patients who received ATO at relapse and did not undergo transplantation was compared to that of patients who received any reinduction therapy, including ATO and subsequent auto-HCT. Overall survival was calculated from two months post relapse with left truncation at date of CR2 for patients receiving ATO alone and date of transplant for those receiving auto-HCT. Cox proportional hazard regression was used to estimate the univariate and multivariate associations with overall survival. Potential prognostic factors included age, disease risk based on WBC >10,000/µl, the presence of extramedullary disease, and duration of CR1, in addition to the primary comparison of auto-HCT and ATO-based reinduction therapy.Results: 242 patients were identified, of whom 34 were excluded due to missing relapse date (n = 25), death or lost to follow-up before 2 months after relapse (n = 7), or receiving auto-HCT within two months of relapse (n = 2). The median age was 31, 63% of the patients were males and median WBC count at diagnosis was 4,750/µl. The final cohort of 208 patients included 68 receiving ATO alone and 140 receiving auto-HCT. The groups were comparable in terms of age, gender, duration of CR1 and risk group. Fifty-six percent of the auto-HCT patients received ATO-based treatment as salvage therapy and the others received various combinations of chemotherapy and ATRA.There is a statistically significant survival advantage for the HCT group (HR = 0.34 (0.27-0.42), p<0.001) compared to the ATO only group. At 5 years, OS was 42% (95% CI: 31% - 58%) and 78% (95% CI: 71% - 86%) for the ATO-only and HCT groups, respectively. In addition, there was a significant OS association with longer duration of CR1 (p=0.002) and presence of extramedullary disease (p = 0.041); which remained significant in a multivariate model (p < 0.001). Disease risk at diagnosis was not associated with OS.Conclusions: These data suggest that auto-HCT results in improved OS after APL relapse, better than ATO-based therapy alone. Further studies to identify improvements in auto-HCT and which patients treated with ATO alone may be cured are needed. DisclosuresDouer:Gilead: Consultancy. Rowe:Amgen: Consultancy; BioLineRx Ltd.: Consultancy; BioSight Ltd.: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Inverted-U Relationship between Innovation and Survival: Evidence from Firm-Level UK Data

Theoretical and empirical work on innovation and firm survival has produced varied and often conflicting findings. In this paper, we draw on Schumpeterian models of competition and innovation and stochastic models of firm dynamics to demonstrate that the conflicting findings may be due to linear specifications of the innovation-survival relationship. We demonstrate that a quadratic specification is appropriate theoretically and fits the data well. Our findings from an unbalanced panel of 39,705 UK firms from 1997-2012 indicate that an inverted-U relationship holds for different types of R&D expenditures and sources of funding. We also report that R&D intensity is more likely to increase survival when firms are in more concentrated industries and in Pavitt technology classes consisting of specialized suppliers of technology and scale-intensive industries. Finally, we report that the effects of firm and industry characteristics as well as macroeconomic environment indicators are all consistent with prior findings. The results are robust to step-wise modeling, controlling for left truncation and use of lagged values to address potential simultaneity bias.

Risks of miscarriage and inadvertent exposure to artemisinin derivatives in the first trimester of pregnancy: a prospective cohort study in western Kenya.

