Journal of Biometrics & Biostatistics

In recent years, big data analytics has received much attention in the area of healthcare related biomedical research and development. Big data analytics enables research organizations to analyze a mix of structured and unstructured data for identifying valuable medical information and insights in healthcare related biomedical research and development.

We used the 2013 Nigeria Demographic Health Survey (NDHS) data to investigate the determinants of family size in Nigeria using the geo-additive model. The model was used to simultaneously measure the fixed, nonlinear, spatial and random effects. The fixed effect of categorical covariates were modeled using the diffuse prior, P-spline with secondorder random walk for the nonlinear effect of continuous variable, spatial effects followed Markov random field priors while the exchangeable normal priors were used for the random effects of the community and household. The negative binomial distribution was used to handle over dispersion of the dependent variable. Inference was fully Bayesian approach. Results showed a declining effect of secondary and higher education of mother, Yoruba tribe, Christianity, family planning, mother giving birth by caesarean section and having a partner who has secondary education on family size. Family size is positively associated with age at first birth, number of daughters in a household, being gainfully employed, married and living with partner, community and household effects.

Assessment of drug safety typically involves estimation of occurrence rate of adverse events. Most often, the crude percentage (subject incidence) is used to estimate adverse event rate. However, in some situations, the exposure adjusted incidence rate (EAIR) may be a more appropriate measure to account for the potential difference in the duration of drug exposure or the follow-up time among individuals. In this article, we establish the asymptotic properties of the EAIR under certain assumptions, and propose a general and simple approach for variance estimation and for calculating the confidence interval of the rate. Simulation studies are conducted to evaluate the performance of the proposed approach. The results show that the proposed procedures perform well for various scenarios of different follow-up patterns. Data from a clinical trial are used to demonstrate the application of the method. A SAS macro is provided in the appendix.

Myopia is a disorder of ocular refraction with varying rates of progression. Although the disorder has a dynamic nature, prospective longitudinal studies with long term follow-ups have been remarkably few. In this paper, we show how mixed-effects regression splines with different choices of basis functions can be used to model myopia progression data in a flexible way. We show how the estimated model may be used to find prediction curves with corresponding confidence and tolerance intervals for a new myopic subject. We discuss alternative choices of the basis functions such as the truncated polynomial spline functions (2 types) and B-spline functions. Principal component functions may be used for an analysis of the variation of the curves in the population. The theory is collected together and presented in a coherent way as well as illustrated with a careful analysis of myopia progression data from a Finnish myopia study.

MicroRNAs (miRNAs) are small non-coding RNAs (containing about 22 nucleotides) that regulate gene expression. MiRNAs are involved in many different biological processes such as cell proliferation, differentiation, apoptosis, fat metabolism, and human cancer genes; while miRNAs may function as candidates for diagnostic and prognostic biomarkers and predictors of drug response. This paper emphasizes the statistical methods in the analysis of the associations of miRNA gene expression with human cancers and related clinical phenotypes: 1) simple statistical methods include chi-square test, correlation analysis, t-test and one-way ANOVA; 2) regression models include linear and logistic regression; 3) survival analysis approaches such as non-parametric Kaplan-Meier method and log-rank test as well as semi-parametric Cox proportional hazards models have been used for time to event data; 4) multivariate method such as cluster analysis has been used for clustering samples and principal component analysis (PCA) has been used for data mining; 5) Bayesian statistical methods have recently made great inroads into many areas of science, including the assessment of association between miRNA expression and human cancers; and 6) multiple testing.

HIV associated deaths have decreased substantially thanks to the ART treatment. However, there is still need for funding of Anti Retroviral Treatment services by donors and Ministry of health so as to prevent the loss caused by AIDS related deaths in the society. The study basically looked at the survival rates of HIV patients under ART treatment in Kilifi county. Various factors affecting the uptake of ARVs for HIV-positive patients were outlined and investigated in this study. A sample of 232 patients was considered from Chasimba health center from Kilifi county for a period of about 5 years. The analysis of the data showed that ARV uptake in females is high compared to their male counterparts. Opportunistic infections, the kind of marital status of patients and counseling session attendance by patients on ART affected their survival. Thus, the survival of patients under ART programs can be improved if we improve on the sensitization of the public on the need to access healthcare facilities and to ask the county government to set up as many health facilities as possible, to provide health services at close distance to the people. We can also bring behavioral change among HIV patients to attend counseling session and get pieces of advices on correct health measures and behaviors.

