Making sense of breast cancer risk estimates.

Individual probabilistic assessments on the risk of cancer, primary or secondary, will not be understood by most patients. That is the essence of our arguments in this paper. Greater understanding can be achieved by extensive, intensive, and detailed counseling. But since probability itself is a concept that easily escapes our everyday intuition-consider the famous Monte Hall paradox-then it would also be wise to advise patients and potential patients, to not put undue weight on any probabilistic assessment. Such assessments can be of value to the epidemiologist in the investigation of different potential etiologies describing cancer evolution or to the clinical trialist as a way to maximize design efficiency. But to an ordinary individual we cannot anticipate that these assessments will be correctly interpreted.

Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.

Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.

A transformer model for cause-specific hazard prediction.

BACKGROUD: Modelling discrete-time cause-specific hazards in the presence of competing events and non-proportional hazards is a challenging task in many domains. Survival analysis in longitudinal cohorts often requires such models; notably when the data is gathered at discrete points in time and the predicted events display complex dynamics. Current models often rely on strong assumptions of proportional hazards, that is rarely verified in practice; or do not handle sequential data in a meaningful way. This study proposes a Transformer architecture for the prediction of cause-specific hazards in discrete-time competing risks. Contrary to Multilayer perceptrons that were already used for this task (DeepHit), the Transformer architecture is especially suited for handling complex relationships in sequential data, having displayed state-of-the-art performance in numerous tasks with few underlying assumptions on the task at hand. RESULTS: Using synthetic datasets of 2000-50,000 patients, we showed that our Transformer model surpassed the CoxPH, PyDTS, and DeepHit models for the prediction of cause-specific hazard, especially when the proportional assumption did not hold. The error along simulated time outlined the ability of our model to anticipate the evolution of cause-specific hazards at later time steps where few events are observed. It was also superior to current models for prediction of dementia and other psychiatric conditions in the English longitudinal study of ageing cohort using the integrated brier score and the time-dependent concordance index. We also displayed the explainability of our model's prediction using the integrated gradients method. CONCLUSIONS: Our model provided state-of-the-art prediction of cause-specific hazards, without adopting prior parametric assumptions on the hazard rates. It outperformed other models in non-proportional hazards settings for both the synthetic dataset and the longitudinal cohort study. We also observed that basic models such as CoxPH were more suited to extremely simple settings than deep learning models. Our model is therefore especially suited for survival analysis on longitudinal cohorts with complex dynamics of the covariate-to-outcome relationship, which are common in clinical practice. The integrated gradients provided the importance scores of input variables, which indicated variables guiding the model in its prediction. This model is ready to be utilized for time-to-event prediction in longitudinal cohorts.

Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.

There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and metformin use has also been associated with reduced cognitive decline and cancer incidence. In this paper, we dig more deeply into whether metformin prevents cancer by emulating target randomized trials comparing metformin to sulfonylureas as first line diabetes therapy using data from Clinical Practice Research Datalink, a U.K. primary care database (1987-2018). We included individuals with diabetes, no prior cancer diagnosis, no chronic kidney disease, and no prior diabetes therapy who initiated metformin (N=93353) or a sulfonylurea (N=13864). In our cohort, the estimated overlap weighted additive separable direct effect of metformin compared to sulfonylureas on cancer risk at 6 years was -1% (.95 CI=-2.2%, 0.1%), which is consistent with metformin providing no direct protection against cancer incidence or substantial protection. The analysis faced two methodological challenges-poor overlap, and pre-cancer death as a competing risk. To address these issues while minimizing nuisance model misspecification, we develop and apply double/debiased machine learning estimators of overlap weighted separable effects in addition to more traditional effect estimates.

Does breast cancer modify the long-term relationship between lifestyle behaviors and mortality? A prospective analysis of breast cancer survivors and population-based controls.

