Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

Tracking Control for Output Probability Density Function of Stochastic Systems Using FPD Method.

Output probability density function (PDF) tracking control of stochastic systems has always been a challenging problem in both theoretical development and engineering practice. Focused on this challenge, this work proposes a novel stochastic control framework so that the output PDF can track a given time-varying PDF. Firstly, the output PDF is characterised by the weight dynamics following the B-spline model approximation. As a result, the PDF tracking problem is transferred to a state tracking problem for weight dynamics. In addition, the model error of the weight dynamics is described by the multiplicative noises to more effectively establish its stochastic dynamics. Moreover, to better reflect the practical applications in the real world, the given tracking target is set to be time-varying rather than static. Thus, an extended fully probabilistic design (FPD) is developed based on the conventional FPD to handle multiplicative noises and to track the time-varying references in a superior way. Finally, the proposed control framework is verified by a numerical example, and a comparison simulation with the linear-quadratic regulator (LQR) method is also included to illustrate the superiority of our proposed framework.

Another look at regression analysis using ranked set samples with application to an osteoporosis study.

Statistical learning with ranked set samples has shown promising results in estimating various population parameters. Despite the vast literature on rank-based statistical learning methodologies, very little effort has been devoted to studying regression analysis with such samples. A pressing issue is how to incorporate the rank information of ranked set samples into the analysis. We propose two methodologies based on a weighted least squares approach and multilevel modeling to better incorporate the rank information of such samples into the estimation and prediction processes of regression-type models. Our approaches reveal significant improvements in both estimation and prediction problems over already existing methods in the literature and the corresponding ones with simple random samples. We study the robustness of our methods with respect to the misspecification of the distribution of the error terms. Also, we show that rank-based regression models can effectively predict simple random test data by assigning ranks to them a posteriori using judgment poststratification. Theoretical results are augmented with simulations and an osteoporosis study based on a real data set from the Bone Mineral Density (BMD) program of Manitoba to estimate the BMD level of patients using easy to obtain covariates.

Ethnic Differences in Experimental Pain Responses Following a Paired Verbal Suggestion With Saline Infusion: A Quasiexperimental Study.

BACKGROUND: Ethnic differences in placebo and nocebo responses are an important, yet underresearched, patient factor that might contribute to treatment disparities. PURPOSE: The purpose of this study was to examine ethnic differences in pain trajectories following a verbal suggestion paired with a masked, inert substance (i.e., saline). METHODS: Using a quasiexperimental design, we examined differences between 21 non-Hispanic Black (NHB) participants and 20 non-Hispanic White (NHW) participants in capsaicin-related pain rating trajectories following a nondirectional verbal suggestion + saline infusion. All participants were told that the substance would "either increase pain sensation, decrease it, or leave it unchanged." A spline mixed model was used to quantify the interaction of ethnicity and time on ratings. RESULTS: There was a significant Ethnicity × Time interaction effect (ß = -0.28, p = .002); NHB individuals reported significantly greater increases in pain following, but not before, the verbal suggestion + saline infusion. Sensitivity analyses showed no change in primary results based on differences in education level, general pain sensitivity, or condition order. CONCLUSIONS: The present results showed ethnic differences in pain response trajectories following a verbal suggestion + saline infusion and suggest that future research rigorously examining possible ethnic differences in placebo/nocebo responses is warranted.

Negative binomial additive model for RNA-Seq data analysis.

BACKGROUND: High-throughput sequencing experiments followed by differential expression analysis is a widely used approach for detecting genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. Existing models assume linear effect of covariates, which is restrictive and may not be sufficient for certain phenotypes. RESULTS: We introduce NBAMSeq, a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. Specifically, we model the logarithm of mean gene counts as sums of smooth functions with the smoothing parameters and coefficients estimated simultaneously within a nested iterative method. The variance is estimated by the Bayesian shrinkage approach to fully exploit the information across all genes. CONCLUSIONS: Based on extensive simulations and case studies of RNA-Seq data, we show that NBAMSeq offers improved performance in detecting nonlinear effect and maintains equivalent performance in detecting linear effect compared to existing methods. The vignette and source code of NBAMSeq are available at http://bioconductor.org/packages/release/bioc/html/NBAMSeq.html.