BackgroundThe artemisinin anti-malarials are widely deployed as artemisinin-based combination therapy (ACT). However, they are not recommended for uncomplicated malaria during the first trimester because safety data from humans are scarce.MethodsThis was a prospective cohort study of women of child-bearing age carried out in 2011–2013, evaluating the relationship between inadvertent ACT exposure during first trimester and miscarriage. Community-based surveillance was used to identify 1134 early pregnancies. Cox proportional hazard models with left truncation were used.ResultsThe risk of miscarriage among pregnancies exposed to ACT (confirmed + unconfirmed) in the first trimester, or during the embryo-sensitive period (≥6 to <13 weeks gestation) was higher than among pregnancies unexposed to anti-malarials in the first trimester: hazard ratio (HR) = 1.70, 95 % CI (1.08–2.68) and HR = 1.61 (0.96–2.70). For confirmed ACT-exposures (primary analysis) the corresponding values were: HR = 1.24 (0.56–2.74) and HR = 0.73 (0.19–2.82) relative to unexposed women, and HR = 0.99 (0.12–8.33) and HR = 0.32 (0.03–3.61) relative to quinine exposure, but the numbers of quinine exposures were very small.ConclusionACT exposure in early pregnancy was more common than quinine exposure. Confirmed inadvertent artemisinin exposure during the potential embryo-sensitive period was not associated with increased risk of miscarriage. Confirmatory studies are needed to rule out a smaller than three-fold increase in risk.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-015-0950-6) contains supplementary material, which is available to authorized users.

Abstract 17352: Predictors of Ventricular Arrhythmias and Sudden Death in a Quebec Cohort With Brugada Syndrome

Introduction: Patients with Brugada syndrome (BrS) are at risk for ventricular arrhythmias (VAs) and sudden death. Identification of high-risk individuals beyond those with syncope or resuscitated SCD remains a major challenge. We sought to assess the value of clinical, electrophysiological, and ECG features of depolarization and repolarization in predicting arrhythmic events and SCD in a Quebec population. Methods: Consecutive patients with BrS diagnosed between 2002 and 2013 were recruited from three major university hospitals in the province of Quebec, Canada. Relevant clinical features were recorded at baseline. Qualifying ECGs with the highest type 1 ST-segment elevations were reviewed and analyzed by two electrophysiologists blinded to clinical history, with the following parameters assessed: S duration in V1, aVR sign, QRS duration in V6, R-J interval in V2, maximal Tp-e, duration, Tp-e dispersion, QRS-f, ST elevation and QTc in V2, and presence of an inferolateral ER pattern. Survival analyses considered a left truncation model to account for potential sampling bias, considering that those who died suddenly before recognition of BrS were necessarily excluded. Results: A total of 105 consecutive patients, 79.8% male, were diagnosed with BrS at a median age of 48 years and were followed for 5.0±1.2 years. Ten (9.5%) had a history of SCD, 37 (35.2%) had syncope, and 7 (6.7%) experienced 20 arrhythmic events during follow-up (7 anti-tachycardia pacing; 13 shocks). In multivariate Cox regression analyses adjusted for left truncation and Firth bias correction, a spontaneous type I ECG pattern (HR 10.80; 95% CI 1.03 to 113.87; p=0.0476)], maximal Tp-e duration ≥100ms (HR 29.73; 95% CI 1.33 to 666.37; p= 0.0325) and QRS duration in V6&gt;110 ms (HR 15.27; 95% CI 1.07 to 217.42; p=0.0443) were independently associated with VAs or aborted SCD. Family history of SCD, AF, and inducible VAs during programmed ventricular stimulation were not associated with the primary endpoint. Conclusion: In a cohort of patients with BrS from the province of Quebec, VAs and SCD were independently associated with standard 12-lead ECG features including a spontaneous type 1 pattern, depolarization (QRS in V6 &gt;110 ms), and repolarization (maximal Tp-e duration) criteria.

Nonparametric association analysis of bivariate left‐truncated competing risks data

We develop time-varying association analyses for onset ages of two lung infections to address the statistical challenges in utilizing registry data where onset ages are left-truncated by ages of entry and competing-risk censored by deaths. Two types of association estimators are proposed based on conditional cause-specific hazard function and cumulative incidence function that are adapted from unconditional quantities to handle left truncation. Asymptotic properties of the estimators are established by using the empirical process techniques. Our simulation study shows that the estimators perform well with moderate sample sizes. We apply our methods to the Cystic Fibrosis Foundation Registry data to study the relationship between onset ages of Pseudomonas aeruginosa and Staphylococcus aureus infections.

