Statistical Methodology for a Clinical Trial Protocol.
|
|
|
- Anna Hubbard
- 7 years ago
- Views:
Transcription
1 Statistical Methodology for a Clinical Trial Protocol. McMaster University Anesthesia Research Interest Group Dinner Meeting December, 3 rd
2 Objectives : Talk about some key concepts for writing a statistical methodology for a research proposal Present guidance to be considered when developing the statistical plan in proposals for clinical research
3 Objectives : Talk about some key concepts for writing a statistical methodology for a research proposal Present guidance to be considered when developing the statistical plan in proposals for clinical research
4 Objectives : Talk about some key concepts for writing a statistical methodology for a research proposal Present guidance to be considered when developing the statistical plan in proposals for clinical research
5 Plan 1 Preliminary questions 2 Study design Sampling and Sample size calculation Statistical analysis plan and data collection 3 Conclusion
6 Preliminary questions When is the time to interact with a statistician? It is never too early to interact with statisticians (see Berverley and Anh (2009) ) No amount of clever analyses can compensate for problems with the design of studies Many clinical investigators are familiar with statistical technics but necessary aware of the design and the way of developing a study protocol
7 Preliminary questions When is the time to interact with a statistician? It is never too early to interact with statisticians (see Berverley and Anh (2009) ) No amount of clever analyses can compensate for problems with the design of studies Many clinical investigators are familiar with statistical technics but necessary aware of the design and the way of developing a study protocol
8 Preliminary questions When is the time to interact with a statistician? It is never too early to interact with statisticians (see Berverley and Anh (2009) ) No amount of clever analyses can compensate for problems with the design of studies Many clinical investigators are familiar with statistical technics but necessary aware of the design and the way of developing a study protocol
9 Preliminary questions When is the time to interact with a statistician? It is never too early to interact with statisticians (see Berverley and Anh (2009) ) No amount of clever analyses can compensate for problems with the design of studies Many clinical investigators are familiar with statistical technics but necessary aware of the design and the way of developing a study protocol
10 Preliminary questions Some key aspects of the statistical analysis plan. What is the research hypothesis. Is the primary hypotheses clearly stated, adequate and realistic? Identify the outcome(s) related to the research question (ex which domain of sf36) What is the type of study design? Randomisation and blinding. What is the type variable and unit measurement? What is the clinically meaningful difference for the primary outcome? Sample size and number of arms (independent or paired)? Allocation ratio, number of evaluation, interval of repeated measurement
11 Preliminary questions Some key aspects of the statistical analysis plan. What is the research hypothesis. Is the primary hypotheses clearly stated, adequate and realistic? Identify the outcome(s) related to the research question (ex which domain of sf36) What is the type of study design? Randomisation and blinding. What is the type variable and unit measurement? What is the clinically meaningful difference for the primary outcome? Sample size and number of arms (independent or paired)? Allocation ratio, number of evaluation, interval of repeated measurement
12 Preliminary questions Some key aspects of the statistical analysis plan. What is the research hypothesis. Is the primary hypotheses clearly stated, adequate and realistic? Identify the outcome(s) related to the research question (ex which domain of sf36) What is the type of study design? Randomisation and blinding. What is the type variable and unit measurement? What is the clinically meaningful difference for the primary outcome? Sample size and number of arms (independent or paired)? Allocation ratio, number of evaluation, interval of repeated measurement
13 Preliminary questions Some key aspects of the statistical analysis plan. What is the research hypothesis. Is the primary hypotheses clearly stated, adequate and realistic? Identify the outcome(s) related to the research question (ex which domain of sf36) What is the type of study design? Randomisation and blinding. What is the type variable and unit measurement? What is the clinically meaningful difference for the primary outcome? Sample size and number of arms (independent or paired)? Allocation ratio, number of evaluation, interval of repeated measurement
14 Preliminary questions Some key aspects of the statistical analysis plan. What is the research hypothesis. Is the primary hypotheses clearly stated, adequate and realistic? Identify the outcome(s) related to the research question (ex which domain of sf36) What is the type of study design? Randomisation and blinding. What is the type variable and unit measurement? What is the clinically meaningful difference for the primary outcome? Sample size and number of arms (independent or paired)? Allocation ratio, number of evaluation, interval of repeated measurement
15 Preliminary questions Some key aspects of the statistical analysis plan. What is the research hypothesis. Is the primary hypotheses clearly stated, adequate and realistic? Identify the outcome(s) related to the research question (ex which domain of sf36) What is the type of study design? Randomisation and blinding. What is the type variable and unit measurement? What is the clinically meaningful difference for the primary outcome? Sample size and number of arms (independent or paired)? Allocation ratio, number of evaluation, interval of repeated measurement
