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At least 19 recordsLinked to original sources
PubMed1993

Charting the process of change: a primer on survival analysis.

Survival analysis is a powerful and useful technique for understanding qualitative change. This article provides a practical, nontechnical introduction to the use of survival analysis for social scientists. Important issues in using survival analysis are discussed, including research design, data preparation and management, and data analysis. Attendance data from a self-help organization are used to illustrate common survival analysis tasks such as describing the overall survival and hazard functions, examining covariate effects, and modeling the form of the hazard function over time. An appendix that discusses the strengths and weaknesses of current survival analysis computer programs is included.

Data Collection↗
PubMed1999

Statistical methodology: IX. survival analysis.

Survival analysis is a group of statistical methods used to analyze data representing the time to an event of interest, e.g., the duration of survival after an out-of-hospital cardiac arrest or the length of time a patient stays in the ED. Survival analysis properly accounts for patients who are lost to follow-up and for patients who have not yet experienced the event of interest at the end of the study's observation period (censored data). This article acquaints the reader with the terminology, methodology, and limitations of survival analysis. Specific methods discussed include life tables, the Kaplan-Meier product limit estimate, the log-rank test, and the multivariate Cox proportional hazards model.

Cardiopulmonary Resuscitation↗
PubMedDate not supplied

A primer on survival analysis.

Survival analysis is the statistical method for studying the time between entry to a study and a subsequent event. Survival is often the most important outcome in both observational and intervention studies. The analysis of survival, however, is not simple because of a number of factors. The time between entry to a study and the end of follow-up varies during the study period, as recruitment time for each subject is different. In addition, most studies have a finite duration during which not all subjects would have experienced the outcome of interest, while some patients might leave the study or are lost to follow-up. Survival analysis is not just concerned with time from treatment to death. The outcome could be any defined event such as time to doubling of serum creatinine concentration or first rejection episode following transplantation. An understanding of the principles involved in the statistical analyses of survival is essential for critical appraisal of studies, which employ these methods, and for designing clinical studies. This brief review describes the principles of survival analysis and its application to studies in nephrology.

Humans↗
PubMed1989

Effect of disease on days open assessed by survival analysis.

Survival analysis, a regression technique for analyzing data in which the time to an event is the measured outcome, was used to quantify the effects of five common herd diseases on days open in dairy cattle. Data were collected retrospectively from health and production records of 467 cows in five herds. In the 5-yr study period, 1059 lactations were followed to a conception or culling event. Retained placenta, nonsystemic metritis, systemic metritis, ovarian cysts, and lameness were associated with a decrease in conception rate and an increase in median days open. The hazard ratios for conception were .66, .83, .70, .70, and .69 and the increase in median days open 5, 15, 13, 22, and 28 d for the five diseases, respectively. Several attributes of survival analysis make it a valuable tool for quantifying days open in dairy cattle, including the ability to use reproductive information from observations which fail to conceive. Additionally, results obtained from survival analysis complement economic and decision analyses and will facilitate accurate cost assessment of suboptimal reproductive performance.

Animals↗
PubMed1985

Psychiatric recidivism in a psychosocial rehabilitation setting: a survival analysis.

Survival analysis, a methodology employed in studies of survival after diagnosis of a life-threatening physical illness, is used to examine psychiatric recidivism. The probability of survival in the community without rehospitalization was calculated for 400 consecutive admissions to Thresholds, a psychiatric rehabilitation center serving formerly hospitalized patients. It was found that the probability of a client's being rehospitalized at least once within 15 months of Thresholds intake was .55 and that the probability of rehospitalization varied with sex, race, age, and number of prior hospitalizations. The authors note that early intervention may be indicated for clients at higher risk of rehospitalization. They suggest that survival analysis be used in studies of psychiatric recidivism because it permits direct statistical comparisons between studies.

Actuarial Analysis↗
PubMed2005

Genetic and environmental factors influencing mortality up to weaning of Bruna dels Pirineus beef calves in mountain areas. A survival analysis.

