Flexible Parametric Proportional Hazards Model Research Articles

Prognostic Value of the Pre-Treatment Prognostic Nutritional Index for Patients with Unresectable Locally-Advanced and Advanced Stage Upper Gastrointestinal Tract Cancer

PurposeThe prognostic nutritional index (PNI) is used to distinguish immune-nutritional status. Previous studies have shown that it is significantly associated with patient outcomes for various malignancies. This study aimed to evaluate the prognostic impact of PNI in patients with unresectable locally-advanced and advanced stage upper gastrointestinal tract cancer, including esophageal cancer and gastric cancer.MethodsA retrospective study of 170 unresectable stage III–IV esophageal cancer and gastric cancer patients was conducted from January 2018 to December 2020. In our retrospective analysis, the pretreatment PNI of patients was calculated and analyzed. The Youden index was estimated to select the optimal cut-off value for PNI. Univariate and multivariate flexible parametric proportional hazards models with restricted cubic splines (RCS) were used to identify independent prognostic factors, and the Kaplan–Meier method was used to estimate survival curves.ResultsThe median follow-up period was 5 months (ranging from 0.06 to 36.92 months). We determined 52.9 as the cut-off value by using the maximum Youden index. Subsequently, patients in the testing group were classified into high PNI and low PNI groups. Kaplan–Meier curves showed the low PNI group had significantly poorer overall survival (OS) than the high PNI group. Median OS in the low PNI group was 4.43 months compared with 8.23 months in the high PNI group (HR 2.42, 95% CI 1.33–4.40, p = 0.004). In the univariate analysis, low PNI, ECOG PS 2, and ECOG PS 3–4 were associated with OS. According to multivariate analysis, low PNI was an independent prognostic factor for OS (HR 2.31, 95% CI 1.24–4.29, p = 0.008).ConclusionPretreatment PNI is useful for independent prognosis of unresectable stage III–IV esophageal cancer and gastric cancer in patients.

Fatty liver disease and changes in dense breasts in pre- and postmenopausal women: the Kangbuk Samsung Health Study.

While increased breast density is a risk factor for breast cancer, the effect of fatty liver disease on breast density is unknown. We investigated whether fatty liver is a risk factor for changes in breast density over ~ 4years of follow-up in pre- and postmenopausal women. This study included 74,781 middle-aged Korean women with mammographically determined dense breasts at baseline. Changes in dense breasts were identified by more screening mammograms during follow-up. Hepatic steatosis (HS) was measured using ultrasonography. Flexible parametric proportional hazards models were used to determine the adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs), and a Weibull accelerated failure time model (AFT) was used to determine the time ratios (TRs) and 95% CIs. During a median follow-up of 4.1years, 4022 women experienced resolution of the dense breasts. The association between HS and dense breast resolution differed by the menopause status (P for interaction < 0.001). After adjusting for body mass index and other covariates, the aHRs (95% CI) for dense breast resolution comparing HS to non-HS were 0.81 (0.70-0.93) in postmenopausal women, while the association was converse in premenopausal women with the corresponding HRs of 1.30 (1.18-1.43). As an alternative approach, the multivariable-adjusted TR (95% CI) for dense breast survival comparing HS to non-HS were 0.81 (0.75-0.87) and 1.19 (1.06-1.33) in premenopausal and postmenopausal women, respectively. The association between HS and changes in dense breasts differed with the menopause status. HS increased persistent dense breast survival in postmenopausal women but decreased it in premenopausal women.

The association of occupational stress and sleep duration with anxiety symptoms among healthy employees: A cohort study.

The aim of this study is to identify occupational stress associated with the development of new-onset anxiety symptoms and the dose-response relationship between sleep duration and the onset of anxiety symptoms. Data from 29,251 healthy employees who had undergone at least two comprehensive health examinations at Kangbuk Samsung Hospital Health Screening Center were analysed. Anxiety symptoms were assessed using the Beck Anxiety Inventory. Occupational stress and sleep duration were measured using a self-reported questionnaire about total sleep time and the Korean Occupational Stress Scale-Short Form (KOSS-SF), respectively. Flexible parametric proportional hazards model used to estimate the hazard ratios. Compared with the groups without case-level anxiety, discomfort in an organizational climate, high job demands, job insecurity, organizational injustice and lack of reward were associated with the onset of case-level anxiety. Compared with less than 6 hr of sleep per day, the beneficial level of sleep duration was 7 ≤ to <9 hr a day. Almost all subscales of job stress were associated with the development of anxiety symptoms. In addition, the efficacious level of sleep duration for reducing the onset of future anxiety symptoms was 7 ≤ to <9 hr a day.