Validation of the prognostic performance in various nodal staging systems for gallbladder cancer: results of a multicenter study.

BACKGROUND: Although the current nodal staging system for gallbladder cancer (GBC) was changed based on the number of positive lymph nodes (PLN), it needs to be evaluated in various situations. METHODS: We reviewed the clinical data for 398 patients with resected GBC and compared nodal staging systems based on the number of PLNs, the positive/retrieved LN ratio (LNR), and the log odds of positive LN (LODDS). Prognostic performance was evaluated using the C-index. RESULTS: Subgroups were formed on the basis of an restricted cubic spline plot as follows: PLN 3 (PLN = 0, 1-2, ≥ 3); PLN 4 (PLN = 0, 1-3, ≥ 4); LNR (LNR = 0, 0-0.269, ≥ 0.27); and LODDS (LODDS < - 0.8, - 0.8-0, ≥ 0). The oncological outcome differed significantly between subgroups in each system. In all patients with GBC, PLN 4 (C-index 0.730) and PLN 3 (C-index 0.734) were the best prognostic discriminators of survival and recurrence, respectively. However, for retrieved LN (RLN) ≥ 6, LODDS was the best discriminator for survival (C-index 0.852). CONCLUSION: The nodal staging system based on PLN was the optimal prognostic discriminator in patients with RLN < 6, whereas the LODDS system is adequate for RLN ≥ 6. The following nodal staging system considers applying different systems according to the RLN.

A Dose-Response Meta-analysis of Coffee Consumption and Colorectal Cancer Risk in the Japanese Population: Application of a Cubic-Spline Model.

BACKGROUND: A recent meta-analysis compared the relative risks of colorectal cancer between the highest and lowest levels of coffee consumption in the Japanese population. However, this analysis did not define the risks with respect to specific exposure values when considering levels of coffee consumption per day in the study population. METHODS: We conducted a two-stage random-effects dose-response meta-analysis of the association between coffee consumption and colorectal cancer among the Japanese. This was performed by modeling coffee consumption using restricted cubic splines to be able to examine a potential nonlinear relation. RESULTS: We identified a total of 26 studies from seven articles, which were distributed separately according to sex and colon/rectum cancers. Data from 14 cohort studies showed that the pooled relative risks for colorectal cancers were less than 1.0 in cases with coffee consumption of 1-3 cups/day and 1.0 in cases with consumption of 4 cups/day or more, although these results were not statistically significant. Data from 12 case-control studies showed that the pooled odds ratios for cancer risk were significantly less than 1.0 in cases with coffee consumption of 1-6 cups/day. CONCLUSIONS: Findings from this meta-analysis indicate that moderate coffee consumption may not be associated or may be weakly inversely associated with the risk of colorectal cancer in the Japanese population.

Methods for simulating nutritional requirement and response studies with all organisms to increase research efficiency.

Nutritional requirements and responses of all organisms are estimated using various models representing the response to different dietary levels of the nutrient in question. To help nutritionists design experiments for estimating responses and requirements, we developed a simulation workbook using Microsoft Excel. The objective of the present study was to demonstrate the influence of different numbers of nutrient levels, ranges of nutrient levels and replications per nutrient level on the estimates of requirements based on common nutritional response models. The user provides estimates of the shape of the response curve, requirements and other parameters and observation to observation variation. The Excel workbook then produces 1-1000 randomly simulated responses based on the given response curve and estimates the standard errors of the requirement (and other parameters) from different models as an indication of the expected power of the experiment. Interpretations are based on the assumption that the smaller the standard error of the requirement, the more powerful the experiment. The user can see the potential effects of using one or more subjects, different nutrient levels, etc., on the expected outcome of future experiments. From a theoretical perspective, each organism should have some enzyme-catalysed reaction whose rate is limited by the availability of some limiting nutrient. The response to the limiting nutrient should therefore be similar to enzyme kinetics. In conclusion, the workbook eliminates some of the guesswork involved in designing experiments and determining the minimum number of subjects needed to achieve desired outcomes.