BACKGROUND: Modifiable lifestyle factors are known to impact survival. It is less clear whether this differs between postmenopausal women ever diagnosed with breast cancer and unaffected women. METHODS: Women diagnosed with breast cancer and unaffected women of comparable age were recruited from 2002 to 2005 and followed up until 2020. Using baseline information, a lifestyle adherence score (range 0-8; categorized as low [0-3.74], moderate [3.75-4.74], and high [≥4.75]) was created based on the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) cancer prevention recommendations. Cox regression and competing risks analysis were used to analyze the association of adherence to WCRF/AICR lifestyle recommendations with overall mortality and with death due to cardiovascular diseases and cancer, respectively. RESULTS: A total of 8584 women were included (2785 with breast cancer and 5799 without). With a median follow-up of 16.1 years there were 2006 total deaths. Among the deaths of known causes (98.6%), 445 were cardiovascular-related and 1004 were cancer-related. The average lifestyle score was 4.2. There was no differential effect of lifestyle score by case-control status on mortality. After adjusting for covariates, moderate (hazard ratio [HR], 0.66; 95% confidence interval [CI], 0.57-0.76) and high (HR, 0.54; 95% CI, 0.47-0.63) adherence to WCRF/AICR lifestyle recommendations were significantly associated with a decrease in overall mortality. Similarly, in competing risks analysis, moderate and high adherence were associated with decreased mortality from cardiovascular diseases and from cancer. CONCLUSIONS: A healthy lifestyle can substantially reduce mortality risk in women. With low adherence to all WCRF/AICR guidelines in about a third of study participants, health interventions are warranted.

Semiparametric marginal regression for clustered competing risks data with missing cause of failure.

Clustered competing risks data are commonly encountered in multicenter studies. The analysis of such data is often complicated due to informative cluster size (ICS), a situation where the outcomes under study are associated with the size of the cluster. In addition, the cause of failure is frequently incompletely observed in real-world settings. To the best of our knowledge, there is no methodology for population-averaged analysis with clustered competing risks data with an ICS and missing causes of failure. To address this problem, we consider the semiparametric marginal proportional cause-specific hazards model and propose a maximum partial pseudolikelihood estimator under a missing at random assumption. To make the latter assumption more plausible in practice, we allow for auxiliary variables that may be related to the probability of missingness. The proposed method does not impose assumptions regarding the within-cluster dependence and allows for ICS. The asymptotic properties of the proposed estimators for both regression coefficients and infinite-dimensional parameters, such as the marginal cumulative incidence functions, are rigorously established. Simulation studies show that the proposed method performs well and that methods that ignore the within-cluster dependence and the ICS lead to invalid inferences. The proposed method is applied to competing risks data from a large multicenter HIV study in sub-Saharan Africa where a significant portion of causes of failure is missing.

First trimester risk of preeclampsia and rate of spontaneous birth in patients without preeclampsia.

BACKGROUND: First-trimester screening for preeclampsia using a combination of maternal risk factors and mean arterial pressure, uterine artery pulsatility index, and placental growth factor, as proposed by the Fetal Medicine Foundation, provides effective prediction of preterm preeclampsia. Placental dysfunction is a potential precursor of spontaneous birth. OBJECTIVE: The objective of this study was to examine if the estimated risk of preeclampsia is associated with the gestational age at onset of spontaneous delivery in the absence of preeclampsia. STUDY DESIGN: This was a secondary analysis of the data from the Screening programme for pre-eclampsia trial in which there was a comparison of the performance of first-trimester screening for preterm preeclampsia using the Fetal Medicine Foundation model vs a traditional history-based risk scoring system. A subgroup of women from the trial with spontaneous onset of delivery (labor with intact membranes or preterm prelabor rupture of membranes) was included in this study and was arbitrarily divided into 3 groups according to the risk for preterm preeclampsia as determined by the Fetal Medicine Foundation model at 11 to 13 weeks' gestation as follows: group 1 low risk (˂1/100); group 2 intermediate risk (1/50 to 1/100); and group 3 high risk (˃1/50). A survival analysis was carried out using a Kaplan-Meier estimator and a Cox regression analysis with stratification by the 3 preeclampsia risk groups. Occurrence of spontaneous birth in the study groups was compared using log-rank tests and hazard ratios. RESULTS: The study population comprised 10,820 cases with delivery after spontaneous onset of labor among the 16,451 cases who participated in the Screening programme for pre-eclampsia trial. There were 9795 cases in group 1, 583 in group 2, and 442 in group 3. The gestational age at delivery was <28, <32, <35, <37, and <40 weeks in 0.29%, 0.64%, 1.68%, 4.52%, and 44.97% of cases, respectively, in group 1; 0.69%, 1.71%, 3.26%, 7.72%, and 55.23% of cases, respectively, in group 2; and 0.45%, 1.81%, 5.66%, 13.80%, and 63.12% of cases, respectively, in group 3. The curve profile of gestational age at spontaneous birth in the 3 study groups was significantly different overall and in pairwise comparisons (P values <.001). The Cox regression analysis showed that risks increased for spontaneous birth by 18% when the intermediate-risk group was compared with the low-risk group (P˂.001) and by 41% when the high-risk group was compared with the low-risk group (P˂.001). CONCLUSION: In this study that investigated birth after spontaneous onset of labor in women without preeclampsia, there were 2 major findings. First, the duration of pregnancy decreased with increasing first-trimester risk for preeclampsia. Second, in the high-risk group, when compared with the low-risk group, the risk for spontaneous birth was 4 times higher at a gestational age of 24 to 26 weeks, 3 times higher at 28 to 32 weeks, and 2 times higher at 34 to 39 weeks. These differences present major clinical implications for antepartum counselling, monitoring, and interventions in these pregnancies.

