Institute of Applied Statistics and Data Science

Seminar on Monday, 18 December 2017 at 9 am
2017-12-18 09:00 - 10:30 UTC
Title: A motivational discussion on higher studies

Venue, date and time: ISRT seminar room, 18 December 2017 (Monday), 9 am

Speaker: Professor Abdus Wahed, Director of PhD Graduate Program, Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, USA

Abstract: Students and young colleagues planing for higher studies in any branch of Statistics are highly encouraged to attend this discussion. Students can ask any questions related to opportunities and challenges of getting admission with funding for higher studies in Statistics, and Professor Wahed will answer your questions.

Seminar on Sunday, December 17 at 11 am
2017-12-17 11:00 - 12:30
Title: Testing equality of two normal means using combined samples of paired and unpaired data

Speaker: Professor Nizam Uddin , Department of Statistics, University of Central Florida

Abstract: A test statistic for testing equality of two normal means when data consist of both paired and unpaired observations is proposed. The proposed test statistic is compared with two other standard methods known in the literature with respect to the type I error rate and power using simulation results.

He will also talk about the Big data analytics PhD program offered in the University of Central Florida.

Seminar on Tuesday, December 12 at 3 pm
2017-12-12 15:00 - 16:00 | isrt seminar room
Title: Statisticians in the Data Science Era

Speaker: Enayetur Raheem, Data Scientist at Carolinas HealthCare System, North Carolina, USA

Abstract: In this talk I will discuss what it really means to be a data scientist from a statistician’s perspective. In particular, I will share my experience about the US job market for statisticians. This will give you some ideas about your future job prospects in the US and in Bangaldesh.

Seminar on Tuesday, November 28 at 2:30 pm
2017-11-28 14:30 - 16:00
Title: Business Intelligence and Data Science

Speaker: Business Intelligence team, Grameenphone

Summary: The main focus of the seminar will be- to shed some light on the current business trends, and the challenges the industry faces in this 21st century, and also to delineate the prospects of Business Intelligence and Data Science in Bangladesh. The sessions will be conducted by a team of 8-10 presenters/speakers from the Business Intelligence Department of Grameenphone.

Seminar on Sunday, November 19 at 11 am
2017-11-19 11:00 - 13:00
Title: Survival Ensembles and Representative Trees for Cancer Prognostication

Speaker: Mousumi Banerjee, PhD
Research Professor of Biostatistics
School of Public Health & Comprehensive Cancer Center,
Director of Biostatistics
Center for Healthcare Outcomes and Policy
The University of Michigan, Ann Arbor, USA.

Abstract: Tree-based methods have become popular for analyzing right censored survival data where the primary goal is the prognostic stratification of patients. Ensemble techniques such as random forest improve the accuracy in prediction and address the instability in a single tree by growing an ensemble of trees and aggregating. However, individual trees are lost in the forest. This talk will first provide an overview of the methodological aspects of tree-based modeling in the censored data setting. Next, we propose a methodology for identifying the most representative trees in a forest for survival data, based on several tree distance metrics. For any two trees, the metrics are chosen to (1) measure similarity of the covariates used to split the trees; (2) reflect similar clustering of patients in the terminal nodes of the trees; and (3) measure similarity in predictions from the two trees. While the latter focuses on prediction, the first two metrics focus on the architectural similarity between two trees. The most representative trees in the forest are chosen based on the average distance between a tree and all other trees in the forest. Out of bag estimate of error rate is obtained using neighborhoods of representative trees. Simulations and data examples show gains in predictive accuracy when averaging over such neighborhoods. Although our focus is on trees for censored data, the ideas are also applicable to classification and regression trees. We illustrate our methods using data from a thyroid cancer study.

