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PubMed1993

[From multiple regression analysis to logistic model, proportional hazard model and log linear model. Its concept and application].

Recently logistic model, proportional hazard model and log linear model have been used frequently in the medical literatures. Here, each model is reviewed briefly from basics to its application, pointing out pitfalls in its application, some of which are common to any regression analysis. The logistic model is especially useful for the analysis of retrospective data where odds ratio is utilized to evaluate the outcome probability. On the other hand, proportional hazard model is useful when we analyze censored data, utilizing hazard function. Log linear model has been used where contingency table has more than three independent variables, the situation where its applicability in clinical medicine is wide. Familiarity with these statistical methods would enable us to evaluate data more effectively and efficiently and ultimately to read literature more easily.

Models, Statistical↗
PubMed1991

The investigation of age at onset as a risk factor for mortality in persons with insulin-dependent diabetes mellitus using Cox proportional hazards models.

Proportional hazards models measuring the effect of age at onset of insulin-dependent diabetes mellitus on mortality risk are presented. The study population consisted of 924 insulin-dependent diabetic patients who were seen within 1 year of diagnosis at Children's Hospital of Pittsburgh between 1950 and 1981 and were more than 20 years old at follow-up. Age at diabetes onset was categorized as prepubertal or pubertal, defined by age. Individuals with pubertal onset of diabetes had a significantly higher risk of mortality compared with those with prepubertal onset by diabetes duration but not by attained age. It is proposed that age at onset is an independent determinant of mortality in diabetic individuals and may represent either heterogeneity within insulin-dependent diabetes mellitus with respect to long-term prognosis or an interactive effect of diabetes duration and puberty on prognosis.

Adolescent↗
PubMed1989

[Explanatory essay for researchers who plan to analyse their clinical data using Cox's proportional hazard model].

Proportional hazard model was proposed by Cox as a multivariate model suitable for evaluation of prognostic factors and/or therapeutic effects. This paper aims at explaining fundamental concepts of the model for clinical researchers, as clear as possible. The relationship between hazard ratio and survival curves is demonstrated using an example of survivals calculated from an exponential function. Usage of computer programme SAS is described in brief for the readers who plan to analyse their own clinical data by this model.

Analysis of Variance↗
PubMed1992

Flexible covariate effects in the proportional hazards model.

The proportional hazards model is frequently used in analyzing the results of clinical trials, when it is often the case that the outcomes are right-censored. This model allows one to measure treatment effects and simultaneously identify and adjust for prognostic factors that might influence the outcome. In this paper, we outline a class of semiparametric models that allows one to model prognostic factors nonlinearly, and have the data suggest the form of their effect. The methods are illustrated in an analysis of data from a breast cancer clinical trial.

Adult↗
PubMed2003

Analysis of the relationship between type traits, inbreeding, and functional survival in Jersey cattle using a Weibull proportional hazards model.

A Weibull proportional hazards model was used to analyze the effects of 13 linear type traits, final score, and inbreeding on the functional survival of 268,008 US Jersey cows in 2416 herds with first calving from 1981 to 2000. Functional survival was defined as the number of days from first calving until involuntary culling or censoring. The statistical model included the time-dependent effects of herd-year-season of calving, parity by stage of lactation interaction, and within-herd-year quintile for mature equivalent milk yield, as well as the time-independent effects of inbreeding, age at first calving, and linear type traits or final score (analyzed one at a time). Each type trait was divided into 10 classes, and the relative risk of involuntary culling was calculated for animals in each class after accounting for the aforementioned management factors. Type traits with the greatest contribution to the likelihood function were udder depth, fore udder attachment, front teat placement, and udder support. Cows with low scores for these traits had a risk of culling that was 1.3 to 1.8 times that of cows with intermediate scores. Cows with high scores for udder depth and udder support had a risk of culling only 0.7 to 0.85 as great as that of cows with intermediate scores. Intermediate scores were desirable for rear leg set, dairy form, and strength, but stature, rump angle, and rump width had negligible effects on survival. Cows with low final scores had a risk of culling that was 1.35 times that of cows with intermediate scores, whereas cows with high final scores had a risk of culling that was 0.8 times that of cows with intermediate scores. Animals with inbreeding coefficients greater than 10% had a slightly higher risk of culling than animals with inbreeding coefficients less than 5%.

