scholarly journals Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator

2016 ◽  
Vol 27 (2) ◽  
pp. 323-335 ◽  
Author(s):  
SJW Willems ◽  
A Schat ◽  
MS van Noorden ◽  
M Fiocco
Keyword(s):  
Dependent Censoring ◽  
Data Set ◽  
Mathematical Skills ◽  
Weighted Estimator ◽  
Inverse Probability ◽  
Kaplan Meier ◽  
Outcome Monitoring ◽  
Censored Observations ◽  
Event Times ◽  
User Friendly

Censored data make survival analysis more complicated because exact event times are not observed. Statistical methodology developed to account for censored observations assumes that patients’ withdrawal from a study is independent of the event of interest. However, in practice, some covariates might be associated to both lifetime and censoring mechanism, inducing dependent censoring. In this case, standard survival techniques, like Kaplan–Meier estimator, give biased results. The inverse probability censoring weighted estimator was developed to correct for bias due to dependent censoring. In this article, we explore the use of inverse probability censoring weighting methodology and describe why it is effective in removing the bias. Since implementing this method is highly time consuming and requires programming and mathematical skills, we propose a user friendly algorithm in R. Applications to a toy example and to a medical data set illustrate how the algorithm works. A simulation study was carried out to investigate the performance of the inverse probability censoring weighted estimators in situations where dependent censoring is present in the data. In the simulation process, different sample sizes, strengths of the censoring model, and percentages of censored individuals were chosen. Results show that in each scenario inverse probability censoring weighting reduces the bias induced in the traditional Kaplan–Meier approach where dependent censoring is ignored.

scholarly journals Hidden Imputations and the Kaplan-Meier Estimator

2020 ◽  
Vol 189 (11) ◽  
pp. 1408-1411 ◽  
Author(s):  
Stephen R Cole ◽  
Jessie K Edwards ◽  
Ashley I Naimi ◽  
Alvaro Muñoz
Keyword(s):  
Survival Data ◽  
Survival Function ◽  
Data Set ◽  
Kaplan Meier ◽  
Event Times

Abstract The Kaplan-Meier (KM) estimator of the survival function imputes event times for right-censored and left-truncated observations, but these imputations are hidden and therefore sometimes unrecognized by applied health scientists. Using a simple example data set and the redistribution algorithm, we illustrate how imputations are made by the KM estimator. We also discuss the assumptions necessary for valid analyses of survival data. Illustrating imputations hidden by the KM estimator helps to clarify these assumptions and therefore may reduce inappropriate inferences.

scholarly journals Were VCF patients at higher risk of mortality following the 2009 publication of the vertebroplasty “sham” trials?

2017 ◽  
Vol 29 (2) ◽  
pp. 375-383 ◽  
Author(s):  
K. L. Ong ◽  
D. P. Beall ◽  
M. Frohbergh ◽  
E. Lau ◽  
J. A. Hirsch
Keyword(s):  
Mortality Risk ◽  
Cox Regression ◽  
Compression Fracture ◽  
Balloon Kyphoplasty ◽  
Data Set ◽  
Mortality Risks ◽  
Medicare Data ◽  
Kaplan Meier ◽  
Risk Of Mortality ◽  
Insight Into

Abstract Summary The 5-year period following 2009 saw a steep reduction in vertebral augmentation volume and was associated with elevated mortality risk in vertebral compression fracture (VCF) patients. The risk of mortality following a VCF diagnosis was 85.1% at 10 years and was found to be lower for balloon kyphoplasty (BKP) and vertebroplasty (VP) patients. Introduction BKP and VP are associated with lower mortality risks than non-surgical management (NSM) of VCF. VP versus sham trials published in 2009 sparked controversy over its effectiveness, leading to diminished referral volumes. We hypothesized that lower BKP/VP utilization would lead to a greater mortality risk for VCF patients. Methods BKP/VP utilization was evaluated for VCF patients in the 100% US Medicare data set (2005–2014). Survival and morbidity were analyzed by the Kaplan-Meier method and compared between NSM, BKP, and VP using Cox regression with adjustment by propensity score and various factors. Results The cohort included 261,756 BKP (12.6%) and 117,232 VP (5.6%) patients, comprising 20% of the VCF patient population in 2005, peaking at 24% in 2007–2008, and declining to 14% in 2014. The propensity-adjusted mortality risk for VCF patients was 4% (95% CI, 3–4%; p < 0.001) greater in 2010–2014 versus 2005–2009. The 10-year risk of mortality for the overall cohort was 85.1%. BKP and VP cohorts had a 19% (95% CI, 19–19%; p < 0.001) and 7% (95% CI, 7–8%; p < 0.001) lower propensity-adjusted 10-year mortality risk than the NSM cohort, respectively. The BKP cohort had a 13% (95% CI, 12–13%; p < 0.001) lower propensity-adjusted 10-year mortality risk than the VP cohort. Conclusions Changes in treatment patterns following the 2009 VP publications led to fewer augmentation procedures. In turn, the 5-year period following 2009 was associated with elevated mortality risk in VCF patients. This provides insight into the implications of treatment pattern changes and associated mortality risks.

