This document discusses the evolving role of statisticians in clinical research. It begins with an introduction of the presenter. The presentation then outlines how statisticians contribute throughout the different stages of a clinical study, including study planning, start-up, maintenance, risk-based monitoring, data monitoring committees, end of study analysis, and reporting. It emphasizes that statisticians can add more value through activities such as data management programming, cross-study analyses, organizational statistics, and training. The presentation concludes by encouraging clinical research organizations to keep statisticians involved, ask them questions, and make full use of their skills and expertise throughout all phases of a clinical study.
How to Craft the "Significance” & "Innovation" Sections of a Grant Applicatio...UCLA CTSI
William Parks, PhD
Professor of Medicine at Cedars-Sinai Medical Center and UCLA
Associate Dean for Graduate Research Education
Scientific Director, Women’s Guild Lung Institute
NIH Grant Proposals (SF 424): K08 - K23 Applications and Individual Career De...UCLA CTSI
Learn about NIH Grant Proposals (SF 424). Dr. Salusky discusses K08 - K23 applications and Individual Career Development Awards. Provided by the UCLA Clinical and Translational Science Institute. More information on K /CDA awards can be found at https://ctsi.ucla.edu/education/pages/edtools.
Presenter:
Isidro B. Salusky, MD
Distinguished Professor of Pediatrics at UCLA
Chief, Division of Pediatric Nephrology
Director, Clinical Translational Research Center
Associate Dean of Clinical Research
How to Craft the "Significance” & "Innovation" Sections of a Grant Applicatio...UCLA CTSI
William Parks, PhD
Professor of Medicine at Cedars-Sinai Medical Center and UCLA
Associate Dean for Graduate Research Education
Scientific Director, Women’s Guild Lung Institute
NIH Grant Proposals (SF 424): K08 - K23 Applications and Individual Career De...UCLA CTSI
Learn about NIH Grant Proposals (SF 424). Dr. Salusky discusses K08 - K23 applications and Individual Career Development Awards. Provided by the UCLA Clinical and Translational Science Institute. More information on K /CDA awards can be found at https://ctsi.ucla.edu/education/pages/edtools.
Presenter:
Isidro B. Salusky, MD
Distinguished Professor of Pediatrics at UCLA
Chief, Division of Pediatric Nephrology
Director, Clinical Translational Research Center
Associate Dean of Clinical Research
A well recognised form of research is called systematic reviews on specific point. Why do we need them and How they can be done?? this talk is trying to answer these questions in a simple way
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
For a School of Information class on medical librarianship, this presentation was created to provide a very basic introduction and overview of the concepts, expectations, and experience of the librarian portion of working in a systematic review team.
The Role and Responsibilities of Statisticians in Clinical Trials Presentation to MedicReS 5th World Congress on October 19-25,2015 in New York by Shing Lee, PhD
Narrative review | Systematic review | Data extractionPubrica
When conducting a systematic review of prospective cohort studies, it’s crucial to extract relevant data from the included studies in a consistent and structured manner. Here are some variables to consider when creating a data extraction form for your systematic review.
Visit us @ https://pubrica.com/services/medical-data-collection/
A well recognised form of research is called systematic reviews on specific point. Why do we need them and How they can be done?? this talk is trying to answer these questions in a simple way
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
For a School of Information class on medical librarianship, this presentation was created to provide a very basic introduction and overview of the concepts, expectations, and experience of the librarian portion of working in a systematic review team.
The Role and Responsibilities of Statisticians in Clinical Trials Presentation to MedicReS 5th World Congress on October 19-25,2015 in New York by Shing Lee, PhD
Narrative review | Systematic review | Data extractionPubrica
When conducting a systematic review of prospective cohort studies, it’s crucial to extract relevant data from the included studies in a consistent and structured manner. Here are some variables to consider when creating a data extraction form for your systematic review.
Visit us @ https://pubrica.com/services/medical-data-collection/
Systematic literature review | Meta analysis | Retrospective versusPubrica
Systematic review for prospective studies is a meticulous and essential process ensuring research findings’ reliability and validity. The key to success lies in adhering to a well-structured methodology that includes defining the research question, developing a comprehensive search strategy, screening studies based on pre-defined criteria, and critically appraising the selected articles.