Animals↗
PubMed1997

Derivation of the linear-logistic model and Cox's proportional hazard model from a canonical system description.

The linear-logistic regression model and Cox's proportional hazard model are widely used in epidemiology. Their successful application leaves no doubt that they are accurate reflections of observed disease processes and their associated risks or incidence rates. In spite of their prominence, it is not a priori evident why these models work. This article presents a derivation of the two models from the framework of canonical modeling. It begins with a general description of the dynamics between risk sources and disease development, formulates this description in the canonical representation of an S-system, and shows how the linear-logistic model and Cox's proportional hazard model follow naturally from this representation. The article interprets the model parameters in terms of epidemiological concepts as well as in terms of general systems theory and explains the assumptions and limitations generally accepted in the application of these epidemiological models.

Communicable Diseases↗
PubMed2005

Bayesian model selection and averaging in additive and proportional hazards models.

Although Cox proportional hazards regression is the default analysis for time to event data, there is typically uncertainty about whether the effects of a predictor are more appropriately characterized by a multiplicative or additive model. To accommodate this uncertainty, we place a model selection prior on the coefficients in an additive-multiplicative hazards model. This prior assigns positive probability, not only to the model that has both additive and multiplicative effects for each predictor, but also to sub-models corresponding to no association, to only additive effects, and to only proportional effects. The additive component of the model is constrained to ensure non-negative hazards, a condition often violated by current methods. After augmenting the data with Poisson latent variables, the prior is conditionally conjugate, and posterior computation can proceed via an efficient Gibbs sampling algorithm. Simulation study results are presented, and the methodology is illustrated using data from the Framingham heart study.

Age of Onset↗
PubMed2006

Inference for a family of survival models encompassing the proportional hazards and proportional odds models.

For survival data regression, the Cox proportional hazards model is the most popular model, but in certain situations the Cox model is inappropriate. Various authors have proposed the proportional odds model as an alternative. Yang and Prentice recently presented a number of easily implemented estimators for the proportional odds model. Here we show how to extend the methods of Yang and Prentice to a family of survival models that includes the proportional hazards model and proportional odds model as special cases. The model is defined in terms of a Box-Cox transformation of the survival function, indexed by a transformation parameter rho. This model has been discussed by other authors, and is related to the Harrington-Fleming G(rho) family of tests and to frailty models. We discuss inference for the case where rho is known and the case where rho must be estimated. We present a simulation study of a pseudo-likelihood estimator and a martingale residual estimator. We find that the methods perform reasonably. We apply our model to a real data set.

Chlamydia Infections↗
PubMed2002

Two-level proportional hazards models.

We extend the proportional hazards model to a two-level model with a random intercept term and random coefficients. The parameters in the multilevel model are estimated by a combination of EM and Newton-Raphson algorithms. Even for samples of 50 groups, this method produces estimators of the fixed effects coefficients that are approximately unbiased and normally distributed. Two different methods, observed information and profile likelihood information, will be used to estimate the standard errors. This work is motivated by the goal of understanding the determinants of contraceptive use among Nepalese women in the Chitwan Valley Family Study (Axinn, Barber, and Ghimire, 1997). We utilize a two-level hazard model to examine how education and access to education for children covary with the initiation of permanent contraceptive use.

Adolescent↗
PubMed2005

A version of the EM algorithm for proportional hazard model with random effects.

Proportional hazard models with multivariate random effects (frailties) acting multiplicatively on the baseline hazard have recently become a topic of an intensive research. One of the main practical problems related to the models is the estimation of parameters. To this aim, several approaches based on the EM algorithm have been proposed. The major difference between these approaches is the method of the computation of conditional expectations required at the E-step. In this paper an alternative implementation of the EM algorithm is proposed, in which the expected values are computed with the use of the Laplace approximation. The method is computationally less demanding than the approaches developed previously. Its performance is assessed based on a simulation study and compared to a non-EM based estimation approach proposed by Ripatti and Palmgren (2000).