Maternal vascular indices at 36 weeks' gestation in the prediction of preeclampsia.

BACKGROUND: Epidemiological studies have shown that women with preeclampsia (PE) are at increased long term cardiovascular risk. This risk might be associated with accelerated vascular ageing process but data on vascular abnormalities in women with PE are scarce. OBJECTIVE: This study aimed to identify the most discriminatory maternal vascular index in the prediction of PE at 35 to 37 weeks' gestation and to examine the performance of screening for PE by combinations of maternal risk factors and biophysical and biochemical markers at 35 to 37 weeks' gestation. STUDY DESIGN: This was a prospective observational nonintervention study in women attending a routine hospital visit at 35 0/7 to 36 6/7 weeks' gestation. The visit included recording of maternal demographic characteristics and medical history, vascular indices, and hemodynamic parameters obtained by a noninvasive operator-independent device (pulse wave velocity, augmentation index, cardiac output, stroke volume, central systolic and diastolic blood pressures, total peripheral resistance, and fetal heart rate), mean arterial pressure, uterine artery pulsatility index, and serum concentration of placental growth factor and soluble fms-like tyrosine kinase-1. The performance of screening for delivery with PE at any time and at <3 weeks from assessment using a combination of maternal risk factors and various combinations of biomarkers was determined. RESULTS: The study population consisted of 6746 women with singleton pregnancies, including 176 women (2.6%) who subsequently developed PE. There were 3 main findings. First, in women who developed PE, compared with those who did not, there were higher central systolic and diastolic blood pressures, pulse wave velocity, peripheral vascular resistance, and augmentation index. Second, the most discriminatory indices were systolic and diastolic blood pressures and pulse wave velocity, with poor prediction from the other indices. However, the performance of screening by a combination of maternal risk factors plus mean arterial pressure was at least as high as that of a combination of maternal risk factors plus central systolic and diastolic blood pressures; consequently, in screening for PE, pulse wave velocity, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and soluble fms-like tyrosine kinase-1 were used. Third, in screening for both PE within 3 weeks and PE at any time from assessment, the detection rate at a false-positive rate of 10% of a biophysical test consisting of maternal risk factors plus mean arterial pressure, uterine artery pulsatility index, and pulse wave velocity (PE within 3 weeks: 85.2%; 95% confidence interval, 75.6%-92.1%; PE at any time: 69.9%; 95% confidence interval, 62.5%-76.6%) was not significantly different from a biochemical test using the competing risks model to combine maternal risk factors with placental growth factor and soluble fms-like tyrosine kinase-1 (PE within 3 weeks: 80.2%; 95% confidence interval, 69.9%-88.3%; PE at any time: 64.2%; 95% confidence interval, 56.6%-71.3%), and they were both superior to screening by low placental growth factor concentration (PE within 3 weeks: 53.1%; 95% confidence interval, 41.7%-64.3%; PE at any time: 44.3; 95% confidence interval, 36.8%-52.0%) or high soluble fms-like tyrosine kinase-1-to-placental growth factor concentration ratio (PE within 3 weeks: 65.4%; 95% confidence interval, 54.0%-75.7%; PE at any time: 53.4%; 95% confidence interval, 45.8%-60.9%). CONCLUSION: First, increased maternal arterial stiffness preceded the clinical onset of PE. Second, maternal pulse wave velocity at 35 to 37 weeks' gestation in combination with mean arterial pressure and uterine artery pulsatility index provided effective prediction of subsequent development of preeclampsia.