Survival analysis techniques were used to analyze survival up to weaning of beef calves in the Pyrenean mountains areas of Catalonia, Spain. The Kaplan-Meier curve showed that the survival experience was not constant throughout the lactation period, as the mortality rate was more pronounced during the first month of life. The proportional hazards model analysis showed that several factors influenced the instantaneous mortality rate, with the herd-year effect having the strongest influence. Calves born in the first part of the breeding season, from September to February, had the lowest mortality risk (P < 0.001), showing that mortality risk increases as births accumulate. Calves from cows younger than 1,300 d of productive life had a higher risk of mortality (P < 0.05). Unassisted calvings presented the smallest risk of mortality, and mortality risk increased up to five times as birth became more difficult (P < 0.001). This risk also tended to increase slightly when calf birth weight was small (P < 0.10); for bigger calves, no increase of risk was detected, probably because calving difficulty was included in the model. These results suggest the need for improving the environment in the second part of the breeding period and paying more attention to births from younger cows. The survival curve fitted a parametric piecewise exponential function very well, with cut points at 16 and 32 d. The lower risk corresponded to the period of 33 to 180 d, the risk for the periods 17 to 32 d and 1 to 16 d being multiplied by 7 and 26, respectively. Confirming the robustness of the Cox model, the relative risks estimated for the different factors under this piecewise exponential model or a Weibull time-dependent model were similar to those reported above, as well as to those estimated under a frailty model, including the sire as a random effect. The modal estimates of sire variance under different baseline functions were close to 0.3, although the standard errors were very large. At weaning, the heritability estimate in the binary scale reached a value of only 0.037 because the survival at weaning was very high (96.9%) in this population. Nevertheless, in populations with a higher mortality, the inclusion of survival to weaning in the breeding objective might be justified. Overall, these results show that survival analysis is a powerful tool to analyze the mortality curve until weaning of beef calves.

Age Factors↗
PubMed2003

Impact of type traits on functional herd life of Quebec Holsteins assessed by survival analysis.

Survival analysis was used to study the effects of composite and descriptive linear type traits on functional herd life of Quebec Holsteins. Functional herd life was defined as the length of life from first calving to death, culling, or censoring, and was adjusted for 305-d milk production. The dataset contained information from 331,105 cows from Quebec province calving for the first time between 1981 and 1995; 58% of the records had type information. Weibull models were fitted to analyze the data. The hazard function was described as the product of a baseline hazard function and the time-independent effects of age at first calving and type, and the time-dependent effects of year of calving, stage of lactation x lactation number, annual change in herd size, 305-d milk production, and herd-year (random). Analyses were done one at a time for each type trait. The strongest relationships between survival and composite type traits were found for final score, mammary system, and feet and legs. Among the linear type traits, the highest impact on functional herd life was found for traits related to the udder.

Animals↗
PubMed1999

Applications in survival analysis.

Survival or failure time traits such as herd life and days open are both important economically and pose a number of challenges to an analysis based on linear mixed models. The main features of a survival trait are that it is the time until some event occurs, and some of the observations are censored. Survival models and the associated estimation procedures provide a flexible means of modeling survival traits. In this paper I will discuss the application of survival analysis based on the Weibull distribution. The components that make up a survival model will be presented along with their interpretation. Issues related to the model construction and estimation will be presented.

Animals↗
PubMed1981

A new prognostic classification of chronic lymphocytic leukemia derived from a multivariate survival analysis.