16 Preliminary questions Some key aspects of the statistical analysis plan. What is the research hypothesis. Is the primary hypotheses clearly stated, adequate and realistic? Identify the outcome(s) related to the research question (ex which domain of sf36) What is the type of study design? Randomisation and blinding. What is the type variable and unit measurement? What is the clinically meaningful difference for the primary outcome? Sample size and number of arms (independent or paired)? Allocation ratio, number of evaluation, interval of repeated measurement
17 Preliminary questions Some key aspects of the statistical analysis plan. What is the research hypothesis. Is the primary hypotheses clearly stated, adequate and realistic? Identify the outcome(s) related to the research question (ex which domain of sf36) What is the type of study design? Randomisation and blinding. What is the type variable and unit measurement? What is the clinically meaningful difference for the primary outcome? Sample size and number of arms (independent or paired)? Allocation ratio, number of evaluation, interval of repeated measurement
18 Preliminary questions Example 1 based of quality of life outcome. compare the impact of pain on daily activities while on a maintenance dose of Pain-Free vs. Relief Assessments during maintenance is important
19 Preliminary questions Example 1 based of quality of life outcome. compare the impact of pain on daily activities while on a maintenance dose of Pain-Free vs. Relief Assessments during maintenance is important
20 Preliminary questions Example 1 based of quality of life outcome. compare the impact of pain on daily activities while on a maintenance dose of Pain-Free vs. Relief Assessments during maintenance is important
21 Preliminary questions Example 2 based on quality of life outcome. compare the time to 20% improvement in the severity of pain with Pain-Free vs. Relief Earlier assessments are the basis of defining the outcome and should be frequent enough to detect differences between the two regimens
22 Preliminary questions Example 2 based on quality of life outcome. compare the time to 20% improvement in the severity of pain with Pain-Free vs. Relief Earlier assessments are the basis of defining the outcome and should be frequent enough to detect differences between the two regimens
23 1 The study design 2 Sampling and Sample size calculation 3 Statistical analysis plan and data collection
24 1 The study design 2 Sampling and Sample size calculation 3 Statistical analysis plan and data collection
25 1 The study design 2 Sampling and Sample size calculation 3 Statistical analysis plan and data collection
26 Study design The statistical aspects of the design relate essentially to the structure of the study and all aspects of the collection of data and the choice of measurement (Altman 1997) Study designs include case-control, cohort, crossover, factorial design
27 Study design The statistical aspects of the design relate essentially to the structure of the study and all aspects of the collection of data and the choice of measurement (Altman 1997) Study designs include case-control, cohort, crossover, factorial design
28 Study design The statistical aspects of the design relate essentially to the structure of the study and all aspects of the collection of data and the choice of measurement (Altman 1997) Study designs include case-control, cohort, crossover, factorial design
29 Sampling and Sample size calculation Example of study design.
30 Sampling and Sample size calculation The following factors are essential in sampling : 1 Sampling methods 2 Sample size Calculation 3 Participation (response) 4 Inclusion/ exclusion criteria, consent form
31 Sampling and Sample size calculation The following factors are essential in sampling : 1 Sampling methods 2 Sample size Calculation 3 Participation (response) 4 Inclusion/ exclusion criteria, consent form
32 Sampling and Sample size calculation The following factors are essential in sampling : 1 Sampling methods 2 Sample size Calculation 3 Participation (response) 4 Inclusion/ exclusion criteria, consent form
33 Sampling and Sample size calculation The following factors are essential in sampling : 1 Sampling methods 2 Sample size Calculation 3 Participation (response) 4 Inclusion/ exclusion criteria, consent form
34 Sampling and Sample size calculation Example of sampling methods. Figure 1 : Sampling Methods. Produced by Agriculture and Consumer
35 Sampling and Sample size calculation Requirement for the sample size calculation The variable type of primary outcome measurement must be defined before sample size and power calculation Sample size is needed for primary outcome Sample size for secondary outcomes is often helpful for the reviewers
36 Sampling and Sample size calculation Requirement for the sample size calculation The variable type of primary outcome measurement must be defined before sample size and power calculation Sample size is needed for primary outcome Sample size for secondary outcomes is often helpful for the reviewers
37 Sampling and Sample size calculation Requirement for the sample size calculation The variable type of primary outcome measurement must be defined before sample size and power calculation Sample size is needed for primary outcome Sample size for secondary outcomes is often helpful for the reviewers
38 Sampling and Sample size calculation Requirement for the sample size calculation The variable type of primary outcome measurement must be defined before sample size and power calculation Sample size is needed for primary outcome Sample size for secondary outcomes is often helpful for the reviewers
39 Sampling and Sample size calculation Example of pain outcomes in a systematic review