Competitive Risk Analysis of Prognosis in Older Adults with Sigmoid Colon Adenocarcinoma: A Population-Based Study.

BACKGROUND: The purpose of this study is to employ a competing risk model based on the Surveillance, Epidemiology, and End Results (SEER) database to identify prognostic factors for elderly individuals with sigmoid colon adenocarcinoma (SCA) and compare them with the classic Cox proportional hazards model. METHODS: We extracted data from elderly patients diagnosed with SCA registered in the SEER database between 2010 and 2015. Univariate analysis was conducted using cumulative incidence functions and Gray's test, while multivariate analysis was performed using both the Fine-Gray and Cox proportional hazards models. RESULTS: Among the 10,712 eligible elderly patients diagnosed with SCA, 5595 individuals passed away: 2987 due to sigmoid colon adenocarcinoma and 2608 from other causes. The results of one-way Gray's test showed that age, race, marital status, AJCC stage, differentiation grade, tumor size, surgical status, liver metastasis status, lung metastasis status, brain metastasis status, radiotherapy status, and chemotherapy status all affected the prognosis of SCA (P < .05). Multivariate analysis showed that sex, age, race, marital status, and surgical status affected the prognosis of SCA (P < .05). Multifactorial Fine-Gray analysis revealed that key factors influencing the prognosis of SCA patients include age, race, marital status, AJCC stage, grade classification, surgical status, tumor size, liver metastasis, lung metastasis, and chemotherapy status (P < .05). CONCLUSION: Data from the SEER database were used to more accurately estimate CIFs for sigmoid colon adenocarcinoma-specific mortality and prognostic factors using competing risk models.

Efficient estimation for left-truncated competing risks regression for case-cohort studies.

The case-cohort study design provides a cost-effective study design for a large cohort study with competing risk outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risk data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis. Existing inverse probability weighting methods for case-cohort studies with competing risk data not only have not addressed left truncation, but also are inefficient in regression parameter estimation for fully observed covariates. We propose an augmented inverse probability-weighted estimating equation for left-truncated competing risk data to address these limitations of the current literature. We further propose a more efficient estimator when extra information from the other causes is available. The proposed estimators are consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is unbiased and leads to estimation efficiency gain in the regression parameter estimation. We analyze the Atherosclerosis Risk in Communities study data using the proposed methods.

Absolute risk from double nested case-control designs: cause-specific proportional hazards models with and without augmented estimating equations.

We estimate relative hazards and absolute risks (or cumulative incidence or crude risk) under cause-specific proportional hazards models for competing risks from double nested case-control (DNCC) data. In the DNCC design, controls are time-matched not only to cases from the cause of primary interest, but also to cases from competing risks (the phase-two sample). Complete covariate data are available in the phase-two sample, but other cohort members only have information on survival outcomes and some covariates. Design-weighted estimators use inverse sampling probabilities computed from Samuelsen-type calculations for DNCC. To take advantage of additional information available on all cohort members, we augment the estimating equations with a term that is unbiased for zero but improves the efficiency of estimates from the cause-specific proportional hazards model. We establish the asymptotic properties of the proposed estimators, including the estimator of absolute risk, and derive consistent variance estimators. We show that augmented design-weighted estimators are more efficient than design-weighted estimators. Through simulations, we show that the proposed asymptotic methods yield nominal operating characteristics in practical sample sizes. We illustrate the methods using prostate cancer mortality data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Study of the National Cancer Institute.

