Survival Analysis Commons^™

Detecting Outliers And Influential Observations In Survival Model., Nor Akmal Md Noh

Student Works (2010-2019)

This study proposes outlier and influential observation detection procedures for Cox proportional hazard model. In the estimation process, the parameters for Cox proportional hazard model are estimated using partial likelihood method, while the baseline hazard estimates are obtained using Nelson-Aalen method. The procedure of outlier detection is based on three types of residuals; deviance, log-odd and normal deviate residuals. We study their properties and compare their performance in detecting outliers via simulation. On the other hand, we propose a procedure of identifying influential observation using forward search method. The method has been shown to be effective in detecting influential observations …

The Joint Distribution Of Bivariate Exponential Under Linearly Related Model, Norou Diawara, Kumer Pial Das

Mathematics & Statistics Faculty Publications

In this paper, fundamental results of the joint distribution of the bivariate exponential distributions are established. The positive support multivariate distribution theory is important in reliability and survival analysis, and we applied it to the case where more than one failure or survival is observed in a given study. Usually, the multivariate distribution is restricted to those with marginal distributions of a specified and familiar lifetime family. The family of exponential distribution contains the absolutely continuous and discrete case models with a nonzero probability on a set of measure zero. Examples are given, and estimators are developed and applied to …

Linear Dependency For The Difference In Exponential Regression, Indika Sathish, Norou Diawara

Mathematics & Statistics Faculty Publications

In the field of reliability, a lot has been written on the analysis of phenomena that are related. Estimation of the difference of two population means have been mostly formulated under the no-correlation assumption. However, in many situations, there is a correlation involved. This paper addresses this issue. A sequential estimation method for linearly related lifetime distributions is presented. Estimations for the scale parameters of the exponential distribution are given under square error loss using a sequential prediction method. Optimal stopping rules are discussed using concepts of mean criteria, and numerical results are presented.

Mean Survival Time From Right Censored Data, Ming Zhong, Kenneth R. Hess

COBRA Preprint Series

A nonparametric estimate of the mean survival time can be obtained as the area under the Kaplan-Meier estimate of the survival curve. A common modification is to change the largest observation to a death time if it is censored. We conducted a simulation study to assess the behavior of this estimator of the mean survival time in the presence of right censoring.

We simulated data from seven distributions: exponential, normal, uniform, lognormal, gamma, log-logistic, and Weibull. This allowed us to compare the results of the estimates to the known true values and to quantify the bias and the variance. Our …

Survival Analysis With Error-Prone Time-Varying Covariates: A Risk Set Calibration Approach, Xiaomei Liao, David M. Zucker, Yi Li, Donna Spiegelman

Harvard University Biostatistics Working Paper Series

No abstract provided.

Analyzing Bivariate Survival Data With Interval Sampling And Application To Cancer Epidemiology, Hong Zhu, Mei-Cheng Wang

Johns Hopkins University, Dept. of Biostatistics Working Papers

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To …

Neurodevelopmental Outcome & Mr Spectroscopy Of Therapeutic Hypothermia After Pediatric Drowning, Sharon Mieras Perugini

Loma Linda University Electronic Theses, Dissertations & Projects

Despite advances in medical treatment and technology, outcome following pediatric drowning can vary widely from mild to severe impairments and death. Prognosis is often difficult to predict given a number of contributing factors. As such, this study examined the relationship between clinical indicators including submersion duration, initial GCS and PRISM scores, and waking time with outcome as well as metabolite ratios based on magnetic resonance spectroscopy. Research stemming from the area of cardiac arrest as well as anecdotal case study reports of cold water drownings suggests that lowering the body temperature may be helpful and protective. As such, the use …

Comparing Risk Scoring Systems Beyond The Roc Paradigm In Survival Analysis, Hajime Uno, Lu Tian, Tianxi Cai, Isaac S. Kohane, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Marginalized Frailty Models For Multivariate Survival Data, Megan Othus, Yi Li

Harvard University Biostatistics Working Paper Series

No abstract provided.

On The C-Statistics For Evaluating Overall Adequacy Of Risk Prediction Procedures With Censored Survival Data, Hajime Uno, Tianxi Cai, Michael J. Pencina, Ralph B. D'Agostino, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Barrow Island Ute Guide - Non-Indigenous Insects, Spiders And Snails, Mike Grimm, Kristofer Collett, Peter Davis, Marc Widmer, Andras Szito, Rob Emery, Peter Mangano

Biosecurity published reports

Barrow Island, the second largest island in Western Australia, is a Class A Nature Reserve and home to a variety of rare and endangered fauna and flora, some of which occur nowhere else. The island lies about 70 km off the north-west coast of Western Australia and 150 km west of Karratha.

