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Factors affecting aseptic failure of fixation after primary Charnley total hip arthroplasty. Multivariate survival analysis.

Multivariate survival analysis with use of the Cox proportional-hazards model was applied to a consecutive series of 293 primary Charnley total hip arthroplasties performed on 246 patients. The purpose of the analysis was to identify risk factors for, and to quantitate their effects on, aseptic failure of fixation. The duration of follow-up ranged from one month to twenty-three years (average, thirteen years). The end point of survival was defined as radiographic evidence of failure of fixation or as a revision operation. Failure of fixation of the acetabular component was defined as complete demarcation or migration. Failure of the femoral component was defined as progression of at least one of five postoperative signs (subsidence, demarcation of the cement, separation of the component from the cement, fracture of the cement, and endosteal cavitation) or as the occurrence of at least two of these signs. Twenty-four specific items of data for each acetabular component and thirty specific items for each femoral component formed the sets of variables for the analysis. With use of radiographic evidence of failure as the end point, the sixteen-year rates of survival (with 95 per cent confidence interval) were 83.6 +/- 5.6 per cent for the acetabular components and 90.9 +/- 4.1 per cent for the femoral components. With use of revision as the end point, the sixteen-year rates of survival were 92.3 +/- 4.0 per cent and 95.6 +/- 3.2 per cent, respectively. The most important risk factor affecting radiographic loosening of the acetabular component was rapid wear of the polyethylene (0.2 millimeter or more annually), followed by the classification of the osteoarthrosis (as hypertrophic, normotrophic, or atrophic) according to the extent of osteophyte formation. The sockets in the hips that had hypertrophic osteoarthrosis survived longer than those in the other two groups. Survival of the acetabular component as determined on the basis of revision was affected only by rapid wear of the polyethylene. Survival of the femoral component, with either radiographic failure of fixation or revision as the end point, was affected by an unfavorable geometry of the medullary canal (a so-called stovepipe canal or a large canal). Patients who have rapid wear of the polyethylene, little osteophyte formation, or an unfavorable geometry of the canal should be followed carefully. These risk factors warrant additional evaluation.

Acetabulum↗

CANSURV: A Windows program for population-based cancer survival analysis.

Patient survival is one of the most important measures of cancer patient care (the diagnosis and treatment of cancer). The optimal method for monitoring the progress of patient care across the full spectrum of provider settings is through the population-based study of cancer patient survival, which is only possible using data collected by population-based cancer registries. The probability of cure, "statistical cure", is defined for a cohort of cancer patients as the percent of patients whose annual death rate equals the death rate of general cancer-free population. Mixture cure models have been widely used to model failure time data. The models provide simultaneous estimates of the proportion of the patients cured from cancer and the distribution of the failure times for the uncured patients (latency distribution). CANSURV (CAN-cer SURVival) is a Windows software fitting both the standard survival models and the cure models to population-based cancer survival data. CANSURV can analyze both cause-specific survival data and, especially, relative survival data, which is the standard measure of net survival in population-based cancer studies. It can also fit parametric (cure) survival models to the individual data. The program is available at . The colorectal cancer survival data from the Surveillance, Epidemiology and End Results (SEER) program [Surveillance, Epidemiology and End Results Program, The Portable Survival System/Mainframe Survival System, National Cancer Institute, Bethesda, 1999.] of the National Cancer Institute, NIH is used to demonstrate the use of CANSURV program.

Computer Simulation↗

Cause-specific actuarial survival analysis: a useful method for reporting survival data in men with clinically localized carcinoma of the prostate.

The primary objective of this retrospective 15-year survival analysis of 57 men with clinical stage B1 carcinoma of the prostate undergoing radical prostatectomy between 1951 and 1963 is to compute the cause-specific curve for these men and argue why it is a useful method to report survival data in men with clinically localized prostatic cancer. Historically, survival following radical prostatectomy and radiotherapy has been reported using all cause survival analysis. Cause-specific survival focuses on the impact of a disease process on survival, since men dying of causes unrelated to carcinoma of the prostate are considered lost to followup as of date of death of such unrelated causes. Cause-specific survival analysis reduces the impact of age, medical condition and other risk factors on survival rates. The cause-specific 15-year actuarial survival rate in our patients was 86 per cent. The 95 per cent confidence interval for this 15-year survival rate was 76.9 to 90.1 per cent. Based upon this series of patients with clinical stage B1 carcinoma of the prostate who undergo radical prostatectomy one may state that the chance of death of carcinoma of the prostate within 15 years of surgery averages 14 +/- 5 per cent. The cause-specific survival curve reached a plateau at 10 years, indicating that most men who survive 10 years are cured of the disease. With the Cox model regression analysis, mortality in this series was related positively to age at operation when the outcome variable was death of all causes (p equals 0.003) but it was unrelated to age when the outcome variable was prostate cancer mortality (p equals 0.85). Cause-specific survival rates are a more precise indicator of the impact of a therapeutic modality on survival and, therefore, they are useful to report survival data in men with localized carcinoma of the prostate and other disease when death from competing causes is an important consideration.