Regression-derived optimal milk thistle in growing quail's diet.

A study was conducted to evaluate the nutritional benefits of milk thistle (Silybum marianum) in quail nutrition as an additive containing antioxidant compounds such as silymarin. A total of 300, 14-d old Japanese quail chicks were randomly allotted to 5 treatments with 6 replicates and 10 birds each. The experimental diets, including a basal diet and 4 diets containing 10, 20, 30, and 40 g/kg milk thistle, were used from d 14 to 35 and spline and segmented models were applied to fit data. The optimized values of dietary milk thistle (breakpoints) for optimum amounts of serum albumin (ALB), total protein (TP), glucose (Glu), magnesium (Mg), calcium (Ca), phosphorus (P), iron (Fe), and water holding capacity (WHC) in meat samples, as predicted by the regression models, were 24.14, 20.00, 20.00, 24.50, 20.00, 10.43, 23.75, and 25.85 g/kg of diet, respectively, based on maximum R2 and minimum Sy.x. While the breakpoints for minimum cooking loss, drip loss, malondialdehyde after 10 and 30 d (MDA10 and MDA30), triglyceride (TG), lactate dehydrogenase (LDH), aspartate aminotransferase (AST), cholesterol (CHOL), uric acid (UA), and creatinine (CRT) were 27.00, 15.82, 15.78, 33.09, 27.39, 17.99, 20.00, 20.00, 20.90, and 32.57 g/kg of diet, respectively. The use of spline models revealed an objective estimate of the optimal amounts of milk thistle for optimizing physiological responses in growing quails. The present analysis showed that higher dietary levels of milk thistle were needed for optimizing meat quality compared to other physiological responses.

Association of urinary polycyclic aromatic hydrocarbon metabolites with gestational diabetes mellitus and gestational hypertension among pregnant women in Southwest China: A cross-sectional study.

The association of polycyclic aromatic hydrocarbons (PAHs) with gestational diabetes mellitus (GDM) and gestational hypertension during pregnancy has not yet been established. To investigate the association between PAH exposure and GDM and gestational hypertension, we conducted a cross-sectional study of 4206 pregnant women from the Zunyi birth cohort in southwestern China. Gas chromatography/mass spectrometry was used to detect the urinary levels of 10 monohydroxylated PAHs (OH-PAHs). GDM and gestational hypertension were diagnosed and the relevant information was documented by specialist obstetricians and gynecologists. Logistic regression and restricted cubic spline regression were employed to investigate their single and nonlinear associations. Stratified analyses of pregnancy and body mass index data were conducted to determine their moderating effects on the abovementioned associations. Compared with the first quartile of urinary ∑OH-PAHs, the third or fourth quartile in all study participants was associated with an increased risk of GDM (quartile 3: odds ratio [OR] = 1.35, 95% confidence interval [CI]: 1.03-1.77) and gestational hypertension (quartile 3: OR = 1.88, 95% CI: 1.26-2.81; quartile 4: OR = 1.58, 95% CI: 1.04-2.39), respectively. Nonlinear associations of 1-OH-PYR with GDM (cutoff level: 0.02 µg/g creatinine [Cr]) and 1-OH-PHE with gestational hypertension (cutoff level: 0.06 µg/g Cr) were also observed. In pregnant women with overweight or obesity, 1-OH-PHE and 3-OH-PHE were more strongly associated with gestational hypertension. Our results indicate that exposure to PAH during pregnancy may significantly increase the maternal risks of GDM and gestational hypertension; however, this finding still needs to be confirmed through larger-scale prospective studies and biological evidence.