Weighting estimation in the cause-specific Cox regression with partially missing causes of failure.

Complex diseases are often analyzed using disease subtypes classified by multiple biomarkers to study pathogenic heterogeneity. In such molecular pathological epidemiology research, we consider a weighted Cox proportional hazard model to evaluate the effect of exposures on various disease subtypes under competing-risk settings in the presence of partially or completely missing biomarkers. The asymptotic properties of the inverse and augmented inverse probability-weighted estimating equation methods are studied with a general pattern of missing data. Simulation studies have been conducted to demonstrate the double robustness of the estimators. For illustration, we applied this method to examine the association between pack-years of smoking before the age of 30 and the incidence of colorectal cancer subtypes defined by a combination of four tumor molecular biomarkers (statuses of microsatellite instability, CpG island methylator phenotype, BRAF mutation, and KRAS mutation) in the Nurses' Health Study cohort.

An augmented illness-death model for semi-competing risks with clinically immediate terminal events.

Preeclampsia is a pregnancy-associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time-to-event setting of "semi-competing risks" in which a non-terminal event of interest is subject to the occurrence of a terminal event of interest. The semi-competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non-terminal event, which we call "clinically immediate" and "non-immediate" terminal events. Though such phenomena arise in many clinical contexts, to-date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty-based illness-death model with a binary submodel to distinguish risk of immediate terminal event following the non-terminal event. The model admits direct dependence of the terminal event on the non-terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions.

Prognostic Differences Between Surveillance and Active Treatment After Initial Orchiectomy in Patients With Stage I Mixed Germ Cell Tumors of the Testis: A Propensity Score Matching Analysis.

INTRODUCTION: The prognosis and optimal treatment approach for stage I mixed germ cell cancers of the testis are not well-established. This study aimed to assess contemporary treatment rates and their correlation with the cancer-specific mortality (CSM) and other-cause mortality (OCM) in patients with stage I testicular mixed germ cell tumors (TMGCT) who underwent orchiectomy, comparing surveillance with active treatment, including chemotherapy (CHT) and retroperitoneal lymph node dissection (RPLND). METHODS: Retrospective analysis of clinical data from stage I TMGCT patients who underwent orchiectomy was conducted using the Surveillance, Epidemiology, and End Results database from 2004 to 2019. The annual percentage change (APC) in the use of surveillance, postoperative CHT, and RPLND was examined. Propensity score matching (PSM) and cumulative incidence, analyses were employed to compare differences in CSM and OCM between surveillance and active treatment, as well as between CHT and RPLND. Multivariate competing-risks regression models were utilized to investigate independent factors affecting CSM and OCM among stage I TMGCT patients. RESULTS: The study included 5743 individuals with stage I TMGCT that underwent surveillance (61.6%), CHT(27.2%), or RPLND (11.2%). Among them, 82 deaths were attributed to TMGCT, and 82 deaths resulted from other causes. Surveillance rates increased over time (APC: 0.635%, P = 0.008), as did CHT rates (APC: 0.863%, P < 0.001), while RPLND rates declined (APC: -0.96%, P < 0.001). After PSM, multivariate competing-risks regression analysis showed that, active treatment, compared to surveillance, was not an independent factor for CSM and OCM. In contrast, when compared to CHT, RPLND was an independent factor associated with lower CSM (hazard ratio = 0.247, 95% confidence interval: 0.08-0.761; P = 0.015), but not OCM (hazard ratio = 0.946, 95% confidence interval: 0.377-2.37; P = 0.91). CONCLUSIONS: Surveillance and CHT rates have increased over time for patients with stage I TMGCT following initial orchiectomy, while RPLND utilization has decreased. There was no significant difference in CSM between surveillance and active treatment groups, but RPLND demonstrated significantly lower CSM than CHT in active treatment. Our findings suggest that the usage of RPLND in patients with stage I TMGCT should be reconsidered.

Alcohol use disorder and risk of specific methods of suicide death in a national cohort.

