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Results for “Survival analysis”

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PubMed2005

Modeling late entry bias in survival analysis.

In a failure time analysis, we sometimes observe additional study subjects who enter during the study period. These late entries are treated as left-truncated data in the statistical literature. However, with real data, there is a substantial possibility that the delayed entries may have extremely different hazards compared to the other standard subjects. We focus on a situation in which such entry bias might arise in the analysis of survival data. The purpose of the present article is to develop an appropriate methodology for making inference about data including late entries. We construct a model that includes parameters for the effect of delayed entry bias having no specification for the distribution of entry time. We also discuss likelihood inference based on this model and derive the asymptotic behavior of estimates. A simulation study is conducted for a finite sample size in order to compare the analysis results using our method with those using the standard method, where independence between entry time and failure time is assumed. We apply this method to mortality analysis among atomic bomb survivors defined in a geographical study region.

Algorithms↗

PubMedDate not supplied

Flexible modelling in survival analysis. Structuring biological complexity from the information provided by tumor markers.

The aim of the present article is to introduce and discuss the problem of optimal modelling of the prognostic information provided by putative prognostic variables, possibly measured on a quantitative scale. A number of methodological aspects will be treated, with particular reference to the role of spline functions and artificial neural networks, which will be discussed in the context of the analysis of survival data. The problem of the evaluation and the choice of the optimal statistical models will be examined, with particular attention to the critical aspects related to the definition of prognostic indexes on the basis of the results of the selected models. Clinical examples in breast cancer on the evaluation of the prognostic impact of several tumor markers are provided. This paper is addressed to all researchers who are interested in the evaluation of the prognostic role of tumor markers, therefore we will stress the necessity of integrating the methodologies of biological, clinical and statistical research in the assessment of prognosis.

Biomarkers, Tumor↗

PubMed1998

Prognostic relevance of apoptotic cell death in non-Hodgkin's lymphomas: a multivariate survival analysis including Ki67 and p53 oncoprotein expression.

AIMS: To evaluate the independent prognostic value of apoptotic versus proliferative fractions in a series of 92 patients with non-Hodgkin's lymphomas (NHL). METHODS AND RESULTS: Apoptotic fractions were quantified by use of the TdT (terminal deoxynucleotidyl-transferase)-mediated in-situ end-labelling technique (TUNEL), the percentage of positive cells constituting the apoptotic index (AI). Proliferative rate was expressed as percentage of Ki67 positive cells (Ki67 LI). Tissues were also stained for p53 protein with the DO-1 antibody. Patients were followed up until death (n = 33) or for an average of 63 months (n = 56). AI increased with malignancy grade and proliferative activity but was not related to location, cell of origin, clinical stage, bone marrow involvement and p53 expression. In multivariate analysis, overall survival was independently influenced by grade, stage, p53 LI and chemotherapy. The independent predictors of disease-free survival were Ki67 LI location and chemotherapy. AI turned out to be the only independent (negative) predictor of post-relapse survival. On the other hand, a low Ki67 LI increased the risk of relapse (logistic regression analysis) whereas a low p53 LI increased the probability of complete response. CONCLUSIONS: Our results suggest that the combined assessment of apoptotic fraction, proliferative rate and p53 expression may provide important prognostic information independent of other clinicopathological parameters in NHL.

Adolescent↗

PubMedDate not supplied

Survival analysis in quality improvement: the Diabetic Kidney Disease Project extrapolation group estimates.

Survival analysis is used in a wide variety of research settings to maximize the information extracted from a group of timed observations. Measures employed in continuous quality improvement (CQI) efforts often involve such observations. Yet to date, survival analysis has not been widely used to guide CQI efforts. This article presents the features of survival analysis that are most applicable to CQI efforts and illustrates the application of these techniques to a quality improvement project focused on diabetic kidney disease. Results are compared with those from a "standard" analysis. The interpretation of results is discussed in the context of constraints typical of CQI efforts. The article concludes with a recommendation for broader application of this valuable analytic methodology.

Data Collection↗

PubMed1997

Censoring issues in survival analysis.

A key characteristic that distinguishes survival analysis from other areas in statistics is that survival data are usually censored. Censoring occurs when incomplete information is available about the survival time of some individuals. We define censoring through some practical examples extracted from the literature in various fields of public health. With few exceptions, the censoring mechanisms in most observational studies are unknown and hence it is necessary to make assumptions about censoring when the common statistical methods are used to analyze censored data. In addition, we present situations in which censoring mechanisms can be ignored. The effects of the censoring assumptions are demonstrated through actual studies.

Bias↗

PubMed2001

Survival analysis for degree of compliance with supportive periodontal therapy.