A semiparametric regression cure model for interval-censored and left-truncated data

ABSTRACTMedical advancements have made it possible for patients to be cured of certain types of diseases. In follow-up studies, the disease event time can be subject to left truncation and interval censoring. In this article, we propose a semiparametric nonmixture cure model for the regression analysis of left-truncated and interval-censored (LTIC) data. We develop semiparametric maximum likelihood estimation for the nonmixture cure model with LTIC data. A simulation study is conducted to investigate the performance of the proposed estimators.

Joint modelling of longitudinal and survival data: incorporating delayed entry and an assessment of model misspecification.

A now common goal in medical research is to investigate the inter‐relationships between a repeatedly measured biomarker, measured with error, and the time to an event of interest. This form of question can be tackled with a joint longitudinal‐survival model, with the most common approach combining a longitudinal mixed effects model with a proportional hazards survival model, where the models are linked through shared random effects. In this article, we look at incorporating delayed entry (left truncation), which has received relatively little attention. The extension to delayed entry requires a second set of numerical integration, beyond that required in a standard joint model. We therefore implement two sets of fully adaptive Gauss–Hermite quadrature with nested Gauss–Kronrod quadrature (to allow time‐dependent association structures), conducted simultaneously, to evaluate the likelihood. We evaluate fully adaptive quadrature compared with previously proposed non‐adaptive quadrature through a simulation study, showing substantial improvements, both in terms of minimising bias and reducing computation time. We further investigate, through simulation, the consequences of misspecifying the longitudinal trajectory and its impact on estimates of association. Our scenarios showed the current value association structure to be very robust, compared with the rate of change that we found to be highly sensitive showing that assuming a simpler trend when the truth is more complex can lead to substantial bias. With emphasis on flexible parametric approaches, we generalise previous models by proposing the use of polynomials or splines to capture the longitudinal trend and restricted cubic splines to model the baseline log hazard function. The methods are illustrated on a dataset of breast cancer patients, modelling mammographic density jointly with survival, where we show how to incorporate density measurements prior to the at‐risk period, to make use of all the available information. User‐friendly Stata software is provided. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Vandenbroucke and Pearce Respond to “Incident and Prevalent Exposures and Causal Inference”

We are grateful for the opportunity to reply to the comments by Hernan (1) and Brookhart (2) on our paper about incident and prevalent exposures and causal inference, in which we argued that limiting studies to persons who are followed from first exposure onward may damage epidemiology (3). In an imaginary world, the ideal study would start from first exposure and follow all study participants until the end of their lives. In the real world, the issue is not whether there will be missing data for some time periods. There always will be. Rather, the issue is which time periods we should focus on. The question is not whether “left truncation is okay” but whether “left truncation or right censoring is a more serious problem in this study of this hypothesis.” It is not a matter of idealism versus pragmatism but which type of pragmatism is most appropriate in a given situation. In a study of alcohol consumption and car accidents, missing data on the 24 hours after drinking (i.e., left truncation) would be disastrous. In a study of occupational cancer, the first 5 years after exposure may not be of interest, whereas data for the period of more than 10 years after first exposure are crucial. Hernan's analogy that we are complacent about seat-belt use because risks are small (1) is misleading. The issue is not about wearing a seat belt but whether “not wearing a seat belt” is the most serious problem. When taking an airplane, would you rather 1) wear a seat belt during takeoff, 2) know that the plane had enough fuel to reach its destination, or 3) know that the landing gear worked? The ideal trip has all 3, and the ideal epidemiologic study has perfect and infinite follow-up. In the real world, however, we need to make choices: We are almost always faced with accepting left truncation, right censoring, or both, and the best decision depends on the hypothesis under study. Fundamentally, we disagree when Hernan sees the purpose of epidemiologic research as specifically to support practice and limits epidemiology to studies that resemble randomized trials. We conduct epidemiologic studies for many reasons, including to assess causality for factors that will not lead to immediate interventions, that is, to explore etiology. Many studies involve complex exposures (e.g., socioeconomic status, climate change) that are relevant to public health policy but do not correspond to a randomized trial, even in theory. Hernan chooses to define epidemiology in a narrow way because this fits the elegant (and often incredibly useful) randomized trial-based theory that he advocates. This ultimately means restricting epidemiology to studying the short- or medium-term effects of very specific interventions and not studying many important public health problems (has there ever been an incident-exposures study of smoking and lung cancer or of a gene and cancer at middle age?). However much we admire Brookhart's ideas about the treatment decision design (2) and hope that this might be applied in pharmacoepidemiology, it does not solve the problems we raise. What are the rest of us supposed to do? Should we tell society, “Sorry, we can't give you answers, or even evidence, about major scientific issues because of ‘left truncation’?” Should we throw out all knowledge on the adverse effects of drugs on the basis of periods of use (oral contraceptives, nonsteroidal antiinflammatory drugs), even if it is clear that the adverse effect occurs only during use and is unlikely to be modified by time since first use? Whether a study is based on incident or prevalent exposures, it is important to document this and to carefully assess the likely biases. What we have is a choice between different types of pragmatism: Which type works best depends on the hypothesis under study and the populations available to study it. What we need is problem-based methods rather than theory that tautologically infers that some study designs are inherently and always better than others.

