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Background Birth control is the conscious control of the birth rate by methods which temporarily prevent conception by interfering with the normal process of ovulation, fertilization, and implantation. High contraceptive prevalence rate is always expected for controlling births for those countries that are experiencing high population growth rate. The factors that influence contraceptive prevalence are also important to know for policy implication purposes in Bangladesh. This study aims to explore the socio-economic, demographic and others key factors that influence the use of contraception in Bangladesh. Methods The contraception data are extracted from the 2014 Bangladesh Demographic and Health Survey (BDHS) data which were collected by using a two stage stratified random sampling technique that is a source of nested variability. The nested sources of variability must be incorporated in the model using random effects in order to model the actual parameter effects on contraceptive prevalence. A mixed effect logistic regression model has been implemented for the binary contraceptive data, where parameters are estimated through generalized estimating equation by assuming exchangeable correlation structure to explore and identify the factors that truly affect the use of contraception in Bangladesh. Results The prevalence of contraception use by currently married 15–49 years aged women or their husbands is 62.4%. Our study finds that administrative division, place of residence, religion, number of household members, woman’s age, occupation, body mass index, breastfeeding practice, husband’s education, wish for children, living status with wife, sexual activity in past year, women amenorrheic status, abstaining status, number of children born in last five years and total children ever died were significantly associated with contraception use in Bangladesh. Conclusions The odds of women experiencing the outcome of interest are not independent due to the nested structure of the data. As a result, a mixed effect model is implemented for the binary variable ‘contraceptive use’ to produce true estimates for the significant determinants of contraceptive use in Bangladesh. Knowing such true estimates is important for attaining future goals including increasing contraception use from 62 to 75% by 2020 by the Bangladesh government’s Health, Population & Nutrition Sector Development Program (HPNSDP).

In studies of recurrent events, such as epileptic seizures, there can be a large amount of information about a cohort over a period of time, but current methods for these data are often unable to utilize all of the available information. The paper considers data which include post-treatment survival times for individuals experiencing recurring events, as well as a measure of the base-line event rate, in the form of a pre-randomization event count. Standard survival analysis may treat this pre-randomization count as a covariate, but the paper proposes a parametric joint model based on an underlying Poisson process, which will give a more precise estimate of the treatment effect.

Much current analysis of cancer registry data uses the semiparametric proportional hazards Cox model. In this paper, the time-dependent effect of various prognostic indicators on breast cancer survival times from the West Midlands Cancer Intelligence Unit are investigated. Using Bayesian methodology and Markov chain Monte Carlo estimation methods, we develop a parametric dynamic survival model which avoids the proportional hazards assumption. The model has close links to that developed by both Gamerman and Sinha and co-workers: the log-base-line hazard and covariate effects are piecewise constant functions, related between intervals by a simple stochastic evolution process. Here this evolution is assigned a parametric distribution, with a variance that is further included as a hyperparameter. To avoid problems of convergence within the Gibbs sampler, we consider using a reparameterization. It is found that, for some of the prognostic indicators considered, the estimated effects change with increasing follow-up time. In general those prognostic indicators which are thought to be representative of the most hazardous groups (late-staged tumour and oldest age group) have a declining effect.

A class of parametric dynamic survival models are explored in which only limited parametric assumptions are made, whilst avoiding the assumption of proportional hazards. Both the log-baseline hazard and covariate effects are modelled by piecewise constant and correlated processes. The method of estimation is to use Markov chain Monte Carlo simulations: Gibbs sampling with a Metropolis-Hastings step. In addition to standard right censored data sets, extensions to accommodate interval censoring and random effects are included. The model is applied to two well known and illustrative data sets, and the dynamic variability of covariate effects investigated.

Practical Bayesian statistics with realistic models usually gives posterior distributions that are analytically intractable, and inferences must be made via numerical integration. In many cases, the integrands can be transformed into periodic functions on the unit d-dimensional cube, for which quasirandom sequences are known to give efficient numerical integration rules. This paper reviews some relevant theory, defines new criteria for identifying suitable quasirandom sequences and suggests some extensions to the basic integration rules. Various quasirandom methods are then compared on the sort of integrals that arise in Bayesian inference and are shown to be much more efficient than Monte Carlo methods.

A natural extension of generalised linear models is to use a cubic spline as the underlying regression function. The resulting models are usually analytically intractable, but recent developments in numerical approaches to Bayesian statistics allow the models to be analysed efficiently. The models are illustrated with an example from biological assay.

Numerical integration techniques now exist which permit a very flexible approach to Bayesian modelling. Low dimensional summaries can be extracted from joint posterior distributions of up to 15 dimensions. The sensitivity of particular summaries to changes in both the model and the prior can thus be investigated. This paper discusses the various aspects of sensitivity in a Bayesian analysis and demonstrates something of the power of numerical integration via two examples.

One of the main obstacles to the routine implementation of Bayesian methods has been the absence of efficient algorithms for carrying out the computational tasks implicit in the Bayesian approach. In this paper, recent progress towards overcoming this problem is reviewed. In particular, novel numerical integration and interpolation methods, which exploit the opportunities offered by modern interactive computing and graphics facilities, are outlined and illustrated.

Current and potential salmonid aquaculture sites in the Bay d'Espoir estuarine fjord on the south coast of Newfoundland were surveyed using multibeam SWATH sonar. In 1997, shallow sites were surveyed using the CSS Puffin EM3000-POS/MV system, and deeper sites were surveyed in 1998 using the CCGS Creed hull mounted EM1000. Sediment cores from representative areas were collected during this period and analyzed for organic matter content, and pore water ammonium and sulfate. We discuss the correlation between the sediment core profiles and the results of the side scan and sun-illuminated bathymetric imagery. Bay d'Espoir is a natural depositional area, and that, coupled with the unique backscatter properties of fish farm wastes, increases the difficulty of interpreting these multibeam sonar images. A fairly accurate broad scale characterization of sediment quality can be made from high-resolution images. However, much of the fine scale detail and inherent variation of sediment characteristics associated with impacts from aquaculture cannot be determined from multibeam imagery.