INTRODUCTION: Alcohol use disorder (AUD) is among the strongest correlates of suicide death, but it is unclear whether AUD status is differentially associated with risk of suicide by particular methods. METHODS: The authors used competing risks models to evaluate the association between AUD status and risk of suicide by poisoning, suffocation, drowning, firearm, instruments, jumping, or other means in a large Swedish cohort born 1932-1995 (total N = 6,581,827; 48.8% female). Data were derived from Swedish national registers, including the Cause of Death Register and a range of medical registers. RESULTS: After adjusting for sociodemographic factors and familial liability to suicidal behavior, AUD was positively associated with risk of suicide for each method evaluated (cumulative incidence differences: 0.006-1.040 for females, 0.046-0.680 for males), except the association with firearm suicide in females. AUD was most strongly associated with risk of suicide by poisoning. Sex differences in the effects of AUD and family liability were observed for some, but not all, methods. Furthermore, high familial liability for suicidal behavior exacerbated AUD's impact on risk for suicide by poisoning (both sexes) and suffocation and jumping (males only), while the inverse interaction was observed for firearm suicide (males only). CONCLUSIONS: AUD increases risk of suicide by all methods examined and is particularly potent with respect to risk of suicide by poisoning. Differences in risk related to sex and familial liability to suicidal behavior underscore AUD's nuanced role in suicide risk. Future research should investigate targeted means restriction effectiveness among persons with AUD.

Adverse events in single-arm clinical trials with non-fatal time-to-event efficacy endpoint: from clinical questions to methods for statistical analysis.

BACKGROUND: In any single-arm trial on novel treatments, assessment of toxicity plays an important role as occurrence of adverse events (AEs) is relevant for application in clinical practice. In the presence of a non-fatal time-to-event(s) efficacy endpoint, the analysis should be broadened to consider AEs occurrence in time. The AEs analysis could be tackled with two approaches, depending on the clinical question of interest. Approach 1 focuses on the occurrence of AE as first event. Treatment ability to protect from the efficacy endpoint event(s) has an impact on the chance of observing AEs due to competing risks action. Approach 2 considers how treatment affects the occurrence of AEs in the potential framework where the efficacy endpoint event(s) could not occur. METHODS: In the first part of the work we review the strategy of analysis for these two approaches. We identify theoretical quantities and estimators consistent with the following features: (a) estimators should address for the presence of right censoring; (b) theoretical quantities and estimators should be functions of time. In the second part of the work we propose the use of alternative methods (regression models, stratified Kaplan-Meier curves, inverse probability of censoring weighting) to relax the assumption of independence between the potential times to AE and to event(s) in the efficacy endpoint for addressing Approach 2. RESULTS: We show through simulations that the proposed methods overcome the bias due to the dependence between the two potential times and related to the use of standard estimators. CONCLUSIONS: We demonstrated through simulations that one can handle patients selection in the risk sets due to the competing event, and thus obtain conditional independence between the two potential times, adjusting for all the observed covariates that induce dependence.

First-trimester prediction of preterm pre-eclampsia and prophylaxis by aspirin: Effect on spontaneous and iatrogenic preterm birth.

OBJECTIVE: To report the predictive performance for preterm birth (PTB) of the Fetal Medicine Foundation (FMF) triple test and National Institute for health and Care Excellence (NICE) guidelines used to screen for pre-eclampsia and examine the impact of aspirin in the prevention of PTB. DESIGN: Secondary analysis of data from the SPREE study and the ASPRE trial. SETTING: Multicentre studies. POPULATION: In SPREE, women with singleton pregnancies had screening for preterm pre-eclampsia at 11-13 weeks of gestation by the FMF method and NICE guidelines. There were 16 451 pregnancies that resulted in delivery at ≥24 weeks of gestation and these data were used to derive the predictive performance for PTB of the two methods of screening. The results from the ASPRE trial were used to examine the effect of aspirin in the prevention of PTB in the population from SPREE. METHODS: Comparison of performance of FMF method and NICE guidelines for pre-eclampsia in the prediction of PTB and use of aspirin in prevention of PTB. MAIN OUTCOME MEASURE: Spontaneous PTB (sPTB), iatrogenic PTB for pre-eclampsia (iPTB-PE) and iatrogenic PTB for reasons other than pre-eclampsia (iPTB-noPE). RESULTS: Estimated incidence rates of sPTB, iPTB-PE and iPTB-noPE were 3.4%, 0.8% and 1.6%, respectively. The corresponding detection rates were 17%, 82% and 25% for the triple test and 12%, 39% and 19% for NICE guidelines, using the same overall screen positive rate of 10.2%. The estimated proportions prevented by aspirin were 14%, 65% and 0%, respectively. CONCLUSION: Prediction of sPTB and iPTB-noPE by the triple test was poor and poorer by the NICE guidelines. Neither sPTB nor iPTB-noPE was reduced substantially by aspirin.