scholarly journals SHI7 Is a Self-Learning Pipeline for Multipurpose Short-Read DNA Quality Control

mSystems ◽  
2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Gabriel A. Al-Ghalith ◽  
Benjamin Hillmann ◽  
Kaiwei Ang ◽  
Robin Shields-Cutler ◽  
Dan Knights
Keyword(s):  
Quality Control ◽  
Dna Sequences ◽  
Sequence Data ◽  
Background Knowledge ◽  
Sequencing Technology ◽  
Data Set ◽  
Short Read ◽  
Dna Quality ◽  
Public Data ◽  
User Friendly

ABSTRACT Next-generation sequencing technology is of great importance for many biological disciplines; however, due to technical and biological limitations, the short DNA sequences produced by modern sequencers require numerous quality control (QC) measures to reduce errors, remove technical contaminants, or merge paired-end reads together into longer or higher-quality contigs. Many tools for each step exist, but choosing the appropriate methods and usage parameters can be challenging because the parameterization of each step depends on the particularities of the sequencing technology used, the type of samples being analyzed, and the stochasticity of the instrumentation and sample preparation. Furthermore, end users may not know all of the relevant information about how their data were generated, such as the expected overlap for paired-end sequences or type of adaptors used to make informed choices. This increasing complexity and nuance demand a pipeline that combines existing steps together in a user-friendly way and, when possible, learns reasonable quality parameters from the data automatically. We propose a user-friendly quality control pipeline called SHI7 (canonically pronounced “shizen”), which aims to simplify quality control of short-read data for the end user by predicting presence and/or type of common sequencing adaptors, what quality scores to trim, whether the data set is shotgun or amplicon sequencing, whether reads are paired end or single end, and whether pairs are stitchable, including the expected amount of pair overlap. We hope that SHI7 will make it easier for all researchers, expert and novice alike, to follow reasonable practices for short-read data quality control. IMPORTANCE Quality control of high-throughput DNA sequencing data is an important but sometimes laborious task requiring background knowledge of the sequencing protocol used (such as adaptor type, sequencing technology, insert size/stitchability, paired-endedness, etc.). Quality control protocols typically require applying this background knowledge to selecting and executing numerous quality control steps with the appropriate parameters, which is especially difficult when working with public data or data from collaborators who use different protocols. We have created a streamlined quality control pipeline intended to substantially simplify the process of DNA quality control from raw machine output files to actionable sequence data. In contrast to other methods, our proposed pipeline is easy to install and use and attempts to learn the necessary parameters from the data automatically with a single command.

scholarly journals SARS-CoV-2 Persistence in Immunocompromised Children

2021 ◽  
Author(s):  
Susan Dolan ◽  
Jean Mulcahy Levy ◽  
Angla Moss ◽  
Kelly Pearce ◽  
Samuel Dominguez ◽  
...  
Keyword(s):  
Viral Load ◽  
Median Time ◽  
Immunosuppressive Treatment ◽  
Viral Persistence ◽  
High Viral Load ◽  
Viral Loads ◽  
Mild Disease ◽  
Kaplan Meier ◽  
Diagnostic Panel ◽  
Event Times

Introduction/Objectives: We evaluated the length of time immunocompromised children (ICC) remain positive for SARS-CoV-2, identified factors associated with viral persistence and determined cycle threshold (CT) values of children with viral persistence as a surrogate of viral load. Methods: We conducted a retrospective cohort study of ICC at a pediatric hospital from March 2020-2021. Immunocompromised status was defined as primary, secondary or acquired due to medical comorbidities/immunosuppressive treatment. The primary outcome was time to first-of-two consecutive negative SARS-CoV-2 Polymerase chain reaction (PCR) tests at least 24 hours apart. Testing of sequential clinical specimens from the same subject was conducted using the Centers for Disease Control (CDC) 2019-nCoV Real-Time RT-PCR Diagnostic Panel assay. Descriptive statistics, Kaplan-Meier curve median event times and log-rank-sum tests were used to compare outcomes between groups. Results: Ninety-one children met inclusion criteria. Median age was 15.5 years (IQR 8-18 yrs), 64% were male, 58% were white, and 43% were Hispanic/Latinx. Most (67%) were tested in outpatient settings and 58% were asymptomatic. The median time to two negative tests was 42 days (IQR 25.0,55.0), with no differences in median time by illness presentation or level of immunosuppression. Seven children had >1 sample available for repeat testing, and 5/7 (71%) children had initial CT values of <30, (moderate to high viral load); 4 children had CT values of <30 3-4 weeks later, suggesting persistent moderate to high viral loads. Conclusions: Most ICC with SARS-CoV-2 infection had mild disease, with prolonged viral persistence >6 weeks and moderate to high viral load.

