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author:("Li, julong")
1.  The association between CCR5 Δ32 polymorphism and susceptibility to breast cancer 
Oncotarget  2017;8(47):82796-82802.
Background
Chemokine C-C motif receptor 5 (CCR5) gene polymorphisms have been proposed to play important roles in tumors. Δ32 polymorphism of this gene might correlate with breast cancer (BC) susceptibility. Nevertheless, inconsistent conclusions have been achieved as yet. We carried out this meta-analysis to draw a more comprehensive and convincing conclusion on this issue.
Results
No significant correlation of CCR5 Δ32 polymorphism with individual susceptibility to BC was detected in either total analysis (Δ32 vs. WT: OR=1.12, 95% CI=0.76-1.65; WT/Δ32 vs. WT/WT: OR=1.21, 95% CI=0.81-1.80) or subgroup analyses by ethnicity and control source.
Methods
All eligible studies were searched from electronic databases including Chinese National Knowledge Infrastructure (CNKI), PubMed, EMBASE, and Google Scholar Web. Strength of association between CCR5 Δ32 polymorphism and BC susceptibility was evaluated using pooled odds ratios (ORs) with their corresponding 95% confidence intervals (95% CIs). To further detect their correlation in specific populations, subgroup analyses were performed based on ethnicity and control source. Sensitivity analysis was conducted in this meta-analysis to test statistical stability of the final results. Publication bias among included studies was inspected with Begg’s funnel plot and Egger’s test.
Conclusion
CCR5 Δ32 polymorphism may not independently affect the risk of BC.
doi:10.18632/oncotarget.19959
PMCID: PMC5669929
CCR5; polymorphism; breast cancer; susceptibility; meta-analysis
2.  Major depressive disorder subtypes to predict long-term course 
Depression and anxiety  2014;31(9):765-777.
Background
Variation in course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here.
Methods
Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs) followed by k-means cluster analysis, are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) Surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode; number of with an episode lasting most of the year) and severity (hospitalization for MDD; disability due to MDD).
Results
Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity and was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor.
Conclusions
Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data.
doi:10.1002/da.22233
PMCID: PMC5125445  PMID: 24425049
Epidemiology; Depression; Anxiety/Anxiety Disorders; Suicide/Self Harm; Panic Attacks
3.  A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies 
Biometrics  2015;72(3):877-887.
Summary
To evaluate a new therapy versus a control via a randomized, comparative clinical study or a series of trials, due to heterogeneity of the study patient population, a pre-specified, predictive enrichment procedure may be implemented to identify an “enrichable” subpopulation. For patients in this subpopulation, the therapy is expected to have a desirable overall risk-benefit profile. To develop and validate such a “therapy-diagnostic co-development” strategy, a three-step procedure may be conducted with three independent data sets from a series of similar studies or a single trial. At the first stage, we create various candidate scoring systems based on the baseline information of the patients via, for example, parametric models using the first data set. Each individual score reflects an anticipated average treatment difference for future patients who share similar baseline profiles. A large score indicates that these patients tend to benefit from the new therapy. At the second step, a potentially promising, enrichable subgroup is identified using the totality of evidence from these scoring systems. At the final stage, we validate such a selection via two-sample inference procedures for assessing the treatment effectiveness statistically and clinically with the third data set, the so-called holdout sample. When the study size is not large, one may combine the first two steps using a “cross-training-evaluation” process. Comprehensive numerical studies are conducted to investigate the operational characteristics of the proposed method. The entire enrichment procedure is illustrated with the data from a cardiovascular trial to evaluate a beta-blocker versus a placebo for treating chronic heart failure patients.
doi:10.1111/biom.12461
PMCID: PMC4916037  PMID: 26689167
Cox model; Cross-validation; Stratified medicine; Survival analysis; Therapy-diagnostic co-development
4.  Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports 
Molecular psychiatry  2016;21(10):1366-1371.
Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.
doi:10.1038/mp.2015.198
PMCID: PMC4935654  PMID: 26728563
5.  Predicting U.S. Army suicides after hospitalizations with psychiatric diagnoses in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) 
JAMA psychiatry  2015;72(1):49-57.
IMPORTANCE
The U.S. Army experienced a sharp rise in suicides beginning in 2004. Administrative data show that among those at highest risk are soldiers in the 12 months after inpatient treatment of a psychiatric disorder.
OBJECTIVE
To develop an actuarial risk algorithm predicting suicide in the 12 months after US Army soldier inpatient treatment of a psychiatric disorder to target expanded post-hospital care.
DESIGN, SETTING, AND PARTICIPANTS
There were 53,769 hospitalizations of active duty soldiers in 2004–2009 with ICD-9-CM psychiatric admission diagnoses. Administrative data available prior to hospital discharge abstracted from a wide range of data systems (socio81 demographic, Army career, criminal justice, medical/pharmacy) were used to predict suicides in the subsequent 12 months using machine learning methods (regression trees, penalized regressions) designed to evaluate cross-validated linear, nonlinear, and interactive predictive associations.
MAIN OUTCOME
Suicides of soldiers hospitalized with psychiatric disorders in the 12 months after hospital discharge.