Predictors of faster virological suppression in early treated infants with perinatal HIV from Europe and Thailand.

To identify predictors of faster time to virological suppression among infants starting combination antiretroviral therapy (cART) early in infancy. Cohort study of infants from Europe and Thailand included in studies participating in the European Pregnancy and Paediatric HIV Cohort Collaboration. Infants with perinatal HIV starting cART aged less than 6 months with at least 1 viral load measurement within 15 months of cART initiation were included. Multivariable interval-censored flexible parametric proportional hazards models were used to assess predictors of faster virological suppression, with timing of suppression assumed to lie in the interval between last viral load at least 400 and first viral load less than 400 copies/ml. Of 420 infants, 59% were female and 56% from Central/Western Europe, 26% United Kingdom/Ireland, 15% Eastern Europe and 3% Thailand; 46 and 54% started a boosted protease inhibitor-based or nonnucleoside reverse transcriptase inhibitor-based regimen, respectively. At cART initiation, the median age, CD4% and viral load were 2.9 [interquartile range (IQR): 1.4-4.1] months, 34 (IQR: 24-45)% and 5.5 (IQR: 4.5-6.0) log10 copies/ml, respectively. Overall, an estimated 89% (95% confidence interval: 86-92%) achieved virological suppression within 12 months of cART start. In multivariable analysis, younger age [adjusted hazard ratio (aHR): 0.84 per month older; P < 0.001], higher CD4% (aHR: 1.11 per 10% higher; P = 0.010) and lower log10 viral load (aHR: 0.85 per log10 higher; P < 0.001) at cART initiation independently predicted faster virological suppression. We observed a significant independent effect of age at cART initiation, even within a narrow 6 months window from birth. These findings support the earliest feasible cART initiation in infants and suggest that early therapy influences key virological and immunological parameters that could have important consequences for long-term health.

Reliability-based covariate analysis for complex systems in heterogeneous environment: Case study of mining equipment

Operational heterogeneity and harsh environment lead to major variations in production system performance and safety. Traditional probabilistic model is dealt with time-to-event data analysis, which does not have the capability of quantifying and simulation of these types of complexities. This research proposes an integrated methodology for analyzing the impact of dominant explanatory variables on the complex system reliability. A flexible parametric proportional hazards model is developed by focusing on standard parametric Cox regression model for reliability evaluation in complex systems. To achieve this, natural cubic splines are utilized to create a smooth and flexible baseline hazards function where the standard parametric distribution functions do not fit into the failure data set. A real case study is considered to evaluate the reliability for multi-component mechanical systems such as mining equipment. Different operational and environmental explanatory variables are chosen for the analysis process. Research findings revealed that precise estimation of the baseline hazards function is a major part of the reliability evaluation in heterogeneous environment. It is concluded that an appropriate maintenance strategy potentially mitigate the equipment failure intensity.

Comparison of the flexible parametric survival model and Cox model in estimating Markov transition probabilities using real-world data.

Background and objectiveMarkov micro-simulation models are being increasingly used in health economic evaluations. An important feature of the Markov micro-simulation model is its ability to consider transition probabilities of heterogeneous subgroups with different risk profiles. A survival analysis is generally performed to accurately estimate the transition probabilities associated with the risk profiles. This study aimed to apply a flexible parametric survival model (FPSM) to estimate individual transition probabilities.Materials and methodsThe data were obtained from a cohort study investigating ischemic stroke outcomes in Western China. In total, 585 subjects were included in the analysis. To explore the goodness of fit of the FPSM, we compared the estimated hazard ratios and baseline cumulative hazards, both of which are necessary to the calculate individual transition probabilities, and the Markov micro-simulation models constructed using the FPSM and Cox model to determine the validity of the two Markov micro-simulation models and cost-effectiveness results.ResultsThe flexible parametric proportional hazards model produced hazard ratio and baseline cumulative hazard estimates that were similar to those obtained using the Cox proportional hazards model. The simulated cumulative incidence of recurrent ischemic stroke and 5-years cost-effectiveness of Incremental cost-effectiveness Ratios (ICERs) were also similar using the two approaches. A discrepancy in the results was evident between the 5-years cost-effectiveness and the 10-years cost-effectiveness of ICERs, which were approximately 0.9 million (China Yuan) and 0.5 million (China Yuan), respectively.ConclusionsThe flexible parametric survival model represents a good approach for estimating individual transition probabilities for a Markov micro-simulation model.