Actuarial Analysis↗

Alternative tree-structured survival analysis based on variance of survival time.

Tree-structured survival analysis (TSSA) is a popular alternative to the Cox proportional hazards regression in medical research of survival data. Several methods for constructing a tree of different survival profiles have been developed, including TSSA based on log-rank statistics, martingale residuals, Lp Wasserstein metrics between Kaplan-Meier survival curves, and a method based on a weighted average of the within-node impurity of the death indicator and the within-node loss function of follow-up times. Lu and others used variance of restricted mean lifetimes as an index of degree of separation (DOS) to measure the efficiency in separations of survival profiles by a classification method. Like tree-based regression analysis that uses variance as a criterion for node partition and pruning, the variance of restricted mean lifetimes between different groups can be an alternative index to log-rank test statistics in construction of survival trees. In this article, the authors explore the use of DOS in TSSA. They propose an algorithm similar to the least square regression tree for survival analysis based on the variance of the restricted mean lifetimes. They apply the proposed method to prospective cohort data from the Study of Osteoporotic Fracture that motivated the research and then compare their classification rule to those rules based on the conventional TSSA mentioned above. A limited simulation study suggests that the proposed algorithm is a competitive alternative to the log-rank or martingale residual-based TSSA approaches.

Aged↗

Survival analysis: a practical approach.

Survival analysis is a statistical method used to calculate the probability of an event such as death or relapse of disease occurring in a patient over time. Survival analysis is important in the interpretation of clinical research, and is frequently encountered in the colorectal literature. In this article, the terminology used in survival analysis is explained, specific examples are presented, and common methods of calculation demonstrated.

Humans↗

A longitudinal study of caries onset in initially caries-free children and baseline salivary mutans streptococci levels: a Kaplan-Meier survival analysis.

OBJECTIVES: To apply survival analysis to a longitudinal study of the relationship between salivary mutans streptococci (MS) levels at baseline in initially caries-free children and caries onset in deciduous, mixed, and permanent dentition. METHODS: The Kaplan-Meier survival analysis method was used to compare survival times to caries onset for initially caries-free children with low levels of MS at baseline with survival times to caries onset for initially caries-free children with high levels of MS at baseline. RESULTS: Data from a 6-year longitudinal study of caries risk in initially caries-free children in Rochester and the Finger Lakes Region of western New York were utilized for this study. Of 464 children analyzed, 327 had a low level of MS and 137 had a high level of MS at baseline. Survival analyses showed that children with a low level of MS at baseline remained caries-free for a longer period than children with a high level of MS at baseline. Statistically significant relationships [hazard ratios (HR)] with onset of caries in deciduous, mixed and permanent teeth were found with high and low levels of salivary MS. CONCLUSIONS: Based on our analysis, we concluded that children who were caries-free at baseline and who had high salivary MS levels at baseline would be at greater risk, i.e. more susceptible to caries onset, at any given time than caries-free children who had low salivary MS levels at baseline. Survival functions for deciduous, mixed and permanent dentitions with their 95% confidence limits have been calculated. Survival analysis for the exploration of longitudinal caries studies has an advantage over traditional statistical methods, as it takes into account censored observations and incorporates the concept of risk over time. Hence, survival analysis is well suited for studying transitions from one health state to another, in this case, from a caries-free state to a caries-active state.

Child↗

Survival analysis: caveats and pitfalls.

