Evaluating the impact of a trial of labor after cesarean section on labor duration: a retrospective cohort study

BACKGROUND

Cesarean section (C-section) rates are increasing globally, and repeated C-sections are associated with increased maternal morbidity. Trial of labor after C-section (TOLAC) is an approach to reduce the recurrence of C-sections. However, limited research exists on the impact of cesarean scars on labor duration in TOLAC, considering the termination of labor through C-section and selection bias. This study aimed to investigate the impact of cesarean scars on labor duration in TOLAC participants, accounting for potential confounding factors and biases.

METHODS

This retrospective cohort study included 2,964 women who attempted vaginal birth at a single center in Japan from 2012 to 2021. The study categorized participants into TOLAC (n = 187) and non-TOLAC (n = 2,777) groups. Propensity scores were calculated based on 14 factors that could influence labor duration, and inverse probability of treatment weighting (IPTW) was applied. Cox proportional hazards regression analysis estimated hazard ratios (HRs) for labor duration, with and without IPTW adjustment. Sensitivity analyses used propensity score matching, bootstrapping, and interval censoring to address potential biases, including recall bias in the reported onset of labor.

RESULTS

The unadjusted HR for labor duration in the TOLAC group compared to the non-TOLAC group was 0.83 (95% CI: 0.70-0.98, P = 0.027), indicating a longer labor duration in the TOLAC group. After adjusting for confounding factors using IPTW, the HR was 0.98 (95% CI: 0.74-1.30, P = 0.91), suggesting no significant difference in labor duration between the groups. Sensitivity analyses using propensity score matching, bootstrapping, and interval censoring yielded consistent results. These findings suggested that the apparent association between TOLAC and longer labor duration was because of confounding factors rather than TOLAC itself.

CONCLUSIONS

After adjusting for confounding factors and addressing potential biases, cesarean scars had a limited impact on labor duration in TOLAC participants. Maternal and fetal characteristics may have a more substantial influence on labor duration.

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.

Metabolic risk factors among prediabetic individuals and the trajectory toward the diabetes incidence

BACKGROUND

This study investigates the trajectory of the risk factors of prediabetes progression to overt diabetes.

METHODS

The study retrospectively investigated 1610 prediabetic individuals. The trajectory of metabolic indicators was investigated using the generalized estimated equation method with autoregressive working correlation structure through a linear model with the identity link function.

RESULTS

During 15 years of follow-up, the trajectories of metabolic risk factors changed from 3 years before diabetes occurrence for fasting plasma glucose (FPG) and 2-hour plasma glucose (2hPG), 6 years for waist circumference (WC), 9 years for high-density lipoprotein cholesterol (HDL-C), and earlier for body mass index, triglyceride (TG), and TG:HDL ratio. It was shown that the differences in the trajectory of WC and HDL were stable after adjustment for other metabolic risk factors. The trajectories of FPG and 2hPG remained stable after considering multiple insulin resistance markers.

CONCLUSIONS

Deterioration of metabolic risk factor status can be a predictor of diabetes many years before its occurrence, but the abrupt change in plasma glucose is evident 3 years before diabetes mellitus onset. It seems that the HDL-C and WC trajectories are two independent predictors for diabetes incidence. It was also found that when the rising trend in plasma glucose starts, preventive strategies to lessen insulin resistance might not be efficient.

Dynamic prediction models improved the risk classification of type 2 diabetes compared with classical static models

OBJECTIVE

Dynamic prediction models use the repeated measurements of predictors to estimate coefficients that link the longitudinal predictors to a static model (i.e. Cox regression). This study aims to develop and validate a dynamic prediction for incident type 2 diabetes (T2DM) as the outcome.

STUDY DESIGN AND SETTING

Data from the Tehran lipid and glucose study was used to develop (n = 5291 individuals; phases 1 to 3) and validate (n = 3147 individuals; phases 3 to 6) the dynamic prediction model among individuals aged ≥ 20 years. We used repeated measurements of fasting plasma glucose (FPG) or waist circumference (WC) in the framework of the joint modeling (JM) of longitudinal and time-to-event analysis.