Oil discoveries in the 1960s and the development of massive Northwest Shelf gas reserves since the 1990s made Barrow Island an important site for these industries. Strict environmental and quarantine conditions are placed on industry seeking permits to drill oil wells and construct gas processing plants. Oil production …

Estimating Subject-Specific Dependent Competing Risk Profile With Censored Event Time Observations, Yi Li, Lu Tian, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

A Class Of Semiparametric Mixture Cure Survival Models With Dependent Censoring, Megan Othus, Yi Li, Ram C. Tiwari

Harvard University Biostatistics Working Paper Series

No abstract provided.

Analysis Of Randomized Comparative Clinical Trial Data For Personalized Treatment Selections, Tianxi Cai, Lu Tian, Peggy H. Wong, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Selecting Optimal Treatments Based On Predictive Factors, Eric C. Polley, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

No abstract provided.

Optimal Cutpoint Estimation With Censored Data, Mithat Gonen, Camelia Sima

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

We consider the problem of selecting an optimal cutpoint for a continuous marker when the outcome of interest is subject to right censoring. Maximal chi square methods and receiver operating characteristic (ROC) curves-based methods are commonly-used when the outcome is binary. In this article we show that selecting the cutpoint that maximizes the concordance, a metric similar to the area under an ROC curve, is equivalent to maximizing the Youden index, a popular criterion when the ROC curve is used to choose a threshold. We use this as a basis for proposing maximal concordance as a metric to use with …

A New Class Of Rank Tests For Interval-Censored Data, Guadalupe Gomez, Ramon Oller Pique

Harvard University Biostatistics Working Paper Series

No abstract provided.

Calibrating Parametric Subject-Specific Risk Estimation, Tianxi Cai, Lu Tian, Hajime Uno, Scott D. Solomon, L. J. Wei

Harvard University Biostatistics Working Paper Series

No abstract provided.

Estimation For Arbitrary Functionals Of Survival, Kyle Rudser, Michael L. Leblanc, Scott S. Emerson

UW Biostatistics Working Paper Series

No abstract provided.

Joint Spatial Modeling Of Recurrent Infection And Growth With Processes Under Intermittent Observation, Farouk S. Nathoo

COBRA Preprint Series

In this article we present new statistical methodology for longitudinal studies in forestry where trees are subject to recurrent infection and the hazard of infection depends on tree growth over time. Understanding the nature of this dependence has important implications for reforestation and breeding programs. Challenges arise for statistical analysis in this setting with sampling schemes leading to panel data, exhibiting dynamic spatial variability, and incomplete covariate histories for hazard regression. In addition, data are collected at a large number of locations which poses computational difficulties for spatiotemporal modeling. A joint model for infection and growth is developed; wherein, a …

Nonparametric Regression Using Local Kernel Estimating Equations For Correlated Failure Time Data, Zhangsheng Yu, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Semiparametric Maximum Likelihood Estimation In Normal Transformation Models For Bivariate Survival Data, Yi Li, Ross L. Prentice, Xihong Lin

Harvard University Biostatistics Working Paper Series

No abstract provided.

Multiple Imputation Of Timing Of Mother-To-Child Transmission Of Hiv, Elizabeth Brown, Ying Qing Chen

UW Biostatistics Working Paper Series

In this paper, we present a model for imputing timing of mother-to- child transmission (MTCT) of HIV. The method re ects the three modes of MTCT of HIV: in utero, during delivery and via breastfeeding and can accomodate shapes for the baseline hazard that vary between infants. Ad- ditionally, it allows that the majority of infants do not experience MTCT of HIV. Final analyses from the imputed data sets are combined in a mul- tiple imputation framework. The methods is illustrated on a large trial designed to assess the use of antibiotics in preventing MTCT of HIV and is validated …

Immediate Implant Placement In Extraction Sites With Periapical Lesions: A Retrospective Study, Yuan-Lung Hung

Loma Linda University Electronic Theses, Dissertations & Projects

Immediate implant placement into fresh extraction sites has become a relatively routine clinical procedure with a favorable prognosis. However, immediate placement into extraction sockets with lesions has not been extensively documented in humans. Therefore, the purpose of this study was to retrospectively determine the survival rate of implants placed into extraction sockets with visible periapical lesions.