Challenges with Estimating Long-Term Overall Survival in Extensive Stage Small-Cell Lung Cancer: A Validation-Based Case Study.

Objective: The study aimed to explore methods and highlight the challenges of extrapolating the overall survival (OS) of immunotherapy-based treatment in first-line extensive stage small-cell lung cancer (ES-SCLC). Methods: Standard parametric survival models, spline models, landmark models, mixture and non-mixture cure models, and Markov models were fitted to 2-year data of the CASPIAN Phase 3 randomised trial of PD-L1 inhibitor durvalumab added to platinum-based chemotherapy (NCT03043872). Extrapolations were compared with updated 3-year data from the same trial and the plausibility of long-term estimates assessed. Results: All models used provided a reasonable fit to the observed Kaplan-Meier (K-M) survival data. The model which provided the best fit to the updated CASPIAN data was the mixture cure model. In contrast, the landmark analysis provided the least accurate fit to model survival. Estimated mean OS differed substantially across models and ranged from (in years) 1.41 (landmark model) to 4.81 (mixture cure model) for durvalumab plus etoposide and platinum and from 1.01 (landmark model) to 2.00 (mixture cure model) for etoposide and platinum. Conclusion: While most models may provide a good fit to K-M data, it is crucial to assess beyond the statistical goodness-of-fit and consider the clinical plausibility of the long-term predictions. The more complex cure models demonstrated the best predictive ability at 3 years, potentially providing a better representation of the underlying method of action of immunotherapy; however, consideration of the models' clinical plausibility and cure assumptions need further research and validation. Our findings underscore the significance of adopting a clinical perspective when selecting the most appropriate approach to model long-term survival, particularly when considering the use of more complex models.

Quantitative relationship between grip strength and quality of life in the older adult based on a restricted cubic spline model.

Background: Grip strength have been showed diverse associations with quality of life for the older adult population in the literature, still there is lack of evidence of the threshold value of grip strength for maintaining good quality of life in older adults. The purpose of this study was to study the dose-effect relationship between grip strength and quality of life in the older adult, and to explore the factors affecting quality of life in the older adult, so as to provide effective theoretical basis for realizing healthy aging. Methods: A total of 105 older adult people over 60 years old were selected from 3 communities in Beijing. Grip strength was measured by hand dynamometer apparatus and quality of life was assessed by 36-item Short-Form (SF-36). On the basis of controlling confounding factors, the dose-effect relationship between grip strength and quality of life was analyzed with the restricted cubic spline model. Results: The results showed that there was a dose-effect relationship between grip strength and physical component summary (PCS) (p < 0.01). However, grip strength was not significantly associated with mental component summary (MCS) (p > 0.05). The threshold value of grip strength for male and female is 34.75 and 23.2 kg, for normal weight group and overweight and obesity group is 24.82 and 29.00 kg, for 60-69, 70-79, 80+ years group is 24.88, 23.37, and 22.97 kg, respectively. When the grip strength value is lower than the threshold value, the increase of grip strength was related to significant improvement of quality of life of the older adult, and when the grip strength value is higher than the threshold value, the quality of life can be maintained in good condition. Conclusion: A dose-effect relationship was found between grip strength and physical health in quality of life. Results of our study indicated that the grip strength of the older adults needed to be greater than certain threshold values to maintain good quality of life.

Modeling and estimating a threshold effect: An application to improving cardiac surgery practices.