RESULTS

Compared with the Cox which used just baseline data, JM showed the same discrimination, better calibration, and higher clinical usefulness (i.e. with a net benefit considering both true and false positive decisions); all were shown with repeated measurements of FPG/WC. Additionally, in our study, the dynamic models improve the risk reclassification (net reclassification index 33% for FPG and 24% for WC model).

CONCLUSION

Dynamic prediction models, compared with the static one could yield significant improvements in the prediction of T2DM. The complexity of the dynamic models could be addressed by using decision support systems.

Regression models for interval censored data using parametric pseudo-observations

Background

Time-to-event data that is subject to interval censoring is common in the practice of medical research and versatile statistical methods for estimating associations in such settings have been limited. For right censored data, non-parametric pseudo-observations have been proposed as a basis for regression modeling with the possibility to use different association measures. In this article, we propose a method for calculating pseudo-observations for interval censored data.

Methods

We develop an extension of a recently developed set of parametric pseudo-observations based on a spline-based flexible parametric estimator. The inherent competing risk issue with an interval censored event of interest necessitates the use of an illness-death model, and we formulate our method within this framework. To evaluate the empirical properties of the proposed method, we perform a simulation study and calculate pseudo-observations based on our method as well as alternative approaches. We also present an analysis of a real dataset on patients with implantable cardioverter-defibrillators who are monitored for the occurrence of a particular type of device failures by routine follow-up examinations. In this dataset, we have information on exact event times as well as the interval censored data, so we can compare analyses of pseudo-observations based on the interval censored data to those obtained using the non-parametric pseudo-observations for right censored data.

Results

Our simulations show that the proposed method for calculating pseudo-observations provides unbiased estimates of the cumulative incidence function as well as associations with exposure variables with appropriate coverage probabilities. The analysis of the real dataset also suggests that our method provides estimates which are in agreement with estimates obtained from the right censored data.

Conclusions

The proposed method for calculating pseudo-observations based on the flexible parametric approach provides a versatile solution to the specific challenges that arise with interval censored data. This solution allows regression modeling using a range of different association measures.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12874-021-01227-8).

Estimating trends in the incidence rate with interval censored data and time-dependent covariates

We propose a multiple imputation method for estimating the incidence rate with interval censored data and time-dependent (and/or time-independent) covariates. The method has two stages. First, we use a semi-parametric G-transformation model to estimate the cumulative baseline hazard function and the effects of the time-dependent (and/or time-independent covariates) on the interval censored infection times. Second, we derive the participant's unique cumulative distribution function and impute infection times conditional on the covariate values. To assess performance, we simulated infection times from a Cox proportional hazards model and induced interval censoring by varying the testing rate, e.g., participants test 100%, 75%, 50% of the time, etc. We then compared the incidence rate estimates from our G-imputation approach with single random-point and mid-point imputation. By comparison, our G-imputation approach gave more accurate incidence rate estimates and appropriate standard errors for models with time-independent covariates only, time-dependent covariates only, and a mixture of time-dependent and time-independent covariates across various testing rates. We demonstrate, for the first time, a multiple imputation approach for incidence rate estimation with interval censored data and time-dependent (and/or time-independent) covariates.

Shelf-life dating of shelf-stable strawberry juice based on survival analysis of consumer acceptance information

BACKGROUND

Accurate shelf-life dating of food products is crucial for consumers and industries. Therefore, in this study we applied a science-based approach for shelf-life assessment, including accelerated shelf-life testing (ASLT), acceptability testing and the screening of analytical attributes for fast shelf-life predictions. Shelf-stable strawberry juice was selected as a case study.

RESULTS

Ambient storage (20 °C) had no effect on the aroma-based acceptance of strawberry juice. The colour-based acceptability decreased during storage under ambient and accelerated (28-42 °C) conditions. The application of survival analysis showed that the colour-based shelf-life was reached in the early stages of storage (≤11 weeks) and that the shelf-life was shortened at higher temperatures. None of the selected attributes (a^* and ΔE^* value, anthocyanin and ascorbic acid content) is an ideal analytical marker for shelf-life predictions in the investigated temperature range (20-42 °C). Nevertheless, an overall analytical cut-off value over the whole temperature range can be selected.

