statistical models and procedures in programming 3rd ... phuse sde mum… · mumbai 2016 | phuse...

Mumbai 2016 | PhUSE Single Day Event 1

MumbaiSingle Day Event

Statistical Models and Procedures in Programming3rd December 2016Country Inn & Suites By Carlson

2 PhUSE Single Day Event | Mumbai 2016 Mumbai 2016 | PhUSE Single Day Event 3

Dear Single Day Event Attendees,

On behalf of the Organizing Committee, it is our great pleasure to welcome you all to the PhUSE Mumbai Single Day Event (SDE) ‘Statistical Models and Procedures in Programming’. We hope that you will find this event beneficial in both content and structure and that it will provide you with a great opportunity to network, learn, reflect and to meet colleagues from different companies.

With a growing need for analysis of clinical data in the pharma industry, statistical models and procedures have evolved, and are still evolving with regular upgrades. This SDE will serve as a great platform for all the stakeholders to share, discuss and enhance their knowledge in understanding the application of these models and procedures in their day-to-day journey of analysis. Overall, it is a great stage for collaborative discussion on the various topics of the statistical analysis in our industry.

A big thanks to our sponsors, Chiltern, doLoop Technologies, Ephicacy, ICON, PPD, and Sciformix for their generous support of this SDE. Additional thanks to the Organizing Committee, who have contributed greatly to the successful preparation and management of the event.

Our presenters are the cornerstones of the event, and we are grateful for the time and effort they have dedicated in choosing interesting and relevant topics and providing enjoyable presentations. The majority of the presentations are ‘never seen before’, demonstrating the enthusiasm of our community in ensuring you receive new information.

As usual, your feedback matters to us and we will be issuing an online feedback survey after the event.

Many thanks for your participation. We hope you will have a pleasant and constructive day.

Best regards, Nikita Agnihotri & Sarvesh SinghPhUSE Asia Single Day Event Organizing Committee

Introduction

Sarvesh SinghPhUSE Asia Director

Your guide to the day

AgendaTime Title and Speaker

08:30–09:00 Registration & Welcome Refreshments

09:00–09:30 Welcome & Overview of PhUSESarvesh Singh, PhUSE Asia Director

09:30–10:00 Keynote SpeechSanjay Jankar, Glenmark

10:00–10:30 Keynote SpeechSanjeev Sabnis, IIT Bombay

10:30–10:55 Glimpse of Advance Regression Model – Non-parametric ApproachSuman Kapoor & Neetu Badhoniya, Quintiles

10:55–11:10 Morning Break

11:10–11:35 Calculation of Incidence Rate, Instantaneous Risk or Force of Mortality – PROC PHREGRanjit Limbore, Chiltern

11:35–12:00 Statistical Techniques for Immunogenicity Analysis in Vaccine StudiesSridhar Vijendra, Ephicacy

12:00–12:25 Bayesian Modeling Using SAS MCMC ProcedureSaidulu Aithagoni, PAREXEL

12:25–12:50 Repeated Measure AnalysisVinod Bambure, PPD

12:50–13:50 Lunch & Networking

13:50–14:15 Propensity Score AnalysisGarima Joshi & Bhagyashree Patil, Sciformix

14:15–14:40 Analysis of Variance (ANOVA)Mahesh Ganupooru, ICON

14:40–15:05 Novel Designs in Oncology Clinical Trials Swapna Deshpande, Cytel

15:05–15:30 Sensitivity of Variable Type in Statistical Analysis using Selective SAS/STAT Procedures Sakthivel Sivam, Quartesian

15:30–15:55 Sensitivity Analysis for Missing Data – A Shift from the MAR Assumption Swati Rizhwani, TCS

15:55–16:10 Afternoon Break

16:10–16:35 Missing Values & Statistical ModelingKrishnendu Biswas, Cognizant

16:35–17:00 An Overview of Survival Analysis in Clinical Trials, Particularly in OncologyAniruddha Kulkarni, inVentiv