Screening for pre-eclampsia with competing-risks model using placental growth factor measurement in blood samples collected before 11 weeks' gestation.

OBJECTIVES: To describe the distributional properties and assess the performance of placental growth factor (PlGF) measured in blood samples collected before 11 weeks' gestation in the prediction of pre-eclampsia (PE). METHODS: The study population consisted of pregnant women included in the Pre-eclampsia Screening in Denmark (PRESIDE) study with a PlGF measurement from the routine combined first-trimester screening (cFTS) blood sample collected at 8-14 weeks' gestation. PRESIDE was a prospective multicenter study investigating the predictive performance of the Fetal Medicine Foundation (FMF) first-trimester screening algorithm for PE in a Danish population. In the current study, serum concentration of PlGF in the cFTS blood samples was analyzed in batches between January and June 2021. RESULTS: A total of 8386 pregnant women were included. The incidence of PE was 0.7% at < 37 weeks' gestation and 3.0% at ≥ 37 weeks. In blood samples collected at 10 weeks' gestation, PlGF multiples of the median (MoM) were significantly lower in pregnancies with preterm PE < 37 weeks compared to unaffected pregnancies. However, PlGF MoM did not differ significantly between pregnancies with PE and unaffected pregnancies in samples collected before 10 weeks' gestation. CONCLUSIONS: The gestational-age range for PlGF sampling may be expanded from 11-14 to 10-14 weeks when assessing the risk for PE using the FMF first-trimester screening model. There is little evidence to support the use of PlGF in blood samples collected before 10 weeks' gestation. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Comparison of different methods of first-trimester screening for preterm pre-eclampsia: cohort study.

OBJECTIVE: To compare the predictive performance of three different mathematical models for first-trimester screening of pre-eclampsia (PE), which combine maternal risk factors with mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) and serum placental growth factor (PlGF), and two risk-scoring systems. METHODS: This was a prospective cohort study performed in eight fetal medicine units in five different regions of Spain between September 2017 and December 2019. All pregnant women with singleton pregnancy and a non-malformed live fetus attending their routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation were invited to participate in the study. Maternal characteristics and medical history were recorded and measurements of MAP, UtA-PI, serum PlGF and pregnancy-associated plasma protein-A (PAPP-A) were converted into multiples of the median (MoM). Risks for term PE, preterm PE (< 37 weeks' gestation) and early PE (< 34 weeks' gestation) were calculated according to the FMF competing-risks model, the Crovetto et al. logistic regression model and the Serra et al. Gaussian model. PE classification was also performed based on the recommendations of the National Institute for Health and Care Excellence (NICE) and the American College of Obstetricians and Gynecologists (ACOG). We estimated detection rates (DR) with their 95% CIs at a fixed 10% screen-positive rate (SPR), as well as the area under the receiver-operating-characteristics curve (AUC) for preterm PE, early PE and all PE for the three mathematical models. For the scoring systems, we calculated DR and SPR. Risk calibration was also assessed. RESULTS: The study population comprised 10 110 singleton pregnancies, including 32 (0.3%) that developed early PE, 72 (0.7%) that developed preterm PE and 230 (2.3%) with any PE. At a fixed 10% SPR, the FMF, Crovetto et al. and Serra et al. models detected 82.7% (95% CI, 69.6-95.8%), 73.8% (95% CI, 58.7-88.9%) and 79.8% (95% CI, 66.1-93.5%) of early PE; 72.7% (95% CI, 62.9-82.6%), 69.2% (95% CI, 58.8-79.6%) and 74.1% (95% CI, 64.2-83.9%) of preterm PE; and 55.1% (95% CI, 48.8-61.4%), 47.1% (95% CI, 40.6-53.5%) and 53.9% (95% CI, 47.4-60.4%) of all PE, respectively. The best correlation between predicted and observed cases was achieved by the FMF model, with an AUC of 0.911 (95% CI, 0.879-0.943), a slope of 0.983 (95% CI, 0.846-1.120) and an intercept of 0.154 (95% CI, -0.091 to 0.397). The NICE criteria identified 46.7% (95% CI, 35.3-58.0%) of preterm PE at 11% SPR and ACOG criteria identified 65.9% (95% CI, 55.4-76.4%) of preterm PE at 33.8% SPR. CONCLUSIONS: The best performance of screening for preterm PE is achieved by mathematical models that combine maternal factors with MAP, UtA-PI and PlGF, as compared to risk-scoring systems such as those of NICE and ACOG. While all three algorithms show similar results in terms of overall prediction, the FMF model showed the best performance at an individual level. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Ophthalmic artery Doppler at 36 weeks' gestation in prediction of pre-eclampsia: validation and update of previous model.