CONCLUSIONS

Colour changes of strawberry juice during storage are shelf-life limiting. Combining ASLT with acceptability testing allowed to gain faster insight into the change in colour-based acceptability and to perform shelf-life predictions relying on scientific data. An analytical marker is a convenient tool for shelf-life predictions in the context of ASLT. © 2017 Society of Chemical Industry.

Punch biopsies shorten time to clearance of high-risk human papillomavirus infections of the uterine cervix

Background

The primary objective was to determine human papilloma virus (HPV) clearance rate after cervical biopsy among women with persistent high-risk HPV infection compared with spontaneous HPV clearance rate in the absence of biopsy.

Methods

We collected data from a dedicated screening program of women aged 30–70 years old. Inclusion criteria for the baseline non-interventional cohort were a positive HPV test (hybrid capture 2, HC2) and normal cytology. In the baseline cohort women were followed with approximately yearly HPV-tests and cytology until HPV regressed (one negative HPV test) or interventions in the form of diagnostic biopsies or therapy. Women who had a diagnostic biopsy were included in the biopsy cohort and followed until HPV regression or therapy. Observed HPV regression rates and time to HPV regression were compared between baseline and biopsy cohorts. For the comparison, we used Fisher’s exact test for the HPV regression rates and interval-censored, accelerated failure time model for time to HPV regression.

Results

Among the 1079 women included in the baseline cohort, 499 (46.3%) had HPV clearance and 475 were referred for colposcopy with biopsy. The biopsy cohort comprised all women who were not treated and had at least one HC2 test after biopsy (201/475; 42.3%). Of those, 138 (68.7%) experienced HPV regression. In the biopsy cohort, time to clearance of HPV infection was approximately halved (0.46, 95% CI 0.38–0.56) compared with the baseline cohort. This result was robust in a wide range of sensitivity analyses.

Conclusions

A higher proportion of women cleared their HPV infection, and time to HPV clearance was shorter in the biopsy cohort than in the baseline cohort. It is reassuring for clinicians to know that conservative management of patients with HPV persistency is successful when colposcopy with biopsies excludes high-grade disease.

Incidence rate estimation, periodic testing and the limitations of the mid-point imputation approach

Background

It is common to use the mid-point between the latest-negative and earliest-positive test dates as the date of the infection event. However, the accuracy of the mid-point method has yet to be systematically quantified for incidence studies once participants start to miss their scheduled test dates.

Methods

We used a simulation-based approach to generate an infectious disease epidemic for an incidence cohort with a high (80-100%), moderate (60-79.9%), low (40-59.9%) and poor (30-39.9%) testing rate. Next, we imputed a mid-point and random-point value between the participant's latest-negative and earliest-positive test dates. We then compared the incidence rate derived from these imputed values with the true incidence rate generated from the simulation model.

Results

The mid-point incidence rate estimates erroneously declined towards the end of the observation period once the testing rate dropped below 80%. This decline was in error of approximately 9%, 27% and 41% for a moderate, low and poor testing rate, respectively. The random-point method did not introduce any systematic bias in the incidence rate estimate, even for testing rates as low as 30%.

Conclusions

The mid-point assumption of the infection date is unjustified and should not be used to calculate the incidence rate once participants start to miss the scheduled test dates. Under these conditions, we show an artefactual decline in the incidence rate towards the end of the observation period. Alternatively, the single random-point method is straightforward to implement and produces estimates very close to the true incidence rate.

Statistical methods to derive efficacy estimates of anti-malarials for uncomplicated Plasmodium falciparum malaria: pitfalls and challenges

The Kaplan-Meier (K-M) method is currently the preferred approach to derive an efficacy estimate from anti-malarial trial data. In this approach event times are assumed to be continuous and estimates are generated on the assumption that there is only one cause of failure. In reality, failures are captured at pre-scheduled time points and patients can fail treatment due to a variety of causes other than the primary endpoint, commonly termed competing risk events. Ignoring these underlying assumptions can potentially distort the derived efficacy estimates and result in misleading conclusions. This review details the evolution of statistical methods used to derive anti-malarial efficacy for uncomplicated Plasmodium falciparum malaria and assesses the limitations of the current practices. Alternative approaches are explored and their implementation is discussed using example data from a large multi-site study.

