Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score

BACKGROUND

Pneumonia poses a major global health challenge, necessitating accurate severity assessment tools. However, conventional scoring systems such as CURB-65 have inherent limitations. Machine learning (ML) offers a promising approach for prediction. We previously introduced the Blood Culture Prediction Index (BCPI) model, leveraging solely on complete blood count (CBC) and differential leukocyte count (DC), demonstrating its effectiveness in predicting bacteremia. Nevertheless, its potential in assessing pneumonia remains unexplored. Therefore, this study aims to compare the effectiveness of BCPI and CURB-65 in assessing pneumonia severity in an emergency department (ED) setting and develop an integrated ML model to enhance efficiency.

METHODS

This retrospective study was conducted at a 3400-bed tertiary medical center in Taiwan. Data from 9,352 patients with pneumonia in the ED between 2019 and 2021 were analyzed in this study. We utilized the BCPI model, which was trained on CBC/DC data, and computed CURB-65 scores for each patient to compare their prognosis prediction capabilities. Subsequently, we developed a novel Cox regression model to predict in-hospital mortality, integrating the BCPI model and CURB-65 scores, aiming to assess whether this integration enhances predictive performance.

RESULTS

The predictive performance of the BCPI model and CURB-65 score for the 30-day mortality rate in ED patients and the in-hospital mortality rate among admitted patients was comparable across all risk categories. However, the Cox regression model demonstrated an improved area under the ROC curve (AUC) of 0.713 than that of CURB-65 (0.668) for in-hospital mortality (p<0.001). In the lowest risk group (CURB-65=0), the Cox regression model outperformed CURB-65, with a significantly lower mortality rate (2.9% vs. 7.7%, p<0.001).

CONCLUSIONS

The BCPI model, constructed using CBC/DC data and ML techniques, performs comparably to the widely utilized CURB-65 in predicting outcomes for patients with pneumonia in the ED. Furthermore, by integrating the CURB-65 score and BCPI model into a Cox regression model, we demonstrated improved prediction capabilities, particularly for low-risk patients. Given its simple parameters and easy training process, the Cox regression model may be a more effective prediction tool for classifying patients with pneumonia in the emergency room.

Effect of marital status on the survival outcomes of cervical cancer: a retrospective cohort study based on SEER database

BACKGROUND

Cervical cancer is the fourth most common malignant tumor troubling women worldwide. Whether marital status affects the prognosis of cervical cancer is still unclear. Here, we investigate the prognostic value of marital status in patients with cervical cancer based on the seer database.

MATERIAL/METHODS

The demographic and clinical data of patients with cervical cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1975 to 2017. Patients were divided into two groups (married and unmarried) according to marital status, and then the clinical characteristics of each group were compared using the chi-square test. Propensity score matching (PSM) was used to reduce differences in baseline characteristics. The overall survival (OS) and cervical cancer-specific survival (CCSS) were assessed by the Kaplan-Meier method, univariate and multivariate Cox regression models, and stratified analysis. Moreover, univariate and multivariate competing risk regression models were performed to calculate hazard ratios (HR) of death risk.

RESULTS

A total of 21,148 patients were included in this study, including 10,603 married patients and 10,545 unmarried patients. Married patients had better OS(P < 0.05) and CCSS (P < 0.05) compared to unmarried patients, and marital status was an independent prognostic factor for both OS (HR: 0.830, 95% CI: 0.798-0.862) and CCSS (HR: 0.892, 95% CI: 0.850-0.937). Moreover, after eliminating the competing risk, married patients (CCSD: HR:0.723, 95% CI: 0.683-0.765, P < 0.001) had a significantly decreased risk of death compared to unmarried patients. In stratified analysis, the married patients showed better OS and CCSS than the unmarried patients diagnosed in 1975-2000 and 2001-2017.

CONCLUSIONS

Being married was associated with a favorable prognosis of cervical cancer, and marital status was an independent prognostic factor for cervical cancer.

Magnetocardiography at rest predicts cardiac death in patients with acute chest pain

Introduction

Sudden cardiac arrest is a major cause of morbidity and mortality worldwide and remains a major public health problem for which better non-invasive prediction tools are needed. Primary preventive therapies, such as implantable cardioverter defibrillators, are not personalized and not predictive. Most of these devices do not deliver life-saving therapy during their lifetime. The individual relationship between fatal arrhythmias and cardiac function abnormalities in predicting cardiac death risk has rarely been explored.

Methods

We retrospectively analyzed the measurements at rest for 191 patients with acute chest pain (ACP) magnetocardiographically. Our recently introduced analyses are able to detect inhomogeneities of the depolarization and repolarization. Moreover, electrically silent phenomena-intracellular ionic currents as well as vortex currents-can be measured and quantified. All included ACP patients were recruited in 2009 at Yonsei University Hospital and were followed up until 2022.

Results

During half of the follow-up period (6.5 years), 11 patients died. Out of all the included nine clinical, eight magnetocardiographical, and nine newly introduced magnetoionographical parameters we tested in this study, three parameters revealed themselves to be outstanding at predicting death: heart rate-corrected QT (QTc) prolongation, depression of repolarization current IKr + IKs, and serum creatinine (all significant in Cox regression, p < 0.05). They clearly predicted cardiac death over the 6.5 years duration (sensitivity 90.9%, specificity 85.6%, negative predictive accuracy 99.4%). Cardiac death risk was more than ninefold higher in patients with low repolarization reserve and QTc prolongation in comparison with the remaining patients with ACP (p < 0.001). The non-parametric Kaplan-Meier statistics estimated significantly lower survival functions from their lifetime data (p < 0.001).

Discussion

To the best of our knowledge, these are the first data linking magnetocardiographical and magnetoionographical parameters and subsequent significant fatal events in people, suggesting structural and functional components to clinical life-threatening ventricular arrhythmogenesis. The findings support investigation of new prevention strategies and herald those new non-invasive techniques as complementary risk stratification tools.

Seven oxidative stress-related genes predict the prognosis of hepatocellular carcinoma

Predicting the prognosis of hepatocellular carcinoma (HCC) is a major medical challenge and of guiding significance for treatment. This study explored the actual relevance of RNA expression in predicting HCC prognosis. Cox's multiple regression was used to establish a risk score staging classification and to predict the HCC patients' prognosis on the basis of data in the Cancer Genome Atlas (TCGA). We screened seven gene biomarkers related to the prognosis of HCC from the perspective of oxidative stress, including Alpha-Enolase 1(ENO1), N-myc downstream-regulated gene 1 (NDRG1), nucleophosmin (NPM1), metallothionein-3, H2A histone family member X, Thioredoxin reductase 1 (TXNRD1) and interleukin 33 (IL-33). Among them we measured the expression of ENO1, NGDP1, NPM1, TXNRD1 and IL-33 to investigate the reliability of the multi-index prediction. The first four markers' expressions increased successively in the paracellular tissues, the hepatocellular carcinoma samples (from patients with better prognosis) and the hepatocellular carcinoma samples (from patients with poor prognosis), while IL-33 showed the opposite trend. The seven genes increased the sensitivity and specificity of the predictive model, resulting in a significant increase in overall confidence. Compared with the patients with higher-risk scores, the survival rates with lower-risk scores are significantly increased. Risk score is more accurate in predicting the prognosis HCC patients than other clinical factors. In conclusion, we use the Cox regression model to identify seven oxidative stress-related genes, investigate the reliability of the multi-index prediction, and develop a risk staging model for predicting the prognosis of HCC patients and guiding precise treatment strategy.

Influencing factors of death in patients with MDR-TB based on Bayesian Cox regression model

OBJECTIVES

Multidrug-resistant tuberculosis (MDR-TB) has a high mortality and is always one of the major challenges in global TB prevention and control. Analyzing the factors that may impact the adverse outcomes of MDR-TB patients is helpful for improving the systematic management and optimizing the treatment strategies for MDR-TB patients. For follow-up data, the Cox proportional hazards regression model is an important multifactor analysis method. However, the method has significant limitations in its application, such as the fact that it is difficult to deal with the impacts of small sample sizes and other practical issues on the model. Therefore, Bayesian and conventional Cox regression models were both used in this study to analyze the influencing factors of death in MDR-TB patients during the anti-TB therapy, and compare the differences between these 2 methods in their application.

METHODS

Data were obtained from 388 MDR-TB patients treated at Lanzhou Pulmonary Hospital from November 1, 2017 to March 31, 2021. Survival analysis was employed to analyze the death of MDR-TB patients during the therapy and its influencing factors. Conventional and Bayesian Cox regression models were established to estimate the hazard ratios (HR) and their 95% confidence interval (95% CI) for the factors affecting the death of MDR-TB patients. The reliability of parameter estimation in these 2 models was assessed by comparing the parameter standard deviation and 95% CI of each variable. The smaller parameter standard deviation and narrower 95% CI range indicated the more reliable parameter estimation.

