Peto Test Research Articles

Modified Weibull multi-state models for the analysis of animal carcinogenicity data

The Peto test is the standard method of analysis used in carcinogenicity studies to compare tumor incidence in groups of animals. It assumes that tumors are either instantly fatal or have no effect on mortality and requires a judgement of the lethality of each tumor. To avoid this requirement, parametric multi-state models have been proposed. In addition these allow estimation of tumor onset and mortality rates. This paper considers two such models and presents a modification. It is shown that the modified models provide a better fit to carcinogenicity data and simulated data are used to show that the modified models provide a modest increase in test power relative to the Peto test.

Survival Analysis, a Self-Learning Text

Chapter 1. The basic ideas are considered: like event, survival time, censoring, survival function, hazard function. Data layouts and some descriptive measures are discussed. The principles of confounding and interaction are discussed. In a multivariable example the similarities and differences between linear, logistic regression and survival regression and the concept of a hazard ratio are discussed. This chapter was overall clear. In this book the hazard is introduced as the instantaneous potential per unit time for the event to occur. The derivation of the relationship between the hazard function and the survival function is not given (the author finds it not important, since a computer can do the calculations), while the calculations are easy to perform. We also missed a warning that censoring should be independent of the occurrence of an event. Competing risks and left censoring are mentioned but not treated in detail. Chapter 2. This chapter is about Kaplan-Meier curves, the log-rank test and the Peto test. The proof of the Kaplan-Meier formula is difficult to follow, just because the author is so hard trying to make it easy to understand. The log-rank test and Peto test are clearly explained. Chapter 3. Here the Cox Proportional Hazards model is discussed. The chapter starts with the computer output of a rather complex example with a treatment effect, a confounder plus an interaction term. Hereafter the general model is discussed. The partial likelihood is discussed but the formula is not given. The interpretation of the coefficients is discussed using the data example. Somewhere in the midst of several examples, the general rule is given, which is confusing. Chapter 4. Four different ways of checking the assumptions of the Cox model are discussed: log-log plots, comparing observed with model survival curves, Schonfeld's goodness of fit test and time dependent covariate methods. Suggestions are given about how to proceed in practice. Overall this is a good chapter, although the part about log-log plots is too elaborate. Chapter 5 is about stratification in the Cox model. There is a discussion of when to use stratification (if the PH assumption does not hold), and the possibility of interaction between the stratification variables and the other predictors is considered, with statistical tests. In Chapter 6 time dependent covariates are discussed. A distinction is made between two different situations. The first situation is a variable that changes in time (like employment status, smoking status). The second situation is a variable that is constant in time, but its effect on the hazard changes in time, so that the proportional hazards assumption does not hold. By multiplying the variable by some function of time, the model can be improved.

Does hepatitis C virus co-infection accelerate clinical and immunological evolution of HIV-infected patients?

To study the influence of hepatitis C virus (HCV) co-infection on clinical and immunological evolution of HIV-infected patients. A longitudinal study of HIV-infected individuals with or without HCV infection, identified at the Infectious Diseases Department of Dijon University Hospital and enrolled in a historical cohort, was performed. One hundred and nineteen HIV-infected people co-infected with HCV and 119 matched individuals infected with HIV alone were included in the cohort (median participation time 3 years; range, 2 months to 11.5 years). Clinical progression was defined as one or more of the following: a 30% decrease in the Karnofsky index; a 20% loss of body weight; an AIDS-defining illness (for non-AIDS patients); death (except by accident, suicide or overdose). Immunological progression was defined as a 50% decrease in the initial CD4 T-cell count (for patients with an initial count > 100 x 10(6) cells/l). Effects of HCV co-infection were evaluated using Kaplan-Meier survival analysis and significance was tested using univariate (log-rank and Peto's tests) and multivariate methods (Cox's model). In univariate analysis, immunological progression was not statistically different between the HCV-positive group and the HCV-negative group, whereas clinical progression was significantly faster in HCV-positive patients (P < 0.005, log-rank test). In a multivariate Cox model, clinical progression remained significantly associated with infection by HCV [hazard ratio (HR), 1.64; 95% confidence interval (CI), 1.06-2.55; P < 0.05]. Stratified multivariable analysis retained HCV as a significant prognostic factor of clinical progression (HR, 10.9; 95% CI, 1.09-109.3; P < 0.05) and immunological progression (HR, 2.31; 95% CI, 1.16-4.62; P < 0.02) for patients with an initial CD4 count above 600 x 10(6) cells/l. Clinical progression is more rapid in HIV-HCV co-infected patients than in HIV-seropositive patients are not infected by HCV. The prognostic value of HCV infection for both clinical and immunological progression is significant at early stages of HIV infection. These findings may argue for active management of hepatitis C infection in co-infected individuals, especially for asymptomatic patients whose CD4 count is above 600 x 10(6) cells/l, to predict and prevent accelerated progression of HCV and HIV diseases.