Point: incident exposures, prevalent exposures, and causal inference: does limiting studies to persons who are followed from first exposure onward damage epidemiology?

The idea that epidemiologic studies should start from first exposure onward has been advocated in the past few years. The study of incident exposures is contrasted with studies of prevalent exposures in which follow-up may commence after first exposure. The former approach is seen as a hallmark of a good study and necessary for causal inference. We argue that studying incident exposures may be necessary in some situations, but it is not always necessary and is not the preferred option in many instances. Conducting a study involves decisions as to which person-time experience should be included. Although studies of prevalent exposures involve left truncation (missingness on the left), studies of incident exposures may involve right censoring (missingness on the right) and therefore may not be able to assess the long-term effects of exposure. These considerations have consequences for studies of dynamic (open) populations that involve a mixture of prevalent and incident exposures. We argue that studies with prevalent exposures will remain a necessity for epidemiology. The purpose of this paper is to restore the balance between the emphasis on first exposure cohorts and the richness of epidemiologic information obtained when studying prevalent exposures.

Composite Estimating Equation Method for the Accelerated Failure Time Model with Length‐biased Sampling Data

AbstractLength‐biased sampling data are often encountered in the studies of economics, industrial reliability, epidemiology, genetics and cancer screening. The complication of this type of data is due to the fact that the observed lifetimes suffer from left truncation and right censoring, where the left truncation variable has a uniform distribution. In the Cox proportional hazards model, Huang &amp; Qin (Journal of the American Statistical Association, 107, 2012, p. 107) proposed a composite partial likelihood method which not only has the simplicity of the popular partial likelihood estimator, but also can be easily performed by the standard statistical software. The accelerated failure time model has become a useful alternative to the Cox proportional hazards model. In this paper, by using the composite partial likelihood technique, we study this model with length‐biased sampling data. The proposed method has a very simple form and is robust when the assumption that the censoring time is independent of the covariate is violated. To ease the difficulty of calculations when solving the non‐smooth estimating equation, we use a kernel smoothed estimation method (Heller; Journal of the American Statistical Association, 102, 2007, p. 552). Large sample results and a re‐sampling method for the variance estimation are discussed. Some simulation studies are conducted to compare the performance of the proposed method with other existing methods. A real data set is used for illustration.