RESULTS

The median survival time (1st quartile, 3rd quartile) of the 388 MDR-TB patients included in the study was 10.18 (4.26, 18.13) months, with the longest survival time of 31.90 months. Among these patients, a total of 12 individuals died of MDR-TB and the mortality was 3.1%. The median survival time (1st quartile, 3rd quartile) for the deceased patients was 4.78(2.63, 6.93) months. The majority of deceased patients, accounting for 50%, experienced death within the first 5 months of anti-TB therapy, with the last mortality case occurring within the 13th month of therapy. The results of the conventional Cox regression model showed that the risk of death in MDR-TB patients with comorbidities was approximately 6.96 times higher than that of patients without complications (HR=6.96, 95% CI 2.00 to 24.24, P=0.002) and patients who received regular follow-up had a decrease in the risk of death by approximately 81% compared to those who did not receive regular follow-up (HR=0.19, 95% CI 0.05 to 0.77, P=0.020). In the results of Bayesian Cox regression model, the iterative history plot and Blue/Green/Red (BGR) plot for each parameter showed the good model convergence, and parameter estimation indicated that the risk of death in patients with a positive first sputum culture was lower than that of patients with a negative first sputum culture (HR=0.33, 95% CI 0.08 to 0.87). Additionally, compared to patients without complications, those with comorbidities had an approximately 6.80-fold increase in the risk of death (HR=7.80, 95% CI 1.90 to 21.91). Patients who received regular follow-up had a 90% reduction in the risk of death compared to those who did not receive regular follow-up (HR=0.10, 95% CI 0.01 to 0.30). The comparison between these 2 models showed that the parameter standard deviations and corresponding 95% CI ranges of other variables in the Bayesian Cox model were significantly smaller than those in the conventional model, except for parameter standard deviations of receiving regular follow-up (Bayesian model was 0.77; conventional model was 0.72) and pulmonary cavities (Bayesian model was 0.73; conventional model was 0.73).

CONCLUSIONS

The first year of anti-TB therapy is a high-risk period for mortality in MDR-TB patients. Complications are the main risk factors of death in MDR-TB patients, while patients who received regular follow-up and had positive first sputum culture presented a lower risk of death. For data with a small sample size and low incidence of outcome, the Bayesian Cox regression model provides more reliable parameter estimation than the conventional Cox model.

Survival times of HIV/AIDS in different AIDS Diagnostic and Treatment Guidelines from 2006 to 2020 in Liuzhou, China

BACKGROUND

To compare the survival rates of four timing of treatment initiation for people living with HIV/AIDS provided in China in 2006, 2011, 2015, and 2018, and to investigate the factors impacting survival time.

METHODS

A people living with HIV/AIDS retrospective cohort study was in Liuzhou City from April 2006 to December 2020. The information was obtained from the National Comprehensive AIDS Prevention and Control Information System. Life tables and the Kaplan-Meier method were used to calculate participant survival rates and time. The univariate and multivariate Cox regression models were used to investigate the factors related to survival.

RESULTS

18,543 participants were included in this study. In four periods, the 1-year survival rates were 81%, 87%, 95%, and 95%. The 2-year survival rates were 76%, 85%, 93%, and 94%. The 3-year survival rates were 73%, 84%, 92%, and 94%. Results of multivariate Cox regression showed that sex, age of HIV diagnosis, ethnicity, household registration, occupation, marital status, the timing of treatment, education level, route of HIV transmission, whether receiving antiretroviral therapy (ART), and the count of CD4+T cells at baseline (count of CD4+T cells at HIV diagnosis) were factors that are significantly correlated with mortality caused by HIV infection.

CONCLUSIONS

With the Guidelines updated from 2006 to 2020, the 1-, 2-, and 3-year survival rates of people living with HIV/AIDS in four periods tended to increase. The timing of treatment initiation of the updated edition of the AIDS Diagnostic and Treatment Guidelines (Guidelines) significantly prolonged the survival time of people living with HIV/AIDS.

Predictive value of triglycerides to high-density lipoprotein cholesterol and triglyceride glycemic index for diabetes incidence in pre-diabetes patients: a prospective cohort study

BACKGROUND

The triglyceride glucose (TyG) and triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-c) are the indices that can predict the progression of pre-diabetes to type 2 diabetes mellitus (T2DM). This study aimed to examine the relationship between TyG and TG/HDL-c indices with the incidence of T2DM in pre-diabetes patients.

METHODS

A total of 758 pre-diabetic patients aged 35-70 years who were enrolled in a prospective Fasa Persian Adult Cohort were followed up for 60 months. TyG and TG/HDL-C indices were obtained at baseline data and divided into quartiles. The 5-year cumulative incidence of T2DM was analyzed by Cox proportional hazards regression analysis while controlling for baseline covariates.

RESULTS

During 5 years of follow-up, there were 95 incident cases of T2DM, with an overall incidence rate of 12.53%. After adjusting for age, sex, smoking, marital status, socioeconomic status, body mass index, waist circumference, hip circumference, hypertension, total cholesterol, and dyslipidemia, the multivariate-adjusted hazard ratios (HRs) demonstrated that patients with the highest TyG and TG/HDL-C indices quartile were at higher risk of T2DM (HR = 4.42, 95%CI 1.75-11.21) and (HR = 2.15, 95%CI 1.04-4.47), respectively, compared to participants in the lowest quartile. As the quantiles of these indices increase, the HR value shows a significant increment (P < 0.05).

CONCLUSION

The results of our study showed that the TyG and TG/HDL-C indices can be important independent predictors for the progression of pre-diabetes to T2DM. Therefore, controlling the components of these indicators in pre-diabetes patients can prevent developing T2DM or delay its occurrence.

Influence of chronic diseases on the occurrence of depression: A 13-year follow-up study from the Survey of Health, Ageing and Retirement in Europe

The causal association between chronic diseases and depression remains unclear. This study aimed to explore the effects of types and number of chronic diseases on the risk of depression using data from the Survey of Health, Ageing and Retirement in Europe (SHARE). A self-admitted questionnaire was used to obtain data on 14 predefined chronic diseases and the European-Depression Scale (EURO-D) was used to assess depression. Among the 16,080 baseline depression-free participants aged 50+, 31.29% (5032) developed depression over 13 years. Multivariate Cox regression models showed that individuals with any chronic diseases were at higher risk of new onset depression compared to disease-free participants. The risk of new onset depression increased with an increasing number of diseases among both younger (50-64) and older (65+) adults. Individuals with heart attack, stroke, diabetes, chronic lung disease, and arthritis were at increased risk of depression across age groups. However, some age-specific associations were observed, with cancer increasing depression risk among younger- and peptic ulcer, Parkinson's disease and cataracts increasing depression risk among older adults. These findings highlight the importance of managing chronic diseases, especially among those with more than two diseases, to prevent the development of depression among middle-aged and older adults.

Demographic Factors and Instantaneous Lower Extremity Injury Occurrence in a National Collegiate Athletic Association Division I Population

CONTEXT

Temporal prediction of the lower extremity (LE) injury risk will benefit clinicians by allowing them to better leverage limited resources and target those athletes most at risk.

OBJECTIVE

To characterize the instantaneous risk of LE injury by demographic factors of sex, sport, body mass index (BMI), and injury history.

DESIGN

Descriptive epidemiologic study.

SETTING

National Collegiate Athletic Association Division I athletic program.

PATIENTS OR OTHER PARTICIPANTS

A total of 278 National Collegiate Athletic Association Division I varsity student-athletes (119 males, 159 females; age = 19.07 ± 1.21 years, height = 175.48 ± 11.06 cm, mass = 72.24 ± 12.87 kg).

MAIN OUTCOME MEASURE(S)

Injuries to the LE were tracked for 237 ± 235 consecutive days. Sex-stratified univariate Cox regression models were used to investigate the association between time to first LE injury and sport, BMI, and LE injury history. The instantaneous LE injury risk was defined as the injury risk at any given point in time after the baseline measurement. Relative risk ratios and Kaplan-Meier curves were generated. Variables identified in the univariate analysis were included in a multivariate Cox regression model.

RESULTS

Female athletes displayed similar instantaneous LE injury risk to male athletes (hazard ratio [HR] = 1.29; 95% CI= 0.91, 1.83; P = .16). Overweight athletes (BMI >25 kg/m2) had similar instantaneous LE injury risk compared with athletes with a BMI of <25 kg/m2 (HR = 1.23; 95% CI = 0.84, 1.82; P = .29). Athletes with previous LE injuries were not more likely to sustain subsequent LE injury than athletes with no previous injury (HR = 1.09; 95% CI = 0.76, 1.54; P = .64). Basketball (HR = 3.12; 95% CI = 1.51, 6.44; P = .002) and soccer (HR = 2.78; 95% CI = 1.46, 5.31; P = .002) athletes had a higher risk of LE injury than cross-country athletes. In the multivariate model, instantaneous LE injury risk was greater in female than in male athletes (HR = 1.55; 95% CI = 1.00, 2.39; P = .05), and it was greater in male athletes with a BMI of >25 kg/m2 than that in all other athletes (HR = 0.44; 95% CI = 0.19, 1.00; P = .05), but these findings were not significantly different.

CONCLUSIONS

In a collegiate athlete population, previous LE injury was not a contributor to the risk of future LE injury, whereas being female or being male with a BMI of >25 kg/m2 resulted in an increased risk of LE injury. Clinicians can use these data to extrapolate the LE injury risk occurrence to specific populations.