BACKGROUND, AIMS: The purpose of this investigation was to evaluate the degree of compliance for supportive periodontal therapy (SPT) and to determine factors in relation to patients who failed to continue SPT programs. METHOD: A sample of 1896 patients who were treated between 1988 and 1999 was studied. The patients were classified by gender, age group and the distance between their house and the hospital. The number of visits was counted based on an electronic patient record for SPT. Data were analyzed by survival analysis. Survival probabilities in SPT were estimated by the Kaplan-Meier method and compared by the generalized Wilcoxon test. RESULTS: 28% of patients did not comply with the first visit for SPT. The older patients had higher tendencies to continue the SPT program than the younger patients. No significant differences in compliance were found between males and females or between short-distance and long-distance groups. However, when these factors were adjusted by age, significantly different patterns were shown on the curves of survival probability: in males, significant differences were found between the 20 s and 30 s (p<0.00001) and between the 50 s and 60 s (p<0.01). In females, significant differences were found between the 40 s and 50 s (p<0.001) and between the 60 s and 70 s (p<0.001). The differences of the survival probability by age group were greater in the short-distance group than in the long-distance group. CONCLUSIONS: The results suggest that age is the most important factor for compliance of the patients with SPT, not only alone but also in relation to the other factors.

Adult↗

PubMed2001

Building a database for survival analysis.

The AIRT built a national data base for survival data, collecting follow up information regarding incident cases from 14 cancer registries and 2 childhood cancer registries operating in Italy. In this study, 210,661 cases incident during the period 1990-1994, followed up until 1999, were analysed. The Veneto Cancer Registry did the data quality checks, the conversion in SAS format and developed a SAS procedure to perform the survival analysis. The procedure allows to select the cancer registry, the period and the cancer site to analyse, running the Hakulinens' software for relative survival analysis and exporting the results in Excel sheet to produce a standard layout.

Adult↗

PubMed2002

Survival analysis of human meniscal transplantations.

We describe a prospective survival analysis of 63 consecutive meniscal allografts transplanted into 57 patients. The lateral meniscus was transplanted in 34, the medial meniscus in 17, and both menisci (combined) in the same knee in six. For survival analysis we used persistent pain or mechanical damage as clinical criteria of failure. A total of 13 allografts failed (5 lateral, 7 medial, 1 medial and lateral). A significant negative correlation (p = 0.003) was found between rupture of the anterior cruciate ligament (ACL) and successful meniscal transplantation. A significant difference (p = 0.004) in the clinical results was found between lateral and medial meniscal transplants. The cumulative survival rate of the lateral, medial and combined allografts in the same knee, based on the life-table method and the Kaplan-Meier calculation, was 76%, 50% and 67%, respectively. The survival of medial meniscal allografts may improve when reconstruction of the ACL is carried out at the same time as meniscal transplantation in an ACL-deficient knee.

Adult↗

PubMed2005

Survival analysis applied to genetic evaluation for female fertility in dairy cattle.

The objective of this research was to study whether survival analysis results in a more accurate genetic evaluation for female fertility traits compared with the usual methodology based on linear models. The fertility trait studied was interval between calving and last insemination. A stochastic simulation describing the reproductive cycle of first-parity cows was done, in which true breeding values for conception rate were created. A model containing effects of sire and herd was used both with survival analysis and with mixed linear model analysis to predict sire breeding values. Correlations between true breeding values for conception rate and breeding values for calving to last insemination predicted by the best survival analysis model or the best linear model were 0.77 and 0.68, respectively. The results showed that when pregnancy status is known, survival analysis is a better method than linear models for genetic evaluation of conception rate when using observations on the interval between calving and last insemination.

Animals↗

PubMedDate not supplied

Bilateral lateral rectus recession for exotropia: a survival analysis.

Exotropia may progress during the childhood years, and the ideal time for surgical intervention remains uncertain. We reviewed our results following bilateral lateral rectus recession in 57 consecutive patients. In order to identify possible predictive covariates, we performed survival analysis using survival time as the time from surgery to failure. The patients' age at the time of surgery ranged from 11 months to 50 years (mean, 68 months). Follow up after surgery ranged from 1 to 105 months (mean, 47 months). Outcomes were considered failures if there was (1) a distance exodeviation greater than 10 prism diopters at any time after surgery, (2) a distance esodeviation greater than 10 delta more than 6 months postoperatively, or (3) further surgery for exotropia or esotropia. By these criteria, surgery was successful in 58% of cases. Results of the survival analysis indicated an estimated mean time to failure of 68 months. Patients with intermittent exotropia were more likely to remain aligned postoperatively than were those with constant deviations. The age at onset of exotropia, age at surgery, angle of deviation at different fixation distances, early postoperative alignment, and presence of symptoms, amblyopia, anisometropia, or incomitance before surgery were not predictive of success. We conclude that exotropia surgery may be performed with the same likelihood of success at any age, but patients with intermittent deviations do better.

Adolescent↗

PubMed1989

A survival analysis of hospitalization among patients with acquired immunodeficiency syndrome.

Survival analysis techniques were used in estimating lifetime inpatient utilization among patients diagnosed with acquired immunodeficiency syndrome (AIDS) using data on 863 members of the Kaiser Permanente Medical Care Program in the Northern California Region diagnosed with AIDS between January 1, 1981 and June 30, 1987. Using information on both deceased and living patients, we estimated means of 40.3 lifetime inpatient days and 3.3 hospitalizations among all AIDS patients. Those presenting with Kaposi's sarcoma experienced a mean of 7.6 fewer lifetime inpatient days than those presenting with Pneumocystis carinii pneumonia (95% confidence interval = .61, 14.6) and a mean of 11.0 (3.9, 18.6) fewer inpatient days than all other AIDS patients. Older patients had fewer hospital admissions than younger ones. Year of diagnosis does not appear to be related to lifetime utilization, and there is an indication that increased survival has been accompanied by decreased inpatient utilization intensity as measured on a per person-year basis. We recommend the use of survival analysis methods in the study of utilization among groups of patients with incomplete follow-up.