40 Sampling and Sample size calculation Type of outcomes Categorical : binary (gender : Male vs Female), nominal (e.g. blood group), ordinal (e.g. level of pain : minimal, severe, unboreable) Numerical data : discrete (number of children), continuous (body temperature) Other types : ranks (preference of treatment), rate and ratios (rate of growth per decade), scores (ASA scores), latent scores, visual analogue scale, censored data
41 Sampling and Sample size calculation Type of outcomes Categorical : binary (gender : Male vs Female), nominal (e.g. blood group), ordinal (e.g. level of pain : minimal, severe, unboreable) Numerical data : discrete (number of children), continuous (body temperature) Other types : ranks (preference of treatment), rate and ratios (rate of growth per decade), scores (ASA scores), latent scores, visual analogue scale, censored data
42 Sampling and Sample size calculation Type of outcomes Categorical : binary (gender : Male vs Female), nominal (e.g. blood group), ordinal (e.g. level of pain : minimal, severe, unboreable) Numerical data : discrete (number of children), continuous (body temperature) Other types : ranks (preference of treatment), rate and ratios (rate of growth per decade), scores (ASA scores), latent scores, visual analogue scale, censored data
43 Sampling and Sample size calculation Type of outcomes Categorical : binary (gender : Male vs Female), nominal (e.g. blood group), ordinal (e.g. level of pain : minimal, severe, unboreable) Numerical data : discrete (number of children), continuous (body temperature) Other types : ranks (preference of treatment), rate and ratios (rate of growth per decade), scores (ASA scores), latent scores, visual analogue scale, censored data
44 Sampling and Sample size calculation Determination of the effect size effect size : Measure of the magnitude of treatment effect and represent a clinically or biologically important difference (SMD, MID,... ) It is more a clinical question than a statistical one represent the difference we need to have ability to detect any effect This determination is based on clinical knowledge of the primary endpoint The statistician and the investigator should examine the literature, past research to determine a study effect size
45 Sampling and Sample size calculation Determination of the effect size effect size : Measure of the magnitude of treatment effect and represent a clinically or biologically important difference (SMD, MID,... ) It is more a clinical question than a statistical one represent the difference we need to have ability to detect any effect This determination is based on clinical knowledge of the primary endpoint The statistician and the investigator should examine the literature, past research to determine a study effect size
46 Sampling and Sample size calculation Determination of the effect size effect size : Measure of the magnitude of treatment effect and represent a clinically or biologically important difference (SMD, MID,... ) It is more a clinical question than a statistical one represent the difference we need to have ability to detect any effect This determination is based on clinical knowledge of the primary endpoint The statistician and the investigator should examine the literature, past research to determine a study effect size
47 Sampling and Sample size calculation Determination of the effect size effect size : Measure of the magnitude of treatment effect and represent a clinically or biologically important difference (SMD, MID,... ) It is more a clinical question than a statistical one represent the difference we need to have ability to detect any effect This determination is based on clinical knowledge of the primary endpoint The statistician and the investigator should examine the literature, past research to determine a study effect size
48 Sampling and Sample size calculation Determination of the effect size effect size : Measure of the magnitude of treatment effect and represent a clinically or biologically important difference (SMD, MID,... ) It is more a clinical question than a statistical one represent the difference we need to have ability to detect any effect This determination is based on clinical knowledge of the primary endpoint The statistician and the investigator should examine the literature, past research to determine a study effect size
49 Sampling and Sample size calculation Determination of the effect size effect size : Measure of the magnitude of treatment effect and represent a clinically or biologically important difference (SMD, MID,... ) It is more a clinical question than a statistical one represent the difference we need to have ability to detect any effect This determination is based on clinical knowledge of the primary endpoint The statistician and the investigator should examine the literature, past research to determine a study effect size
50 Sampling and Sample size calculation Do all the studies need sample size calculation? No, but pilot studies may need a power analysis because they are about testing the protocol than test analysis For the pilot or/ and feasibility study, the sample size calculation is not a requirement (see Arain et al and Thabane et al. 2010) The use of pilot study should be used cautiously. When the sample size of pilot study is too small, the subsequent sample size calculation cannot be realistic.
51 Sampling and Sample size calculation Do all the studies need sample size calculation? No, but pilot studies may need a power analysis because they are about testing the protocol than test analysis For the pilot or/ and feasibility study, the sample size calculation is not a requirement (see Arain et al and Thabane et al. 2010) The use of pilot study should be used cautiously. When the sample size of pilot study is too small, the subsequent sample size calculation cannot be realistic.