17:00–17:45 Panel Discussion & Q&ASanjay Jankar, Glenmark, Nikita Agnihotri, TCS & Sanjeev Sabnis, IIT Bombay

17:45–18:00 Wrap-up & Closing Remarks

Welcome to Mumbai


Speakers and abstracts Speakers and abstracts

Sanjeev Sabnis, IIT Bombay

BiographySanjeev Sabnis is a faculty member in the Department of Mathematics at IIT Bombay. He obtained his BSc and MSc from the University of Pune and his PhD from Old Dominion University, Norfolk, Virginia, USA. His research areas are reliability theory and industrial statistics. He has so far guided more than 60 MSc projects (of two-semester duration) and 3 PhD students; he is presently guiding 1 PhD student.

Sanjeev offers consulting services to industries in and around Mumbai and Pune, and conducts in-house workshops on 'Statistical Modeling' for them. Sanjeev was awarded the Excellence in Teaching award from IIT Bombay in 2011.

Keynote Speech10:00–10:30

Suman Kapoor & Neetu Badhoniya, Quintiles

AbstractThe aim of regression analysis is to establish a reasonable relationship between unknown response function and independent variables. Many approaches are adapted to analyze the unknown response. Parametric is one of the famous approaches; it’s fully described by a finite set of parameters. Although it’s easily interpretable, there are many assumptions that need consideration before performing the parametric approach. Sometimes data does not follow a particular pattern and those assumptions do not satisfy accurately, which may lead to wrong analysis. The non-parametric approach is smooth and flexible. It lets the data determine the shape of the relationship and also relaxes the usual assumption of linearity and allows you to reveal the relationships between response variable and predictive variable. Understanding the non-parametric approach and interpretation of the outcome is more challenging than an ordinary parametric regression approach. The aim of the presentation is to examine the data and apply a proper non-parametric approach and interpret the outcome in a meaningful way.

BiographySuman has been working as a biostatistician for six years in pharmaceutical clinical research. At Quintiles, Suman has been working as a study biostatistician on multiple Phase I to Phase IV clinical studies, with responsibility for providing sample size calculations, input to protocol development and cRF, developing SAP and TFL shells, reviewing and validating statistical analysis datasets and results and providing input to clinical study reports.

Neetu has a science doctorate in statistics, with more than 10 years of experience as a biostatistician in clinical research and healthcare (six years in the field of public health and over four years in the field of clinical research). He deals with therapeutic areas such as immunology, oncology, diabetes, analgesics, smoking control, pharmacokinetics and oral healthcare for all phases of clinical trials. Neetu has 13 publications in international journals in the medical and healthcare fields.

Neetu has a good knowledge and excellent understanding of applications of statistics in clinical trials. He is a proven qualified technical lead and deals with various stakeholders (DM, Programming, MW, Client Stats) and is efficient in managing resources and project planning.

Glimpse of Advance Regression Model – Non-parametric Approach

10:30–10:55

Ranjit Limbore, Chiltern

AbstractSurvival analysis is a branch of statistics which deals with analysis of time duration until one or more events happen. Mostly in clinical studies, the main interest is in measuring the occurrence of an outcome event due to a study drug. So, ultimately for almost every longitudinal trial, the focus is on understanding and analyzing time-to-event occurrence (e.g. heart attack, death, etc.).

For survival analysis model factors that influence the time to an event, or time to “failure”, we cannot use a linear regression method since the outcome is binary and the data doesn’t follow a normal distribution assumption.

This presentation will cover the very popular survival analysis method in the pharma industry and how to deal with Cox proportional hazards regression using PROC PHREG in SAS, including: • difficulty of survival analysis• events and censoring• measuring time and event• hazard function/ratio• Cox proportional hazards regression• understanding models and hypothesis testing• calculation and interpretation using SAS.

BiographyRanjit has worked as a Senior Statistical Programmer at Chiltern International since April 2016. Overall, he has six years’ industry experience working in clinical research in the pharmaceutical industry and holds a master’s degree in statistics. He has been involved with SAS programming tasks for safety and efficacy analysis in several therapeutic areas including respiratory, neuroscience, asthma, diabetes and oncology. Previously, Ranjit was employed at inVentiv Health Clinical and Tata Consultancy Services.