BACKGROUND: Survival analysis in clinical studies is important to assess the effectiveness of a given treatment and to understand the effect of various disease characteristics. A number of methods exist to estimate the survival rate and its standard error. However, one cannot be certain that these methods have been handled appropriately. The widespread use of computers has made it possible to carry out survival analysis without expert guidance, but using inappropriate methods can give rise to erroneous conclusions. The majority of the biomedical journals now recommend that a statistical review of each manuscript should be carried out by an experienced bio-statistician, in addition to obtaining expert referees' comments on the article. The problem is compounded in papers from third-world countries where bio-statisticians may not be available in all institutions to guide clinicians as to the selection of proper techniques. METHODS: The present paper deals with the various techniques of survival analysis and their interpretation, using a modal data set of malignant upper-aerodigestive tract melanoma patients treated in the Regional Cancer Centre, Trivandrum since 1982. RESULTS: The Kaplan-Meier method was found to be the most suitable for survival analysis. The median survival time is a better method of summarizing data than the mean. Rothman's method of estimation of the confidence limit is better than Peto's method as the confidence limit for survival probability tends to go beyond the range of 0-1.0 when calculated by Peto's method, especially when the sample size is small. CONCLUSION: The results from the present study suggest that survival analysis should be carried out by the Kaplan-Meier method. The median survival time should be provided wherever possible, rather than relying on mean survival. Confidence limits should be calculated as a measure of variability. A suitable rank test should be used to compare two or more survival curves, rather than a Z-test. Stratified analysis and Cox's model, when stratified analysis fails, can be used to define the impact of prognostic factors on survival.

Confidence Intervals↗

Survival analysis: caveats and pitfalls.

BACKGROUND: Survival analysis in clinical studies is important to assess the effectiveness of a given treatment and to understand the effect of various disease characteristics. A number of methods exist to estimate the survival rate and its standard error. However, one cannot be certain that these methods have been handled appropriately. The widespread use of computers has made it possible to carry out survival analysis without expert guidance, but using inappropriate methods can give rise to erroneous conclusions. The majority of the biomedical journals now recommend that a statistical review of each manuscript should be carried out by an experienced bio-statistician, in addition to obtaining expert referees' comments on the article. The problem is compounded in papers from third-world countries where bio-statisticians may not be available in all institutions to guide clinicians as to the selection of proper techniques. METHODS: The present paper deals with the various techniques of survival analysis and their interpretation, using a modal data set of malignant upper-aerodigestive tract melanoma patients treated in the Regional Cancer Centre, Trivandrum since 1982. RESULTS: The Kaplan-Meier method was found to be the most suitable for survival analysis. The median survival time is a better method of summarizing data than the mean. Rothman's method of estimation of the confidence limit is better than Peto's method as the confidence limit for survival probability tends to go beyond the range of 0-1.0 when calculated by Peto's method, especially when the sample size is small. CONCLUSION: The results from the present study suggest that survival analysis should be carried out by the Kaplan-Meier method. The median survival time should be provided wherever possible, rather than relying on mean survival. Confidence limits should be calculated as a measure of variability. A suitable rank test should be used to compare two or more survival curves, rather than a Z-test. Stratified analysis and Cox's model, when stratified analysis fails, can be used to define the impact of prognostic factors on survival.

Digestive System Neoplasms↗

Estimating genetic influences on the age-at-menarche: a survival analysis approach.

A survival analysis regression model is described for analyzing twin data on the age-at-menarche. The model includes latent genetic and environmental covariates and allows one to test hypotheses regarding the nature of familial aggregation for age-at-onset. Additionally, the model accommodates a variety of baseline survival distributions and therefore may be used to test different developmental hypotheses. Model-fitting results indicate that a survival model with a baseline gamma distribution gives an adequate fit to recalled age-at-menarche of 1,888 pairs of Australian female monozygotic and dizygotic twins. Further, results show that additive genetic and dominance genetic effects contribute to shared variation in age-at-menarche. If there are common environmental influences on the timing of menarche, they are completely obscured by nonadditivity in genetic factors, and information from other relationships would be required to detect their effect.

Adolescent↗

Survival analysis in practice.

Survival analysis is a versatile statistical tool that has been used in dentistry to evaluate such diverse subjects as the longevity of restorations, the natural history of caries and caries risk. It is hoped that this article will help readers to understand better such studies and make their own interpretation of the results, and even to carry out similar evaluations among their own patients.

Humans↗

A re-analysis of a caries clinical trial by survival analysis.