Survivals of two series of CLL patients (99 from a retrospective series and 196 from a prospective series) were studied separately. The three main staging systems (Rai, Binet, Rundles) agreed well, but as far as survival is concerned, too many stages are defined. The authors performed a Cox multivariate analysis of survival in order to isolate important prognostic factors at diagnosis and to use them to build a simple three-stage classification. Thrombopenia and anemia appeared as the most important risk factors. Among the nonanemic and nonthrombopenic patients, the number of involved areas was clearly related to prognosis in the authors' two series. This study allowed the authors to propose a new classification in three prognostic groups. Group C: anemia (Hb less than 10 g) and/or thrombopenia (platelets less than 100,000/mm3); about 15% of the patients; median of 2 years. Group B: no anemia, no thrombopenia, three or more involved areas (counting as one each of the following: axillary, cervical, inguinal, lymph nodes, whether unilateral or bilateral, spleen and liver); about 30% of patients; median of 7 years. Group A: no anemia, no thrombopenia, less than three involved areas; about 55% of patients; the survival of this group does not seem different from that of the French population of the same age and sex distribution. This three-stage classification only requires clinical examination and routine hemogram, has a good prognostic value which was confirmed on the series of Montserrat and Rozman (146 patients), and should therefore be helpful in planning new clinical trials.

Aged↗
PubMed2001

Accuracy of point predictions in survival analysis.

Survival time prediction is important in many applications, particularly for patients diagnosed with terminal diseases. A measure of prediction error taken from the medical literature is advocated as a practicable method of quantifying reliability of point predictions. Optimum predictions are derived for familiar survival models and the accuracy of these predictions is investigated. We argue that poor predictive capability is inherent to standard survival models with realistic parameter values. A lung cancer example is used to illustrate difficulties in prediction in practice.

Carcinoma, Non-Small-Cell Lung↗
PubMed1986

Conditional regression models for transient state survival analysis.

Survival models are important tools for the analysis of data when a disease event occurs in time and subjects are lost to follow-up. Many models, however, can also be adapted for use when an event is characterized by transitions through intermediate states of disease with increasing severity. In this presentation, such adaptations will be demonstrated for a class of conditional regression models for the analysis of transient state events occurring among grouped event times. The type of conditioning that will be described is useful in providing comparisons of specific disease states and an assessment of transition-dependent risk factor effects. An example will be given based on the Framingham Heart Study.

Adult↗
PubMed1994

A technique for using neural network analysis to perform survival analysis of censored data.

The purpose of this study was to demonstrate how a form of neural network analysis could be used to perform survival analysis on censored data, and to compare neural network analysis with the most commonly used technique for this type of analysis, Cox regression. In this study computer simulated data sets were used. The underlying rules connecting prognostic information to the hazard of death were defined to allow the construction of data sets with specific realistic properties that could be used to demonstrate situations in which neural network analysis had particular strengths in comparison with Cox regression modeling. Using these simulated data sets neural network analysis could produce successful predictive models, find interactions between variables, and recognize the importance of variables that contributed to the hazard rate as a complex function of the variables value and in situations where the proportionality of hazards assumption was violated. It was also demonstrated that neural network analysis was not a 'black box', but could lead to useful insights into the roles played by different prognostic variables in determining patient outcome.

Humans↗
PubMed2025

CoxKAN: Kolmogorov-Arnold networks for interpretable, high-performance survival analysis.

MOTIVATION: Survival analysis is a branch of statistics that is crucial in medicine for modeling the time to critical events such as death or relapse, in order to improve treatment strategies and patient outcomes. Selecting survival models often involves a trade-off between performance and interpretability; deep learning models offer high performance but lack the transparency of more traditional approaches. This poses a significant issue in medicine, where practitioners are reluctant to use black-box models for critical patient decisions. RESULTS: We introduce CoxKAN, a Cox proportional hazards Kolmogorov-Arnold Network for interpretable, high-performance survival analysis. Kolmogorov-Arnold Networks (KANs) were recently proposed as an interpretable and accurate alternative to multi-layer perceptrons. We evaluated CoxKAN on four synthetic and nine real datasets, including five cohorts with clinical data and four with genomics biomarkers. In synthetic experiments, CoxKAN accurately recovered interpretable hazard function formulae and excelled in automatic feature selection. Evaluations on real datasets showed that CoxKAN consistently outperformed the traditional Cox proportional hazards model (by up to 4% in C-index) and matched or surpassed the performance of deep learning-based models. Importantly, CoxKAN revealed complex interactions between predictor variables and uncovered symbolic formulae, which are key capabilities that other survival analysis methods lack, to provide clear insights into the impact of key biomarkers on patient risk. AVAILABILITY AND IMPLEMENTATION: CoxKAN is available at GitHub and Zenodo.