Mortality among twin individuals exposed to loss of a co-twin

BACKGROUND

Although the death of a child, sibling or spouse has been associated with elevated risk of mortality, less is known about the survival of twin siblings exposed to a co-twin loss.

METHODS

In a Swedish population-based sibling-matched cohort, we compared the mortality of 5548 twin individuals who lost their co-twin between 1932 and 2011 with that of 27 740 age-matched and sex-matched twin individuals without such a loss and 6772 full siblings of these exposed twin individuals. Cox regression models were used to estimate the hazard ratios (HRs) of all-cause and cause-specific mortality.

RESULTS

We found increased risk of all-cause mortality among twin individuals exposed to a co-twin loss compared with matched unexposed twin individuals (HR = 1.30, 95% CI: 1.18-1.43) and their full siblings (HR = 1.10, 95% CI: 0.96-1.27) after adjusting for multiple covariates. The all-cause mortality risk was greater for loss of a co-twin due to unnatural deaths (HR = 1.54, 95% CI: 1.17-2.03) than natural deaths (HR = 1.26, 95% CI: 1.14-1.40). For cause-specific mortality, co-twin loss was associated with a higher risk of unnatural deaths both among twin individuals who lost their co-twin due to unnatural deaths (HR = 1.98, 95% CI: 1.27-3.10) and those whose loss was due to natural deaths (HR = 1.48, 95% CI: 1.07-2.06). The risk elevations were generally stronger for loss of a monozygotic co-twin than loss of a dizygotic co-twin.

CONCLUSION

Loss of a co-twin, especially a monozygotic co-twin, was associated with increased mortality, particularly of unnatural causes, among the surviving twin individuals. The excess mortality is likely attributable to both shared disease susceptibility within the twin pair and the adverse health sequelae of bereavement.

Safety and feasibility of laparoscopic liver resection for intrahepatic cholangiocarcinoma: a propensity score-matched study

BACKGROUND

Laparoscopic liver resection (LLR) is controversial in treating intrahepatic cholangiocarcinoma (ICC). Therefore, this study aimed to evaluate the safety and feasibility of LLR for the treatment of ICC and explored the independent factors affecting the long-term prognosis of ICC.

METHODS

We included 170 patients undergoing hepatectomy for ICC from December 2010 to December 2021 and divided them into LLR group and open liver resection (OLR) group. We used propensity score matching (PSM) analysis to reduce the impact of data bias and confounding variables and then compared the short-term and long-term prognosis of LLR and OLR in treating ICC; Cox proportional hazards regression model was adopted to explore the independent factors affecting the long-term prognosis of ICC.

RESULTS

A total of 105 patients (70 in the LLR group and 35 in the OLR group) were included after 2:1 PSM analysis. There was no difference in demographic characteristics and preoperative indexes between the two groups. The perioperative results of the OLR group were worse than those of the LLR group, that is, the intraoperative blood transfusion rate (24 (68.6) vs 21 (30.0)), blood loss (500 (200-1500) vs 200 (100-525)), and the morbidity of major postoperative complications (9 (25.7) vs 6 (8.5)) in the OLR group were worse than those in LLR group. LLR could enable patients to obtain an equivalent long-term prognosis compared to OLR. The Cox proportional hazards regression model exhibited that no matter before or after PSM, preoperative serum CA12-5 and postoperative hospital stay were independent factors affecting overall survival, while only lymph node metastasis independently influenced recurrence-free survival.

CONCLUSIONS

Compared with ICC treated by OLR, the LLR group obtained superior perioperative period outcomes. In the long run, LLR could enable ICC patients to receive an equivalent long-term prognosis compared to OLR. In addition, ICC patients with preoperative abnormal CA12-5, lymph node metastasis, and more extended postoperative hospital stay might suffer from a worse long-term prognosis. However, these conclusions still need multicenter extensive sample prospective research to demonstrate.

Low Serum Vascular Endothelial Growth Factor Level Predicts Adverse Outcomes in Neonates with Respiratory Distress Syndrome

Objective: Preterm neonates are susceptible to neonatal respiratory distress syndrome (NRDS). Lower levels of cord blood vascular endothelial growth factor (VEGF) are implicated in NRDS. This study aims to explore whether the serum VEGF level has prognostic values on neonates with respiratory distress syndrome (RDS). Methods: A total of 80 infants diagnosed with NRDS were enrolled, with 70 preterm neonates without NRDS as controls. Cord blood samples before treatment and venous blood samples after treatment were collected and clinical information was recorded. The serum VEGF level was measured using enzyme-linked immunosorbent assay kits. Receiver operating characteristic (ROC) curve was used to evaluate whether serum VEGF is a biomarker of NRDS. Newborns were followed up for 1 month to collect survival information. The influence of VEGF levels on overall survival was analyzed using the Kaplan-Meier method. The univariate and multivariate Cox regression models were adopted to assess the prognostic factor of NRDS. Results: VEGF level was decreased in sera of neonates with RDS. The area under the ROC curve of VEGF level in distinguishing neonates with RDS from neonates without RDS was 0.949, with a cutoff value of 39.72 (87.50% sensitivity, 87.14% specificity). Serum VEGF was a biomarker of NRDS. Neonates with RDS with high VEGF levels had longer periods of survival than those with low VEGF levels. NRDS grade and VEGF level were independent prognostic factors affecting the overall survival of neonates with RDS. Conclusion: Decreased serum VEGF level in RDS neonates can predict the poor prognosis of NRDS, and VEGF level might be an independent prognostic factor for the overall survival of RDS neonates. Clinical Trial Registration No. 201901A.

Correlation analysis and prognostic value of miR-29a-3p expression and CYP2C19 genotypes in exfoliated cells from tongue coating of patients with gastroesophageal reflux disease

BACKGROUND

Gastroesophageal reflux disease (GERD) is a highly prevalent and troublesome disease. Several differentially expressed microRNAs (miRNAs) have been found in GERD.

OBJECTIVE

This study was to analyze the correlation of miR-29a-3p expression and CYP2C19 genotypes in exfoliated cells from tongue coating of GERD patients and its prognostic value.

METHODS

Tongue coating specimens were collected from 130 GERD patients and 70 healthy volunteers and their clinical baseline information was recorded. miR-29a-3p expression in exfoliated cells from tongue coating was determined via RT-qPCR, and its diagnostic efficiency on GERD was evaluated via receiver operating characteristic curve. CYP2C19 genotypes and their correlation with miR-29a-3p were analyzed via polymerase chain reaction restriction fragment length polymorphism technique. The adverse events of patients were documented via 12-month follow-up. The impact of miR-29a-3p expression on the healing rate of patients was analyzed via Kaplan-Meier method. Qualification of miR-29a-3p as an independent prognostic factor of GERD patients was analyzed via multivariate Cox regression analysis.

RESULTS

miR-29a-3p was highly-expressed in exfoliated cells from tongue coating of GERD patients. miR-29a-3p expression had high specificity and sensitivity in diagnosing GERD. CYP2C19 genotypes in GERD patients comprised rapid metabolizers, intermedia metabolizers, and poor metabolizers. miR-29a-3p expression showed a correlation with CYP2C19 genotypes. Higher miR-29a-3p expression predicted higher cumulative incidences of adverse outcomes. Highly-expressed miR-29a-3p was an independent prognostic factor for adverse outcomes of GERD patients.

CONCLUSION

High expression of miR-29a-3p aided the diagnosis and predicted poor prognosis of GERD patients.

Thin highly cross-linked polyethylene liners combined with large femoral heads in primary total hip arthroplasty show excellent survival and low wear rates at a mean follow-up of 12.8 years

AIMS

Several short- and mid-term studies have shown minimal liner wear of highly cross-linked polyethylene (HXLPE) in total hip arthroplasty (THA), but the safety of using thinner HXLPE liners to maximize femoral head size remains uncertain. The objective of this study was to analyze clinical survival and radiological wear rates of patients with HXLPE liners, a 36 mm femoral head, and a small acetabular component with a minimum of ten years' follow-up.

METHODS

We retrospectively identified 55 patients who underwent primary THA performed at a single centre, using HXLPE liners with 36 mm cobalt-chrome heads in acetabular components with an outer diameter of 52 mm or smaller. Patient demographic details, implant details, death, and all-cause revisions were recorded. Cox regression and Kaplan-Meier survival was used to determine all-cause and liner-specific revision. Of these 55 patients, 22 had a minimum radiological follow-up of seven years and were assessed radiologically for linear and volumetric wear.

RESULTS

Overall survival rate for all-cause revision was 94.5% (95% confidence interval (CI) 81.7% to 97.2%) at a mean follow-up of 12.8 years (10.9 to 18.7). Three patients were revised, none for liner wear, fracture, or dissociation. A total of 22 patients were included in the radiological analysis (mean follow-up 9.9 years (7.5 to 13.7)). Mean linear liner wear was 0.085 mm (95% CI -0.086 to 0.257) and the volumetric wear rate was 11.097 mm³/year (95% CI -6.5 to 28.7).

CONCLUSION

Using HXLPE liners with 36 mm heads in 52 mm acetabular components or smaller is safe, with excellent survival and low rates of linear and volumetric wear at medium-term follow-up. Patients did not require revision surgery for liner complications such as fracture, dissociation, or wear. Our results suggest that the advantages of using larger heads outweigh the potential risks of using thin HXLPE liners.Cite this article: Bone Joint J 2023;105-B(1):29-34.