Estimating excess hazard ratios and net survival when covariate data are missing: strategies for multiple imputation.

Net survival is the survival probability we would observe if the disease under study were the only cause of death. When estimated from routinely collected population-based cancer registry data, this indicator is a key metric for cancer control. Unfortunately, such data typically contain a non-negligible proportion of missing values on important prognostic factors (eg, tumor stage). We carried out an empirical study to compare the performance of complete records analysis and several multiple imputation strategies when net survival is estimated via a flexible parametric proportional hazards model that includes stage, a partially observed categorical covariate. Starting from fully observed cancer registry data, we induced missingness on stage under three scenarios. For each of these scenarios, we simulated 100 incomplete datasets and evaluated the performance of the different strategies. Ordinal logistic models are not suitable for the imputation of tumor stage. Complete records analysis may lead to grossly misleading estimates of net survival, even when the missing data mechanism is conditionally independent of survival time given the covariates and the bias on the excess hazard ratios estimates is negligible. As key covariates are unlikely missing completely at random, studies estimating net survival should not use complete records. When the missingness can be inferred from available data, appropriate multiple imputation should be performed. In the context of flexible parametric proportional hazards models with a partially observed stage covariate, a multinomial logistic imputation model for stage should be used and should include the Nelson-Aalen cumulative hazard estimate and the event indicator.

Cholesteatoma in cleft lip and palate: A population‐based follow‐up study of children after ventilation tubes

To investigate the association of cleft conditions and the development of secondary cholesteatoma following middle ear ventilation tube insertion (MEVTI) in children. A retrospective cohort study of all children born after 1980 who underwent at least one MEVTI in a Western Australian hospital from 1980 to 2009 using administrative health data. The timing and number of MEVTIs, adenoidectomy, type of cleft conditions and cholesteatoma were identified along with demographic variables. Flexible parametric proportional hazards models and hazard functions using age as a time scale were used to estimate the relative rate of developing cholesteatoma. There were 56,949 children who underwent at least one MEVTI during the study period. There were 869 (1.5%) children who also had a diagnosis of a cleft condition. Overall, 594 (1.0%) children developed a secondary cholesteatoma. After taking length of follow-up into account, 6.9% (95% confidence interval [CI], 5.0-9.6) of children with cleft conditions developed cholesteatoma by 18 years of age compared to 1.5% (95% CI, 1.3-1.6) of children without cleft conditions. After adjusting for demographic and clinical variables, children with cleft conditions developed cholesteatoma 7.5 (95% CI, 3.8-18.2) times faster after first MEVTI compared to children without cleft conditions, although by the third MEVTI this difference was no longer statistically significant (P = .257). The rate of developing cholesteatoma in 2005 to 2009 was 0.5 (95% CI, 0.3-0.8) times that of 1980 to 1990. Children with cleft conditions are at increased risk of developing cholesteatoma compared to other children who had one or two MEVTIs, although the overall rate of cholesteatoma is declining.

Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions

BackgroundCompeting risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening.MethodsThere are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework.ResultsAn illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects.ConclusionA key advantage of using this approach is that smooth estimates of both the cause-specific hazard rates and the cumulative incidence functions can be obtained. It is also relatively easy to incorporate time-dependent effects which are commonly seen in epidemiological studies.

Flexible parametric estimation of duration and competing risk models

AbstractIn this paper we specify and estimate a flexible parametric proportional hazards model. The model specification is flexibly parametric in the sense that the baseline hazard is non parametric while the effect of the covariates takes a particular functional form. We also add parametric heterogeneity to the underlying hazard model specification. We specify a flexible parametric proportional competing risks model which permits unrestricted correlation among the risks. Unemployment duration data are then analysed using the flexible parametric duration and competing risks specifications. We find an important effect arising from the exhaustion of unemployment insurance and significantly different hazards for the two types of risks, new jobs and recalls.