Training Programs on SPSS and Stata
2017-10-14 - 2017-11-14
ISRT is going to organize the regular training programs on SPSS and Stata from October 14, 2017 to November 14, 2017. The deadline for the registration is October 08, 2017. Details can be found on the following link: http://www.isrt.ac.bd/training

Seminar on Tuesday, October 10 at 2 pm
2017-10-10 14:00 - 15:00
Title: Conditional dependence in joint modelling of longitudinal non-Gaussian outcomes

Speaker: Mili Roy, MSc
Research Assistant
Werklund School of Education
University of Calgary, Canada

Abstract: The study is motivated by the limitations of conventional joint modelling strategies based

on linear and generalized linear mixed models (LMMs/GLMMs). The class of so-called Gaussian
copula mixed models (GCMMs), introduced by Wu and de Leon (2014) to generalize conventional
LMMs/GLMMs to non-Gaussian settings, was adopted, and simulations were conducted to in-
vestigate the impact of incorrectly ignoring the conditional dependence between outcomes, given
the random effects, on the performance of maximum likelihood estimates (MLEs). A variety of
scenarios involving shared or correlated random effects were considered, and implementation of
the correct and misspeci ed joint models was done in SAS’s PROC NLMIXED. Although MLEs of
fixed effects were only slightly impacted by the conditional independence misspeci cation, MLEs
based on the correct GCMM yielded generally better performances than those from the incorrect
model. Data on pediatric pain (Weiss, 2005; Withanage et al., 2015) were used for illustration.

Seminar on Wednesday, October 4 at 10 am
2017-10-04 10:00 - 11:30
Big Data: Opportunities to Explore

Presenter: DataSoft Systems Bangladesh Limited

Photosession of Teachers and MS Students
2017-08-23 11:30
A photosession of MS students with faculty members will take place on August 23 at 11:30 AM

Seminar on Monday, August 21, 2017
2017-08-21 14:00 - 15:00 | isrt seminar room
Title: Spectrum sharing in cellular networks: Optimisation and post-optimisation techniques

Speaker: Md Asaduzzaman, PhD
Associate Professor
ISRT, University of Dhaka

Abstract:

Dynamic spectrum sharing aims to provide secondary access to under-utilized spectrum in cellular networks. The main aim of the talk is twofold. Firstly, the secondary operator aims to borrow spectrum bandwidths under the assumption that more spectrum resources exist considering a merchant mode. Two optimization models are proposed using stochastic and optimization models in which the secondary operator (i) spends the minimal cost to achieve the target grade of service assuming unrestricted budget or (ii) gains the maximal profit to achieve the target grade of service assuming restricted budget. Results obtained from each model are then compared with results derived from algorithms in which spectrum borrowings are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimization framework is significantly higher than heuristic counterparts. Secondly, post-optimization performance analysis of the operators in the form of blocking probability in various scenarios is investigated to determine the probable performance gain and degradation of the secondary and primary operators respectively. We mathematically modelled the sharing agreement scenario and derive the closed form solution of blocking probabilities for each operator. Results showed how the secondary operator perform in terms of blocking probability under various offered loads and sharing capacity.

Seminar on Monday, August 7, 2017
2017-08-07 14:00 - 15:00 | isrt seminar room
Title: Energy Expenditure Prediction from Raw Accelerometer Data: A Comparison between Linear and Nonlinear Models

Speaker: Dr. Munni Begum
Professor of Mathematical Sciences
Ball State University, USA

Abstract:

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models. For the thighworn accelerometer, there were no differences in correlations or RMSE between linear and ANN models. Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers. For studies using wrist-worn accelerometers, machine-learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip or thigh-worn accelerometers.

Classes will resume on July 30, 2017
2017-07-30
All B.S. and M.S. classes will resume on 30-th of July, 2017 after second midterm examination.

Seminar on Monday, May 29, 2017
2017-05-29 10:00 - 11:00
Construction of Ensembles by Exploiting the Richness of Feature Variables in High-Dimensional Data with Application in Protein Homology

May 23, 2017 – 7:13pm

Full Title:
Construction of Ensembles by Exploiting the Richness of Feature Variables in High-Dimensional Data with Application in Protein Homology

Speaker:
Dr. Jabed Tomal

Assistant Professor
Department of Computer and Mathematical Sciences
The University of Toronto Scarborough, Canada.

Date/Time:
Monday, May 29, 2017, 10 a.m.