[Analysis of risk factors for readmission of pneumoconiosis patients in Hunan]

Objective: To explore the risk factors of pneumoconiosis patients' re-admission to provide a scientific basis for improving the treatment effect of pneumoconiosis, reducing the rate of re-admission, and reducing the burden of disease. Methods: In June 2020, The clinical data of 470 patients with pneumoconiosis who had hospitalization records from February 8, 2014, to February 8, 2020, in the Hunan Provincial Occupational Disease Prevention and Treatment Institute were retrospectively analyzed. The patients' general data and emotional state at the first admission were collected through questionnaires and telephone follow-ups. The entire group of patients completed at least one follow-up, with readmission as the end event. First, the Kaplan-Meier method was used for univariate analysis. The multivariate COX regression model analysis was performed on meaningful variables to explore the risk factors that affect the patient's re-admission. Results: A total of 470 patients with pneumoconiosis were included in this study, with an average age of 55.88 years (34-81 years old) and all the participants were male. During the first admission, the number of participants diagnosed as stage III pneumoconiosis, with complications of COPD, fatty liver, or severe pulmonary diffusion dysfunction was 215 (45.74%) , 179 (38.09%) , 51 (10.85%) , and 44 (9.36%) , respectively. Six patients (1.28%) have had suicidal tendencies almost every day since they became ill. A total of 345 patients (73.40%) were re-admitted to the hospital. Multivariate Cox regression model analysis showed that compared with the suspected pneumoconiosis group, patients in the first, second, and third-stage pneumoconiosis groups had an increased risk of readmission (OR=2.43, 2.96, 2.35, P=0.000) . Compared with the age of 30-50 years old, 50-70 years old and ≥70 years old have an increased risk of readmission (OR=1.28, 2.32, P<0.05) . Patients with tricuspid regurgitation (OR=1.33, P<0.05) and elevated triglyceride level (>2.26 mmol/L) (OR=1.40, P<0.05) have increased risks of readmission. Compared with the normal group, patients with severe pulmonary diffusion dysfunction in pneumoconiosis have an increased risk of readmission (OR=1.96, P<0.05) . Compared with the normal group, pneumoconiosis patients in the suicidal group had an increased risk of re-admission to the hospital almost every day (OR=2.92, P<0.05) . Conclusion Age of onset, stage of pneumoconiosis, tricuspid regurgitation, high triglycerides, severe diffuse pulmonary dysfunction, and suicidal tendency are independent risk factors that affect the readmission of patients with pneumoconiosis. The management of chronic diseases and mental health of patients with pneumoconiosis should be strengthened to reduce the risk of readmission. Conclusion: Age of onset, stage of pneumoconiosis, tricuspid regurgitation, high triglycerides, severe diffuse pulmonary dysfunction, and suicidal tendency are independent risk factors that affect the readmission of patients with pneumoconiosis. The management of chronic diseases and the mental health of patients with pneumoconiosis should be strengthened to reduce the risk of readmission.

A robust hazard ratio for general modeling of survival-times

Hazard ratios (HR) associated with the well-known proportional hazard Cox regression models are routinely used for measuring the impact of one factor of interest on a time-to-event outcome. However, if the underlying real model does not fit with the theoretical requirements, the interpretation of those HRs is not clear. We propose a new index, gHR, which generalizes the HR beyond the underlying survival model. We consider the case in which the study factor is a binary variable and we are interested in both the unadjusted and adjusted effect of this factor on a time-to-event variable, potentially, observed in a right-censored scenario. We propose non-parametric estimations for unadjusted gHR and semi-parametric regression-induced techniques for the adjusted case. The behavior of those estimators is studied in both large and finite sample situations. Monte Carlo simulations reveal that both estimators provide good approximations of their respective inferential targets. Data from the Health and Lifestyle Study are used for studying the relationship of the tobacco use and the age of death and illustrate the practical application of the proposed technique. gHR is a promising index which can help facilitate better understanding of the association of one study factor on a time-dependent outcome.

Unified estimation for Cox regression model with nonmonotone missing at random covariates

This article investigates a unified estimator for Cox regression model (Cox, 1972) when covariate data are missing at random (Rubin, 1976). It extends the idea of using parametric working models (Zhao and Liu, 2021) to extract the partial information contained in the incomplete observations. The working models are flexible and convenient to deal with nonmonotone missing data patterns. It can also incorporate auxiliary variables into the analysis to reduce estimation bias and improve efficiency. The unified estimator is consistent and more efficient than the (weighted) complete case estimator. Similar to multiple imputation (MI) method (Rubin, 1987 and 1996), the proposed method is also based on standard (weighted) complete data analysis and can be easily implemented in standard software. Simulation studies comparing the unified estimator with the substantive model compatible modification of the fully conditional specification MI (SMC-FCS) estimator (Bartlett et al., 2015) in various settings indicate that the unified estimator is consistent and as efficient as SMC-FCS estimator. Data from a clinical trial in patients with early breast cancer are analyzed for illustration.

Risk factors associated with hospitalization in patients with asymptomatic or mild COVID-19 in public accommodation facilities in Tokyo

Introduction

In Japan, patients with coronavirus disease 2019 (COVID-19) who do not require medical intervention are provided care in recovery accommodation facilities (RAFs). However, some patients may require hospitalization if their symptoms become more severe during their stay. We conducted an observational study using epidemiological data of patients with COVID-19 admitted to RAFs in Tokyo.

Methods

This was an observational cohort study using data from COVID-19 patients admitted to one of the RAFs in Tokyo from December 2020 to November 2021. Admissions to the facilities were limited to patients with asymptomatic or mild COVID-19 with no underlying disease or at least stable underlying disease at the time of admission. Patients were hospitalized when they required oxygen administration or when they had, or persistent fever, or severe respiratory symptoms. We evaluated the association between hospitalization and the risk factors for hospitalization using a Cox regression model.

Results

The number of patients with COVID-19 admitted to the RAF was 6176. The number of hospitalized patients was 393 (6.4%), and the median length of stay was 5.50 days (IQR: 4.50, 6.50). In the Cox regression analysis, the hazard ratio increased with age and was significantly higher among patients aged >60 years (HR = 10.23, 95% CI: 6.72–15.57) than those in other age groups. This trend is similar to that observed in the sensitivity analysis.

Conclusion

Patients with diabetes, the elderly, obesity, and medications for gout and psychiatric diseases may be at a high risk of hospitalization. In particular, an age over 60 years was strongly associated with hospitalization.

Long-term exposure to road traffic noise and all-cause and cause-specific mortality: a Danish Nurse Cohort study

BACKGROUND

Long-term road traffic noise exposure is linked to cardio-metabolic disease morbidity, whereas evidence on mortality remains limited.

OBJECTIVES

We investigated association of long-term exposure to road traffic noise with all-cause and cause-specific mortality.

METHODS

We linked 22,858 females from the Danish Nurse Cohort (DNC), recruited into the Danish Register of Causes of Death up to 2014. Road traffic noise levels since 1970 were modelled by Nord2000 as the annual mean of a weighted 24 h average (L_den). Cox regression models examined the associations between L_den (5-year and 23-year means) and all-cause and cause-specific mortalities, adjusting for lifestyle and exposure to PM_2.5 (particulate matter with diameter < 2.5 μm) and NO₂ (nitrogen dioxide).

RESULTS

During follow-up (mean 17.4 years), 3902 nurses died: 1622 from cancer, 922 from CVDs (289 from stroke), 338 from respiratory diseases (186 from chronic obstructive pulmonary disease, 114 from lower respiratory tract infections [ALRIs]), 234 from dementia, 95 from psychiatric disorders, and 79 from diabetes. Hazard ratios (95% confidence intervals) for all-cause mortality from fully-adjusted models were 1.06 (1.01, 1.11) and 1.09 (1.03, 1.15) per 10 dB of 5-year and 23-year mean L_den, respectively, which attenuated slightly in our main model (fully-adjusted plus PM_2.5: 1.04 [1.00, 1.10]; 1.08 [1.02, 1.13]). Main model estimates suggested the strongest associations between 5-year mean L_den and diabetes (1.14: 0.81, 1.61), ALRIs (1.13: 0.84, 1.54), dementia (1.12: 0.90, 1.38), and stroke (1.10: 0.91, 1.31), whereas associations with 23-year mean L_den were suggested for respiratory diseases (1.15: 0.95, 1.39), psychiatric disorders (1.11: 0.78, 1.59), and all cancers (1.08: 0.99, 1.17).

DISCUSSION

Among the female nurses from the DNC, we observed that long-term exposure to road traffic noise led to premature mortality, independently of air pollution, and its adverse effects may extend well beyond those on the cardio-metabolic system to include respiratory diseases, cancer, neurodegenerative and psychiatric disorders.