Humans↗
PubMed1994

Surgical treatment of endometriosis-associated infertility: meta-analysis compared with survival analysis.

OBJECTIVE: Our purpose was to evaluate the role of surgery in the treatment of endometriosis associated with infertility. STUDY DESIGN: We used a prospective cohort analysis of pregnancy rates and variables affecting pregnancy rates for surgical, medical, and no treatment. Our studies were combined with those reported by Hughes et al. (Fertil Steril 1993; 59:963-70), and the meta-analysis was expanded to include additional comparisons. Treatment was performed by a single surgeon in a referral reproductive endocrinology and surgery private practice. Results from 579 women with endometriosis and infertility in our study and the meta-analysis of 25 studies by Hughes et al. were examined. Interventions consisted of no treatment, medical treatment, or surgical treatment by laparoscopy or laparotomy. The main outcome measure was pregnancy rates. RESULTS: For minimal and mild disease, no treatment, laparoscopy, and laparotomy had equivalent 3-year estimated cumulative life-table pregnancy rates (67% +/- 12%, 68% +/- 4%, and 74% +/- 8%, respectively) that were higher than medical treatment pregnancy rates (Breslow p = 0.003). For moderate and severe disease, all but 11 patients were treated surgically. The 3-year estimated cumulative life-table pregnancy rates were 62% + 6% [corrected] for 120 laparoscopy cases and 44% + 6% [corrected] for 102 laparotomy cases (Breslow p = 0.054). For endometriomas, 48 laparoscopy patients had a 3-year estimated cumulative life-table pregnancy rate of 52% +/- 9% and 52 laparotomy patients had a 3-year estimated cumulative life-table pregnancy rate of 46% +/- 9% (Breslow p = 0.48). For 28 patients with complete cul-de-sac obliteration, the 3-year estimated cumulative life-table pregnancy rates were 30% +/- 14% after laparoscopy and 24% +/- 12% after laparotomy (Breslow p = 0.084). Comparison of our results with the expanded meta-analysis revealed deficiencies in the design of meta-analysis studies and the impact of our using life-table pregnancy rates controlled for factors influencing outcome (survival analysis with fixed covariates) rather than the simple pregnancy rates used in the meta-analysis. Benefits of sophisticated statistical techniques, including propensity scores, to adjust for noncomparability of groups in prospective cohort studies were identified. CONCLUSION: Both our study and the meta-analysis show that either no treatment or surgery is superior to medical treatment for minimal and mild endometriosis associated with infertility. For moderate and severe disease, surgery is usually used. In these patients experienced surgeons utilizing good clinical judgment can achieve results at operative laparoscopy at least equivalent to those at laparotomy, even in cases involving endometriomas and complete cul-de-sac obliteration. Prospective randomized trials should be performed to confirm these findings.

Cohort Studies↗
PubMed2002

Effectiveness of venlafaxine, extended release formulation, in the short-term and long-term treatment of generalized anxiety disorder: results of a survival analysis.

A survival analysis of data from two placebo-controlled, randomized, long-term (6-month) studies was used to examine the effectiveness of venlafaxine, extended release (XR) formulation, in patients with generalized anxiety disorder (GAD). Patients in a placebo-controlled, flexible-dose study received 75 to 225 mg/day venlafaxine XR, while patients in a placebo-controlled, fixed-dose study received once-daily venlafaxine XR doses of 37.5 mg, 75 mg, or 150 mg. The survival analysis was based on the clinician's decision to discontinue treatment in a placebo-controlled study and incorporated data from all patients who were randomized. In each study, placebo-treated patients discontinued treatment due to lack of efficacy more frequently and earlier than those receiving venlafaxine XR (p < 0.001, log-rank test). A dose-response relationship was apparent, with the lowest rate of withdrawal seen at the highest venlafaxine XR dose. Survival curves for discontinuations due to adverse events did not differ significantly in either study. These results were consistent with the conventional intent-to-treat efficacy assessments of changes in anxiety severity, highlighting the superiority of venlafaxine XR over placebo in the long-term treatment of GAD. Overall, these results demonstrate the clinical effectiveness of venlafaxine XR in the short-term and long-term treatment of GAD.