Calculation of Incidence Rate, Instantaneous Risk or Force of Mortality – PROC PHREG

11:10–11:35



Sridhar Vijendra, Ephicacy

AbstractDespite the simple design of a vast majority of vaccine studies, the immunogenicity analysis of vaccines presents an interesting opportunity to explore and understand statistical techniques in clinical trial data analysis. Due to the nature of the laboratory tests used to study immune response of a vaccine (titer data from assays such as HI, MN, etc.), immunogenicity data from vaccine studies are often summarized after logarithmic transformations. Commonly presented reports in immunogenicity analysis include comparison of geometric mean titers and percentage of subjects achieving a target titer ratio post baseline. The GLM and CATMOD procedures in SAS are used to arrive at adjusted and unadjusted estimates of differences and confidence intervals between treatment groups. Covariates commonly used are age group, baseline titer value and health status. Vaccine data analyses are also characterized by tables that present a significant number of subgroup analyses, including site, age group, health status, gender and ethnicity. In this session, the use of PROC GLM and PROC CATMOD for vaccine immunogenicity analysis will be presented.

BiographySridhar Vijendra has a master’s degree in biomedical engineering from the University of Texas at Arlington and more than eight years’ experience in clinical trial data analysis and reporting. He also has prior experience in the application of signal and image processing techniques on biomedical data. He currently works at Ephicacy, India as a Principal Programmer.

Statistical Techniques for Immunogenicity Analysis in Vaccine Studies

11:35–12:00

Saidulu Aithagoni, PAREXEL

AbstractIn recent decades, modeling and simulation has played a vital role in clinical trials. Simulation and modeling can function as a part of decision support systems (go-no-go decision) to optimize the study design. The Bayesian model, by using prior distribution and likelihood function in clinical trials, helps to better understand the study outcomes. Bayesian can be performed by using procedures such as FMM, GENMOD, PHREG, LIFEREG and MCMC. Among these procedures, Markov chain Monte Carlo (MCMC) has good features such as advancement of computational algorithms and power, and also has a wide range of complex Bayesian statistical models and random statement (used for linear & non-linear random effects models).

BiographySaidulu Aithagoni is a Principal Statistical Programmer at PAREXEL International, with over nine years of experience in the clinical domain. Saidulu holds a master’s degree in statistics from Osmania University, Hyderabad. Prior to joining PAREXEL, he worked at Novartis Healthcare for more than five years. He started his career at Sristek. Saidulu is certified in Executive Education Program in Biostatistics from Manipal University.

Bayesian Modeling Using SAS MCMC Procedure

12:00–12:25

Vinod Bambure, PPD

AbstractThe term ‘repeated measures’ refers to data with multiple observations on the same sampling unit. In most cases, the multiple observations are taken over time, but they could be over experimental units such as individuals, animals, or machines. It is usually plausible to assume that observations on the same unit are correlated. Hence, statistical analysis of repeated measures data must address the issue of covariation between measures on the same unit. The objectives of repeated measures data analysis are to examine and compare response trends over time. This can involve comparisons of groups at specific times, or averaged over time.

Many methods have been used for the analysis of repeated measures data. The classical approach is to treat the experimental units in a repeated measures study as blocks in a blocked design. Repeated measures analyses in the SAS GLM procedure involves the traditional univariate and multivariate approaches. The SAS MIXED procedure employs a more general covariance structure approach. In this presentation, we define repeated measures designs, and discuss their analysis with both PROC GLM and PROC MIXED; some numerical examples illustrate many of the key similarities and differences.

BiographyVinod N Bambure, based in Bangalore since 2006, was born and studied in the Dharwad district of Karnataka. Vinod has a master’s degree in statistics from Karnataka University, Dharwad. Vinod has worked as ‘Manager, Biostatistics’ at PPD, Bangalore, India since May 2015 and, overall, has 10 years of industry experience across pharma and CROs, working as a statistician in different phases of clinical trials across various therapeutic areas.