Estimating population-averaged hazard ratios in the presence of unmeasured confounding

The Cox regression model and its associated hazard ratio (HR) are frequently used for summarizing the effect of treatments on time to event outcomes. However, the HR's interpretation strongly depends on the assumed underlying survival model. The challenge of interpreting the HR has been the focus of a number of recent papers. Several alternative measures have been proposed in order to deal with these concerns. The marginal Cox regression models include an identifiable hazard ratio without individual but populational causal interpretation. In this work, we study the properties of one particular marginal Cox regression model and consider its estimation in the presence of omitted confounder from an instrumental variable-based procedure. We prove the large sample consistency of an estimation score which allows non-binary treatments. Our Monte Carlo simulations suggest that finite sample behavior of the procedure is adequate. The studied estimator is more robust than its competitor (Wang et al.) for weak instruments although it is slightly more biased for large effects of the treatment. The practical use of the presented techniques is illustrated through a real practical example using data from the vascular quality initiative registry. The used R code is provided as Supplementary material.

Accurate prediction and further dissection of neonicotinoid elimination in the water treatment by CTS@AgBC using multihead attention-based convolutional neural network combined with the time-dependent Cox regression model

Imidacloprid (IMI), as the most widely used neonicotinoid insecticide, poses a serious threat to the water ecosystem due to the inefficient elimination in the traditional water treatment. Chitosan (CTS)-stabilized biochar (BC)-supported Ag nanoparticles (CTS@AgBC) are applied to eliminate the IMI in the water treatment effectively. Batch experiments depict that the modification of BC by CTS and Ag nanoparticles remarkably improve its adsorption performance. The pseudo-second-order and Elovich models have good performance in simulating the adsorption processes of CTS@AgBC and BC. This indicates that the chemical adsorption on real surfaces plays the dominant role in the adsorption of IMI by CTS@AgBC and BC. In addition, the multihead attention (MHA)-based convolutional neural network (CNN) combined with the time-dependent Cox regression model are initially applied to predict and dissect the adsorption elimination processes of IMI by CTS@AgBC. The proposed MHA-CNN model achieves more accurate concentration prediction of IMI than traditional models. According to influence weights by MHA module, biochar category, pH, and treatment temperature are considered the three dominant environmental variables to determine the IMI elimination processes. This study provides insights into roles of environmental variables in the elimination of IMI by CTS@AgBC and the accurate prediction of IMI concentration.

Identification and Validation of Hub Genes Predicting Prognosis of Hepatocellular Carcinoma

INTRODUCTION

The aim of this study is selecting the hub genes associated with hepatocellular carcinoma (HCC) to construct a Cox regression model for predicting prognosis in HCC patients.

METHODS

Using HCC patient data from the ICGC and TCGA databases, screened for 40 core genes highly correlated with histological grade of HCC. Univariate and multivariate Cox regression analyses were performed on the genes highly associated with HCC prognosis, and the model was established. The expression of those genes was measured by immunohistochemistry in 110 HCC patients who underwent the surgery in the First Affiliated Hospital of Wenzhou Medical University. The survival of HCC patients was analyzed by the Kaplan-Meier method.

RESULTS

Eight genes (CDC45, CENPA, MCM10, MELK, CDC20, ASF1B, FANCD2, and NCAPH) were correlated with prognosis, and the same result was observed in 110 HCC patients. Using the regression model, the HCC patients in the training set were classified as high- and low-risk groups. The overall survival of patients in the high-risk group was shorter than that in the low-risk group, and the same results were obtained in the verification set.

CONCLUSION

This study found that the risk model according to these 8 genes can be used as a predictor of prognosis in HCC. These genes may become alternative biomarkers and therapeutic targets and provide new therapeutic strategies for HCC.

Melamine exposure threshold in early chronic kidney disease patients - A benchmark dose approach

Environmental melamine exposure has been associated with deteriorating kidney function in early-stage chronic kidney disease patients. In this study, a benchmark dose (BMD) approach was used to establish melamine exposure threshold in 293 patients with eGFR≥30 ml/min per 1.73 m². The patients were enrolled 2006-2010 and followed-up for a median of 7.0 years to monitor kidney outcomes. Average daily intakes (AvDI) of melamine were estimated using one-spot urine samples collected at enrollment. BMDs and corresponding one-sided 95% lower bound (BMDLs) were derived using established dose-response models relating estimated AvDIs and dichotomous kidney outcomes: doubling of serum creatine levels, eGFR decreased > 3 ml/min per 1.73 m² per year, and >30% decline in eGFR during the first 2 years. In addition, survival time to doubling of serum creatinine and eGFR decline over time were assessed as continuous endpoints. Given a benchmark response of 0.10, BMDLs ranged from 0.74 to 2.03 μg/kg_bw/day after Bayesian model averaging, a range one to two orders lower than the current WHO recommended tolerable daily intake of 200 μg/kg_bw/day and the US FDA's 63 μg/kg_bw/day. Our results suggest that early-stage CKD patients should strictly refrain from using melamine tableware and related melamine-made products.

Comparing statistical methods for detecting and estimating waning efficacy of rotavirus vaccines in developing countries

INTRODUCTION

Vaccination has significantly reduced morbidity and mortality resulting from rotavirus infection worldwide. However, rotavirus vaccine efficacy (VE) appears to wane over the first 2 years since vaccination, particularly in developing countries. Statistical methods for detecting VE waning and estimating its rate have been used in a few studies, but comparisons of methods for evaluating VE waning have not yet been performed. In this work we present and compare three methods - Durham's method, Tian's method, and time-dependent covariate (TDC) method - based on generalizations of the Cox proportional hazard model.

METHODS

We developed a new stochastic agent-based simulation model to generate data from a hypothetical rotavirus vaccine trial where the protective efficacy of the vaccine may vary over time. Input parameters to the simulation model were obtained from studies on rotavirus infections in four developing countries. We applied each of the methods to four simulated datasets and compared the type-1 error probabilities and the powers of the resulting statistical tests. We also compared estimated and true values of VE over time.

RESULTS

Durham's method had the highest power of detecting true VE waning of the three methods. This method also provided quite accurate estimates of VE in each period and of the per-period drop in VE.

CONCLUSIONS

Durham's method is somewhat more powerful than the other two Cox proportional hazards model-based methods for detecting VE waning and provides more information about the temporal behavior of VE.

PD-L1+NEUT, Foxp3+Treg, and NLR as New Prognostic Marker with Low Survival Benefits Value in Hepatocellular Carcinoma

Background: This presented study was aimed to evaluate the diagnostic and prognostic value of PD-L1⁺Neutrophils (PD-L1⁺NEUT) and neutrophil to lymphocyte ratio (NLR) based on our previous experience of Foxp3⁺Treg in transplantation. Methods: the NLR cutoff value of 1.79 was used to include 136 cases from the 204 patients with hepatocellular carcinoma (HCC) confirmed by clinical pathology, which were divided into highly-moderately and poorly differentiated HCC groups. The expressions of PD-L1⁺NEUT and Foxp3⁺Treg in peripheral blood and cancer tissue were detected with flow cytometry, meanwhile, PD-L1 and Foxp3 expressed in carcinoma and para-carcinoma tissues were marked by immunohistochemistry. Survival rates, including overall survival and disease-free survival, were calculated by the Kaplan–Meier curve and evaluated with the log-rank test. Finally, Cox risk regression model was used to analyze the independent risk factors for prognostic survival. Results: The level of PD-L1⁺NEUT, Foxp3⁺Treg, and NLR in peripheral blood of patients with poorly differentiated HCC were significantly increased (all P < .001). Both PD-L1⁺NEUT and NLR were positively correlated with Foxp3⁺Treg (r = 0.479, P = .0017; r = 0.58, P < .0001). The level of PD-L1⁺NEUT and Foxp3⁺Treg as well as PD-L1 and Foxp3 in cancer tissue and patients with poorly differentiated HCC were obviously increased (all P < .01), respectively. Cox regression analysis indicated that PD-L1⁺NEUT, NLR, and Foxp3⁺Treg were independent risk factors for the prognosis (P = .000, .000, .006) with a RR and 95%CI of 2.704-(2.155-3.393), 3.139-(2.361-4.173), 1.409-(1.105-1.798), respectively. Conclusion: PD-L1⁺NEUT, NLR, and Foxp3⁺Treg are independent risk factors for prognosis which maybe new marker of lower survival benefits.

Thromboembolic and bleeding complications following primary total knee arthroplasty : a Danish nationwide cohort study

AIMS

The aim of this study is to compare the effectiveness and safety of thromboprophylactic treatments in patients undergoing primary total knee arthroplasty (TKA).

METHODS

Using nationwide medical registries, we identified patients with a primary TKA performed in Denmark between 1 January 2013 and 31 December 2018 who received thromboprophylactic treatment. We examined the 90-day risk of venous thromboembolism (VTE), major bleeding, and all-cause mortality following surgery. We used a Cox regression model to compute hazard ratios (HRs) with 95% confidence intervals (CIs) for each outcome, pairwise comparing treatment with dalteparin or dabigatran with rivaroxaban as the reference. The HRs were both computed using a multivariable and a propensity score matched analysis.