52 Sampling and Sample size calculation Do all the studies need sample size calculation? No, but pilot studies may need a power analysis because they are about testing the protocol than test analysis For the pilot or/ and feasibility study, the sample size calculation is not a requirement (see Arain et al and Thabane et al. 2010) The use of pilot study should be used cautiously. When the sample size of pilot study is too small, the subsequent sample size calculation cannot be realistic.
53 Sampling and Sample size calculation Do all the studies need sample size calculation? No, but pilot studies may need a power analysis because they are about testing the protocol than test analysis For the pilot or/ and feasibility study, the sample size calculation is not a requirement (see Arain et al and Thabane et al. 2010) The use of pilot study should be used cautiously. When the sample size of pilot study is too small, the subsequent sample size calculation cannot be realistic.
54 Sampling and Sample size calculation Software and online Resources Gpower, R/ Splus, STATA, SAS, SPSS,... Online resources : http :// rollin/stats/ssize/ http ://
55 Statistical analysis plan and data collection Statistical analysis plan and data collection Always driven by the research question, the study design and the type of measurement Provide a plan that includes a detailed description of statistical procedures for each variable, group and subgroups (e.g. baseline characteristics, comorbid conditions,â) specify these methods in advance in order to minimize bias and maintain the integrity Use confidence limits and p values Explain how statistical assumptions or model diagnostics will be examined (validation and calibration). Explain how to deal with missing data
56 Statistical analysis plan and data collection Statistical analysis plan and data collection Always driven by the research question, the study design and the type of measurement Provide a plan that includes a detailed description of statistical procedures for each variable, group and subgroups (e.g. baseline characteristics, comorbid conditions,â) specify these methods in advance in order to minimize bias and maintain the integrity Use confidence limits and p values Explain how statistical assumptions or model diagnostics will be examined (validation and calibration). Explain how to deal with missing data
57 Statistical analysis plan and data collection Statistical analysis plan and data collection Always driven by the research question, the study design and the type of measurement Provide a plan that includes a detailed description of statistical procedures for each variable, group and subgroups (e.g. baseline characteristics, comorbid conditions,â) specify these methods in advance in order to minimize bias and maintain the integrity Use confidence limits and p values Explain how statistical assumptions or model diagnostics will be examined (validation and calibration). Explain how to deal with missing data
58 Statistical analysis plan and data collection Statistical analysis plan and data collection Always driven by the research question, the study design and the type of measurement Provide a plan that includes a detailed description of statistical procedures for each variable, group and subgroups (e.g. baseline characteristics, comorbid conditions,â) specify these methods in advance in order to minimize bias and maintain the integrity Use confidence limits and p values Explain how statistical assumptions or model diagnostics will be examined (validation and calibration). Explain how to deal with missing data
59 Statistical analysis plan and data collection Statistical analysis plan and data collection Always driven by the research question, the study design and the type of measurement Provide a plan that includes a detailed description of statistical procedures for each variable, group and subgroups (e.g. baseline characteristics, comorbid conditions,â) specify these methods in advance in order to minimize bias and maintain the integrity Use confidence limits and p values Explain how statistical assumptions or model diagnostics will be examined (validation and calibration). Explain how to deal with missing data
60 Statistical analysis plan and data collection Statistical analysis plan and data collection Always driven by the research question, the study design and the type of measurement Provide a plan that includes a detailed description of statistical procedures for each variable, group and subgroups (e.g. baseline characteristics, comorbid conditions,â) specify these methods in advance in order to minimize bias and maintain the integrity Use confidence limits and p values Explain how statistical assumptions or model diagnostics will be examined (validation and calibration). Explain how to deal with missing data
61 Statistical analysis plan and data collection Data collection Writing a codebook is important : serve as a reference for the team, help newcomers and the statistician to understand the project, facilitate communication Should contains : variables names, variables labels, variables codes, variables formats (prevent typo and help to identify missing), Suggest a way to code missing and have an unique code for date variables, provides range Deidentified and protect data
62 Statistical analysis plan and data collection Data collection Writing a codebook is important : serve as a reference for the team, help newcomers and the statistician to understand the project, facilitate communication Should contains : variables names, variables labels, variables codes, variables formats (prevent typo and help to identify missing), Suggest a way to code missing and have an unique code for date variables, provides range Deidentified and protect data
