M. Ataharul Islam

Inconsistent use of oral contraceptives in rural Bangladesh

The purpose of this study is to explore predictors of inconsistent use of oral contraceptives (OCs) in rural Bangladesh. A total of 801 rural OC users were included in the study, about half of them (49%) missed one or more active pill(s) during the 6 months before the survey.Multivariate analysis revealed that Muslim women were 60% more likely to be inconsistent OC users compared to their non-Muslim counterparts. Women who lacked knowledge about contraindications were 60% more likely to take the pill inconsistently than were women who had the knowledge. Women who were not visited by family planning workers or did not have access to mass media were 40% more likely to be inconsistent OC users.OC users need increased information about correct OC use, which could be provided via improved access to mass media with specific messages on how to use OCs properly. Better access to the community clinics could improve the pill-taking behaviors of rural Bangladeshi women.

A generalized bivariate Bernoulli model with covariate dependence

Dependence in outcome variables may pose formidable difficulty in analyzing data in longitudinal studies. In the past, most of the studies made attempts to address this problem using the marginal models. However, using the marginal models alone, it is difficult to specify the measures of dependence in outcomes due to association between outcomes as well as between outcomes and explanatory variables. In this paper, a generalized approach is demonstrated using both the conditional and marginal models. This model uses link functions to test for dependence in outcome variables. The estimation and test procedures are illustrated with an application to the mobility index data from the Health and Retirement Survey and also simulations are performed for correlated binary data generated from the bivariate Bernoulli distributions. The results indicate the usefulness of the proposed method.

Assessing Proportional Hazards Assumption in Competing Risk Situation

This paper extends the QUANTIN et al.s (1996) Regression Survival Model for testing the proportional hazards assumption to the competing risk framework. Observed level and power of the test are investigated through simulation experiments for a binary covariate. An illustration of the test is given using the Stanford Heart Transplant Data.

Analysis of Repeated Measures Data

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Comparison of missing value estimation techniques in rainfall data of Bangladesh

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Probability Distributions: Continuous

The presence of missing values in daily rainfall data may hamper the analyses to determine effective results for solving problems of hydrological, agricultural, and climatological issues. The study attempts to select an appropriate method for estimating the missing value of daily rainfall data of Bangladesh. For this purpose, eight methods and seven comparison techniques are employed. For imputation of missing values employing these methods, three sets of daily rainfall data (1, 5, and 10% missing values) with 1000 repetitions are considered randomly for five regions of the country. These samples are artificially created as missing and then imputation for these missing values is made applying the selected methods. The relative performance of the methods are examined using some comparison criteria. The following observations can be made from the study regarding the choice of the appropriate missing value estimation technique: for imputation of the missing values of daily rainfall data, the arithmetic average method for rainfall stations Chittagong and Rajshahi in the south-east region and the north-west region, respectively, is found as the best methods. Further, the single best estimator method for rainfall stations Sylhet and Dhaka in the north-east region and the mid-region, respectively, and the EM-MCMC method for rainfall station Khulna of the south-east region are also identified as the best methods in respect of Kolmogorov-Smirnov test, the lowest bias of estimate, the value of S index, etc.

Basic Concepts, Organizing, and Displaying Data

Two of the most essential continuous distributions, the normal probability distribution and the standard normal probability distribution, are discussed in this chapter with specific focus to the need of users who want to use these distributions in their applications. In this chapter, examples are displayed in a way that the users will be able to learn the applications of these distributions without ambiguity. The general rules of a continuous distribution are illustrated and the relationship between probability in an interval and cumulative probability is shown. The computational procedures of probabilities are illustrated with many examples and figures using the standard normal probability distributions.

Basic Summary Statistics

This chapter introduces biostatistics as a discipline that deals with designing studies, analyzing data, and developing new statistical techniques to address the problems in the fields of life sciences. This includes collection, organization, summarization, and analysis of data in the fields of biological, health, and medical sciences including other life sciences. One major objective of a biostatistician is to find the values that summarize the basic facts from the sample data and to make inference about the representativeness of the estimates using the sample data to make inference about the corresponding population characteristics. The basic concepts are discussed along with examples and sources of data, levels of measurement, and types of variables. Various methods of organizing and displaying data are discussed for both ungrouped and grouped data. The construction of table is discussed in details. This chapter includes methods of constructing frequency bar chart, dot plot, pie chart, histogram, frequency polygon, and ogive. In addition, the construction of stem-and-leaf display is discussed in details. All these are illustrated with examples. As the raw materials of statistics are data, a brief section on designing of sample surveys including planning of a survey and major components is introduced in order to provide some background about collection of data.

Probability Distributions: Discrete

This chapter includes the basic measures of summary statistics, namely measures of central tendency, measures of dispersion, measures of skewness, and measures of kurtosis. These summary measures of descriptive statistics characterize the underlying features of data. The measures of central tendency include arithmetic mean, median, and mode with examples, advantages and disadvantages of the measures are highlighted. The measures of dispersion are discussed along with their properties. The measures of dispersion include range, variance, standard deviation, coefficient of variation, and interquartile range. In statistics and biostatistics, the first measure, central tendency or location, provides the central value around which all other values in the data set are located. In other words, the measure of central tendency provides the most important representative value of the data set. The dispersion quantifies or measures the variability in the data where variability between values of the data or difference between each value and the measure of central tendency may reflect how scattered the data are from a central value or what extent in variation among values of the data is there. The third measure is used to know whether there is symmetry from the central value or there exists asymmetry from the central value. The fourth measure indicates whether the data are more concentrated with high peak, low or normal peaks, or frequencies indicating the extent of concentration of values.

Basic Probability Concepts

The discrete probability distributions are introduced in this chapter. The general rules of a discrete probability distributions are illustrated and the concepts of expected value and variance are shown. The rules are illustrated with a number of examples. This chapter includes brief introduction of the Bernoulli distribution, binomial distribution, Poisson distribution, geometric distribution, multinomial distribution, hypergeometric distribution and negative binomial distribution. The important properties of these distributions are discussed and illustrated with examples. The applications of these useful distributions are given high priority.