Acquired Immunodeficiency Syndrome↗

PubMed1998

Survival analysis with uncertain endpoints.

In some survival analysis applications, the endpoint of interest has a degree of uncertainty associated with it. These events are typically classified by the investigator or by an endpoint committee as true or false according to some decision rule, and the analysis proceeds using only the true endpoints. This procedure has two drawbacks: The cut point for the decision rule is somewhat arbitrary, and the information contained in the level of certainty is lost. This paper introduces a modification of the Cox regression model that allows all potential endpoints to be included in the analysis along with the level of certainty of each. Simulation results show this procedure to considerably increase the power of the standard procedure in a wide range of situations.

Angioplasty, Balloon↗

PubMed2006

Incorporating time-dependent covariates in survival analysis using the LVAR method.

In survival analysis, use of the Cox proportional hazards model requires knowledge of all covariates under consideration at every failure time. Since failure times rarely coincide with observation times, time-dependent covariates (covariates that vary over time) need to be inferred from the observed values. In this paper, we introduce the last value auto-regressed (LVAR) estimation method and compare it to several other established estimation approaches via a simulation study. The comparison shows that under several time-dependent covariate processes this method results in a smaller mean square error when considering the time-dependent covariate effect.

Computer Simulation↗

PubMed1999

On the use of survival analysis techniques to estimate medical care costs.

Measurement of treatment costs is important in the evaluation of medical interventions. Accurate cost estimation is problematic, when cost records are incomplete. Methods from the survival analysis literature have been proposed for estimating costs using available data. In this article, we clarify assumptions necessary for validity of these techniques. We demonstrate how assumptions needed for valid survival analysis may be violated when these methods are applied to cost estimation. Our observations are confirmed through simulations and empirical data analysis. We conclude that survival analysis approaches are not generally appropriate for the analysis of medical costs and review several valid alternatives.

Costs and Cost Analysis↗

PubMed2003

Survival analysis is a better estimate of recurrent disc herniation.

Studies on recurrent disc herniation quote rates of recurrence without regard to the times of recurrence and the influence of longer follow-up. Our objective was to assess the use of survival analysis to measure the rate of revision after lumbar microdiscectomy. We undertook a retrospective analysis of the hospital records of 993 patients who underwent lumbar microdiscectomy over a period of ten years. After calculating the overall rate of revision for the mean length of follow-up, we carried out a survival analysis using the life-table method. During the study period 49 patients had a revision microdiscectomy. This gave an overall rate of revision of 4.9% at a mean follow-up of 5.25 years. Using survival analysis, the rate of revision was 7.9% at a follow-up of ten years when the number at risk was 84. Survival analysis gives a more accurate estimation of the true rate of recurrence for patients undergoing lumbar microdiscectomy. The method allows better comparison between different interventions for disc herniation.

Adult↗

PubMed1993

Cox regression models for quality adjusted survival analysis.

We develop a method for incorporating covariates as regressors in a quality adjusted survival analysis (Q-TWiST) using Cox's proportional hazards model. The standard Q-TWiST method assumes that patients progress through a series of health states which differ in quality of life. The Kaplan-Meier product limit method is used to estimate the mean duration of each state by estimating the survival curves for the health state transition times. These estimates provide the basis for quality adjusted survival analysis. In this paper, the survival curves are modelled using Cox's proportional hazards regression. Quality adjusted survival is estimated given sets of covariate values, allowing one to profile patients. The results are useful for investigating how prognostic factors affect treatment benefits in terms of quality of life. We give a brief review of the standard Q-TWiST method and illustrate the extended methodology with an example from the International Breast Cancer Study Group Trial V comparing short duration versus long duration chemotherapy in the treatment of node-positive breast cancer.

Antineoplastic Agents↗

PubMed2004

Modelling converging hazards in survival analysis.

The Cox proportional hazards model has become the standard model for survival analysis. It is often seen as the null model in that "... explicit excuses are now needed to use different models" (Keiding, Proceedings of the XIXth International Biometric Conference, Cape Town, 1998). However, converging hazards also occur frequently in survival analysis. The Burr model, which may be derived as the marginal from a gamma frailty model, is one commonly used tool to model converging hazards. We outline this approach and introduce a mixed model which extends the Burr model and allows for both proportional and converging hazards. Although a semi-parametric model in its own right, we demonstrate how the mixed model can be derived via a gamma frailty interpretation, suggesting an E-M fitting procedure. We illustrate the modelling techniques using data on survival of hospice patients.

Algorithms↗

PubMed2000

Survival analysis in clinical trials: past developments and future directions.

The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association 53, 457-481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report 50, 163-170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods.

Biometry↗