Read more @ https://pubrica.com/academy/manuscript-editing/conduct-a-systematic-review-for-prospective-studies/
Epoch provides training to students, professionals and corporate on SAS®, Data Management Activities and soft skills. Training includes Software Programming, Clinical, Analysis and Analytics modules, which can be availed by professionals with IT, Life Sciences, Medical, Statistics, MBA and such other backgrounds. Epoch is the pioneer in the courses designed of SAS designed for Clinical Programming world.
www.epoch.co.in, info@epoch.co.in
#bigdata
#hadoop
#sastraining
#epochsastraining
#sasonlinetraining
#clinicalprogramming
#epochsasonlinetraining
#epochresearchinstitute
Speaker Presentation from U.S. News Healthcare of Tomorrow leadership summit, November 2-4, 2016 in Washington, DC. Find out more about this forum at www.usnewshot.com.
Hitting the Sweet Spot with Predictive Analytics (Michael Draugelis)Ashleigh Kades
Speaker Presentation from U.S. News Healthcare of Tomorrow leadership summit, November 2-4, 2016 in Washington, DC. Find out more about this forum at www.usnewshot.com.
Protocol Design & Development: What You Need to Know to Ensure a Successful S...Brook White, PMP
Solid protocol design is critical to clinical development. No matter how well executed a clinical study is, if the underlying design is flawed, it wasn’t worth doing. In this presentation, Dr. David Shoemaker, SVP R&D, and Dr. Karen Kesler, AVP Operations, will walk through the process of developing a protocol, explain the major considerations, and point out common mistakes and challenges.
Researcher KnowHow session presented by Carrol Gamble, Anna Kearney and Paula Williamson, Department of Health Data Science. University of Liverpool and Trials Methodology Research Partnership.
Retrospective versus | Meta analysis | Systematic literature reviewPubrica
Systematic review for prospective studies is a meticulous and essential process ensuring research findings’ reliability and validity. The key to success lies in adhering to a well-structured methodology that includes defining the research question, developing a comprehensive search strategy, screening studies based on pre-defined criteria, and critically appraising the selected articles.
https://pubrica.com/academy/manuscript-editing/conduct-a-systematic-review-for-prospective-studies/
Systematic review and meta analysis is considered as the highest body of evidence in research evidence hierarchy. Often misunderstood or skipped over, this powerful tool can broaden our understanding on a specific topic and form basis of practicing evidence based medicine for us.
I presented systematic review and meta analysis as part of my PG seminar and got a good feedback. Now I wanted to share the presentation for a broader audience.
Any kind of constructive feedback is welcome.
Dr. Anik Chakraborty
JR3, Dept. Of Community Medicine
Pt. B. D. Sharma PGIMS, Rohtak
K-to-R Workshop: How to Structure the "Approach" Section (Part 1)UCLA CTSI
UCLA CTSI K-to_R Workshop, October 29, 2015
Presenter:
David Elashoff, PhD
Professor of Biostatistics & Medicine
Program Leader, CTSI Biostatistics and Computational Biology
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Novas diretrizes da OMS para os cuidados perinatais de mais qualidade
ARCS Presentation 2015
1. The Evolving Role of the
Statistician in Clinical Research
Dr Elisa Young
2. About me
• Education:
• BSc (Med Lab Sci)
• PhD (Pharmacology)
• MSc (Statistics)
• Career: (>10 years)
• Statistician, Project Management, Laboratory
Management, Diagnostic Laboratory Scientist, R&D
Scientist
• Novotech (2 years)
• Statistical lead and statistical programmer on over 30
studies
3. Disclaimer
• This presentation represents my thoughts
and perspectives, based on my
experience.
• Perspectives are based on working for a
CRO with a small-scale statistics team and
may not be representative of other CROs,
nor pharmaceutical companies.
4. Hot Topic
Agenda
• How does the statistician fit into a clinical study team?
• How does the statistician contribute during each of the study
stages?
• How can an organisation get added-value from statisticians?
10. Randomization
Static: Assigns treatment in a sequence established
prior to any patients entering the study.
Dynamic: Generates random treatment assignments
based on the stratification levels of the patients that
have entered the trial as well as the patient entering the
study.
12. Risk Based Monitoring
• Driven by “key risk indicators”
• Review of statistical data over the course of the trial
• Very broad range of metrics to be monitored
• May involve complex modelling to estimate overall
risk and unveil data patterns
• Ownership and responsibilities?
