Estimating the optimal linear combination of predictors using spherically constrained optimization.

BACKGROUND: In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing the area under the receiver operating characteristic curve. For ordinal responses, the optimal predictor combination can similarly be obtained by maximization of the hypervolume under the manifold (HUM). Since the empirical HUM is discontinuous, non-differentiable, and possibly multi-modal, solving this maximization problem requires a global optimization technique. Estimation of the optimal coefficient vector using existing global optimization techniques is computationally expensive, becoming prohibitive as the number of predictors and the number of outcome categories increases. RESULTS: We propose an efficient derivative-free black-box optimization technique based on pattern search to solve this problem, which we refer to as Spherically Constrained Optimization Routine (SCOR). Through extensive simulation studies, we demonstrate that the proposed method achieves better performance than existing methods including the step-down algorithm. Finally, we illustrate the proposed method to predict the severity of swallowing difficulty after radiation therapy for oropharyngeal cancer based on radiation dose to various structures in the head and neck. CONCLUSIONS: Our proposed method addresses an important challenge in combining multiple biomarkers to predict an ordinal outcome. This problem is particularly relevant to medical research, where it may be of interest to diagnose a disease with various stages of progression or a toxicity with multiple grades of severity. We provide the implementation of our proposed SCOR method as an R package, available online at https://CRAN.R-project.org/package=SCOR .

Change-point analysis through integer-valued autoregressive process with application to some COVID-19 data.

In this article, we consider the problem of change-point analysis for the count time series data through an integer-valued autoregressive process of order 1 (INAR(1)) with time-varying covariates. These types of features we observe in many real-life scenarios especially in the COVID-19 data sets, where the number of active cases over time starts falling and then again increases. In order to capture those features, we use Poisson INAR(1) process with a time-varying smoothing covariate. By using such model, we can model both the components in the active cases at time-point t namely, (i) number of nonrecovery cases from the previous time-point and (ii) number of new cases at time-point t. We study some theoretical properties of the proposed model along with forecasting. Some simulation studies are performed to study the effectiveness of the proposed method. Finally, we analyze two COVID-19 data sets and compare our proposed model with another PINAR(1) process which has time-varying covariate but no change-point, to demonstrate the overall performance of our proposed model.

Pressure injuries among admissions to a hospital in the tropics.

We report incidence rates for pressure injuries seen in an acute hospital in Singapore that were classified as Stage 3 or Stage 4. The characteristics of patients and the factors that explain variation in the primary outcome of duration of hospital stay are summarized. Existing data were available from Singapore General Hospital for all admissions from January 2016 to December 2019. Univariable analysis was done and a multivariable Poisson regression model estimated. Incidence rates declined from 4.05 to 3.4 per 1000 admissions in the 48 months between 2016 and 2019. The vast majority were community acquired with 75% in admission from the patients' home. Factors that explain variation in length of stay were, ethnicity; site of injury; community versus healthcare associated; inter-hospital transfer; fracture as reason for admission; and the number of days between admission and assessment of wound by specialist nurse. Stage 3 and 4 injuries arise in a home environment most often and are subsequently managed in acute hospital at high cost. These are novel epidemiological data from a hospital in the tropics where the potential to improve outcomes, implement screening and prevention, and thus increase the performance of health services is strong.

The Effectiveness of Web-Based Tailored Smoking Cessation Interventions on the Quitting Process (Project Quit): Secondary Analysis of a Randomized Controlled Trial.

BACKGROUND: Project Quit was a randomized Web-based smoking cessation trial designed and conducted by researchers from the University of Michigan, where its primary outcome was the 7-day point prevalence. One drawback of such an outcome is that it only focuses on smoking behavior over a very short duration, rather than the quitting process over the entire study period. OBJECTIVE: The aim of this study was to consider the number of quit attempts during the 6-month study period as an alternative outcome, which would better reflect the quitting process. We aimed to find out whether tailored interventions (high vs low) are better in reducing the number of quit attempts for specific subgroups of smokers. METHODS: To identify interactions between intervention components of smoking cessation and individual smoker characteristics, we employed Poisson regression to analyze the number of quit attempts. This approach allowed us to construct data-driven, personalized interventions. RESULTS: A negative effect of the number of cigarettes smoked per day (P=.03) and a positive effect of education (P=.03) on the number of quit attempts were detected from the baseline covariates (n=792). Thus, for every 10 extra cigarettes smoked per day, there was a 5.84% decrease in the expected number of quit attempts. Highly educated participants had a 15.49% increase in their expected number of quit attempts compared with their low-educated counterparts. A negative interaction between intervention component story and smoker's education was also detected (P=.03), suggesting that a high-tailored story given to highly educated people results in 13.50% decrease in the number of quit attempts compared with a low-tailored story. CONCLUSIONS: A highly individually tailored story is significantly more effective for smokers with a low level of education. This is consistent with prior findings from Project Quit based on the 7-day point prevalence.