Antidepressive Agents, Second-Generation↗
PubMed2006

Establishing the change in antibiotic resistance of Enterococcus faecium strains isolated from Dutch broilers by logistic regression and survival analysis.

In this study, we investigated the change in the resistance of Enterococcus faecium strains isolated from Dutch broilers against erythromycin and virginiamycin in 1998, 1999 and 2001 by logistic regression analysis and survival analysis. The E. faecium strains were isolated from caecal samples that had been randomly collected from six slaughterhouses. Moreover, between the sample collection in 1998 and the sample collection in 1999, virginiamycin and the macrolide antibiotics (of which erythromycin is a member) have been banned in The Netherlands from use in broiler feeds as growth promoter. In the logistic regression analysis we used the internationally accepted cut-off values to determine whether bacteria were resistant or not. In the survival analysis, inhibition of bacterial growth was the event and time to event was replaced by concentration of antibiotic to event. As a consequence, changes in the growth of bacteria can be tested over an entire range of concentrations and no cut-off value for resistance has to be determined. We performed the survival analysis by use of a Cox logistic model with an odds ratio (OR) for the increase of the odds of the basic hazard rate as outcome. Both the logistic regression and the survival analyses showed that resistance to erythromycin and virginiamycin decreased during the study period. In the logistic regression model the ORs associated with the fraction of bacteria inhibited by the antibiotics in 2001 as compared to 1998 were 3.76 (2.57-5.49) for erythromycin and 11.65 (7.68-17.66) for virginiamycin. The corresponding ORs from the survival analysis were lower; 2.88 (2.21-3.76) and 2.11 (1.80-2.49), respectively. The reason for the differences between the ORs of the survival analysis and the logistic regression analysis is probably because most changes in resistance included the cut-off value and logistic regression specifically examines those changes.

Abattoirs↗
PubMed2005

Assessment of the impact of somatic cell count on functional longevity in Holstein and Jersey cattle using survival analysis methodology.

Survival analysis in a Weibull proportional hazards model was used to evaluate the impact of somatic cell count (SCC) on the involuntary culling rate of US Holstein and Jersey cows with first calvings from 1990 to 2000. The full data set, consisting of records from 978,043 Holstein and 250,835 Jersey cows, was divided into subsets (5 for Holsteins and 3 for Jerseys) based on herd average lactation SCC values. Functional longevity (also known as herd life or length of productive life) was defined as days from first calving until culling or censoring, after correcting for milk production. Our model included the time-dependent effects of herd-year-season, parity by stage of lactation interaction, within-herd-year quintile ranking for mature equivalent production, and lactation average SCC (rounded to the nearest 50,000 cells/mL), as well as the time-independent effect of age at first calving. Parameters of the Weibull distribution, as well as variance components for herd-year-season effects, were estimated within each group of herds. Mean failure and censoring times decreased as herd average SCC increased, and a nonlinear relationship was observed between SCC and longevity in all groups. The risk of culling for Holstein cows with lactation average SCC > 700,000 cells/mL was 3.4, 2.7, or 2.3 times greater, respectively, than that of Holstein cows with SCC of 200,000 to 250,000 cells/mL in herds with low, medium, or high average SCC. Likewise, the risk of culling for Jersey cows with lactation average SCC > 700,000 cells/mL was 4.0, 2.9, or 2.2 times greater, respectively, than that of Jersey cows with SCC of 200,000 to 250,000 cells/mL in low, medium, or high SCC herds. These trends may reflect more stringent culling of high SCC cows in herds with few mastitis problems. In addition, cows with lactation average SCC <100,000 cells/mL had a slightly higher risk of culling than cows with SCC of 100,000 to 200,000 cells/mL in both breeds, particularly in herds with high average SCC, where exposure to mastitis pathogens was likely.

Aging↗