The decline in caries prevalence, the increases in the level of fluoride exposure, and the lack of placebo control subjects have complicated caries clinical trials in recent times. There has been a substantial increase in the numbers of subjects required for the detection of statistically significant differences between dental products, and hence, the cost of these trials has grown enormously. This study uses a new statistical approach to the analysis of the data from these trials with the ultimate aim of providing a more sensitive method of analysis. The new approach uses survival analysis, where the outcome measure is the survival time of an individual tooth surface. It exploits recent developments in the analysis of clustered survival data where survival times within the same cluster or subject are correlated. To illustrate, the new method of analysis was used for the North Wales, UK, caries clinical trial. It is concluded that survival analysis uses most of the data available in a caries clinical trial, an outcome measure that is easily understood and may lead to a more sensitive method of analysis.

Analysis of Variance↗

Analysis of undetectable HIV RNA using survival analysis: results must be interpreted carefully.

This methodological note exemplifies the drawbacks of conventional survival analysis for studying the determinants of the evolution of HIV RNA after the initiation of a HAART regimen in observational cohort studies. The potential for loss of information might yield differential misclassification biases and therefore unreliable results. Longitudinal models are better suited to analyze the repeated measures of a continuous outcome such as HIV RNA.

Antiretroviral Therapy, Highly Active↗

A demonstration of interval-censored survival analysis.

Interval-censoring occurs in survival analysis when the time until an event of interest is not known precisely (and instead, only is known to fall into a particular interval). Such censoring commonly is produced when periodic assessments (usually clinical or laboratory examinations) are used to assess if the event has occurred. My objectives were to raise awareness about interval-censoring including its existence, the potential ramifications of ignoring its existence, the different types of interval-censored data, and the analytical methods to analyze such data (including availability in standard statistical software). Asynchronous interval-censored survival analysis was demonstrated by parametric evaluation of risk factors for the time to first detected shedding of Salmonella muenster (identified by repeated periodic fecal cultures) for a herd of dairy cows. These results were compared with those from survival analyses which ignored or approximated the interval-censoring. Ignoring or approximating the asynchronous interval-censoring in the survival analysis generally resulted in the risk factors' regression coefficients having the same signs and a decrease (often >50%) in their absolute size. All the standard errors from the three methods of approximating the interval-censoring were <40% of their interval-censored counterparts. The conclusions drawn from the asynchronous interval-censored analysis versus those from the approximations varied dramatically. (The general conclusion from the approximations was that none of the risk factors for this example warranted further consideration.) That ignoring or approximating the left- and interval-censored nature of the dependent variable resulted in biased results was consistent with the literature. In the currently available asynchronous interval-censored models, the inclusion of time-dependent covariates that vary continuously is awkward. Statistical models for the semi-parametric estimation of asynchronous interval-censored survival analysis are not generally available in standard statistical software.

Animals↗

Genetic improvement of laying hens viability using survival analysis.

The survival of about eight generations of a large strain of laying hens was analysed separating the rearing period (RP) from the production period (PP), after hens were housed. For RP (respectively PP), 97.8% (resp., 94.1% ) of the 109,160 (resp., 100,665) female records were censored after 106 days (resp., 313 days) on the average. A Cox proportional hazards model stratified by flock (= season) and including a hatch-within-flock (HWF) fixed effect seemed to reasonably fit the RP data. For PP, this model could be further simplified to a non-stratified Weibull model. The extension of these models to sire-dam frailty (mixed) models permitted the estimation of the sire genetic variances at 0.261 +/- 0.026 and 0.088 +/- 0.010 for RP and PP, respectively. Heritabilities on the log scale were equal to 0.48 and 0.19. Non-additive genetic effects could not be detected. Selection was simulated by evaluating all sires and dams, after excluding all records from the last generation. Then, actual parents of this last generation were distributed into four groups according to their own pedigree index. Raw survivor curves of the progeny of extreme parental groups substantially differed (e.g., by 1.7% at 300 days for PP), suggesting that selection based on solutions from the frailty models could be efficient, despite the very large proportion of censored records.

Journal Article↗

Sparse Bayesian kernel survival analysis for modeling the growth domain of microbial pathogens.