Algorithms↗
PubMed1998

Analysis of failure times for multiple infections following bone marrow transplantation: an application of the multiple failure time proportional hazards model.

We report an application of the proportional hazards model for multiple failure times in a study arising from the Bone Marrow Transplant Database at the University of Minnesota. The study compared the risk of infections after transplantation for patients who received allogeneic bone marrow transplants from unrelated donors (URD) versus related donors (RD). In 249 patients there was a total of 365 infections in 2.5 years of follow-up. The multiple failure time model uses all the data and provides empirical estimates of standard errors that incorporate the within-person dependencies in the data. The estimate of relative risk associated with URD was 1.4 (naive 95 per cent confidence interval 1.14 to 1.73, empirical 1.08 to 1.79), compared to the estimate 1.6 (naive or empirical, 1.1 to 2.1) from the proportional hazards model on 165 first infections only. The multivariate model gives great flexibility in modelling, for example, in accommodating a separate base hazard function for each type of failure and in allowing analysis of intervals between infections as an alternative to analysis of time from a marker event, here transplantation.

Bone Marrow Transplantation↗
PubMed2004

Analysis of the relationship between type traits and functional survival in US Holstein cattle using a Weibull proportional hazards model.

Survival analysis with a Weibull proportional hazards model was used to evaluate the effects of 15 linear type traits, 5 composite traits, and final score on the functional longevity of US Holstein cows. Culling data and type classification scores (measured in first lactation) from 891,524 cows with first calving from 1993 to 2000 were used. The data were divided into 9 geographical regions to determine whether the relationship between type traits and longevity differed according to climate or management system. Functional survival was defined as the number of days from first calving until culling or censoring, after correction for 305-d mature equivalent combined fat and protein yield. The Weibull model included time-dependent effects of herd-year-season, parity-stage of lactation, and within herd-year quintile ranking for combined fat and protein yield (nested within biennium), as well as time-independent effects of age at first calving and type classification score (type traits were analyzed one at a time). Type classification scores were rounded to the nearest 5 points, and the impact of each type trait on functional survival in each region was evaluated. Mean failure time ranged from 694 d in the South to 758 d in the North East. Risk of culling differed by region for several linear type traits, and differences were greatest for regions that were most dissimilar in climate and herd management (e.g., South East, East North Central, and West). Udder depth, fore udder attachment, udder cleft, and rear legs side view were consistently associated with functional longevity, regardless of region, but, the importance of some secondary traits, such as stature or dairy form, differed by region. The survival model applied in this study easily described both linear and nonlinear relationships between type traits and longevity while accounting for important time-dependent and time-independent explanatory variables.

Animals↗
PubMed2005

Generating survival times to simulate Cox proportional hazards models.

Simulation studies present an important statistical tool to investigate the performance, properties and adequacy of statistical models in pre-specified situations. One of the most important statistical models in medical research is the proportional hazards model of Cox. In this paper, techniques to generate survival times for simulation studies regarding Cox proportional hazards models are presented. A general formula describing the relation between the hazard and the corresponding survival time of the Cox model is derived, which is useful in simulation studies. It is shown how the exponential, the Weibull and the Gompertz distribution can be applied to generate appropriate survival times for simulation studies. Additionally, the general relation between hazard and survival time can be used to develop own distributions for special situations and to handle flexibly parameterized proportional hazards models. The use of distributions other than the exponential distribution is indispensable to investigate the characteristics of the Cox proportional hazards model, especially in non-standard situations, where the partial likelihood depends on the baseline hazard. A simulation study investigating the effect of measurement errors in the German Uranium Miners Cohort Study is considered to illustrate the proposed simulation techniques and to emphasize the importance of a careful modelling of the baseline hazard in Cox models.