13. Data Monitoring Committees and Interim Analyses
• Includes: FDA safety reports, SRCs, DSMBs,
IAs ……
• Varying purposes: Safety, efficacy, dose selection,
sample size re-estimation, futility
• Responsibilities:
• Organising provision of study output
• Committee member
• Statistical risk assessments
14. End of Study Analysis
• SAS programming
• Validation and QC review
• Interpretation
• Reporting
16. CDISC
• Clinical data standards for submission of databases
and supporting documentation to regulatory bodies
• To become mandatory in the next few years.
• Primary responsibilities statisticians &
programmers
• Entirely new work processes
• Entirely new skillset
• Significant affects on timelines, budgets & resources
18. Getting the most out of your
statistician/programmers
• Data Management Programming
o Data transposition for easier review
o Identify protocol deviations and eligibility violations
o Complex data review
• Comparing old and refreshed data
• Merging data from several sources
• Reconcile CRF data with external vendor data (e.g.
laboratory results, randomisation data, PK data)
19. • Cross-study analyses
• Combine/compare similar studies
• Site performance
• Organisational statistics
• Training
Getting the most out of your
statistician/programmers
During my presentation this afternoon I aim to talk about 3 aspects of clinical statistics, being how does the statistician fit into the clinical study team, how and what does the statistician contribute through a clinical study. And lastly, how can an organisation get the most out of their statisticians.
Throughout the presentation I’m going to highlight some “hot topics” which will hopefully provide some insight into some of the challenges currently being faced by clinical statisticians.
Taking a quick look at a typical clinical study team, there are of course many team members, representing various departments and areas of expertise. The statistician is just one of the team members of any study team and works closely with most of the other study members, as well as other departments such as QA, BD and HR.
For the purpose of this presentation I’ve split out a clinical study into 5 phases, planning, start-up and maintenance, interim analyses and data monitoring committees, and end of study and reporting.
During the planning phase of studies, statisticians may be asked to provide advise on the study design. The level of involvement varies and may involve advice regarding statistical methodology, schedule of events and timing, or relate to study endpoints.
In addition, we’re often asked to provide estimates regarding sample size, to ensure that the study has adequate power to identify statistical significance.
Of course, none of these discussions are done in isolation. They are done in collaboration with the sponsor and principal investigators, amongst others, to ensure that the study design meets all clinical, regulatory and statistical standards.
One hot topic in the planning stages of clinical studies is the changes we’re seeing in Bioequivalence studies. And there are number of changes happening in this area.
The first is High Variable Drugs. Essentially, BEQ study designs have quite specific guidelines, and study design and sample sizes very much revolve around the variation of a drug’s pharmacokinetic, or PK, profile. The more variable a drug is, the more subjects you’re going to need. Standard BEQ designs only allow for a certain level of variation, and so alternative designs for high variability drugs need to be implemented. This often increases the expense of BEQ studies, places more subjects at risk, and ultimately, limits the availability of generics; and as such sponsors are often looking at exploring newly published study designs, to increase feasibility.
A second shift is in the type of statistics used to analyse BEQ studies, with many studies choosing to use Bayesian statistics. I won’t go into how Bayesian statistics differs to the standard frequentist approach in clinical trial analyses, other than to say that the methodologies and thinking behind the two methodologies is really quite different, and depending on their training, this shift may require statisticians to undergo additional training to be able to keep up.
Other changes in BEQ studies relate to the type of endpoints being used to claim equivalence. Traditional study designs focus on PK outcomes, however studies may instead need to use pharmacodynamic or even clinical endpoints to claim equivalence. These approaches are usually specific to either an indication, such as an eye treatment where systemic PK levels are below the level of detection, or the chemical properties of the drug.
Following the study being planned, the study goes into the Start-Up phase, where the clinical and regulatory teams are hard at work getting everything in order to start to the study. There are also a few activities for the statistician here. Firstly, we may be asked to either review the protocol, with a focus on the statistical section of the protocol, or we may be asked to write the section from scratch. This input is actually really beneficial as it is a great way to familiarise ourselves with the study to ensure that any concerns can be raised before recruitment even begins.
Following ethics and governance approval, we’ll then be involved in generating randomisation schedules, as the study requires, and preparing code break envelopes.
Lastly, during start-up, the statistician is commonly asked to review eCRFs to ensure that the data collection is going to allow appropriate statistical analysis as stipulated by the protocol. We may also be asked to review Consistency Check Specifications, or CCS, which is essentially a list of all the ways that the integrity of the data will be checked, such as making sure that values are within a certain range, that dates make sense, and so on.