Venue:
ISRT seminar room

ABSTRACT
High-dimensional data may contain complementary subsets of useful feature variables which could bevaluable in predicting a response. In this work, I have developed a predictionmodel which exploits the richness of information contained in the complementary subsets ofuseful feature variables in high-dimensional data. The proposed model – which is an aggregated collection of logistic regression models (LRM) – is called an ensemble, where each constituent LRM is fitted to a subset of feature variables. An algorithm is developed to cluster the feature variables into subsets in a way that the variables in a subset are good to put together in an LRM, and the variables in different subsets are good in separate LRMs. Each subset of variables is called a “phalanx”, and the resulting ensemble is called an “ensemble of phalanxes (EPX).” The strength of the ensembledepends on the algorithm’s ability to identify/output strong and diverse subsets of feature variables.
Homologous proteins are considered to havea common evolutionary origin, i.e., the bearers of homologous proteins share a common ancestor. To develop an evolutionary sequence of proteins, a scientist needs to predict their biological homogeneity. The proposed ensembleis applied to the protein homology data, obtained from the 2004 KDD cup competition,and used to predict biological homogeneity of proteins. In this application, the feature variables are various scores representing structural similarity and amino acid sequence identity of proteins. Theunderlying assumption, for model building,is that the structural similarity and amino acid sequence identityare predictive to proteins’ biological homogeneity.As the proportion of homologous proteins is rare, the prediction performances of theensemble are evaluated by checking its ability to rank rare homologous proteins ahead of the non-homologous proteins. While prediction performances of an EPX are competitive to contemporary state-of-the-art ensembles, a big leap of improvement in prediction performances is achieved by aggregating two diverse EPXs obtained from optimizing two complementary evaluation metrics.Here, the algorithm and complementary-metrics guaranteed increased strength and diversity, respectively, among the ensembles of phalanxes to aggregate. Importantly, the performances of the two aggregated EPXs are robust against individual EPX when one EPX is good for detecting close homologs and the other is good for detecting distant homologs. Using parallel computing, the proposed ensemble is shown computationally efficient as well.

Seminar on Wednesday, May 17, 2017
2017-05-17 14:00 - 15:00
Assessment of predictors selected for epidemiologic risk models using automated variable selection methods

May 17, 2017 – 12:57pm

Full Title:
Assessment of predictors selected for epidemiologic risk models using automated variable selection methods

Speaker:
Dr. Haider Mannan

Western Sydney University, Australia

Date/Time:
Wednesday, May 17, 2017, 2 p.m.

Venue:
ISRT Seminar Room

ABSTRACT
TBA

Seminar on Tuesday, May 9, 2017
2017-05-09 14:00 - 15:30
A Tutorial of Creating R Packages under Microsoft Windows

April 23, 2017 – 11:02am

Full Title:
A Tutorial of Creating R Packages under Microsoft Windows

Speaker:
Dr. Md. Hasinur Rahman Khan

Associate Professor, ISRT

Date/Time:
Tuesday, May 9, 2017, 14.00-15.30

Venue:
ISRT Seminar Room

ABSTRACT
Creating an R package is my hobby- a very few number of R programmers or practitioners over the world might claim this but most of them would like to love to document their R code and disseminate their research which is possible only by creating R package because creating R package forces to document own code and provide test examples to ensure that it actually works. This also becomes an ideal way of making sure others have access to own work. This talk is a tutorial of how to create an R package under Windows environment. At the end of the talk, a demonstration with my latest R package called DNAseqtest would be presented as the guideline of gaining confidence for the potential R package builders

Seminar on Tuesday, February 14, 2017
2017-02-14 15:30 - 17:00
Analyzing Repeated Measures Data using Joint Models

February 10, 2017 – 12:47am

Full Title:
Analyzing Repeated Measures Data using Joint Models

Speaker:
Jahida Gulshan

Associate Professor, ISRT, University of Dhaka

Date/Time:
Tuesday, February 14, 2017, 3.30 p.m.

Venue:
ISRT seminar room

ABSTRACT
In many studies, categorical outcomes are measured repeatedly over time. Naturally, those outcomes are correlated and methods based on marginal models are popular choices to analyze such data. However, in reality, marginal models may not provide an appropriate estimation procedure due to lack of proper specification of joint models for outcome variables for repeated measures. As an alternative to marginal approaches, conditional models have also been developed in relatively few studies. However, conditional models are also inadequate to address the problem of modeling correlated data. Both marginal and conditional models fail to represent the underlying dependence in correlated outcome variables. In this study, a joint model is proposed in order to address the limitations of marginal and conditional models. A relative comparison of selected marginal approaches and the proposed model is examined for bivariate outcomes.