RESULTS

We identified 27,736 primary TKA patients who received thromboprophylactic treatment (rivaroxaban (n = 18,846); dalteparin (n = 5,767); dabigatran (n = 1,443); tinzaparin (n = 1,372); and enoxaparin (n = 308)). In the adjusted multivariable analysis and compared with rivaroxaban, treatment with dalteparin (HR 0.68 (95% CI 0.49 to 0.92)) or dabigatran (HR 0.31 (95% CI 0.13 to 0.70)) was associated with a decreased risk of VTE. No statistically significant differences were observed for major bleeding or all-cause mortality. The propensity score matched analysis yielded similar results.

CONCLUSION

Treatment with dalteparin or dabigatran was associated with a decreased 90-day risk of VTE following primary TKA surgery compared with treatment with rivaroxaban. Cite this article: Bone Joint J 2021;103-B(10):1571-1577.

[Construction and Validation of Prognostic Risk Score Model of Autophagy Related Genes in Lung Adenocarcinoma]

BACKGROUND

Autophagy related genes (ARGs) regulate lysosomal degradation to induce autophagy, and are involved in the occurrence and development of a variety of cancers. The expression of ARGs in tumor tissues has a great prospect in predicting the survival of patients. The aim of this study was to construct a prognostic risk score model for lung adenocarcinoma (LUAD) based on ARGs.

METHODS

5,786 ARGs were obtained from GeneCards database. Gene expression profiles and clinical data of 395 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. All ARGs expression data were extracted, and The ARGs differentially expressed were identified by R software. Survival analysis of differentially expressed ARGs was performed to screen for ARGs with prognostic value, and functional enrichment analysis was performed. The least absolute selection operator (LASSO) regression and Cox regression model were used to construct a prognostic risk scoring model for ARGs. The receiver operating characteristic (ROC) curve was drawn to obtain the optimal cut-off value of risk score. According to the cut-off value, the patients were divided into high-risk group and low-risk group. The area under curve (AUC) and the Kaplan-Meier survival curve was plotted to evaluate the model performance, which was verified in external data sets. Finally, univariate and multivariate Cox regression analysis was applied to evaluate the independent prognostic value of the model, and its clinical relevance was analyzed.

RESULTS

Survival analysis, Lasso regression and Cox regression analysis were used to construct a LUAD prognostic risk score model with five ARGs (ADAM12, CAMP, DKK1, STRIP2 and TFAP2A). The survival time of patients with low-risk score in this model was significantly better than that of patients with high-risk score (P<0.001). The model showed good prediction performance for LUAD in both the training set (AUCmax=0.78) and two external validation sets (AUCmax=0.88). Risk score was significantly associated with the prognosis of LUAD patients in univariate and multivariate Cox regression analyses, suggested that risk score could be a potential independent prognostic factor for LUAD. Correlation analysis of clinical characteristic showed that high risk score was closely associated with high T stage, high tumor stage and poor prognosis.

CONCLUSIONS

We constructed a LUAD risk score model consisting of five ARGs, which can provide a reference for predicting the prognosis of LUAD patients, and may be used in combination with tumor node metastasis (TNM) staging for prognosis prediction of LUAD patients in the future.

Predictors of time-to-death on children in Tigray regional state, Ethiopia: a retrospective cross sectional study

BACKGROUND

Although, the clinical and socioeconomic condition of Tigray Regional State mothers has been improved along with the decline in the child death rate. However, children's death rate is still one of the main community serious challenging issues of public health concern. Thus, the main objective of this current investigation was to identify the major predictor factors for short time-to-death in Children in the Tigray Regional State.

METHODS

The study used a secondary data with cross-sectional study design. The information gathered was from 1018 childbirths 5 years prior to the survey. Independent variables such as mother's demographic variables, child demographic variables, healthiness and environmental factors were considered major hazard predictors of children's short time-to-death rate. This current investigation used bivariable and multivariable Cox regression model analysis to identify the major statistically significant associations with children's time-to-death rate.

RESULTS

One thousand eighteen children under 5 years of age were included in the study. Of them, 50% of the children were males, and the median survival time-to-death of children was 26 months. Overall, the prevalence of experiencing child's death rate in the Tigray Regional State was 4.2%. The multivariable Cox regression model analysis showed that living rural place of residence (AHR = 19.8; 95% CI: (7.25-54.049)), being unvaccinated child (no) (AHR = 2.76; 95% CI: (1.071-7.11)), and poor wealth index (AHR = 15.4; 95% CI: (2.83-84)) were statistically significant predictors of time-to-death rate of children's.

CONCLUSION

The study recognized that being a rural place of residence, unvaccinated child status (no) and poor wealth index were statistically significant predictors of children's short time-to-death rate.

Cox regression analysis for distorted covariates with an unknown distortion function

We study inference for censored survival data where some covariates are distorted by some unknown functions of an observable confounding variable in a multiplicative form. An example of this kind of data in medical studies is normalizing some important observed exposure variables by patients' body mass index , weight, or age. Such a phenomenon also appears frequently in environmental studies where an ambient measure is used for normalization and in genomic studies where the library size needs to be normalized for the next generation sequencing of data. We propose a new covariate-adjusted Cox proportional hazards regression model and utilize the kernel smoothing method to estimate the distorting function, then employ an estimated maximum likelihood method to derive the estimator for the regression parameters. We establish the large sample properties of the proposed estimator. Extensive simulation studies demonstrate that the proposed estimator performs well in correcting the bias arising from distortion. A real dataset from the National Wilms' Tumor Study is used to illustrate the proposed approach.

A cohort study on risk factors of high-density lipoprotein cholesterol hypolipidemia among urban Chinese adults

Background

High-density lipoprotein cholesterol (HDL-C) hypolipidemia, a major type of dyslipidemia, has been associated with many kinds of diseases, such as stroke, coronary heart disease, obesity and diabetes, and has displayed an increasing prevalence in China. This study explores the risk factors of HDL-C hypolipidemia and makes recommendations for controlling and preventing HDL-C hypolipidemia and the diseases caused by it.

Methods

Using a retrospective cohort study design, 26,863 urban adults without dyslipidemia, diabetes, cardiovascular and cerebrovascular diseases, hepatosis, renal insufficiency and thyroid diseases were enrolled in the study between 2010 and 2015. Data on each individual were collected at the 2010 baseline year and at a follow-up medical check. A Cox regression model was constructed to evaluate the influence of potential risk factors on the outcome event- HDL-C hypolipidemia.

Results

The incidence of HDL-C hypolipidemia was 5.7% (1531/26863). Sex, age, body mass index (BMI), HDL-C, triglyceride (TG) and urea nitrogen (UN) were significant risk factors of HDL-C hypolipidemia. Men were more likely to develop HDL-C hypolipidemia than women during follow-up medical checks (HR = 1.258, P = 0.014). The incidence of HDL-C hypolipidemia in the over 65 years old group was higher than that of the ≤65 age group (HR = 1.276, P = 0.009). The incidence of HDL-C hypolipidemia increased with increasing BMI (HR = 1.030, P = 0.002), TG (HR = 1.321, P = 0.001) and UN (HR = 1.054, P = 0.019), while falling with increasing HDL-C in the baseline year (HR = 0.002, P < 0.001).

Conclusions

Men, aged over 65, with high BMI were at the highest risk of developing HDL-C hypolipidemia. Measures should be taken to prevent HDL-C hypolipidemia even for healthy urban adults whose blood biochemical indicators were in the normal range when their level of TG, UN and HDL-C are closed to the border of the normal value range.

Analysis of time-to-event for observational studies: Guidance to the use of intensity models

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.

Analysis of Related Factors of Tumor Recurrence or Progression After Transnasal Sphenoidal Surgical Treatment of Large and Giant Pituitary Adenomas and Establish a Nomogram to Predict Tumor Prognosis

BACKGROUND

Pituitary adenoma (PA) is a benign neuroendocrine tumor caused by adenohypophysial cells, and accounts for 10%-20% of all primary intracranial tumors. The surgical outcomes and prognosis of giant pituitary adenomas measuring ≥3 cm in diameter differ significantly due to the influence of multiple factors such as tumor morphology, invasion site, pathological characteristics and so on. The aim of this study was to explore the risk factors related to the recurrence or progression of giant and large PAs after transnasal sphenoidal surgery, and develop a predictive model for tumor prognosis.

METHODS

The clinical and follow-up data of 172 patients with large or giant PA who underwent sphenoidal surgery at the Second Affiliated Hospital of Zhejiang University School of Medicine from January 2011 to December 2017 were retrospectively analyzed. The basic clinical information (age, gender, past medical history etc.), imaging features (tumor size, invasion characteristics, extent of resection etc.), and histopathological characteristics (pathological results, Ki-67, P53 etc.) were retrieved. SPSS 21.0 software was used for statistical analysis, and the R software was used to establish the predictive nomogram.

RESULTS

Seventy out of the 172 examined cases (40.7%) had tumor recurrence or progression. The overall progress free survival (PFS) rates of the patients at 1, 3 and 5 years after surgery were 90.70%, 79.65% and 59.30% respectively. Log-rank test indicated that BMI (P < 0.001), Knosp classification (P < 0.001), extent of resection (P < 0.001), Ki-67 (P < 0.001), sphenoidal sinus invasion (P = 0.001), Hardy classification (P = 0.003) and smoking history (P = 0.018) were significantly associated with post-surgery recurrence or progression. Cox regression analysis further indicated that smoking history, BMI ≥25 kg/m², Knosp classification grade 4, partial resection and ≥3% Ki-67 positive rate were independent risk factors of tumor recurrence or progression (P < 0.05). In addition, the nomogram and ROC curve based on the above results indicated significant clinical value.