Repeated Measure Analysis12:25–12:50

Garima Joshi & Bhagyashree Patil, Sciformix

AbstractIn randomized experiments, the results in the treatment groups may be directly comparable because their patient characteristics are likely to be similar. In observational studies, prognostic and demographic factors may influence treatment allocation among patients. Thus, direct comparison of the treatment groups may be misleading due to the presence of selection bias. In such cases, “Propensity Scores” can be used, which attempts to control such factors to make the treatment groups more comparable and reduce bias.

The aim of this presentation is to understand propensity score analysis and different conditioning methods on the propensity score. We will also demonstrate its application in clinical data.

BiographyGarima Joshi has a postgraduate degree in statistics and has worked at Sciformix Technologies for over four years. Garima started working in the clinical domain as a statistical programmer and has about three years’ experience, and for the past year has been working as a statistician.

Bhagyashree Patil holds a postgraduate degree in statistics and has worked as a statistician at Sciformix Technologies for over two years. He has experience in supporting statistical work on studies across various phases.

Propensity Score Analysis13:50–14:15



Mahesh Ganupooru, ICON

Swapna Deshpande, Cytel

AbstractThis presentation will describe the powerful statistical technique Analysis of Variance (ANOVA) that can be used to compare the means of several samples. The original ideas of analysis of variance (ANOVA) was developed by the English statistician Sir Ronald A. Fisher (1890-1962) in his book “Statistical Methods for Research Workers” (1925). This technique is intended to analyze variability in data in order to infer the inequality among population means. It can be thought of as an extension of the t-test for two independent samples of more than two groups. Analysis of Variance (ANOVA) is the most commonly quoted advanced research method and very useful in revealing important information. The analysis of variance (ANOVA) assumes that observations are normally and

independently distributed with the same variance for each treatment level. PROC GLM and PROC MIXED procedures available in SAS® are useful tools that make it quite easy to conduct the analysis of variance (ANOVA).

BiographyMahesh Ganupooru, Team Lead, Biostatistics and Programming, has been working at ICON for over three years, and has over 10 years’ experience in programming within the clinical industry. He holds a master’s degree in bioinformatics and a bachelor’s degree in biochemistry. He is also Lean Six Sigma Yellow Belt certified. Prior to joining ICON, Mahesh worked at organizations including inVentiv Health, Novartis and Accenture.

AbstractEarly-phase oncology trials aim to determine the maximum-tolerated dose (MTD) using dose escalation strategies. This helps to recommend a dose and/or schedule of new drugs or drug combinations for subsequent late-phase trials. The guiding principle for dose escalation in Phase I trials is to avoid unnecessary exposure of patients to sub-therapeutic doses of an agent while controlling ‘overdosing’ and maintaining rapid accrual. Though conventional 3+3 design is popular due to its ease of understanding and implementation, it has got numerous limitations. In parallel, a number of Bayesian model-based dose-escalation designs have been developed. Model-based approaches such as the Continual Reassessment Method, Bayesian Logistic Regression Model and Toxicity Probability Interval (TPI) are built on a dose–toxicity model that is updated with incoming accumulated real-time data through ongoing trials. This helps to make the estimate of MTD more reliable. In the TPI approach, the aim is to balance target toxicity and safety of the recommended dose.

This presentation will walk through the concept of TPI, with accumulating study data illustrated by an example, and discuss interpretation of its results.

BiographySwapna has more than 15 years’ experience working in the field of biostatistics at various levels within research organizations and the pharmaceutical industry.

Her drug indication and therapeutic area experience includes oncology, Parkinson's disease, HIV/AIDS, diabetes, and vascular diseases. She has authored/co-authored around 15 publications in international journals and is a recipient of the Fogarty fellowship for supportive statistical work in the field of preventive epidemiology of HIV/AIDS.