Seminar on Sunday, February 5, 2017
2017-02-05 15:30 - 17:00
Challenges of Applied Statisticians: Experience from Health Sciences

January 31, 2017 – 8:52am

Full Title:
Challenges of Applied Statisticians: Experience from Health Sciences

Speaker:
Dr Asad Khan

School of Health and Rehabilitation Sciences in The University of Queensland, Australia

Date/Time:
Sunday, February 5, 2017, 3.30 p.m.

Venue:
ISRT seminar room

ABSTRACT
Statistics graduates are taught about how to solve well-defined statistical and mathematical problems, but in the real world, they need to be able to translate the real-life problem into a statistical problem and offer data-based solutions. As applied statisticians, they need to have an understanding of the scientific subject area, a solid understanding of statistics and good communication skills. In this talk, we will also present case studies in the application of statistical modeling in health sciences.

Seminar on Tuesday, January 24, 2017
2017-01-24 14:00 - 15:30
Studies and career in Australia

January 22, 2017 – 4:38pm

Full Title:
Studies and career in Australia

Speaker:
Dr. Shahid Ullah

Flinders University, Australia

Date/Time:
Tuesday, January 24, 2017, 2 p.m.

Venue:
ISRT seminar Room

ABSTRACT
Dr. Shahid Ullah of Flinders University, Australia (a former faculty of ISRT) and some of his Australian colleagues would talk about the opportunities for higher studies and career in Australia.

Seminar on Tuesday, January 24, 2017
2017-01-24 11:30 - 13:00
Youth Leadership for Sustainable Development

January 22, 2017 – 8:45am

Full Title:
Youth Leadership for Sustainable Development

Speaker:
Dr. Atiur Rahman

Professor
Department of Development Studies
University of Dhaka

Date/Time:
Tuesday, January 24, 2017, 11.30 a.m.

Venue:
ISRT seminar room

ABSTRACT
Atiur Rahman is a Bangladeshi economist, writer, and banker. He served as the 10th Governor of Bangladesh Bank, the central bank of Bangladesh. He has also been called “the banker of the poor” for his contribution in developing the Bangladeshi economy.

Seminar on Tuesday, January 10, 2017
2017-01-10 11:30 - 12:30
Regressive Models for Analysing and Predictions of Discrete Time Competing Risks from Repeated Measures

January 2, 2017 – 7:24pm

Full Title:
Regressive Models for Analysing and Predictions of Discrete Time Competing Risks from Repeated Measures

Speaker:
R. I. Chowdhury

Institute of Statistical Research and Training, University of Dhaka

Date/Time:
Tuesday, January 10, 2017, 11.30 a.m. – 12.30 p.m.

Venue:
ISRT Seminar Room

ABSTRACT
In many cohort studies, we may observe repeated outcomes with competing events. Events may occur within an interval or time to events itself are discrete. The occurrences of events at different stages produce a trajectory of events for study subjects. Objectives are to model such data efficiently and prediction of unconditional trajectory probabilities for a subject with specified covariate vectors. We extended the binary regressive logistic model for multinomial outcomes and proposed a framework to predict unconditional trajectory probabilities. Also, multistate Markov model is used for the same and results are compared. The regressive modelling approach is very flexible which allows assessing the effects of past outcomes on the current one and requires to fit a single model for a stage. The proposed model is exemplified using Health and Retirement Study (HRS) data from the USA.

Seminar on Sunday, January 1, 2017
2017-01-01 12:00 - 13:00
A few essential aspects of modern survey sampling

December 20, 2016 – 4:14pm

Full Title:
A few essential aspects of modern survey sampling

Speaker:
Arijit Chaudhuri

Indian Statistical Institute, Kolkata

Date/Time:
Sunday, January 1, 2017, 12.00 p.m.