A hot topic in the Start-Up phase of studies relates to randomization, with many studies now opting to using what’s called Dynamic randomization, as opposed to a standard Static randomization method.
To briefly differentiate, static randomization assigns treatment in a sequence established prior to any patients entering the study. So, the treatment allocation scheme is predefined and unchanged as patients enrol onto the study – the randomization list can be printed off for the whole study ahead of time. This method does not use any information on patients that have entered the trial. As an example you may have a study with a sample size of 6, with 3 active and 3 placebo, where the statistician uses a program to generate a list of the sequential order of treatment allocation. For instance, the first subject will be on active, the second on placebo, the third on placebo, through to the 6th patient.
Dynamic randomization on the other hand generates the random treatment assignment for each subject as they enter the study. This method takes into account the stratification levels, and treatment allocations, of the subjects that have already entered the study, as well as the patient now entering the study, aiming to maintain balance as you continue through enrollment. Because this method is subject-by-subject, there is no randomization schedule available up-front.
The best news about dynamic randomization is that it is done using the eDC platform, and therefore requires very little from the statistician, other than inputting the correct specifications into the back-end.
During much of the maintenance phase, statisticians may very well have very little to do, depending on the length of the study. We tend to kick back into action 3-4 months prior to the proposed database lock date, when we start to prepare a statistical analysis plan, or SAP, which describes, in detail, all of the analyses, including simple data listings, descriptive summaries, inferential analyses and figures, or graphs, which will be output at the end of the study. In conjunction, we’ll put together shells, or mocks, of each output, to detail how the output will look.
Once we have consensus and approval for the SAP and mocks, SAS programming will start to ensure that everything is ready to run by database lock. I’ll be discussing programming in more detail in a few slides time.
Like all other departments, the hot topic for this stage is Risk Based Monitoring.
For statisticians, who have traditionally been involved in the design and analysis of clinical trials at the end, the introduction of RBM will now likely have us be involved in checking the data over the course of the trials. RBM is driven by monitoring indicators of the quality and performance of investigational sites and a common way to evaluate site performance will be through the monitoring of predefined metrics. These will likely include relevant indicators of quality such as accrual rate, frequency of adverse and serious adverse events, frequency of data queries and time taken to resolve them. These metrics will need pre-defined limits or thresholds to trigger actions. Sophisticated RBM approaches go even further, using all available data to build and validate multivariate statistical model to indicate overall risk. For multi-site studies, central statistical monitoring may be required to compare the consistency of the data collected at each site with the data contributed by all other sites. This approach would provide a powerful way to detect abnormal data patterns which may indicate errors, misunderstandings, sloppiness, data fabrication or fraud.
The impact on statisticians is likely to be highly variable, depending on the methodological approaches chosen by the sponsor. Not having direct experience with RBM yet, I think ownership and responsibilities is going to be key. There are so many departments that are critical in RBM, such as clinical operations, data management, statistics and even IT. The other key element will be need for considerable investment in sufficient training to educate people on new processes, and to get everybody comfortable with the new tools and software.
Overall, I think there’s no doubt there will be improvements in data quality by focusing efforts on the data most important to the trial, using statistics and graphics to prioritise and identify warning signs.
This next stage, which is really just a sub-stage of the Maintenance phase, is the occurrence of data monitoring and interim analyses, and also includes study updates submitted to the FDA. There are so many different terms used in studies, but essentially this stage represents any “look” at the data before the end of the study. Theses “looks” can be for a wide variety of reasons, such as reviewing safety or efficacy, or may be for dose selection in sequential dose escalation study designs. It may be re-estimate the sample size, or may be for futility, to determine if the study needs to be stopped early.
The role of the statistician will vary according to purpose of the study, but usually involving organising for the study output to be generated and provided to the appropriate personnel.
Another important role, which is rarely specified upfront, but is essential for the integrity of the study, is for the statistician to prepare a risk assessment, which aims to identify the various risks, and mitigation strategies, related to these data looks, specifically relating to unblinding and bias.
We then come to the end of the study, where things get very busy for a clinical statistician, and statistical programmer. The programmer needs to complete all the programming to generate all the study output, and then all that output needs to be reviewed and validated. To help put things into perspective, each program can easily be over a hundred lines of codes. Depending on the study there can be over 100 pieces of output, including listings, tables and figures.
Once output is validated, the statistician then needs to interpret the findings and prepare a statistical analysis report.