Kaplan-Meier estimation method, Cox proportional hazard model and parametric regression model were applied. The Cox proportional hazard analysis indicated that older ages at first marriage, weight <50 kg, height of >150 cm, follow up of antenatal care, delivery at health center, duration of labour for <2 day, vaginal delivery, length and width of fistula <5 cm and intact of urethra significantly contribute to shorter stay in hospital to treated and physically cured. The result from Weibull regression analysis showed that older age at first marriage (adjusted HR=1.00), weight<50 kg (HR=0.409), height of >150 cm (Adjusted HR=1.00), follow up of antenatal care (adjusted HR=0.263), delivery at health center (adjusted HR=1.00), duration of labour (adjusted HR=0.127 for <2 day), vaginal delivery (adjusted HR=0.241), length of fistula (adjusted HR=0.342 for <2 cm, HR=0.426 for 3-5 cm), width of fistula (adjusted HR=0.147 for <2 cm, HR=0.356 for 3-5 cm) and intact of urethra (adjusted HR=0.439) significantly contribute to a shorter recovery time of a patient. In conclusions: The finding of this study showed that age at first marriage, height, antenatal care, weight, place of delivery, mode of delivery, duration of labour, length and width of fistula, and status of urethra were major factors affecting recovery time of obstetric fistula patient at Yirgalem Hamlin Fistula Hospital. It is recommended to make interventions based on these risk factors.

Accurate cancer stage at diagnosis is essential not only for assessing quality of care and associated prognosis but also for monitoring trends in cancer stages and for assessing effectiveness of early detection interventions. Because the cancer stage is associated with many factors that are not under control of cancer registries, it is infeasible to completely record stages in all cases from registry database. It is necessary to reduce the bias in stage analysis induced by unknown stage cases through statistical adjustment. In this paper, we propose a new adaptive robust method that estimates the distribution of unknown stage cases using both essential and nonessential predictors of cancer stage. Multiple additive regression trees were used to assess the association of explanatory variables (including patient demographics, tumor characteristics, and treatment) with unknown stage. The 2004-2009 incidence data on invasive lung cancer from 38 population-based cancer registries that met NAACCR’s high data quality criteria were used to estimate the population stage distribution of lung cancer over the years. Multiple artificial incomplete datasets with unknown stages and predictors were created from the complete datasets, with varying missing data mechanism and different proportions of missingness. The simulated datasets were used to test the efficiency of the proposed method in estimating population stage distribution. In general, the proposed method is more efficient in terms of estimation accuracy and time consumption, compared with the traditional methods such as multiple imputation method and weighting method.

There are two principal exact tests for evaluation of data in two-by-two contingency tables: the tests of Fisher and Barnard. The latter cannot be a hypothesis test for the causal null hypothesis unless exchangeability can be assumed. Fisher’s exact test is a hypothesis test for the sharp causal null hypothesis (i.e., that there is no effect for all individuals), but not for the weak causal null hypothesis (i.e., that the true risk difference is zero). Rejection of the sharp causal null hypothesis does not mean that the weak causal null hypothesis is rejected (i.e., that the true risk difference is not zero). In this article, we provide exact tests for the weak causal null hypothesis, in the absence of any assumption, in the context of randomized trials. Using the concept of principal stratification, which considers four types of subjects to define four principal strata, we derive an unconditional exact test, for which neither marginal total is fixed, and a conditional exact test, for which one marginal total is fixed. In addition, we show that Fisher’s exact test can be a hypothesis test for the weak causal null hypothesis when monotonicity can be assumed. The derived exact tests are extended to hypothesis testing for non-inferiority trials and to construct confidence intervals linking to the exact tests. The derived exact tests and confidence intervals are illustrated using data from two clinical trials.

A common problem encountered in statistical analysis is that of missing data, which occurs when some variables have missing values in some units. The present paper deals with the analysis of longitudinal continuous measurements with incomplete data due to non-ignorable dropout. In repeated measurements data, as one solution to a problem, the selection model assumes a mechanism of outcome-dependent dropout and jointly both the measurement together with dropout process of repeated measures. We consider the construction of a particular type of selection model that uses a logistic regression model to describe the dependency of dropout indicators on the longitudinal measurement. We focus on the use of the Diggle-Kenward model as a tool for assessing the sensitivity of a selection model in terms of the modeling assumptions. Our main objective here is to investigate the influence on inference that might be exerted on the considered data by the dropout process. We restrict attention to a model for repeated Gaussian measures, subject to potentially non-random dropout. To investigate this, we carry out an application for analyzing incomplete longitudinal clinical trial with dropout by using a practical example in the form of a multi-center clinical trial data.