Venue:
ISRT Seminar Room

ABSTRACT
Conditions on inclusion-probabilities of population units, sample-size determination procedures, availability of variance estimators in systematic sampling, problem of small area estimation, network and adaptive sampling and randomized response techniques as recognized as some of the essentials features in sampling finite populations are presented in brief.

Seminar on Wednesday, November 23, 2016
2016-11-23 09:00 - 10:30
Estimating the cumulative incidence function of dynamic treatment regimes

November 13, 2016 – 8:45am

Full Title:
Estimating the cumulative incidence function of dynamic treatment regimes

Speaker:
Abdus S. Wahed, PhD

Professor and Director of PhD Graduate Program
Department of Biostatistics, Graduate School of Public Health
Editor in chief of JSR
130 Desoto St #7136 Parran (GSPH)
University of Pittsburgh, Pittsburgh, PA 15261

Date/Time:
Wednesday, November 23, 2016, 9.15 a.m.

Venue:
ISRT Seminar Room

ABSTRACT
Recently personalized medicine and dynamic treatment regimes have drawn considerable attention. Dynamic treatment regimes are rules that govern the treatment of subjects depending on their intermediate responses or covariates. Two-stage randomization is a useful set-up to gather data for making inference on such regimes. Meanwhile, the number of clinical trials involving competing risk censoring has risen, where subjects in a study are exposed to more than one possible failure and the speciﬁc event of interest may not be observed because of competing events. We aim to compare several treatment regimes from a two-stage randomized trial on survival outcomes that are subject to competing risk censoring. The cumulative incidence function (CIF) has been widely used to quantify the cumulative probability of occurrence of the target event over time. However, if we use only the data from those subjects who have followed a speciﬁc treatment regime to estimate the CIF, the resulting estimator may be biased. Hence, we propose alternative non-parametric estimators for the CIF by using inverse probability weighting, and we provide inference procedures including procedures to compare the CIFs from two treatment regimes. We show the practicality and advantages of the proposed estimators through numerical studies.

Seminar on Tuesday, November 8, 2016
2016-11-08 15:30 - 17:00
Demystifying ‘Big Data’

November 3, 2016 – 1:24pm

Full Title:
Demystifying ‘Big Data’

Speaker:
Dr Syed Faisal Hasan

Associate Professor of Computer Science and Engineering, Dhaka University

Date/Time:
Tuesday, November 8, 2016, 3.30-4.45

Venue:
ISRT Seminar Room

ABSTRACT
Now-a-days ‘Big Data’ is one of the much talked about topics. Although data analysis is an indispensable part of modern business, with the exponential proliferation of Internet based transactions, social interactions, location based services and amenities both the scale of available data and possibilities of deriving unforeseen information through data analysis is unprecedented. Therefore, it is very important to have a firm grasp about the technology and tools that companies are using to collect, store and analyze these large volume of data sets known as ‘big data’. This talk will provide a high level overview of ‘big data’ analysis explaining briefly about how some of the very successful companies like Uber, Netflix, Target etc have integrated ‘Big Data’ analysis in their day to day business. The key infrastructure/software components behind ‘big data’ will be presented. Later on the talk will finish by illustrating the capabilities and suitability of python for ‘big data’ analysis.

Seminar on Tuesday, May 24, 2016
2016-05-24 15:30 - 17:00
Comparison of adaptive designs for dose finding in phase I clinical trials

May 20, 2016 – 3:45pm

Full Title:
Comparison of adaptive designs for dose finding in phase I clinical trials

Speaker:
Dr. M. Iftakhar Alam

Institute of Statistical Research and Training, University of Dhaka, Dhaka-1000, Bangladesh

Date/Time:
Tuesday, May 24, 2016, 3:30 p.m.

Venue:
ISRT Seminar Room

ABSTRACT
The Continual reassessment method is a model based procedure that has been in the literature to determine the maximum tolerated dose in phase I clinical trials. The maximum tolerated dose can also be found under the framework of D-optimum design. This paper investigates the two methods to explore any potential differences between them. Simulation studies for six plausible dose-response scenarios show that the D-optimum design can work well over the continual reassessment method in many cases. The D-optimum design has also been found to allocate doses from the extremes of design region to the patients in a trial.
Keywords: Dose finding studies; Phase I trial; Maximum tolerated dose; Continual re-assessment method; D-optimum design.