Survival analysis is a branch of statistics concerned with the time elapsing before "failure," with diverse applications in medical statistics and the analysis of the reliability of electrical or mechanical components. We introduce a parametric accelerated life survival analysis model based on kernel learning methods that, at least in principal, is able to learn arbitrary dependencies between a vector of explanatory variables and the scale of the distribution of survival times. The proposed kernel survival analysis method is then used to model the growth domain of Clostridium botulinum, the food processing and storage conditions permitting the growth of this foodborne microbial pathogen, leading to the production of the neurotoxin responsible for botulism. A Bayesian training procedure, based on the evidence framework, is used for model selection and to provide a credible interval on model predictions. The kernel survival analysis models are found to be more accurate than models based on more traditional survival analysis techniques but also suggest a risk assessment of the foodborne botulism hazard would benefit from the collection of additional data.

Artificial Intelligence↗

Longitudinal analysis of deciduous tooth emergence: II. Parametric survival analysis in Bangladeshi, Guatemalan, Japanese, and Javanese children.

We present a form of parametric survival analysis that incorporates exact, interval-censored, and right-censored times to deciduous tooth emergence. The method is an extension of common cross-sectional procedures such as logit and probit analysis, so that data arising from mixed longitudinal and cross-sectional studies can be properly combined. We extended the method to incorporate and estimate a proportion of agenic teeth. While we concentrate on deciduous tooth emergence, the method is relevant to studies of permanent tooth emergence and other developmental events. Deciduous tooth emergence data were analyzed from four longitudinal studies. The samples are 1,271 rural Guatemalan children examined every three months up to age two and every six months thereafter as part of the INCAP study; 397 rural Bangladeshi children examined monthly to age one and quarterly thereafter as part of the Meheran Growth and Development Study; 468 rural Indonesian children examined monthly as part of the Ngaglik study; and 114 urban Japanese children examined monthly in studies from 1910 and 1920. Although all four studies were longitudinal, many observations from the Guatemala and Bangladesh studies were effectively cross-sectionally observed. Three different parametric forms were used to model the eruption process: a normal distribution, a lognormal distribution, and a lognormal distribution with age shifted to shortly after conception. All three distributions produced reliable estimates of central tendencies, but the shifted lognormal distribution produced the best overall estimates of shape (variance) parameters. Estimates of emergence were compared to other studies that used similar methods. Japanese children showed relatively fast emergence times for all teeth. Bangladeshi and Javanese children showed emergence times that were slower than are found in most previous studies. Estimates of agenesis were not significantly different from zero for most teeth. One or two central incisors showed significant agenesis that ranged from 0.1 to 0.8% in three of the samples; even so, failure to model the agenic proportion did not seriously bias the estimates.

Bangladesh↗

Using conformation traits to improve reliability of genetic evaluation for herd life based on survival analysis.

Genetic evaluation for herd life based on survival analysis utilizes information available on all animals, dead (uncensored) and alive (censored), but the reliability of bulls' breeding values depends only on the number of uncensored daughters. Therefore, information on correlated conformation traits scored on daughters during their first lactation may be essential for the evaluation of young bulls with mostly censored daughters. Currently available programs for genetic evaluation based on survival analysis cannot combine indirect information on conformation traits with direct information on herd life, nor can they estimate genetic covariances between herd life and conformation traits. In this study, an alternative approach has been developed and tested using data on Swiss Simmental and Red & White cattle. Genetic covariances were approximated using breeding values for herd life from a survival analysis and BLUP breeding values for 26 linear conformation traits from a separate multivariate analysis. An index combining direct breeding values for herd life and indirect breeding values obtained from conformation traits was constructed. The relative weighting of both information sources varied depending on the amount of available information. The maximum reliability based only on conformation traits was 0.64. Except for old bulls with >100 uncensored daughters, the combined reliability was always higher than the direct reliability from survival analysis.

Animals↗

[Survival analysis techniques].

Statistical methods known as survival analyses are useful for analyzing time-related events, in which time from a benchmark event to an endpoint is the focus of interest. Survival analysis describes not only patient survival statistics (as suggested by the name), but also other dichotomous outcomes such as time of remission, time of breastfeeding, etc. This paper discusses survival analysis techniques, commenting and comparing their utilization, especially in the field of oncology. It also presents and discusses types of epidemiological studies and data sources to which this type of analysis is applied. The authors take into account the difference between hospital-based or clinical series and population-based approaches. Interpretation of results is also discussed.

Data Interpretation, Statistical↗