OBJECTIVE: To validate and extend a model incorporating maternal ophthalmic artery Doppler at 35-37 weeks' gestation in the prediction of subsequent development of pre-eclampsia (PE). METHODS: This was a prospective validation study of screening for PE (defined according to the 2019 American College of Obstetricians and Gynecologists criteria) by maternal ophthalmic artery peak systolic velocity (PSV) ratio in 6746 singleton pregnancies undergoing routine care at 35 + 0 to 36 + 6 weeks' gestation (validation dataset). Additionally, the data from the validation dataset were combined with those of 2287 pregnancies that were previously used for development of the model (training dataset), and the combined data were used to update the original model parameters. The competing-risks model was used to estimate the individual patient-specific risk of delivery with PE at any time and within 3 weeks from assessment by a combination of maternal demographic characteristics and medical history with PSV ratio alone and in combination with the established PE biomarkers of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), serum placental growth factor (PlGF) and serum soluble fms-like tyrosine kinase-1 (sFlt-1). We evaluated the predictive performance of the model by examining, first, the ability to discriminate between the PE and non-PE groups using the area under the receiver-operating-characteristics curve and the detection rate (DR) at fixed screen-positive (SPR) and false-positive rates of 10% and, second, calibration by measuring the calibration slope and calibration-in-the-large. McNemar's test was used to compare the performance of screening by a biophysical test (maternal factors, MAP, UtA-PI and PSV ratio) vs a biochemical test (maternal factors, PlGF and sFlt-1), low PlGF concentration (< 10th percentile) or high sFlt-1/PlGF concentration ratio (> 90th percentile). RESULTS: In the validation dataset, the performance of screening by maternal factors and PSV ratio for delivery with PE within 3 weeks and at any time after assessment was consistent with that in the training dataset, and there was good agreement between the predicted and observed incidence of PE. In the combined data from the training and validation datasets, good prediction for PE was achieved in screening by a combination of maternal factors, MAP, UtA-PI, PlGF, sFlt-1 and PSV ratio, with a DR, at a 10% SPR, of 85.0% (95% CI, 76.5-91.4%) for delivery with PE within 3 weeks and 65.7% (95% CI, 59.2-71.7%) for delivery with PE at any time after assessment. The performance of a biophysical test was superior to that of screening by low PlGF concentration or high sFlt-1/PlGF concentration ratio but not significantly different from the performance of a biochemical test combining maternal factors with PlGF and sFlt-1 for both PE within 3 weeks and PE at any time after assessment. CONCLUSION: Maternal ophthalmic artery PSV ratio at 35-37 weeks' gestation in combination with other biomarkers provides effective prediction of subsequent development of PE. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.