scholarly journals Survival Analysis and Determinants of The First Birth Interval in Indonesia

CAUCHY ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 55
Author(s):  
Alfensi Faruk ◽  
Endro Setyo Cahyono ◽  
Ning Eliyati
Keyword(s):  
Survival Analysis ◽  
Educational Level ◽  
Birth Interval ◽  
Cox Regression ◽  
Wealth Index ◽  
First Birth ◽  
Factors Affecting ◽  
Kaplan Meier ◽  
Censored Observations ◽  
Socioeconomic And Demographic Factors

<p class="Abstract">The first birth interval is one of the indicators of women’s fertility rate. Because in most cases the first birth interval contains censored observations, the only appropriate statistical method to handle such data is survival analysis. The main objective of this study is to analyze several socioeconomic and demographic factors that affect the first birth interval in Indonesia using the univariate and multivariate survival analysis, that is Kaplan-Meier method and Cox regression model, respectively. The sample is obtained from 2012 Indonesian Demographic and Health Survey (IDHS) and consists of 28242 ever married women aged 15-49 at the time of interview. The results show that age at the first birth, women's educational level, husband’s educational level, contraceptive knowledge, wealth index, and employment status are the significant factors affecting the first birth interval in Indonesia.</p>

scholarly journals Association of RASSF1A, DCR2, and CASP8 Methylation with Survival in Neuroblastoma: A Pooled Analysis Using Reconstructed Individual Patient Data

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Waleed M. Hassan ◽  
Mohamed S. Bakry ◽  
Timo Siepmann ◽  
Ben Illigens
Keyword(s):  
Overall Survival ◽  
Individual Patient Data ◽  
Wide Spectrum ◽  
Pooled Analysis ◽  
Patient Data ◽  
Epigenetic Alterations ◽  
Full Data ◽  
Data Set ◽  
Kaplan Meier ◽  
Registration Number

Neuroblastoma (NB) is a heterogeneous tumor affecting children. It shows a wide spectrum of clinical outcomes; therefore, development of risk stratification is critical to provide optimum treatment. Since epigenetic alterations such as DNA methylation have emerged as an important feature of both development and progression in NB, in this study, we aimed to quantify the effect of methylation of three distinct genes (RASSF1A, DCR2, and CASP8) on overall survival in NB patients. We performed a systematic review using PubMed, Embase, and Cochrane libraries. Individual patient data was retrieved from extracted Kaplan–Meier curves. Data from studies was then merged, and analysis was done on the full data set. Seven studies met the inclusion criteria. Methylation of the three genes had worse overall survival than the unmethylated arms. Five-year survival for the methylated arm of RASSF1A, DCR2, and CASP8 was 63.19% (95% CI 56.55-70.60), 57.78% (95% CI 47.63-70.08), and 56.39% (95% CI 49.53-64.19), respectively, while for the unmethylated arm, it was 93.10% (95% CI 87.40–99.1), 84.84% (95% CI 80.04-89.92), and 83.68% (95% CI 80.28-87.22), respectively. In conclusion, our results indicate that in NB patients, RASSF1A, DCR2, and CASP8 methylation is associated with poor prognosis. Large prospective studies will be necessary to confirm definitive correlation between methylation of these genes and survival taking into account all other known risk factors. (PROSPERO registration number CRD42017082264).

scholarly journals CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

2021 ◽  
Author(s):  
Jun Du ◽  
Jinguo Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Early Stage ◽  
Human Tumor ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Cox Proportional Hazard Regression ◽  
The Relationship

Abstract Background: The expression and molecular mechanism of cysteine rich transmembrane module containing 1 (CYSTM1) in human tumor cells remains unclear. The aim of this study was to determine whether CYSTM1 could be used as a potential prognostic biomarker for hepatocellular carcinoma (HCC).Methods: We first demonstrated the relationship between CYSTM1 expression and HCC in various public databases. Secondly, Kaplan–Meier analysis and Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CYSTM1 and the survival of HCC patients which data was downloaded in the cancer genome atlas (TCGA) database. Finally, we used the expression data of CYSTM1 in TCGA database to predict CYSTM1-related signaling pathways through bioinformatics analysis.Results: The expression level of CYSTM1 in HCC tissues was significantly correlated with T stage (p = 0.039). In addition, Kaplan–Meier analysis showed that the expression of CYSTM1 was significantly associated with poor prognosis in patients with early-stage HCC (p = 0.003). Multivariate analysis indicated that CYSTM1 is a potential predictor of poor prognosis in HCC patients (p = 0.036). The results of biosynthesis analysis demonstrated that the data set of CYSTM1 high expression was mainly enriched in neurodegeneration and oxidative phosphorylation pathways.Conclusion: CYSTM1 is an effective biomarker for the prognosis of patients with early-stage HCC and may play a key role in the occurrence and progression of HCC.