Patient charts of 544 immediately placed implants from Loma Linda University School of Dentistry, Center for Prosthodontics and Implant Dentistry were examined. Eighty-six of the 544 implants had been placed immediately into extraction sockets with periapical lesions and they were included in this study. …

A Note On Targeted Maximum Likelihood And Right Censored Data, Mark J. Van Der Laan, Daniel Rubin

U.C. Berkeley Division of Biostatistics Working Paper Series

A popular way to estimate an unknown parameter is with substitution, or evaluating the parameter at a likelihood based fit of the data generating density. In many cases, such estimators have substantial bias and can fail to converge at the parametric rate. van der Laan and Rubin (2006) introduced targeted maximum likelihood learning, removing these shackles from substitution estimators, which were made in full agreement with the locally efficient estimating equation procedures as presented in Robins and Rotnitzsky (1992) and van der Laan and Robins (2003). This note illustrates how targeted maximum likelihood can be applied in right censored data …

Empirical Efficiency Maximization, Daniel B. Rubin, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

It has long been recognized that covariate adjustment can increase precision, even when it is not strictly necessary. The phenomenon is particularly emphasized in clinical trials, whether using continuous, categorical, or censored time-to-event outcomes. Adjustment is often straightforward when a discrete covariate partitions the sample into a handful of strata, but becomes more involved when modern studies collect copious amounts of baseline information on each subject.

The dilemma helped motivate locally efficient estimation for coarsened data structures, as surveyed in the books of van der Laan and Robins (2003) and Tsiatis (2006). Here one fits a relatively small working model …

Regression Analysis Of A Disease Onset Distribution Using Diagnosis Data, Jessica G. Young, Nicholas P. Jewell, Steven J. Samuels

U.C. Berkeley Division of Biostatistics Working Paper Series

We consider methods for estimating the effect of a covariate on a disease onset distribution when the observed data structure consists of right-censored data on diagnosis times and current status data on onset times amongst individuals who have not yet been diagnosed. Dunson and Baird (2001) approached this problem using maximum likelihood, under the assumption that the ratio of the diagnosis and onset distributions is monotonic non-decreasing. As an alternative, we propose a two-step estimator, an extension of the approach of van der Laan, Jewell and Petersen (1997) in the single sample setting, that is computationally much simpler and requires …

Survival Analysis With Large Dimensional Covariates: An Application In Microarray Studies, David A. Engler, Yi Li

Harvard University Biostatistics Working Paper Series

Use of microarray technology often leads to high-dimensional and low- sample size data settings. Over the past several years, a variety of novel approaches have been proposed for variable selection in this context. However, only a small number of these have been adapted for time-to-event data where censoring is present. Among standard variable selection methods shown both to have good predictive accuracy and to be computationally efficient is the elastic net penalization approach. In this paper, adaptation of the elastic net approach is presented for variable selection both under the Cox proportional hazards model and under an accelerated failure time …

Semiparametric Bivariate Quantile-Quantile Regression For Analyzing Semi-Competing Risks Data, Daniel O. Scharfstein, James M. Robins, Mark Van Der Laan

Johns Hopkins University, Dept. of Biostatistics Working Papers

In this paper, we consider estimation of the effect of a randomized treatment on time to disease progression and death, possibly adjusting for high-dimensional baseline prognostic factors. We assume that patients may or may not have a specific type of disease progression prior to death and those who have this endpoint are followed for their survival information. Progression and survival may also be censored due to loss to follow-up or study termination. We posit a semi-parametric bivariate quantile-quantile regression failure time model and show how to construct estimators of the regression parameters. The causal interpretation of the parameters depends on …

Smoothed Rank Regression With Censored Data, Glenn Heller

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

A weighted rank estimating function is proposed to estimate the regression parameter vector in an accelerated failure time model with right censored data. In general, rank estimating functions are discontinuous in the regression parameter, creating difficulties in determining the asymptotic distribution of the estimator. A local distribution function is used to create a rank based estimating function that is continuous and monotone in the regression parameter vector. A weight is included in the estimating function to produce a bounded influence estimate. The asymptotic distribution of the regression estimator is developed and simulations are performed to examine its finite sample properties. …