Causes of death in rodent toxicity and carcinogenicity studies.

Peto test procedures for the statistical evaluation of carcinogenicity studies require that each tumor in an animal that died intercurrently (or was sacrificed in extremis) be classified as either fatal, probably fatal, incidental, or probably incidental. There is considerable controversy as to whether or not the cause of death can be established with accuracy in rodent studies. In the present article, the causes of death or ill-being as found in 10 consecutive carcinogenicity studies--5 studies with 2400 OFA (Sprague-Dawley-derived) and Wistar rats and 5 studies with 2400 OF1 and NMRI mice--were re-examined. A cause of death or moribund state had been established in more than 80% of the cases in rats and in more than 70% in mice. These causes were, in rats, mainly pituitary tumors, chronic progressive nephropathy (males), mammary gland tumors (females), and subcutaneous tumors (males); in mice, mainly hemolymphoreticular tumors, lung tumors, liver tumors (males), and glomerulonephropathy. The criteria used for determining the tumorous or non-tumorous lesions as the cause of death were based on in-life and pathological findings. The validity of such procedures, the possibility of improving criteria in the future, and the usefulness of establishing causes of death in safety assessment are discussed.

Survival of seedling cohorts of a tropical legume on a sand dune system along the Gulf of Mexico: influence of germination date

Chamaecrista chamaecristoides is a legume endemic to the coastal sand dunes along the coasts of Mexico. Hard-coat dormancy of seeds is broken through naturally occurring temperature fluctuations, and seedlings can emerge and establish throughout the year. In the study site, winter storms (called Nortes) are responsible for moving large quantities of sand that bury young seedlings. The objectives of the study were (i) to elucidate the timing of seedling emergence, (ii) to investigate the effect of date of emergence on seedling survival, and (iii) to evaluate different factors of mortality. Seedling survival was monitored under natural conditions, from January 1988 to April 1991. Survivorship curves were compared using the log-rank Peto and Peto test. It was found that the probability of survival or reproduction was not strictly determined by chronological age but by size. Larger sized seedlings survived better during the Nortes. Timing of germination has a great impact on C. chamaecristoides populations, since it affects seedling growth possibilities and thus survival and reproduction. In a changing environment like the sand dunes, there seems to be a selective advantage for maintaining cohorts germinating during different seasons of the year. Key words: tropical sand dunes, seedling survival, Gulf of Mexico, cohorts, seedling size.

On the Representativeness Assumption in Prevalence Tests of Carcinogenicity

SUMMARY Conditions for the validity of the Hoel-Walburg and Peto tests, which compare dose groups with respect to tumour prevalence, are replaced by a more general condition, representativeness. A large carcinogenicity experiment provided a unique opportunity to assess empirically if this condition holds. Though representativeness was generally violated, neither the Hoel-Walburg nor the Peto tests were seriously distorted. Analytic considerations suggest that such robustness can occur in many situations.