Swapna has worked at Cytel for the last five years. She closely works with clients in multi-cultural environments and interacts with cross-functions, analyzes data, prepares reports and works with data monitoring committees. Swapna currently leads a team of study statisticians involved in resource and process management to ensure the quality of the project. Prior to joining Cytel, Swapna worked with eminent research organizations such as the National AIDS Research Institute, the KEM Hospital Diabetes Research Unit and the Tata Institute of Fundamental Research.

Analysis of Variance (ANOVA)

Novel Designs in Oncology Clinical Trials

14:15–14:40

14:40–15:05

Sakthivel Sivam, Quartesian

AbstractVariable data type is one of the key components for statistical procedures in SAS®. The spotlight is drawn towards impact on different SAS/STAT procedures by usage of numeric or character variable type, i.e. discussion would be on how sensitive are the SAS/STAT procedures for the variable type specified. The objective of this presentation is to give a heads-up to programmers to be cautious of the variable types of usage in various SAS/STAT procedures while programming statistical modeling. Few examples worked out on STAT procedures such as PROC FREQ, PROC MIXED, PROC GLM, PROC PHREG, PROC LOGISTIC and PROC LIFESTEST will be discussed extensively.

BiographySakthivel Sivam, or Sakthi, is the Manager of Biostatistics at Quartesian Clinical Research, India and has over 11 years’ experience in biostatistics and statistical programming in the clinical research industry. He currently spearheads all the biostatistics and programming projects and initiatives at Quartesian, India. Sakthi holds a master’s degree and MPhil in

Statistics and is currently pursuing his PhD in Statistics at M.S University, Tamilnadu. He is a certified trainer in this industry. He has written and co-written several research papers that have been published in national as well as international journals. He is a member of a number of societies and associations including ISMS, IASCT, PharmaSUG and PhUSE and was a member of the Board of Studies in the Department of Biostatistics at SDNB Vaishnava College, Chennai. He is an active participant and presenter at events and conferences including ConSPIC, PhUSE and PharmaSUG. Sakthivel has presented his research work at many national and internal conferences. He has been a resource person for many workshops and seminars in both academia and industry. He has also played the role of external expert to conduct the academic audit for the Department of Mathematics, Lady Doak College, Madurai.

His main areas of research interest are statistical inference in categorical data analysis, Bayesian methods, computational statistics, and statistical applications in the field of medical, epidemiological, clinical and social science.

Sensitivity of Variable Type in Statistical Analysis using Selective SAS/STAT Procedures

15:05–15:30


Krishnendu Biswas, Cognizant

Aniruddha Kulkarni, inVentiv

AbstractMissing data is a critical characteristic of clinical trials, and there are several statistical techniques that can accommodate missing data. Missing data arises for a multitude of reasons. There is no single approach that can cope with this variety of reasons. A large number of techniques are available for analyzing incomplete data, ranging from ad hoc ‘fixes’ to sophisticated statistical modeling. The techniques can be grouped under three headings: analysis of completed cases, imputation and modeling. This presentation will cover salient features and the limitations of traditional and modern approaches as well as single and multiple imputations. A real-life scenario of usage of multiple imputations will also be discussed.

BiographyKrishnendu Biswas is Deputy General Manager for Cognizant Technology Solutions. He carries 12 years’ experience in a variety of roles in the biostatistics and programming domain. In his current role, Krish leads the delivery teams and provides strategic direction to the Biostatistics and Statistical Programming practice. Prior to joining Cognizant, he worked with Tata Consultancy Services and Pfizer. He has experience of working in clinical trials from Phase I to IV. He was a Therapeutic Area Lead for the Cardiovascular & Gastro Intestinal TA. He holds a master’s degree in management from the University of Mumbai and a master’s of statistics from the University of Calcutta. He has been an active member of the PhUSE India Working Group since 2014. He co-chaired the PhUSE SDE at Lonavala in December 2015 and serves as an Honorary Lecturer for several educational institutions, including K.J. Somaiya Institute of Management Studies and Research, St. Xavier’s College, Mumbai and the Bombay College of Pharmacy.