Seminar on Tuesday, April 26, 2016
2016-04-26 11:30 - 13:00
Some Models on Diffusion of Innovations

April 24, 2016 – 4:10pm

Full Title:
Some Models on Diffusion of Innovations

Speaker:
Dr. Md. Abud Darda

Associate Professor of Statistics, Natural Science Academic Group, National University, Gazipur 1704.

Date/Time:
Tuesday, April 26, 2016, 11:30 a.m.

Venue:
ISRT Seminar Room

ABSTRACT
Diffusion of innovations models formulates an attempt to study the behavior of agents in the complex network structure, contagion of information and the related consequences. Pioneering approach by F. Bass (1969) is further developed with numerous research works. Later, considerations bring to the introduction of heterogeneity effect and marketing mix variables to the models.Empirical results show that the basic Bass model and its generalizations (GBM) can be studied as a modified form of the basic Logistic model. Recent work by Bemmaor(1994) explains the diffusion dynamics as a mixture of probability distributions obtained from individual level heterogeneity. In this presentation, we will make a short introduction to the diffusion models and also discuss a special heterogeneous agent based diffusion modelling at the aggregate level. Examples also given to explain the natural gas production and their reserves estimates in South Asian countries in terms of the technological diffusion of innovation.

Seminar on Tuesday, January 5, 2016
2016-01-05 15:30 - 17:00
A Multistage Model for Prediction of Sequence of Events

January 5, 2016 – 6:16am

Full Title:
A Multistage Model for Prediction of Sequence of Events

Speaker:
Rafiqul I Chowdhury, MSc

PhD candidate, ISRT, University of Dhaka

Date/Time:
Tuesday, January 5, 2016, 3:30pm

Venue:
ISRT Seminar Room

ABSTRACT
Multi-state models are most commonly used class of methods to analyze sequence of events occurring over time and generating repeated measures with censored observations. The effect of risk factors on the transition hazard from one state to another is measured using the Cox model. In recent years, there is a growing interest to predict the disease status at different stages and endpoints. A key challenge is the simplification and generalization of the existing method for prediction for the large number of events or stages. In this research, a simple alternative method is proposed for risk prediction of the sequence of events using multistage modelling approach. The proposed multi-state model for prediction of a future event for continuous time data may not be appropriate for the discrete time. We also developed the multistage model for discrete time based on the regressive modelling approach. The proposed method of prediction is a new development using a series of events in conditional setting arising from the beginning to the endpoint. The proposed method is based on a marginal-conditional approach to link the events occurring in a trajectory. The probability of a trajectory can be calculated easily. The main improvement of proposed method for risk prediction is that it is a simple approach, compared to the existing ones, and this approach can be generalized to any number of events in the process to the endpoints.

Seminar on Tuesday, August 11, 2015
2015-08-11 15:30 - 16:30
Survival Analysis and Phylogenetics in Infectious Disease Epidemiology

August 11, 2015 – 2:35pm

Full Title:
Survival Analysis and Phylogenetics in Infectious Disease Epidemiology

Speaker:
Eben Kenah

Assistant Professor of Biostatistics at the University of Florida

Date/Time:
Tuesday, August 11, 2015, 3:30pm – 4:30pm

Venue:
Seminar Room, Institute of Statistical Research & Training (3rd floor), University of Dhaka, Dhaka, Bangladesh (map)

ABSTRACT
The analysis of infectious disease transmission data is complicated because disease outcomes in different individuals are inherently dependent. We show how survival analysis provides an elegant statistical framework for handling this dependency. When who-infected-whom is observed, standard methods from survival analysis can be used to estimate the probability of infectious contact as a function of time since the onset of infectiousness—including Cox regression modeling of covariate effects on infectiousness and susceptibility. When who-infects-whom is observed, the likelihood for the model is a sum over all possible transmission trees. These models can be fit using an expectation-maximization (EM) algorithm. Finally, we show how genetic sequence data from pathogen samples can be used to restrict the set of possible transmission trees, resulting in more precise estimates of transmission parameters.