CONCLUSION

The postoperative recurrence or progression of large and giant PAs is related to multiple factors and a prognostic nomogram based on BMI (≥25 kg/m²), Knosp classification (grade 4), extent of resection (partial resection) and Ki-67 (≥3%) can predict the recurrence or progression of large and giant PAs after transnasal sphenoidal surgery.

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.

Testing for change-point in the covariate effects based on the Cox regression model

Models with change-point in covariates have wide applications in cancer research with the response being the time to a certain event. A Cox model with change-point in covariate is considered at which the pattern of the change-point effects can be flexibly specified. To test for the existence of the change-point effects, three statistical tests, namely, the maximal score, maximal normalized score, and maximal Wald tests are proposed. The asymptotic properties of the test statistics are established. Monte Carlo approaches to simulate the critical values are suggested. A large-scale simulation study is carried out to study the finite sample performance of the proposed test statistics under the null hypothesis of no change-points and various alternative hypothesis settings. Each of the proposed methods provides a natural estimate for the location of the change-point, but it is found that the performance of the maximal score test can be sensitive to the true location of the change-point in some cases, while the performance of the maximal Wald test is very satisfactory in general even in cases with moderate sample size. For illustration, the proposed methods are applied to two medical datasets concerning patients with primary biliary cirrhosis and breast cancer, respectively.

The cox-filter method identifies respective subtype-specific lncRNA prognostic signatures for two human cancers

BACKGROUND

The most common histological subtypes of esophageal cancer are squamous cell carcinoma (ESCC) and adenocarcinoma (EAC). It has been demonstrated that non-marginal differences in gene expression and somatic alternation exist between these two subtypes; consequently, biomarkers that have prognostic values for them are expected to be distinct. In contrast, laryngeal squamous cell cancer (LSCC) has a better prognosis than hypopharyngeal squamous cell carcinoma (HSCC). Likewise, subtype-specific prognostic signatures may exist for LSCC and HSCC. Long non-coding RNAs (lncRNAs) hold promise for identifying prognostic signatures for a variety of cancers including esophageal cancer and head and neck squamous cell carcinoma (HNSCC).

METHODS

In this study, we applied a novel feature selection method capable of identifying specific prognostic signatures uniquely for each subtype - the Cox-filter method - to The Cancer Genome Atlas esophageal cancer and HSNCC RNA-Seq data, with the objectives of constructing subtype-specific prognostic lncRNA expression signatures for esophageal cancer and HNSCC.

RESULTS

By incorporating biological relevancy information, the lncRNA lists identified by the Cox-filter method were further refined. The resulting signatures include genes that are highly related to cancer, such as H19 and NEAT1, which possess perfect prognostic values for esophageal cancer and HNSCC, respectively.

CONCLUSIONS

The Cox-filter method is indeed a handy tool to identify subtype-specific prognostic lncRNA signatures. We anticipate the method will gain wider applications.

Identification of seven-gene signature for prediction of lung squamous cell carcinoma

Background and aim: Lung squamous cell carcinoma (LUSC), is a pathological subtype of lung cancer, accounting for 30% of the lung cancers. A reliable model was constructed, based on the whole gene expression profiles, to predict the prognosis of patients with LUSC. Methods: The RNA-Seq data of LUSC was downloaded from the TCGA database, and differentially expressed genes (p<0.05, |log2fold change| >1) were screened out. By univariate and multivariate Cox regression analysis, we identified seven prognosis-related genes. Then, we established a risk score staging system to predict the prognosis of patients with LUSC. Compared with other clinical parameters, the risk score was an independent prognostic factor and had a better performance in predicting prognosis. Finally, GSEA analysis was carried out to determine the enrichment pathway significantly. The risk score models were established by Cox proportional hazard regression analysis; the ROC curve was applied to test the performance of risk score model. All the statistical analysis was accomplished by R packages. Results: In this study, a model was constructed to predict prognosis, which contains seven genes: CSRNP1, CLEC18B, MIR27A, AC130456.4, DEFA6, ARL14EPL, and ZFP42. Based on the model, the risk score of each patient was calculated with LUSC (hazard ratio [HR]=2.673, 95% CI=1.871-3.525). It was found that the risk score can distinguish high-risk and low-risk groups in prognosis of LUSC patients, independently. Furthermore, the model was validated by ROC curves in the testing dataset and the whole dataset. Lastly, by gene set enrichment analysis (GSEA), we showed the main enrichment pathways were DNA damage stimulus, DNA repair, and DNA replication. It was suggested that the risk score may provide a new and reliable method for prognosis prediction. Conclusion: The results of this study suggested that the risk score based on seven-genes could indicate a promising and independent prognostic biomarker for LUSC patients.

Breast cancer survival prediction using seven prognostic biomarker genes

Breast cancer (BC) is one of the most prevalent forms of cancer globally. However, the practical relevance of the RNA expression-based prediction of BC is not clearly understood and requires further study. Using gene expression data downloaded from The Cancer Genome Atlas (TCGA), a risk score staging classification was created using Cox's multiple regression and was used to predict the clinical outcomes of patients with BC. In total, 7 genes, including AC123595.1, leukocyte immunoglobulin-like receptor B5, CD209 molecule, AL049749.1, lymphatic vessel endothelial hyaluronan receptor 1, transmembrane protein 190 and tubulin α 3D chain were identified in association with patient survival. The patients with lower risk scores had considerably improved survival rates than those with higher risk scores. Compared with other clinical factors, the risk score more accurately predicted the clinical outcome of patients with BC. In summary, 7 genes were identified using the Cox regression model, and subsequently used to develop a risk staging model for BC, which may be of use for the medical management of patients.

A novel approach to determine two optimal cut-points of a continuous predictor with a U-shaped relationship to hazard ratio in survival data: simulation and application

BACKGROUND

In clinical and epidemiological researches, continuous predictors are often discretized into categorical variables for classification of patients. When the relationship between a continuous predictor and log relative hazards is U-shaped in survival data, there is a lack of a satisfying solution to find optimal cut-points to discretize the continuous predictor. In this study, we propose a novel approach named optimal equal-HR method to discretize a continuous variable that has a U-shaped relationship with log relative hazards in survival data.

METHODS

The main idea of the optimal equal-HR method is to find two optimal cut-points that have equal log relative hazard values and result in Cox models with minimum AIC value. An R package 'CutpointsOEHR' has been developed for easy implementation of the optimal equal-HR method. A Monte Carlo simulation study was carried out to investigate the performance of the optimal equal-HR method. In the simulation process, different censoring proportions, baseline hazard functions and asymmetry levels of U-shaped relationships were chosen. To compare the optimal equal-HR method with other common approaches, the predictive performance of Cox models with variables discretized by different cut-points was assessed.

RESULTS

Simulation results showed that in asymmetric U-shape scenarios the optimal equal-HR method had better performance than the median split method, the upper and lower quantiles method, and the minimum p-value method regarding discrimination ability and overall performance of Cox models. The optimal equal-HR method was applied to a real dataset of small cell lung cancer. The real data example demonstrated that the optimal equal-HR method could provide clinical meaningful cut-points and had good predictive performance in Cox models.

CONCLUSIONS

In general, the optimal equal-HR method is recommended to discretize a continuous predictor with right-censored outcomes if the predictor has an asymmetric U-shaped relationship with log relative hazards based on Cox regression models.

The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients' survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients' with HCC.

[Association between the change of daily living activities and symptoms of depression in Chinese middle-aged and elderly people]

Objective: To explore the association between the change of daily living activities and the suymtoms of depression in Chinese middle-age and elderly people. Methods: People without symptoms on depression, emotional or spiritual diseases or memories related diseases in baseline survey (2011), those who were aged ≥45 years in the China Health and Retirement Longitudinal Survey (CHARLS) with two waves from 2011 to 2013 were invited to participate in this CHARLS study. Information related to demographic characteristics and health status of the subjects were collected through household-interview. Depression symptom was assessed with the Center for Epidemiological Studies of Depression (CES-D10) scale. Activities of daily living function was assessed by both Basic Activities of Daily Living (BADL) and Instrument Activities of Daily Living (IADL) scales. Symptoms of depression was compared and the differences in various BADL/IADL changing groups were analyzed with Log-rank test. Association between the changes of daily living activities and depression symptom was analyzed by Cox regression model. Results: Results from the log-rank test showed that the differences of depression symptom hazard ratio were statistically significant in different BADL/IADL changing groups. Results from the Cox regression analysis showed that the BADL/IADL in the deteriorating groups was associated with the higher hazard ratio of depression symptom (P<0.01), with the HR value (95%CI) for BADL and IADL deteriorate groups as 1.45 (1.20-1.76) and 1.64 (1.36-1.98), respectively, when comparing to the 'stablized' group. This phenomenon appeared an urban-rural difference. Statistical significances on IADL also happened in both urban and rural areas while BADL only occurred in rurals. Conclusion: There appeared an association between the change of daily living activities and depression symptom in Chinese middle-age and elderly people. Difference between IADL and BADL was also noticed between urban and rural areas.