Electrocardiographic Abnormalities and Treatment with Benznidazole among Children with Chronic Infection by Trypanosoma cruzi: A Retrospective Cohort Study

Background

Chronic infection by Trypanosoma cruzi could cause heart conduction disturbances. We sought to analyze electrocardiographic abnormalities among children with chronic T. cruzi infection with and without trypanocidal treatment with benznidazole.

Methodology/Principal Findings

We studied 111 children 6–16 years of age with asymptomatic chronic T. cruzi infection who were recruited in 1991–1992 in Salta, Argentina. Most children were randomly assigned to benznidazole 5 mg/Kg/day (n = 47) or matching placebo (n = 48) for 60 days. Remaining children (n = 16) received treatment with benznidazole 5 mg/Kg/day open-label. Electrocardiograms were obtained at baseline and in 1995–1996, 1998, 2000 and 2005, and were analyzed using the Buenos Aires method. Among the 94 children with an electrocardiogram at baseline, 8 (8.5%) had electrocardiographic abnormalities, including 4 (4.7%) children with right bundle branch block. Proportion of abnormal electrocardiograms in the full population (n = 111) remained constant over time (media follow-up 8.6 years). Multivariable adjusted prevalence ratios (95% confidence interval [95%CI]) for electrocardiographic abnormalities in 1995–1996, 1998, 2000 and 2005 comparing children treated with benznidazole versus those not treated were 2.76 (0.66, 11.60), 2.33 (0.44, 12.31), 3.06 (0.48, 19.56), and 1.94 (0.33, 11.25), respectively. Among the 86 children with a normal electrocardiogram at baseline, 16 (18.6%) developed electrocardiographic abnormalities during follow-up. The multivariable adjusted hazard ratio for incident electrocardiographic abnormalities comparing children treated with benznidazole versus those not treated was 0.68 (95%CI: 0.25, 1.88).

Conclusions/Significance

Electrocardiographic abnormalities are frequent among children with chronic T. cruzi infection. Treatment with benznidazole for 60 days may not be associated with less electrocardiographic abnormalities.

There are few data available on the natural history of electrocardiographic abnormalities among children with chronic Trypanosoma cruzi infection. Also, few studies analyzed the effect of benznidazole to prevent electrocardiographic abnormalities in this population. In the current study, electrocardiographic abnormalities were frequent among children with chronic T. cruzi infection. Results from the current study also suggest that treatment with benznidazole may not be associated with less electrocardiographic abnormalities. The current study highlights the need of further research to prevent cardiovascular manifestations associated with chronic T. cruzi infection.

Cut-off value of total adiponectin for managing risk of developing metabolic syndrome in male Japanese workers

Aim

To determine the optimal cut-off value of serum total adiponectin for managing the risk of developing metabolic syndrome (MetS) in male Japanese workers.

Methods

A total of 365 subjects without MetS aged 20–60 years were followed up prospectively for a mean of 3.1 years. The accelerated failure-time model was used to estimate time ratio (TR) and cut-off value for developing MetS.

Results

During follow-up, 45 subjects developed MetS. Age-adjusted TR significantly declined with decreasing total adiponectin level (≤ 4.9, 5.0–6.6, 6.7–8.8 and ≥ 8.9 μg/ml, P for trend = 0.003). In multivariate analyses, TR of MetS was 0.12 (95% CI 0.02–0.78; P = 0.03) in subjects with total adiponectin level of 5.0–6.6 μg/ml, and 0.15 (95% CI 0.02–0.97; P = 0.047) in subjects with total adiponectin level ≤ 4.9 μg/ml compared with those with total adiponectin level ≥ 8.9 μg/ml. The accelerated failure-time model showed that the optimal cut-off value of total adiponectin for managing the risk of developing MetS was 6.2 μg/ml. In the multivariate-adjusted model, the mean time to the development of MetS was 78% shorter for total adiponectin level ≤ 6.2 μg/ml compared with > 6.2 μg/ml (TR 0.22, 95% CI: 0.08–0.64, P = 0.005).

Conclusion

Our findings suggest that the cut-off value for managing the risk of developing MetS is 6.2 μg/ml in male Japanese workers. Subjects with total adiponectin level ≤ 6.2 μg/ml developed MetS more rapidly than did those with total adiponectin level > 6.2 μg/ml.