AbstractIn clinical trials, there are many statistical procedures and models available for analyzing various types of data. It’s critical for SAS professionals to determine the right procedure, approach and tool for performing the quality analysis. Determining the right procedure depends on the objective of the trial, particularly the primary objective and the type of data. Objectives differ with the therapeutic area of the trial.

In some trials, response at the end of the treatment period is important, while in others the time taken to getting the response is more important than the actual response. Survival analysis deals with later.

Survival analysis is a class of statistical methods for analyzing the time-dependent data. When we want the results in terms of occurrence of the events rather than the event itself, survival analysis comes into play.

Initially, these methods were most often used for study of deaths where time till death, that is survival time before death, was measured, hence the name survival analysis. Survival analysis consists of techniques for positive-valued variables, e.g. time till death, time to onset (or relapse) of a disease, time of stay in hospital, etc. This presentation will explain in brief the what, why, when and the how of survival analysis.

BiographyAniruddha works as a Senior Statistical Programmer II at inVentiv Health Clinical. He has over seven years of rich experience in clinical SAS programming, with an MSc in Statistics.

Aniruddha is experienced in various therapeutic areas such as dermatology, oncology, cardiovascular and endocrinology, with involvement in all phases (I to IV) of clinical trials. He has experience of working in efficacy and safety analysis, with expertise in oncology-specific studies involving survival analysis. Aniruddha is involved in writing statistical analysis plans (SAPs) for various therapeutic areas and supports the development of programming standards, training, technologies and tracking tools to increase efficiencies in clinical study reporting. He also co-ordinates and oversees activities of the programming teams, as required for selected projects.

Missing Values & Statistical Modeling

An Overview of Survival Analysis in Clinical Trials, Particularly in Oncology

16:10–16:35

16:35–17:00


Swati Rizhwani, TCS

AbstractClinical trial stakeholders face many issues due to missing or incomplete data. The chances of missing data increase even further in the case of outcome variables that involve repeated assessments.

In the presence of missing data for the response or dependent variable, the repeated measures statistical analysis has an underlying assumption that data is Missing At Random (MAR). What if this assumption doesn’t stand true? How does this impact the results? Are the results robust enough to depart from the MAR assumption? Sensitivity analysis for missing data can help us answer these questions.

In this presentation, we explore the impacts resulting from the shift from MAR assumption, i.e. when data is Missing Not At Random (MNAR), and determine how robust our estimates are using a case study based on the pattern mixture model approach.

BiographySwati Rizhwani works as assistant manager in the biostatistics and statistical programming group of TCS Life Sciences and Healthcare. Prior to joining TCS, she worked at Sciformix Technologies as a Senior Statistician. She has six years’ experience in biostatistics and statistical programming on SAS platforms, which includes working in therapeutic areas such as RA, diabetes and immunology, across various clinical trial phases.

Sensitivity Analysis for Missing Data – A Shift from the MAR Assumption

15:30–15:55


“Living with diabetes, I am grateful to know that better drugs are in development that could truly change my life.”

Luke12-year-old living with Type I Diabetes

www.ppdi.com© 2016 Pharmaceutical Product Development, LLC. All rights reserved.

Helping deliver life cHanging tHerapies

At PPD, what we do impacts our clients, our employees and

people around the world. We see the benefits of clinical trials

firsthand and are committed to building strong partnerships

with our clients. Because to us, clinical research isn’t just

business — it’s personal.

“I have diabetes and I’m happy to know that people are working to find a cure.”

Luke12 Year Old Living with Type I Diabetes

“We need more clinical trials and we need to keep innovating

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Survivor and Executive Director, Project Management, PPD

Thanks

ContributorsThank you to all our contributors

for their generous support


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Exhibitors

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Washington National Cathedral

March 19th–21st 2017Silver Spring Civic Center, Silver Spring, Maryland, USA

Computational Science Symposium

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Conference Chair Jules van der Zalm, OCS ConsultingConference Co-Chair Katja Glaß, Bayer PharmaFor more information visit the PhUSE website phuse.eu/annual-conference.aspx

Annual Conference

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Digital Innovation in Healthcare8th–11th OctoberEdinburgh International Conference Centre

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