63 Statistical analysis plan and data collection Data collection Writing a codebook is important : serve as a reference for the team, help newcomers and the statistician to understand the project, facilitate communication Should contains : variables names, variables labels, variables codes, variables formats (prevent typo and help to identify missing), Suggest a way to code missing and have an unique code for date variables, provides range Deidentified and protect data
64 Statistical analysis plan and data collection Data collection Writing a codebook is important : serve as a reference for the team, help newcomers and the statistician to understand the project, facilitate communication Should contains : variables names, variables labels, variables codes, variables formats (prevent typo and help to identify missing), Suggest a way to code missing and have an unique code for date variables, provides range Deidentified and protect data
65 Statistical analysis plan and data collection Selection of variables : beware of univariable screening The use of bivariate selection for selecting variables to be used in multivariate analysis is inappropriate (Guo-Wen et al. (1996)) Wrongly rejects potentially important variables when the relationship between an outcome and a risk factor is confounded by any confounder and when this confounder is not properly controlled
66 Statistical analysis plan and data collection Selection of variables : beware of univariable screening The use of bivariate selection for selecting variables to be used in multivariate analysis is inappropriate (Guo-Wen et al. (1996)) Wrongly rejects potentially important variables when the relationship between an outcome and a risk factor is confounded by any confounder and when this confounder is not properly controlled
67 Statistical analysis plan and data collection Selection of variables : beware of univariable screening The use of bivariate selection for selecting variables to be used in multivariate analysis is inappropriate (Guo-Wen et al. (1996)) Wrongly rejects potentially important variables when the relationship between an outcome and a risk factor is confounded by any confounder and when this confounder is not properly controlled
68 Statistical analysis plan and data collection Results and discussion. Describe the flow of participants through the study (via a diagram for instance) Validation and limitations (non representative sample size, few events per predictor,... ) Discuss about the implications for future research
69 Statistical analysis plan and data collection Results and discussion. Describe the flow of participants through the study (via a diagram for instance) Validation and limitations (non representative sample size, few events per predictor,... ) Discuss about the implications for future research
70 Statistical analysis plan and data collection Results and discussion. Describe the flow of participants through the study (via a diagram for instance) Validation and limitations (non representative sample size, few events per predictor,... ) Discuss about the implications for future research
71 Conclusion Conclusion Study design, sampling, analysis plan and data collection are essential It is a multidisciplinary effort : clinical investigators, statisticians, librarians, research assistants have to collaborate
72 Conclusion Conclusion Study design, sampling, analysis plan and data collection are essential It is a multidisciplinary effort : clinical investigators, statisticians, librarians, research assistants have to collaborate
73 Conclusion Conclusion Study design, sampling, analysis plan and data collection are essential It is a multidisciplinary effort : clinical investigators, statisticians, librarians, research assistants have to collaborate
74 Conclusion Thank you for your attention.
75 Conclusion Bibliography. Adams-Huet, B Ahn, C (2009), Bridging clinical investigators and statisticians : Writing the statistical methodology for a research proposal  Journal of Investigative Medicine, vol 57, no. 8, pp  /JIM.0b013e3181c2996c. Altman, D. G. (1991). Practical statistics for medical research. London : Chapman and Hall. Arain et al. (2010) What is aâ pilotâ or feasibilityâ study? A review of current practice and editorial policy. Thabane L. et al. (2010). A tutorial on pilot studies : the what, why and how.
Study Design and Statistical Analysis
Study Design and Statistical Analysis Anny H Xiang, PhD Department of Preventive Medicine University of Southern California Outline Designing Clinical Research Studies Statistical Data Analysis Designing
Biostat Methods STAT 5820/6910 Handout #6: Intro. to Clinical Trials (Matthews text)
Biostat Methods STAT 5820/6910 Handout #6: Intro. to Clinical Trials (Matthews text) Key features of RCT (randomized controlled trial) One group (treatment) receives its treatment at the same time another
Mary B Codd. MD, MPH, PhD, FFPHMI UCD School of Public Health, Physiotherapy & Pop. Sciences
HRB / CSTAR Grant Applications Training Day Convention Centre Dublin, 9 th September 2010 Key Elements of a Research Protocol Mary B Codd. MD, MPH, PhD, FFPHMI UCD School of Public Health, Physiotherapy
Organizing Your Approach to a Data Analysis
Biost/Stat 578 B: Data Analysis Emerson, September 29, 2003 Handout #1 Organizing Your Approach to a Data Analysis The general theme should be to maximize thinking about the data analysis and to minimize
Guided Reading 9 th Edition. informed consent, protection from harm, deception, confidentiality, and anonymity.