Efficacy of midtrimester short cervix interventions is conditional on intraamniotic inflammation

Midtrimester ultrasound is a valuable method for identifying asymptomatic women at risk for spontaneous preterm delivery (PTD). However, response to various treatments (cerclage, progestogen) has been variable in the clinical setting. It remains unclear how other biomarkers may be used to guide intervention strategies. We applied an amniotic fluid inflammatory scoring system to determine if the degree of inflammation is associated with intervention efficacy in patients with midtrimester short cervix. Women carrying a singleton fetus between 16-24 weeks' gestation with a short cervix (≤25 mm) on transvaginal ultrasound underwent amniocentesis and were assigned to McDonald cerclage, no cerclage, or weekly 17-alpha hydroxyprogesterone caproate (17OHP-C). Our previously described inflammatory risk score (comprised of 14 inflammatory markers) was used to classify patients as high (score ≥8) or low (score <8) risk for inflammation. Gestational age at delivery was compared for each intervention and risk score status. Risk of delivering as a function of the remaining gestation was evaluated using modified Cox proportional hazards models with incorporation of methods to account for both left and right truncation bias. Ninety patients were included: 24 were in the nonintervention control group, 51 received cerclage, and 15 received 17OHP-C. Inflammation status at time of sampling influenced the efficacy of the treatment (P < .001). Compared to the nonintervention control group, in patients with low inflammation (score < 8), both cerclage (adjusted hazard ratio [HR], 2.86; 95% confidence interval [CI], 1.28-6.37) and 17OHP-C (HR, 3.11; 95% CI, 1.04-9.30) were associated with increased hazard of PTD. In contrast, in patients with high inflammation (score ≥8) both cerclage (HR, 0.22; 95% CI, 0.08-0.65) and 17OHP-C (HR, 0.20; 95% CI, 0.05-0.81) were associated with lower hazard of delivering preterm. Cerclage placement or administration of 17OHP-C therapy for midtrimester short cervix for PTD prevention appears beneficial only in the subset of patients with high inflammation. Knowledge of the amniotic fluid inflammatory status may aid in guiding the appropriate therapy for women presenting with midtrimester short cervix who are at increased risk of PTD.

Dynamic prediction models for clustered and interval-censored outcomes: Investigating the intra-couple correlation in the risk of dementia.

The use of settings such as cohorts or clinical trials with interval-censored data and clustered event times are increasingly popular designs. First, the observed outcomes cannot be considered as independent and random effects survival models were introduced. Second, the failure time is not known exactly but it is only known to have occurred within a certain interval. We propose here an extension of shared frailty models to handle simultaneously the interval censoring, the clustering and also left truncation due to delayed entry in the cohort. A simulation study to evaluate the proposed method was conducted. The estimated results are used to obtain dynamic predictions for clustered patients, with interval-censored failure times and with a given history. We apply our method to the Three-City study, a prospective cohort with periodic follow-up in order to study prognostic factors of dementia. In this application scheme, couples are natural clusters and an intra-couple correlation might be present with a possible increased risk for dementia for subjects whose partner already developed incident dementia. No significant intra-couple correlation for the risk of dementia was observed before and after adjustments for covariates. We also present individual predictions of dementia underlining the usefulness of dynamic prognostic tools that can take into account the clustering. The consideration of frailty models for interval-censoring data and left-truncated data permits useful analysis of very complex clustered data. It could help to improve estimation of the impact of proposed prognostic features in a study with clustering. We proposed here a tractable model and a dynamic prediction tool that can easily be implemented using the R package Frailtypack.

Impact of dependent left truncation in semiparametric competing risks methods: A simulation study

ABSTRACTIn this study, we investigated the robustness of the methods that account for independent left truncation when applied to competing risks settings with dependent left truncation. We specifically focused on the methods for the proportional cause-specific hazards model and the Fine–Gray model. Simulation experiments showed that these methods are not in general robust against dependent left truncation. The magnitude of the bias was analogous to the strength of the association between left truncation and failure times, the effect of the covariate on the competing cause of failure, and the baseline hazard of left truncation time.