Cohort Studies↗
PubMed2002

Heritability, reliability of genetic evaluations and response to selection in proportional hazard models.

The purposes of this study were 1) to investigate the heritability, reliability, and selection response for survival traits following a Weibull frailty proportional hazard model; and 2) to examine the relationship between genetic parameters from a Weibull model, a discrete proportional hazard model, and a binary data analysis using a linear model. Both analytical methods and Monte Carlo simulations were used to achieve these aims. Data were simulated using the Weibull frailty model with two different shapes of the Weibull distribution. Breeding values of 100 unrelated sires with 50 to 100 progeny (with different levels of censoring) were generated from a normal distribution and two different sire variances. For analysis of longevity data on the discrete scale, simulated data were transformed to a discrete scale using arbitrary ends of discrete intervals of 400, 800, or 1200 d. For binary data analysis, an individual's longevity was either 0 (when longevity was less than the end of interval) or 1 (when longevity was equal or greater than the end of interval). Three different statistical models were investigated in this study: a Weibull model, a discrete-time model (a proportional hazard model assuming that the survival data are measured on a discrete scale with few classes), and a linear model based upon binary data. An alternative derivation using basic expressions of reliabilities in sire models suggests a simple equation for the heritability on the original scale (effective heritability) that is not dependent on the Weibull parameters. The predictions of reliabilities using the proposed formulae in this study are in very good agreement with reliabilities observed from simulations. In general, the estimates of reliability from either the discrete model or the binary data analysis were close to estimates from the Weibull model for a given number of uncensored records in this simplified case of a balanced design. Although selection response from the binary data analysis depends on the end of interval point, there is a relatively good agreement between selection responses in the Weibull model and the binary data analysis. In general, when the underlying survival data is from a Weibull distribution, it appears that the method of analyzing data does not greatly affect the results in terms of sire ranking or response to selection, at least for the simplified context considered in this study.

Animals↗
PubMed1995

Model inconsistency, illustrated by the Cox proportional hazards model.

We consider problems involving the comparison of two or more treatments where we have the opportunity to adjust for relevant covariates either conditionally in a regression model or implicitly in repeated measures data, for example, in crossover trials. It is seen that for data arising from non-Normal distributions there is the possibility that models adjusting for covariates and those not adjusting for covariates will be inconsistent, that is, at most one of the models can be valid. Alternatively, even if conditional and unconditional models are valid, parameters in each model may have different interpretations. We note that this presents difficulties for the specification and interpretation of the analysis. It is also clear that model validation is critical. Specific attention is paid to survival data analysed by the Cox proportional hazards model.

Analysis of Variance↗
PubMedDate not supplied

Using the proportional hazards model to study heart valve replacement data.

The proportional hazards model is used to study the effect of various concomitant variables on the time to valve failure, mortality, or other complications, for patients who have had artificial heart valves inserted. The data are from a database, which is still being assembled as more information is acquired, at Killingbeck Hospital. A suite of computer programs, not specifically developed with this application in mind, has been used to carry out the exploratory data analysis, the estimation of parameters and the validation of the model. These three elements of the analysis are all illustrated. The present report is seen as a preliminary study to assess the usefulness of the proportional hazards model in this area. Follow-up work as more data are accumulated is intended.

Bioprosthesis↗
PubMed1989

Score tests for homogeneity of regression effect in the proportional hazards model.

A simple model, containing the proportional hazards regression model as a special case, is presented. The purpose of the model is to provide a framework in which specific alternatives to the proportional hazards assumption may be tested. Rank-invariant score tests for linear, quadratic, or exponential trends can, for instance, all be undertaken within this framework. In the case of the two-sample problem the required calculations are shown to take a particularly simple form. Special consideration is given to the two-sample case in which there is an inversion of the regression effect, i.e., where the hazard functions cross at some given point. Both of the motivating examples are concerned with this problem. Computational aspects are relatively straightforward and some discussion on this is provided.

Actuarial Analysis↗