Guided Reading Educational Research: Competencies for Analysis and Applications 9th Edition EDFS 635: Educational Research Chapter 1: Introduction to Educational Research 1. List and briefly describe the
II. DISTRIBUTIONS distribution normal distribution. standard scores
Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,
Chapter 5 Analysis of variance SPSS Analysis of variance
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,
University of Hawai i Human Studies Program. Guidelines for Developing a Clinical Research Protocol
University of Hawai i Human Studies Program Guidelines for Developing a Clinical Research Protocol Following are guidelines for writing a clinical research protocol for submission to the University of
SECOND M.B. AND SECOND VETERINARY M.B. EXAMINATIONS INTRODUCTION TO THE SCIENTIFIC BASIS OF MEDICINE EXAMINATION. Friday 14 March 2008 9.00-9.
SECOND M.B. AND SECOND VETERINARY M.B. EXAMINATIONS INTRODUCTION TO THE SCIENTIFIC BASIS OF MEDICINE EXAMINATION Friday 14 March 2008 9.00-9.45 am Attempt all ten questions. For each question, choose the
Fairfield Public Schools
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
LEVEL ONE MODULE EXAM PART ONE [Clinical Questions Literature Searching Types of Research Levels of Evidence Appraisal Scales Statistic Terminology]
![LEVEL ONE MODULE EXAM PART ONE [Clinical Questions Literature Searching Types of Research Levels of Evidence Appraisal Scales Statistic Terminology]](/thumbs/27/12305234.jpg "LEVEL ONE MODULE EXAM PART ONE [Clinical Questions Literature Searching Types of Research Levels of Evidence Appraisal Scales Statistic Terminology]")
1. What does the letter I correspond to in the PICO format? A. Interdisciplinary B. Interference C. Intersession D. Intervention 2. Which step of the evidence-based practice process incorporates clinical
Reception baseline: criteria for potential assessments
Reception baseline: criteria for potential assessments This document provides the criteria that will be used to evaluate potential reception baselines. Suppliers will need to provide evidence against these
Inclusion and Exclusion Criteria
Inclusion and Exclusion Criteria Inclusion criteria = attributes of subjects that are essential for their selection to participate. Inclusion criteria function remove the influence of specific confounding
Guidelines for AJO-DO submissions: Randomized Clinical Trials June 2015
Guidelines for AJO-DO submissions: Randomized Clinical Trials June 2015 Complete and transparent reporting allows for accurate assessment of the quality of trial and correct interpretation of the trial
TABLE OF CONTENTS. About Chi Squares... 1. What is a CHI SQUARE?... 1. Chi Squares... 1. Hypothesis Testing with Chi Squares... 2
About Chi Squares TABLE OF CONTENTS About Chi Squares... 1 What is a CHI SQUARE?... 1 Chi Squares... 1 Goodness of fit test (One-way χ 2 )... 1 Test of Independence (Two-way χ 2 )... 2 Hypothesis Testing
TUTORIAL on ICH E9 and Other Statistical Regulatory Guidance. Session 1: ICH E9 and E10. PSI Conference, May 2011
TUTORIAL on ICH E9 and Other Statistical Regulatory Guidance Session 1: PSI Conference, May 2011 Kerry Gordon, Quintiles 1 E9, and how to locate it 2 ICH E9 Statistical Principles for Clinical Trials (Issued
WHAT IS A JOURNAL CLUB?
WHAT IS A JOURNAL CLUB? With its September 2002 issue, the American Journal of Critical Care debuts a new feature, the AJCC Journal Club. Each issue of the journal will now feature an AJCC Journal Club
This clinical study synopsis is provided in line with Boehringer Ingelheim s Policy on Transparency and Publication of Clinical Study Data.
abcd Clinical Study for Public Disclosure This clinical study synopsis is provided in line with s Policy on Transparency and Publication of Clinical Study Data. The synopsis which is part of the clinical
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, T-TESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
Parametric and non-parametric statistical methods for the life sciences - Session I
Why nonparametric methods What test to use? Rank Tests Parametric and non-parametric statistical methods for the life sciences - Session I Liesbeth Bruckers Geert Molenberghs Interuniversity Institute
Lean Six Sigma Black Belt Body of Knowledge
General Lean Six Sigma Defined UN Describe Nature and purpose of Lean Six Sigma Integration of Lean and Six Sigma UN Compare and contrast focus and approaches (Process Velocity and Quality) Y=f(X) Input
How to get accurate sample size and power with nquery Advisor R
How to get accurate sample size and power with nquery Advisor R Brian Sullivan Statistical Solutions Ltd. ASA Meeting, Chicago, March 2007 Sample Size Two group t-test χ 2 -test Survival Analysis 2 2 Crossover
Sample Size Planning, Calculation, and Justification
Sample Size Planning, Calculation, and Justification Theresa A Scott, MS Vanderbilt University Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott Theresa
SCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES
SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR
Introduction to Statistics and Quantitative Research Methods
Introduction to Statistics and Quantitative Research Methods Purpose of Presentation To aid in the understanding of basic statistics, including terminology, common terms, and common statistical methods.