A distribution-free smoothed combination method to improve discrimination accuracy in multi-category classification.

Results from multiple diagnostic tests are combined in many ways to improve the overall diagnostic accuracy. For binary classification, maximization of the empirical estimate of the area under the receiver operating characteristic curve has widely been used to produce an optimal linear combination of multiple biomarkers. However, in the presence of a large number of biomarkers, this method proves to be computationally expensive and difficult to implement since it involves maximization of a discontinuous, non-smooth function for which gradient-based methods cannot be used directly. The complexity of this problem further increases when the classification problem becomes multi-category. In this article, we develop a linear combination method that maximizes a smooth approximation of the empirical Hyper-volume Under Manifolds for the multi-category outcome. We approximate HUM by replacing the indicator function with the sigmoid function and normal cumulative distribution function. With such smooth approximations, efficient gradient-based algorithms are employed to obtain better solutions with less computing time. We show that under some regularity conditions, the proposed method yields consistent estimates of the coefficient parameters. We derive the asymptotic normality of the coefficient estimates. A simulation study is performed to study the effectiveness of our proposed method as compared to other existing methods. The method is illustrated using two real medical data sets.

Sample size estimation for comparing dynamic treatment regimens in a SMART: A Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes.

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual's status and progress should be used in practice to decide which treatment (e.g. type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Furthermore, in many health domains, count data are overdispersed-having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation.

Retrospective matched cohort study of incidence rates and excess length of hospital stay owing to pressure injuries in an Asian setting.

Background: Little is known about stage 1 and 2 pressure injuries that are health care-acquired. We report incidence rates of health care-acquired stage 1 and stage 2 pressure injuries, and, estimate the excess length of stay using four competing analytic methods. We discuss the merits of the different approaches. Methods: We calculated monthly incidence rates for stage 1 and 2 health care-acquired pressure injuries occurring in a large Singapore acute care hospital. To estimate excess stay, we conducted unadjusted comparisons with a control cohort, performed linear regression and then generalized linear regression with a gamma distribution. Finally, we fitted a simple state-based model. The design for the cost attribution work was a retrospective matched cohort study. Results: Incidence rates in 2016 were 0.553% (95% confidence interval [CI] 0.55, 0.557) and 0.469% (95% CI 0.466, 0.472) in 2017. For data censored at 60 days' maximum stay, the unadjusted comparisons showed the highest excess stay at 17.68 (16.43-18.93) days and multi-state models showed the lowest at 1.22 (0.19, 2.23) days. Conclusions: Poor-quality methods for attribution of excess length of stay to pressure injury generate inflated estimates that could mislead decision makers. The findings from the multi-state model, which is an appropriate method, are plausible and illustrate the likely bed-days saved from lowering the risk of these events. Stage 1 and 2 pressure injuries are common and increase costs by prolonging the length of stay. There will be economic value investing in prevention. Using biased estimates of excess length of stay will overstate the potential value of prevention.

Neutrophil gelatinase-associated lipocalin prior to cardiac surgery predicts acute kidney injury and mortality.

OBJECTIVE: We aimed to investigate whether preoperative serum neutrophil gelatinase-associated lipocalin (sNGALpre-op) predicted postoperative acute kidney injury (AKI) during hospitalisation and 1-year cardiovascular and all-cause mortality following adult cardiac surgery. METHODS: This study was a post hoc analysis of the Effect of Remote Ischemic Preconditioning on Clinical Outcomes in Patient Undergoing Coronary Artery Bypass Graft Surgery trial involving adult patients undergoing coronary artery bypass graft. Postoperative AKI within 72 hours was defined using the International Kidney Disease: Improving Global Outcomes classification. RESULTS: 1371 out of 1612 patients had data on sNGALpre-op. The overall 1-year cardiovascular and all-cause mortality was 5.2% (71/1371) and 7.7% (105/1371), respectively. There was an observed increase in the incidence of AKI from the first to the third tertile of sNGALpre-op (30.5%, 41.5% and 45.9%, respectively, p<0.001). There was also an increase in both cardiovascular and all-cause mortality from the first to the third tertile of sNGALpre-op, linear trend test with adjusted p=0.018 and p=0.013, respectively. The adjusted HRs for those in the second and third tertiles of sNGALpre-op compared with the first tertile were 1.60 (95% CI 0.78 to 3.25) and 2.22 (95% CI 1.13 to 4.35) for cardiovascular mortality, and 1.25 (95% CI 0.71 to 2.22) and 1.91 (95% CI 1.13 to 3.25) for all-cause mortality at 1 year. CONCLUSION: In a cohort of high-risk adult patients undergoing cardiac surgery, there was an increase in postoperative AKI and 1-year mortality from the first to the third tertile of preoperative serum NGAL. Those in the last tertile (>220 ng/L) had an estimated twofold increase risk of cardiovascular and all-cause mortality at 1 year. CLINICAL TRIAL REGISTRATION: NCT101247545; Post-results.