Genome-scale analysis to identify prognostic markers and predict the survival of lung adenocarcinoma

Lung cancer is one of the most malignant cancers worldwide, and lung adenocarcinoma (LAC) remains the most common histologic subtype. However, the functional significance of RNA expression-based prognosis prediction in LAC is still unclear and needs to be further studied. By utilizing the Cox multivariate regression, we established a risk score staging system to predict the outcome of patients with LAC and subsequently identified 10 genes, including PTPRH, OGFRP1, LDHA, AL365203.1, LINC02178, AL512488.1, LINC01312, AL353746.1, DRAXINP1, and LINC02310, which were closely related to the prognosis of patients with LAC. The identified genes allowed us to classify patients into high-risk group with poor outcome and low-risk group with better outcome. Compared with other clinical factors, the risk score performs better in predicting the outcome of LAC patients. We used Gene-Set Enrichment Analysis to identify the differences between the 2 groups in biological pathways. In conclusion, we identified 10 genes by utilizing Cox regression model and developed a risk staging model for LAC, which might prove significant for the clinical management of LAC patients.

Causal Effect of Donor Source on Survival of Renal Transplantation Using Marginal Structural Models

BACKGROUND

Marginal Structural Models (MSMs) are novel methods to estimate causal effect in epidemiology by using Inverse Probability of Treatment Weighting (IPTW) and Stabilized Weight to reduce confounding effects. This study aimed to estimate causal effect of donor source on renal transplantation survival.

METHODS

In this cohort study, 1354 transplanted patients with a median 42.55 months follow-up in Namazee Hospital Transplantation Center, Shiraz from Mar 1999 to Mar 2009, were included to use marginal structural Cox regression, binomial logistic regression model to estimate causal effect of donor source on the survival of renal transplantation. IPTW and stabilized inverse probability of treatment weighting are used as weights.

RESULTS

The un-weighted (crude) hazard ratios for live unrelated donor and deceased donor in comparison to live related donor as reference group was (HR: 1.03, 95% CI: 0.58-1.83, P=0.89) and (HR: 2.69, 95% CI: 1.67-4.31, P=0.001), respectively. Using a marginal structural Cox regression model and by stabilized weight, the hazard ratios for live-unrelated donor and cadaveric donor were (HR: 1.08, 95% CI: 0.47-2.45, P=0.84) and (HR: 3.63, 95% CI: 1.59-8.26, P=0.002), respectively. There was no difference between estimated effect size from marginal structural Cox regression, marginal structural logistic regression, and marginal structural Weibull regression model.

CONCLUSION

There is no difference between related and unrelated donor source hazard ratio; however, hazard ratio for cadaveric donor was 3.63 times of hazard ratio for related donor and 3.34 times of it for unrelated donor. Therefore, the live donor (related or unrelated) has a better survival of renal transplantation than cadaveric donor.

Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic cancer, with diverse molecular characteristics and clinical outcomes. This study aims to dissect the molecular heterogeneity of NPC, followed by the construction of a microRNA (miRNA)-based prognostic model for prediction of distant metastasis.

METHODS

We retrieved two NPC datasets: GSE32960 and GSE70970 as training and validation cohorts, respectively. Consensus clustering was employed for cluster discovery, and support vector machine was used to build a classifier. Finally, Cox regression analysis was applied to constructing a prognostic model for predicting risk of distant metastasis.

RESULTS

Three NPC subtypes (immunogenic, classical and mesenchymal) were identified that are molecularly distinct and clinically relevant, of which mesenchymal subtype (~ 36%) is associated with poor prognosis, characterized by suppressing tumor suppressor miRNAs and the activation of epithelial--mesenchymal transition. Out of the 25 most differentially expressed miRNAs in mesenchymal subtype, miR-142, miR-26a, miR-141 and let-7i have significant prognostic power (P < 0.05).

CONCLUSIONS

We proposed for the first time that NPC can be stratified into three subtypes. Using a panel of 4 miRNAs, we established a prognostic model that can robustly stratify NPC patients into high- and low- risk groups of distant metastasis.

Cox regression model with doubly truncated data

Truncation is a well-known phenomenon that may be present in observational studies of time-to-event data. While many methods exist to adjust for either left or right truncation, there are very few methods that adjust for simultaneous left and right truncation, also known as double truncation. We propose a Cox regression model to adjust for this double truncation using a weighted estimating equation approach, where the weights are estimated from the data both parametrically and nonparametrically, and are inversely proportional to the probability that a subject is observed. The resulting weighted estimators of the hazard ratio are consistent. The parametric weighted estimator is asymptotically normal and a consistent estimator of the asymptotic variance is provided. For the nonparametric weighted estimator, we apply the bootstrap technique to estimate the variance and confidence intervals. We demonstrate through extensive simulations that the proposed estimators greatly reduce the bias compared to the unweighted Cox regression estimator which ignores truncation. We illustrate our approach in an analysis of autopsy-confirmed Alzheimer's disease patients to assess the effect of education on survival.

Time-dependent ROC analysis for censored biomarker data due to limit of detection

Receiver operating characteristic (ROC) curve is a well-established analysis method to evaluate biomarker's discrimination accuracy for binary outcomes. When the endpoint of interest is time to event outcome such as time to cancer recurrence, a biomarker's time-varying discriminatory performance is often assessed by time-dependent ROC analysis. In practice, biomarkers are often imprecisely measured due to the limitation of assay sensitivity. The values below the limit of detection are not detectable. Ignorance of such data characteristic may lead to inaccurate estimation of marker's potential discriminatory power. The objective of this article is to extend time-dependent ROC method to censored biomarker data by using parameter estimates from the Cox regression model that accommodates censored biomarker measurements. In the simulation study, the proposed methods are shown to outperform the simple substitution method that has been conventionally adopted for handling censored data. Application data are also given to illustrate our methods.

Optimal stratification in outcome prediction using baseline information

A common practice in predictive medicine is to use current study data to construct a stratification procedure, which groups subjects according to baseline information and forms stratum-specific prevention or intervention strategies. A desirable stratification scheme would not only have small intra-stratum variation but also have a clinically meaningful discriminatory capability. We show how to obtain optimal stratification rules with such desirable properties from fitting a set of regression models relating the outcome to baseline covariates and creating scoring systems for predicting potential outcomes. We propose that all available optimal stratifications be evaluated with an independent dataset to select a final stratification. Lastly, we obtain inferential results for this selected stratification scheme with a holdout dataset. When only one study of moderate size is available, we combine the first two steps via crossvalidation. Extensive simulation studies are used to compare the proposed stratification strategy with alternatives. We illustrate the new proposal using an AIDS clinical trial for binary outcomes and a cardiovascular clinical study for censored event time outcomes.

Observational Studies: Matching or Regression?

In observational studies with an aim of assessing treatment effect or comparing groups of patients, several approaches could be used. Often, baseline characteristics of patients may be imbalanced between groups, and adjustments are needed to account for this. It can be accomplished either via appropriate regression modeling or, alternatively, by conducting a matched pairs study. The latter is often chosen because it makes groups appear to be comparable. In this article we considered these 2 options in terms of their ability to detect a treatment effect in time-to-event studies. Our investigation shows that a Cox regression model applied to the entire cohort is often a more powerful tool in detecting treatment effect as compared with a matched study. Real data from a hematopoietic cell transplantation study is used as an example.

Optimal treatment allocation for placebo-treatment comparisons in trials with discrete-time survival endpoints

In many randomized controlled trials, treatment groups are of equal size, but this is not necessarily the best choice. This paper provides a methodology to calculate optimal treatment allocations for longitudinal trials when we wish to compare multiple treatment groups with a placebo group, and the comparisons may have unequal importance. The focus is on trials with a survival endpoint measured in discrete time. We assume the underlying survival process is Weibull and show that values for the parameters in the Weibull distribution have an impact on the optimal treatment allocation scheme in an interesting way. Additionally, we incorporate different cost considerations at the subject and measurement levels and determine the optimal number of time periods. We also show that when many events occur at the beginning of the trial, fewer time periods are more efficient. As an application, we revisit a risperidone maintenance treatment trial in schizophrenia and use our proposed methodology to redesign it and compare merits of our optimal design.

Cox regression model for dissecting genetic architecture of survival time

Common quantitative trait locus (QTL) mapping methods fail to analyze survival traits of skewed normal distributions. As a result, some mapping methods for survival traits have been proposed based on survival analysis. Under a single QTL model, however, those methods perform poorly in detecting multiple QTLs and provide biased estimates of QTL parameters. For sparse oversaturated model used to map survival time loci, the least absolute shrinkage and selection operator (LASSO) for Cox regression model can be employed to efficiently shrink most of genetic effects to zero. Then, a few non-zero genetic effects are re-estimated and statistically tested using the standard maximum Cox partial likelihood method. Simulation shows that the proposed method has higher statistic power for QTL detection than that of the LASSO for logarithmic linear model or the interval mapping based on Cox model, although it somewhat underestimates QTL effects. Especially, computational speed of the method is very fast. An application of this method illustrates mapping main effect and interacting QTLs for heading time in the North American Barley Genome Mapping Project.