Estimating thresholds when a threshold effect exists has important applications in biomedical research. However, models/methods commonly used in the biomedical literature may lead to a biased estimate. For patients undergoing coronary artery bypass grafting (CABG), it is thought that exposure to low oxygen delivery (DO2) contributes to an increased risk of avoidable acute kidney injury. This research is motivated by estimating the threshold of nadir DO2 for CABG patients to help develop an evidence-based guideline for improving cardiac surgery practices. We review several models (sudden-jump model, broken-stick model, and the constrained broken-stick model) that can be adopted to estimate the threshold and discuss modeling assumptions, scientific plausibility, and implications in estimating the threshold. Under each model, various estimation methods are studied and compared. In particular, under a constrained broken-stick model, a modified two-step Newton-Raphson algorithm is introduced. Through comprehensive simulation studies and an application to data on CABG patients from the University of Michigan, we show that the constrained broken-stick model is flexible, more robust, and able to incorporate scientific knowledge to improve efficiency. The two-step Newton-Raphson algorithm has good computational performances relative to existing methods.

Comparison of statistical methods for extrapolating survival in previously untreated diffuse large B-cell lymphoma: results based on the POLARIX study.

OBJECTIVE: The ongoing Phase III randomized POLARIX study (GO39942; NCT03274492) demonstrated significantly improved progression-free survival (PFS) with polatuzumab vedotin plus rituximab, cyclophosphamide, doxorubicin and prednisone (Pola-R-CHP) versus rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) in patients with previously untreated diffuse large B-cell lymphoma (DLBCL). We compared statistical methodologies to extrapolate long-term PFS data from POLARIX. MATERIALS AND METHODS: This analysis explored four different approaches to extrapolate the POLARIX data: standard parametric survival, mixture-cure, landmark, and spline models. The resulting extrapolation curves were validated via comparison with the corresponding Kaplan-Meier (KM) curves from POLARIX and the POLARIX-like population of the Phase III GOYA study (NCT01287741; R-CHOP arm). RESULTS: The R-CHOP PFS KM curve from the GOYA validation set was well aligned with the POLARIX KM curve. As we anticipated that PFS in POLARIX would evolve similarly to that of GOYA, the data from GOYA were used to externally validate the extrapolated modelling results. While all four statistical methods were able to fit the data to the POLARIX KM curve, the mixture-cure model was the most accurate in predicting long-term PFS in the GOYA external validation set. In the mixture-cure model, generalized gamma distribution estimated 64% (95% confidence intervals [CI]: 56-71%) of patients to have long-term remission in the R-CHOP arm of POLARIX and GOYA, and 75% (95% CI: 70-79%) in the Pola-R-CHP arm of POLARIX. A limitation of this study was the comparison of the statistical models only in the PFS KM curves, since it was not possible to determine which statistical method was more appropriate to extrapolate the overall survival KM curves. CONCLUSIONS: Within this analysis, the mixture-cure model provided the best prediction of long-term outcomes from the primary PFS analysis of the POLARIX study.

A mixed finite element discretisation of linear and nonlinear multivariate splines using the Laplacian penalty based on biorthogonal systems.

We consider a mixed finite element method for a linear multivariate spline using the Laplacian penalty. Our discretisation is based on biorthogonal systems leading to a very simple and efficient finite element scheme. We also extend our approach to a nonlinear case and describe a split Bregman iteration scheme for the resulting nonlinear equations. We apply our numerical schemes to remove the mixture of Gaussian and impulsive noise for some test images.•This paper presents a method of discretising a multivariate spline using a finite element method.•The method uses a biorthogonal system to achieve an efficient finite element method.•The method is extended to cover a discretisation scheme for a nonlinear case, including an adaptation of the split Bregman method for the nonlinear case.

Dose-Response Relationship Between Life-Space Mobility and Mortality in Older Japanese Adults: A Prospective Cohort Study.