scholarly journals Targeted Minimum Loss-Based Estimation of Causal Effects in Right-Censored Survival Data with Time-Dependent Covariates: Warfarin, Stroke, and Death in Atrial Fibrillation

2013 ◽  
Vol 1 (2) ◽  
pp. 235-254 ◽  
Author(s):  
Jordan C. Brooks ◽  
Mark J. van der Laan ◽  
Daniel E. Singer ◽  
Alan S. Go
Keyword(s):  
Survival Data ◽  
Causal Effect ◽  
Estimating Equation ◽  
Dependent Censoring ◽  
Time Dependent ◽  
Causal Effects ◽  
Consistent Estimation ◽  
Censored Survival Data ◽  
Inverse Probability ◽  
Time Dependent Covariates

AbstractCausal effects in right-censored survival data can be formally defined as the difference in the marginal cumulative event probabilities under particular interventions. Conventional estimators, such as the Kaplan-Meier (KM), fail to consistently estimate these marginal parameters under dependent treatment assignment or dependent censoring. Several modern estimators have been developed that reduce bias under both dependent treatment assignment and dependent censoring by incorporating information from baseline and time-dependent covariates. In the present article we describe a recently developed targeted minimum loss-based estimation (TMLE) algorithm for general longitudinal data structures and present in detail its application in right-censored survival data with time-dependent covariates. The treatment-specific marginal cumulative event probability is defined via a series of iterated conditional expectations in a time-dependent counting process framework. The TMLE involves an initial estimator of each conditional expectation and sequentially updates these such that the resulting estimator solves the efficient influence curve estimating equation in the nonparametric statistical model. We describe the assumptions required for consistent estimation of statistical parameters and additional assumptions required for consistent estimation of the causal effect parameter. Using simulated right-censored survival data, the mean squared error, bias, and 95% confidence interval coverage probability of the TMLE is compared with those of the conventional KM and the inverse probability of censoring weight estimating equation, conventional maximum likelihood substitution estimator, and the double robustaugmented inverse probability of censoring weighted estimating equation. We conclude the article with estimation of the causal effect of warfarin medical therapy on the probability of “stroke or death” within a 1-year time frame using data from the ATRIA-1 observational cohort of persons with atrial fibrillation. Our results suggest that a fixed policy of warfarin treatment for all patients would result in 2% fewer deaths or strokes within 1-year as compared with a policy of withholding warfarin from all patients.

Plasmin(ogen) serves as a favorable biomarker for prediction of survival in advanced high-grade serous ovarian cancer

2017 ◽  
Vol 398 (7) ◽  
pp. 765-773 ◽  
Author(s):  
Shuo Zhao ◽  
Julia Dorn ◽  
Rudolf Napieralski ◽  
Axel Walch ◽  
Sandra Diersch ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Multivariable Analysis ◽  
Positive Staining ◽  
Mrna Levels ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract In serous ovarian cancer, the clinical relevance of tumor cell-expressed plasmin(ogen) (PLG) has not yet been evaluated. Due to its proteolytic activity, plasmin supports tumorigenesis, however, angiostatin(-like) fragments, derived from PLG, can also function as potent anti-tumorigenic factors. In the present study, we assessed PLG protein expression in 103 cases of advanced high-grade serous ovarian cancer (FIGO III/IV) by immunohistochemistry (IHC). In 70/103 cases, positive staining of tumor cells was observed. In univariate Cox regression analysis, PLG staining was positively associated with prolonged overall survival (OS) [hazard ratio (HR)=0.59, p=0.026] of the patients. In multivariable analysis, PLG, together with residual tumor mass, remained a statistically significant independent prognostic marker (HR=0.49, p=0.009). In another small patient cohort (n=29), we assessed mRNA expression levels of PLG by quantitative PCR. Here, elevated PLG mRNA levels were also significantly associated with prolonged OS of patients (Kaplan-Meier analysis; p=0.001). This finding was validated by in silico analysis of a microarray data set (n=398) from The Cancer Genome Atlas (Kaplan-Meier analysis; p=0.031). In summary, these data indicate that elevated PLG expression represents a favorable prognostic biomarker in advanced (FIGO III/IV) high-grade serous ovarian cancer.

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