RESULTS
68 soldiers died by suicide within 12 months of hospital discharge (12.0% of all Army suicides), equivalent to 263.9 suicides/100,000 person-years compared to 18.5 suicides/100,000 person-years in the total Army. Strongest predictors included socio-demographics (male, late age of enlistment), criminal offenses (verbal violence, weapons possession), prior suicidality, aspects of prior psychiatric inpatient and outpatient treatment, and disorders diagnosed during the focal hospitalizations. 52.9% of post-hospital suicides occurred after the 5% of hospitalizations with highest predicted suicide risk (3,824.1 suicides/100,000 person years). These highest-risk hospitalizations also accounted for significantly elevated proportions of several other adverse post-hospital outcomes (unintentional injury deaths, suicide attempts, re-hospitalizations).
CONCLUSIONS AND RELEVANCE
The high concentration of risk of suicides and other adverse outcomes might justify targeting expanded post-hospital interventions to soldiers classified as having highest post-hospital suicide risk, although final determination requires careful consideration of intervention costs, comparative effectiveness, and possible adverse effects.
doi:10.1001/jamapsychiatry.2014.1754
PMCID: PMC4286426  PMID: 25390793
Army; machine learning; elastic net regression; military; penalized regression; predictive modeling; risk assessment; suicide
6.  Center-Within-Trial Versus Trial-Level Evaluation of Surrogate Endpoints 
Evaluation of candidate surrogate endpoints using individual patient data from multiple clinical trials is considered the gold standard approach to validate surrogates at both patient and trial levels. However, this approach assumes the availability of patient-level data from a relatively large collection of similar trials, which may not be possible to achieve for a given disease application. One common solution to the problem of too few similar trials involves performing trial-level surrogacy analyses on trial sub-units (e.g., centers within trials), thereby artificially increasing the trial-level sample size for feasibility of the multi-trial analysis. To date, the practical impact of treating trial sub-units (centers) identically to trials in multi-trial surrogacy analyses remains unexplored, and conditions under which this ad hoc solution may in fact be reasonable have not been identified. We perform a simulation study to identify such conditions, and demonstrate practical implications using a multi-trial dataset of patients with early stage colon cancer.
doi:10.1016/j.csda.2014.03.011
PMCID: PMC4104720  PMID: 25061255
clinical trials; meta-analysis; surrogate endpoints; survival analysis
7.  Evaluating Marker-Guided Treatment Selection Strategies 
Biometrics  2014;70(3):489-499.
Summary
A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this paper, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.
doi:10.1111/biom.12179
PMCID: PMC4213325  PMID: 24779731
Biomarker-analysis Design; Counterfactual Outcome; Personalized Medicine; Perturbation-resampling; Predictive Biomarkers; Subgroup Analysis
8.  Regression analysis of clustered interval-censored failure time data with the additive hazards model 
Journal of nonparametric statistics  2012;24(4):1041-1050.
This paper discusses regression analysis of clustered failure time data, which means that the failure times of interest are clustered into small groups instead of being independent. Clustering occurs in many fields such as medical studies. For the problem, a number of methods have been proposed, but most of them apply only to clustered right-censored data. In reality, the failure time data is often interval-censored. That is, the failure times of interest are known only to lie in certain intervals. We propose an estimating equation-based approach for regression analysis of clustered interval-censored failure time data generated from the additive hazards model. A major advantage of the proposed method is that it does not involve the estimation of any baseline hazard function. Both asymptotic and finite sample properties of the proposed estimates of regression parameters are established and the method is illustrated by the data arising from a lymphatic filariasis study.
doi:10.1080/10485252.2012.720256
PMCID: PMC4407380  PMID: 25914511
additive hazards model; clustered data; estimating equation; interval censoring; semi-parametric regression analysis
9.  Detection of the SHV genotype polymorphism of the extended-spectrum β-lactamase-producing Gram-negative bacterium 
Biomedical Reports  2015;3(2):261-265.
The prevalence of extended-spectrum β-lactamases (ESBLs) is due to the extensive usage of the extended-spectrum cephalosporins and leads to huge financial loss worldwide, whilst presenting a challenge to the clinical treatment. The aim of the present study was to delineate the frequency of ESBL occurrence in Enterobacteriaceae and confirm the SHV genotype. A random collection of 153 Escherichia coli isolates (E. coli) and 70 Klebsiella pneumoniae isolates were tested. The amplification products obtained by polymerase chain reaction were sequenced. Isolates with novel mutations were transformed to E. coli DH5 α. The minimum inhibitory concentration (MIC) was obtained by a microdilution method. The relevance ratio of ESBL was 67.7% and the proportion of the SHV β-lactamase gene (blaSHV) was 18.5%. A new genotype of β-lactamase was demonstrated and submitted to GenBank. A total of 12 mutational sites were found in 28 ESBL-producing isolates, including four nonsense mutations. Sensitive-rates of 28 ESBL-producing isolates to imipenem were 100%, and resistant-rates to penicillin, amoxicillin and oxacillin were 100%. The MIC of DH5 α-F8 to penicillin, oxacillin, cefoxitin, cefotaxime, cefepime, cefoperazone/sulbactam, imipenem and netilmicin was 512, 512, 2, 0.03, 0.06, 4, 0.015 and 32 respectively. In conclusion, ESBL and SHV-28 is the most prevalent bla. Imipenem is the most effective antibiotic to ESBL, and the 4th-generation cephalosporins and β-lactamase inhibitor compound are also effective. ESBL is mediated by plasmids and able to spread among different Enterobacteriaceae. In conclusion, new mutations of the blaSHV gene exist from at least 2010.
doi:10.3892/br.2015.416
PMCID: PMC4448012  PMID: 26075080
Escherichia coli; Klebsiella pneumoniae; extended-spectrum β-lactamase; SHV genotype; mutation; transconjugation; antibiotic sensitivity

Results 1-9 (9)