OBJECTIVES: Some epidemiological studies of older American adults have reported a relationship between life-space mobility (LSM) and mortality. However, these studies did not show a dose-response relationship and did not include individuals from other countries. Therefore, we evaluated the dose-response relationship between LSM and mortality in older adults. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: We used the data of 10,014 older Japanese adults (aged ≥65 years) who provided valid responses to the Life-Space Assessment (LSA) in the Kyoto-Kameoka study in Japan. METHODS: LSM was evaluated using the self-administered LSA consisting of 5 items regarding life-space from person's bedroom to outside town. The LSM score was calculated by multiplying life-space level by frequency score by independence score, yielding a possible range of 0 (constricted life-space) to 120 (broad life-space). These scores were categorized into quartiles (Qs). Mortality data were collected from July 30, 2011 to November 30, 2016. A multivariate Cox proportional hazards model that included baseline covariates were used to evaluate the relationship between LSM score and mortality risk. RESULTS: A total of 1030 deaths were recorded during the median follow-up period of 5.3 years. We found a negative association between LSM score and overall mortality even after adjusting for confounders [Q1: reference; Q2: hazard ratio (HR) 0.81, 95% CI 0.69-0.95; Q3: HR 0.70, 95% CI 0.59-0.85; Q4: HR 0.68, 95% CI 0.55-0.84, P for trend < .001]. Similar results were observed for the spline model; up to a score of 60 points, LSM showed a strong dose-dependent negative association with mortality, but no significant differences were observed thereafter (L-shaped relationship). CONCLUSIONS AND IMPLICATIONS: Our findings demonstrate an L-shaped relationship between LSM and mortality. This study will be useful in establishing target values for expanding the range of mobility among withdrawn older adults with a constricted life-space.

A Real-Time Comparison of Four Particulate Matter Size Fractions in the Personal Breathing Zone of Paris Subway Workers: A Six-Week Prospective Study.

We developed a Bayesian spline model for real-time mass concentrations of particulate matter (PM10, PM2.5, PM1, and PM0.3) measured simultaneously in the personal breathing zone of Parisian subway workers. The measurements were performed by GRIMM, a gravimetric method, and DiSCmini during the workers' work shifts over two consecutive weeks. The measured PM concentrations were analyzed with respect to the working environment, the underground station, and any specific events that occurred during the work shift. Overall, PM0.3 concentrations were more than an order of magnitude lower compared to the other PM concentrations and showed the highest temporal variation. The PM2.5 levels raised the highest exposure concern: 15 stations out of 37 had higher mass concentrations compared to the reference. Station PM levels were not correlated with the annual number of passengers entering the station, the year of station opening or renovation, or the number of platforms and tracks. The correlation with the number of station entrances was consistently negative for all PM sizes, whereas the number of correspondence concourses was negatively correlated with PM0.3 and PM10 levels and positively correlated with PM1 and PM2.5 levels. The highest PM10 exposure was observed for the station platform, followed by the subway cabin and train, while ticket counters had the highest PM0.3, PM1, and PM2.5 mass concentrations. We further found that compared to gravimetric and DiSCmini measurements, GRIMM results showed some discrepancies, with an underestimation of exposure levels. Therefore, we suggest using GRIMM, calibrated by gravimetric methods, for PM sizes above 1µm, and DiSCmini for sizes below 700 nm.

Investigation of the Utility of Multivariate Meta-Analysis Methods in Estimating the Summary Dose Response Curve.

BACKGROUND: Traditional meta-analyses often assess the effectiveness of different doses of the same intervention separately or examine the overall differences between intervention and placebo groups. The present study aimed to model the effect sizes obtained from different doses in multiple studies using a two-stage dose-response meta-analytic approach while taking dose variations into account. METHODS: Different dose-response meta-analysis models using linear, quadratic, and restricted cubic spline (RCS) functions were fitted. A two-stage approach utilizing multivariate meta-analysis was performed and the obtained results were compared with those of the univariate meta-analysis. A random effect dose-response meta-analysis was performed using data from an existing systematic review on combination therapy with zonisamide and anti-Parkinson drugs for Parkinson's disease. The effective or optimum dose for producing maximum response was also investigated. Moreover, a sensitivity analysis was performed by changing the knots of the RCS model. RESULTS: Dose-response meta-analysis was performed using data from four double-blinded randomized controlled trials with 724 and 309 patients with Parkinson's disease in dose and placebo arms, respectively. The quadratic model yielded the smallest Akaike information criterion (AIC), compared to the linear and RCS models, indicating it to be the best fit for the data. CONCLUSION: Compared to the traditional approach, the two-stage approach could model the dose-dependent effect of zonisamide on the Unified Parkinson's Disease Rating Scale (UPRDS) part III score and predict the outcome for different doses through a single analysis.

