Estimation of the average total cost for treating patients with a particular disease is often complicated by the fact that the survival times are censored on some study subjects and their subsequent costs are unknown. The naive sample average of the observed costs from all study subjects or from the uncensored cases only can be severely biased, and the standard survival analysis techniques are not applicable. To minimize the bias induced by censoring, we partition the entire time period of interest into a number of small intervals and estimate the average total cost either by the sum of the Kaplan-Meier estimator for the probability of dying in each interval multiplied by the sample mean of the total costs from the observed deaths in that interval or by the sum of the Kaplan-Meier estimator for the probability of being alive at the start of each interval multiplied by an appropriate estimator for the average cost over the interval conditional on surviving to the start of the interval. The resultant estimators are consistent if censoring occurs solely at the boundaries of the intervals. In addition, the estimators are asymptotically normal with easily estimated variances. Extensive numerical studies show that the asymptotic approximations are adequate for practical use and the biases of the proposed estimators are small even when censoring may occur in the interiors of the intervals. An ovarian cancer study is provided.
Knowledge of the regression relation between dietary intake reported on a food frequency questionnaire and true average intake is useful in interpreting results from nutritional epidemiologic studies and in planning such studies. Studies which validate a questionnaire against a food record may be used to estimate this regression relation provided the food record is completed by each subject on at least two occasions. Using data collected from women aged 45-69 years during 1985-1986 in the pilot study of the Women's Health Trial, the authors show how variation in diet over time and intraindividual correlation between a questionnaire and food record obtained close together in time affects the estimation of the regression. The authors' method provides estimates of the regression slope and the questionnaire "bias" that are corrected for these effects, together with standard errors. A computer program in the SAS language, for carrying out the analysis, is provided.
In epidemiologic studies, two forms of collinear relationships between the intake of major nutrients, high correlations, and the relative homogeneity of the diet, can yield unstable and not easily interpreted regression estimates for the effect of diet on disease risk. This paper presents tools for assessing the magnitude and source of the corresponding collinear relationships among the estimated coefficients for relative risk regression models. I show how to extend three tools (condition indices, variance decomposition proportions, and standard inflation factors) for diagnosing collinearity in standard regression models to likelihood and partial likelihood estimation for logistic and proportional hazards models. This extension is based on the analogue role of the information matrix in such analyses and the cross-product matrix in the standard linear model. I apply the methodology to relative risk models that relate crude intakes (on the log scale) and nutrient densities to breast cancer cases in the NHANES-I follow-up study. The three diagnostic tools provide complementary evidence of the existence of a strong collinearity in all models that is due largely to homogeneity of the population with respect to our risk scale for the crude intakes. The analysis suggests that the non-significant relative risks for the crude intakes in these models may be due to their involvement in collinear relationships, while the nonsignificant relative risks for the nutrient densities are far less affected by multicollinearity.
For four months we marked and followed through female maturation and adult male mophotypic differentiation, the growth of all 150 individuals in an experimental population of Malaysian giant freshwater prawns (Macrobrachium rosenbergii). Small immature female prawns had high growth rates. Growth of female prawns nearly ceased after maturation. This compensatory growth process produces adult females having a unimodal, symmetrical size distribution with a mean above the size threshold for maturation (about 18-26 g). The small male morphotype has a low growth rate, while the orange claw male morphotype has a high growth rate. As the orange claw males transform to the blue claw morphotype, growth ceases. Examination of changes in size rank during the maturation process supported the leapfrog phenomenon. The fastest growing, largest orange claw male is the first to metamorphose to the blue claw morphotype (at a size of 35 g). As other orange claw males exceed this size, they transform in a sequential process so that the most recent blue claw male is generally the largest blue claw male in the population. Thus, growth of males is depensatory throughout the process of morphotypic differentiation, leading to a wide size range of orange and blue claw males. The leapfrog phenomenon is discussed in terms of the reproductive success of the blue claw males and compared with related growth processes in male poeciliid fishes. Implications of this growth process for aquacultural productivity includes the stimulatory effect on the remaining prawns of selectively harvesting the largest blue claw and orange claw prawns and suggests that the inclusion of a small proportion of large "target" blue claw males might stimulate the rapid growth of orange claw males in a population of smaller prawns.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.