If several different trials are mentioned in one publication, the data of each should be extracted in a separate data extraction form.
General Remarks This template of a data extraction form is intended to help you to start developing your own data extraction form, it certainly has to be adapted to your specific question. Delete unnecessary
Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
This clinical study synopsis is provided in line with Boehringer Ingelheim s Policy on Transparency and Publication of Clinical Study Data.
abcd Clinical Study for Public Disclosure This clinical study synopsis is provided in line with s Policy on Transparency and Publication of Clinical Study Data. The synopsis which is part of the clinical
MRC Autism Research Forum Interventions in Autism
MRC Autism Research Forum Interventions in Autism Date: 10 July 2003 Location: Aim: Commonwealth Institute, London The aim of the forum was to bring academics in relevant disciplines together, to discuss
DATA COLLECTION AND ANALYSIS
DATA COLLECTION AND ANALYSIS Quality Education for Minorities (QEM) Network HBCU-UP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. August 23, 2013 Objectives of the Discussion 2 Discuss
Chapter 1: The Nature of Probability and Statistics
Chapter 1: The Nature of Probability and Statistics Learning Objectives Upon successful completion of Chapter 1, you will have applicable knowledge of the following concepts: Statistics: An Overview and
Data Analysis, Research Study Design and the IRB
Minding the p-values p and Quartiles: Data Analysis, Research Study Design and the IRB Don Allensworth-Davies, MSc Research Manager, Data Coordinating Center Boston University School of Public Health IRB
Conducting Pilot Studies
Conducting Pilot Studies Excerpts adapted from: Simon, M. K. (2011). Dissertation and scholarly research: Recipes for success (2011 ed.). Seattle, WA: Dissertation Success, LLC. http://dissertationrecipes.com/
Research Methods & Experimental Design
Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and
Nursing Journal Toolkit: Critiquing a Quantitative Research Article
A Virtual World Consortium: Using Second Life to Facilitate Nursing Journal Clubs Nursing Journal Toolkit: Critiquing a Quantitative Research Article 1. Guidelines for Critiquing a Quantitative Research
1.0 Abstract. Title: Real Life Evaluation of Rheumatoid Arthritis in Canadians taking HUMIRA. Keywords. Rationale and Background:
1.0 Abstract Title: Real Life Evaluation of Rheumatoid Arthritis in Canadians taking HUMIRA Keywords Rationale and Background: This abbreviated clinical study report is based on a clinical surveillance
UNDERSTANDING THE TWO-WAY ANOVA
UNDERSTANDING THE e have seen how the one-way ANOVA can be used to compare two or more sample means in studies involving a single independent variable. This can be extended to two independent variables
Measurement and Measurement Scales
Measurement and Measurement Scales Measurement is the foundation of any scientific investigation Everything we do begins with the measurement of whatever it is we want to study Definition: measurement
Introduction to Statistics Used in Nursing Research
Introduction to Statistics Used in Nursing Research Laura P. Kimble, PhD, RN, FNP-C, FAAN Professor and Piedmont Healthcare Endowed Chair in Nursing Georgia Baptist College of Nursing Of Mercer University
Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.
Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are
January 26, 2009 The Faculty Center for Teaching and Learning
THE BASICS OF DATA MANAGEMENT AND ANALYSIS A USER GUIDE January 26, 2009 The Faculty Center for Teaching and Learning THE BASICS OF DATA MANAGEMENT AND ANALYSIS Table of Contents Table of Contents... i
Clinical Study Design and Methods Terminology
Home College of Veterinary Medicine Washington State University WSU Faculty &Staff Page Page 1 of 5 John Gay, DVM PhD DACVPM AAHP FDIU VCS Clinical Epidemiology & Evidence-Based Medicine Glossary: Clinical
Descriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
Written Example for Research Question: How is caffeine consumption associated with memory?