Ki67 testing in the clinical management of patients with non-metastatic colorectal cancer: Detecting the optimal cut-off value based on the Restricted Cubic Spline model.

The proliferation of the biomarker Ki67 has been extensively studied in colorectal cancer (CRC). Although numerous Ki67 cut-off values have previously been reported, the optimal cut-off value remains unclear with previous studies providing contrasting results. The present retrospective cohort study aimed to determine the optimal cut-off value for CRC. Ki67 levels and the prognosis of patients with non-metastatic CRC were obtained from the Electronic Health Information System of a tertiary hospital in Kunming City. The Restricted Cubic Spline (RCS) model was used to analyze the non-linear association between Ki67 levels and the risk of patient death and metastasis. Moreover, the RCS model was used to determine the optimal cut-off value of Ki67. Cox proportional hazards models were used to verify the effects of the cut-off value. In total, 210 patients with CRC and a median age of 62.5 years (age range, 23.0-88.0 years) were studied. Results of the present study demonstrated a non-linear association between Ki67 levels and the risk of patient death based on the RCS model, and at Ki67 levels ≥60%, the hazard ratio (HR) of patient death gradually increased. Using multivariate-adjusted Cox proportional hazards models, the results of the present study demonstrated that Ki67 ≥60% indicated a high-risk ratio for both distant metastasis and death [HR, 2.640; 95% confidence interval (CI), 1.066-6.539], compared with Ki67 <60% (HR, 2.558; 95% CI, 1.079-6.064). Therefore, Ki67 ≥60% may be the optimal cut-off value for the prediction of death and metastasis in patients with CRC. Thus, Ki67 may act as a biomarker for predicting the prognosis of patients with CRC, and the optimal cut-off value for the prediction of both death and metastasis of patients with CRC is 60%.

Association Between the Prevalence of Frailty and Doubly Labeled Water-Calibrated Energy Intake Among Community-Dwelling Older Adults.

BACKGROUND: Appropriate energy intake (EI) is essential to prevent frailty. Because self-reported EI is inaccurate and has systematic errors, adequate biomarker calibration is required. This study examined the association between doubly labeled water (DLW)-calibrated EI and the prevalence of frailty among community-dwelling older adults. METHOD: A cross-sectional study was performed using baseline data of 7,022 older adults aged ≥65 years in the Kyoto-Kameoka Study. EI was evaluated using a validated food frequency questionnaire (FFQ), and calibrated EI was obtained from a previously established equation using the DLW method. Physical and comprehensive frailty were defined by the Fried phenotype (FP) model and the Kihon Checklist (KCL), respectively. We used multivariable-adjusted restricted cubic spline logistic regression analysis. RESULTS: The prevalence of physical frailty was 14.8% and 13.6% in women and men, respectively. The spline models showed significant reverse J-shaped or U-shaped relationships between the prevalence of physical or comprehensive frailty against the DLW-calibrated EI, respectively. The lowest prevalence of both types of frailty was found at 1,900-2,000 kcal/d in women and 2,400-2,500 kcal/d in men, which corresponded to approximately 40 kcal/d/kg IBW (ideal body weight = 22 × height2) with DLW-calibrated EI. Uncalibrated EI underestimated approximately 20% compared with calibrated EI; underestimated EI were attenuated by calibration approach. CONCLUSIONS: This study suggests that low EI has a greater detrimental effect compared with excessive EI, particularly on physical frailty. Using biomarkers to calibrate EI holds promise for providing accurate energy requirements to establish guidelines used in public health and clinical nutrition.