Motherhood status and union formation in Moshi,Tanzania 2002–2003

Age at first union is increasing throughout much of sub-Saharan Africa at the same time that not all couples are waiting for marriage before their first sexual intercourse. We assessed the effect of a premarital first birth on entrance into a first union in an urban area in East Africa—Moshi, Tanzania. The data come from the Moshi Infertility Survey of 2002–2003. Women who spent less than a year in single motherhood were significantly more likely than childless women to enter into a first union, although the magnitude of this relationship was weaker for more recent cohorts. Women who had been single mothers for 5 or more years (about two-thirds of women with a premarital birth) were significantly less likely than women without children to enter into a first union.     

Demographic change and response: Social context and the practice of birth control in six countries

This paper expands on Kingsley Davis’s demographic thesis of change and response. Specifically, we consider the social context that accounts for the primacy of particular birth control methods that bring about fertility change during specific time periods. We examine the relevance of state policy (including national family planning programs), the international population establishment, the medical profession, organized religion, and women’s groups using case studies from Japan, Russia, Puerto Rico, China, India, and Cameroon. Some of these countries are undergoing the second demographic transition, others the first. Despite variations in context, heavy reliance on sterilization and/or, abortion as a means of birth control is a major response in most of these countries. The key roles of the medical profession and state policy are discussed, along with the general lack of influence of religion and of women’s groups in these countries.     

Motherhood status and union formation in Moshi, Tanzania 2002-2003

Age at first union is increasing throughout much of sub-Saharan Africa at the same time that not all couples are waiting for marriage before their first sexual intercourse. We assessed the effect of a premarital first birth on entrance into a first union in an urban area in East Africa -- Moshi, Tanzania. The data come from the Moshi Infertility Survey of 2002-2003. Women who spent less than a year in single motherhood were significantly more likely than childless women to enter into a first union, although the magnitude of this relationship was weaker for more recent cohorts. Women who had been single mothers for 5 or more years (about two-thirds of women with a premarital birth) were significantly less likely than women without children to enter into a first union.     

Regression Diagnostics of the Semiparametric Proportional Rate Model for Irregularly Spaced Repeated Measurements

Counting process techniques have been successfully introduced to semiparametric inference of repeated measurements. Cheng and Wei (2000 Cheng , S. C. , Wei , L. J. ( 2000 ). Inference for a semiparametric model with panel data . Biometrika 87 : 89 – 97 .[Crossref], [Web of Science ®] , [Google Scholar]) proposed a simple inference procedure for the semiparametric proportional rate model, which reduces to relative risk regression models for binary data. While the baseline mean functions are completely unspecified, it still requires several assumptions for valid inference. In this article, a goodness-of-fit test for it is proposed based on cumulative residuals. Theoretical justification is provided and an illustration with a dataset from a clinical trial is given. Results of simulation studies to evaluate finite sample performance are also provided.     

Estimation of treatment effects based on possibly misspecified Cox regression

In randomized clinical trials, a treatment effect on a time-to-event endpoint is often estimated by the Cox proportional hazards model. The maximum partial likelihood estimator does not make sense if the proportional hazard assumption is violated. Xu and O'Quigley (Biostatistics 1:423-439, 2000) proposed an estimating equation, which provides an interpretable estimator for the treatment effect under model misspecification. Namely it provides a consistent estimator for the log-hazard ratio among the treatment groups if the model is correctly specified, and it is interpreted as an average log-hazard ratio over time even if misspecified. However, the method requires the assumption that censoring is independent of treatment group, which is more restricted than that for the maximum partial likelihood estimator and is often violated in practice. In this paper, we propose an alternative estimating equation. Our method provides an estimator of the same property as that of Xu and O'Quigley under the usual assumption for the maximum partial likelihood estimation. We show that our estimator is consistent and asymptotically normal, and derive a consistent estimator of the asymptotic variance. If the proportional hazards assumption holds, the efficiency of the estimator can be improved by applying the covariate adjustment method based on the semiparametric theory proposed by Lu and Tsiatis (Biometrika 95:679-694, 2008).     

Mantel-Haenszel estimators for irregular sparse K2×J tables

This paper deals with sparse K2×J(J>2)$K2\times J(J>2)$ tables. Projection-method Mantel–Haenszel (MH) estimators of the common odds ratios have been proposed for K2×J$K2\times J$ tables, which include Greenland's generalized MH estimator as a special case. The method projects log-transformed MH estimators for all K2×2$K2\times 2$ subtables, which were called naive MH estimators, onto a linear space spanned by log odds ratios. However, for sparse tables it is often the case that naive MH estimators are unable to be computed. In this paper we introduce alternative naive MH estimators using a graph that represents K2×J$K2\times J$ tables, and apply the projection to these alternative estimators. The idea leads to infinitely many reasonable estimators and we propose a method to choose the optimal one by solving a quadratic optimization problem induced by the graph, where some graph-theoretic arguments play important roles to simplify the optimization problem. An illustration is given using data from a case–control study. A simulation study is also conducted, which indicates that the MH estimator tends to have a smaller mean squared error than the MH estimator previously suggested and the conditional maximum likelihood estimator for sparse tables.     

Sex change and spatial distribution pattern in an intertidal holothurian Polycheira rufescens in the reproductive season

The patterns of sex change and spatial distribution in an intertidal holothurian, Polycheira rufescens (Chiridotidae; Echinodermata), were investigated on a stony beach in Amakusa, western Kyushu. Field caging experiments revealed that some individuals of P. rufescens underwent a sequential sex change from male to female via hermaphrodite stages and back to male again within a single reproductive season. The sex ratio of the population gradually changed from male dominance at first to equal proportions of males and females as the reproductive season progressed. Toward the end of the reproductive season, immature or spent individuals increased in proportion. Stone size appeared to be an important factor affecting the occurrence of P. rufescens individuals on a stony beach. Analysis of spatial distribution by means of Morisita's index of dispersion and nearest neighbor distances indicated that (i) males showed a slightly stronger tendency to aggregate than females, while females had a tendency toward uniform distribution; (ii) females tended to attract males, as shown by relatively short female-to-male distances; and (iii) there was a substantial variation in male-to-female distances, such that some males were positioned close to females while others were not. It may be suggested that spatial distribution of P. rufescens individuals during the reproductive period is partly dictated by the differential needs of individuals of different sexual states. Received: January 25, 1999 / Accepted: June 10, 1999     

Efficient Regularization Parameter Selection Via Information Criteria

In a nonlinear regression model based on a regularization method, selection of appropriate regularization parameters is crucial. Information criteria such as generalized information criterion (GIC) and generalized Bayesian information criterion (GBIC) are useful for selecting the optimal regularization parameters. However, the optimal parameter is often determined by calculating information criterion for all candidate regularization parameters, and so the computational cost is high. One simple method by which to accomplish this is to regard GIC or GBIC as a function of the regularization parameters and to find a value minimizing GIC or GBIC. However, it is unclear how to solve the optimization problem. In the present article, we propose an efficient Newton–Raphson type iterative method for selecting optimal regularization parameters with respect to GIC or GBIC in a nonlinear regression model based on basis expansions. This method reduces the computational time remarkably compared to the grid search and can select more suitable regularization parameters. The effectiveness of the method is illustrated through real data examples.