Seminar on Thursday, May 28, 2015
2015-05-28 15:00 - 16:00
Generational transmission of cardiovascular risks

May 29, 2015 – 7:54am

Full Title:
Generational transmission of cardiovascular risks: methodological challenges

Speaker:
Abdullah Al Mamun, PhD

University of Queensland
Australia

Date/Time:
Thursday, May 28, 2015, 3:00pm

Venue:
ISRT Seminar Room

ABSTRACT
TBA

Seminar on Wednesday, May 20, 2015
2015-05-20 15:30 - 17:00
Determination of Sample Size for Phase II Clinical Trials

May 22, 2015 – 8:32pm

Full Title:
Determination of Sample Size for Phase II Clinical Trials in Multiple Sclerosis using Lesional Recovery as an Outcome Measure

Speaker:
Md Mahsin, MSc

Institute of Statistical Research and Training (ISRT)
University of Dhaka, Bangladesh

Date/Time:
Wednesday, May 20, 2015, 3:30pm

Venue:
Institute of Statistical Research & Training (3rd floor)

ABSTRACT
Multiple sclerosis (MS) is an inammatory demyelinating disease of the central nervous system. The hallmark feature of the disease is the formation of focal demyelinating lesions accompanied by myelin destruction in the white matter (WM). Magnetic resonance imaging (MRI) is used identify and visualize these lesions. Repeated MRI scanning of patients (most often monthly) over period of months has become a standard protocol for Phase II trials of experimental treatment in MS. The formation of WM lesions in MS is characterized by in ammatory demyelination and then remyelination usually occurs over several months after lesion formation. Hence, a measure reecting lesional recovery is a promising outcome for phase II clinical trials that assess the effect of therapies intended to induce remyelination. Our objective is to provide sample sizes required to detect such an experimental treatment effect with certain statistical power. We consider a parallel group design with two arms of equal number of subjects. The study design is considered as a three level hierarchical data structure where lesions are nested within subjects and are assessed repeatedly over the study period. Variable numbers of new enhancing lesions per subject and variable numbers of measurements at before and after enhancement (depends on the time of the lesion’s appearance) are also considered. The numbers of subjects in each treatment arm necessary to obtain statistical powers of 80% or 90% are determined for different numbers (6; 9; 12) of monthly follow-up scans. A mixed-effects linear regression model is used for this sample size determination.

Seminar on Wednesday, May 13, 2015
2015-05-13 15:30 - 17:00
SamP2CeT: An interactive computer program

May 16, 2015 – 11:01pm

Full Title:
SamP2CeT: An interactive computer program for sample size and power calculation for two-level cost-effectiveness trials

Speaker:
Md. Abu Manju, PhD

Department of Methodology and Statistics
CAPHRI School for Public Health and Primary Care
Maastricht University, Maastricht, The Netherlands

Date/Time:
Wednesday, May 13, 2015, 3:30pm

Venue:
ISRT seminar room

ABSTRACT
The cost-effectiveness of interventions (e.g. new medical therapies or health care technologies) is often evaluated in randomized clinical trials-where individuals are nested within clusters, for instance patients in general practices. In such two-level cost effectiveness trials, one can randomly assign treatments to individuals within clusters (multicentre trial) or to entire clusters (cluster randomized trial (CRT)). Such cost-effectiveness trials (CRTs and multicentre trials) need careful planning to evaluate the cost-effectiveness of interventions within the available financial research resources. The optimal number of clusters and the optimal number of subjects per cluster for both types of cost-effectiveness trials can be determined by using optimal design theory. After presenting some theoretical results on how to calculate samples sizes for these designs, the presentation will continue with a description of a user-friendly computer program SamP2CeT (Sample size and Power calculation for 2-level Cost-effectiveness Trials) enabling researchers to design cost-effectiveness trials with regard to the optimal number of clusters and the optimal number of subjects per cluster. In case of insufficient knowledge on model parameters, the computer program SamP2CeT also provides numbers of clusters and numbers of subjects per cluster starting from a maximin strategy. SamP2CeT can either be used to calculate the minimum budget for a desired level of power, or the largest power for a fixed budget. The computer program will be illustrated for two empirical studies from the literature on cost-effectiveness trials.