Looking at the SAS programming a little further – in order to generate all the output for any given study, the programmer needs to bring together all the study data, which primarily involves the study database, or eDC, but also includes CTMS data such as protocol deviations, safety laboratory results, randomisation schedules, PK and immunogenicity data and pharmacodynamic data, amongst other study specific datasets. All this data is pooled together in SAS, where a study library of datasets is created. The programmer then needs to prepare some derived datasets, which combines some of the datasets, such as combining the coding data for AEs, CMs and Med Hist, together with the raw data from the CRF page. From there, the programmer will then needs to write programs to generate all the listings, tables and figures, as well as transport and CDISC datasets.
Which brings me to the hot topic in this stage of the study, being CDISC.
To provide a brief overview, sponsors have always had to submit database data and supporting documentation through to the FDA, however the requirements, up until recently, have been pretty minimal. CDISC is a not-for-profit organization which established a set of standards for how database data should be managed, collected, archived and submitted, and although up until now these standards have been optional, recent FDA communication indicates that these standards will become the mandatory standard over the next couple of years.
From a CRO perspective, there has been a sharp increase in the past couple of years in the number of requests for clinical data using the CDISC standards. We are now at the stage, where if you are in the clinical trial business, it is now good business to adopt and integrate CDISC standards into your organization.
The integration of CDISC standards into the organisation has primarily fallen on statisticians and statistical programmers. This is no simple integration, with the introduction of CDISC usually requiring significant changes in an organisation’s work flow. The flow of work is no longer from the Data Management straight to analysis datasets. It is now from data management to what’s known as SDTM datasets, which are CDISC-compliant, and then to analysis datasets. This affects timelines, budget, and resources. In general more of everything is going to be needed due to the increase in workload and number of statistical deliverables.
Lastly, there is also a matter skillset. One key element of the CDISC standards is the documentation of the metadata – which is the data about the data, so field names, lengths, formats, etc. A key part of this work requires a knowledge not only of XML but also of XML Schema and XSL, which are programming languages you would learn in an IT course, not statistics.
In regards to reporting the results of any given study, statisticians are first and foremost involved by preparing a Statistical Analysis Report, which summarises all the data and output from the study.
In addition, statisticians may be asked to prepare in-text tables, collate the tables and figures for Section 14 and compile the listings required for Section 16, which is the appendices.
Lastly, we may be involved in the preparation of manuscripts, conference abstracts or presentations.
In addition to the typical activities of statisticians and SAS programmers in the conduct of a clinical study, there’s many ways that you can get more out of them either at an organisational or study level.
The most important area to expand these roles is with Data Management, and in fact many large CROs will have SAS programmers who specifically work with Data Management. In this role, programmers can help data management by transposing data, so that the data is easier to review. This might be organising all the lab data so that a data associate can use Excel filters and pivot tables.
Programs can also be written to pick up protocol deviations and eligibility violations, either as a net to catch anything that slips through the net, or as a more formal check for deviations that may be difficult to pick up without merging and rearranging data.
Programs can also be written to allow more complex data reviews. A few examples that I’ve been involved in have been – comparing old and refreshed data. This allows data associates to focus on data that is new, removed or amended since the last look at the data. It could also be merging data from several sources, which could help with cross-checking eligibility data with protocol violations and waivers. And lastly, SAS programming can be used to reconcile external vendor data with CRF data to ensure that visits, times, and other information lines up.
At a broader level, statisticians and SAS programmers could prepare cross-study analyses. This may be combining or comparing similar but separate studies. Or it could be at an organisational level, to look at site performance or look at recruitment for specific indications or regions, as sort of a higher level risk based monitoring.
There is then organisational statistics, and I really just add this as a way of saying that there are so many departments in a clinical organisation that use statistics. Whether it’s project management tracking how well projects are running, or QA wanting to compile results about CAPAs, or even HR wanting to analyse results of their most recent employee satisfaction survey. Statisticians and programmers can help with any of these, whether it’s with planning the what and how or helping with analysis and interpretation.
Lastly, there is training. I think it’s beneficial for everybody in a clinical study team to understand the fundamentals of clinical statistics, and specifically the effects of dirty or incomplete data on the ability to properly present and interpret such data. Such training should of course be specific for the attendance, so the training one would give to data management would be quite different to that for project management, clinical or regulatory.
In summary, my take away messages are this: Statisticians are a critical element in of any clinical study team. Keep us involved. Ask us questions and ask more of us.