Guide to Writing Your Primary Research Paper Your Research Report should be divided into sections with these headings: Abstract, Introduction, Methods, Results, Discussion, and References. Introduction:
NONPARAMETRIC STATISTICS 1. depend on assumptions about the underlying distribution of the data (or on the Central Limit Theorem)
NONPARAMETRIC STATISTICS 1 PREVIOUSLY parametric statistics in estimation and hypothesis testing... construction of confidence intervals computing of p-values classical significance testing depend on assumptions
ASKING CLINICAL QUESTIONS
MODULE 1: ASKING CLINICAL QUESTIONS There are two main types of clinical questions: background questions and foreground questions. Background questions are those which apply to the clinical topic for all
Guidance for the format and content of the protocol of non-interventional post-authorisation safety studies
26 September 2012 EMA/623947/2012 Patient Health Protection Guidance for the format and content of the protocol of non-interventional post-authorisation Introduction From 10 January 2013, marketing authorisation
Critical appraisal. Gary Collins. EQUATOR Network, Centre for Statistics in Medicine NDORMS, University of Oxford
Critical appraisal Gary Collins EQUATOR Network, Centre for Statistics in Medicine NDORMS, University of Oxford EQUATOR Network OUCAGS training course 25 October 2014 Objectives of this session To understand
Multivariate Analysis of Variance. The general purpose of multivariate analysis of variance (MANOVA) is to determine
2 - Manova 4.3.05 25 Multivariate Analysis of Variance What Multivariate Analysis of Variance is The general purpose of multivariate analysis of variance (MANOVA) is to determine whether multiple levels
U.S. Food and Drug Administration
U.S. Food and Drug Administration Notice: Archived Document The content in this document is provided on the FDA s website for reference purposes only. It was current when produced, but is no longer maintained
Basic Results Database
Basic Results Database Deborah A. Zarin, M.D. ClinicalTrials.gov December 2008 1 ClinicalTrials.gov Overview and PL 110-85 Requirements Module 1 2 Levels of Transparency Prospective Clinical Trials Registry
Advanced Quantitative Methods for Health Care Professionals PUBH 742 Spring 2015
1 Advanced Quantitative Methods for Health Care Professionals PUBH 742 Spring 2015 Instructor: Joanne M. Garrett, PhD e-mail: joanne_garrett@med.unc.edu Class Notes: Copies of the class lecture slides
General Method: Difference of Means. 3. Calculate df: either Welch-Satterthwaite formula or simpler df = min(n 1, n 2 ) 1.
General Method: Difference of Means 1. Calculate x 1, x 2, SE 1, SE 2. 2. Combined SE = SE1 2 + SE2 2. ASSUMES INDEPENDENT SAMPLES. 3. Calculate df: either Welch-Satterthwaite formula or simpler df = min(n
Survey Data Analysis. Qatar University. Dr. Kenneth M.Coleman (Ken.Coleman@marketstrategies.com) - University of Michigan
The following slides are the property of their authors and are provided on this website as a public service. Please do not copy or redistribute these slides without the written permission of all of the
Basic Concepts in Research and Data Analysis
Basic Concepts in Research and Data Analysis Introduction: A Common Language for Researchers...2 Steps to Follow When Conducting Research...3 The Research Question... 3 The Hypothesis... 4 Defining the
Introduction to Longitudinal Data Analysis
Introduction to Longitudinal Data Analysis Longitudinal Data Analysis Workshop Section 1 University of Georgia: Institute for Interdisciplinary Research in Education and Human Development Section 1: Introduction
TEACHING OF STATISTICS IN KENYA. John W. Odhiambo University of Nairobi Nairobi
TEACHING OF STATISTICS IN KENYA John W. Odhiambo University of Nairobi Nairobi In Kenya today statistics is taught at various levels in the education system to various degrees of coverage and sophistication.
Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish
Examining Differences (Comparing Groups) using SPSS Inferential statistics (Part I) Dwayne Devonish Statistics Statistics are quantitative methods of describing, analysing, and drawing inferences (conclusions)
IBM SPSS Statistics for Beginners for Windows
ISS, NEWCASTLE UNIVERSITY IBM SPSS Statistics for Beginners for Windows A Training Manual for Beginners Dr. S. T. Kometa A Training Manual for Beginners Contents 1 Aims and Objectives... 3 1.1 Learning
Correlational Research
Correlational Research Chapter Fifteen Correlational Research Chapter Fifteen Bring folder of readings The Nature of Correlational Research Correlational Research is also known as Associational Research.
EHR4CR ENABLING PROACTIVE RESEARCH
EHR4CR ENABLING PROACTIVE RESEARCH Neelam Patel Neelam Consulting Electronic Health Records for Clinical Research 76 Why change how I currently operate? To more visible to the clinical trial community
Statistics in Medicine Research Lecture Series CSMC Fall 2014
Catherine Bresee, MS Senior Biostatistician Biostatistics & Bioinformatics Research Institute Statistics in Medicine Research Lecture Series CSMC Fall 2014 Overview Review concept of statistical power
Assessing study quality: critical appraisal. MRC/CSO Social and Public Health Sciences Unit
Assessing study quality: critical appraisal MRC/CSO Social and Public Health Sciences Unit Assessing study quality: why? Why critical appraise? Evidence informed decision-making assumes people make better
Introduction to Analysis of Variance (ANOVA) Limitations of the t-test
Introduction to Analysis of Variance (ANOVA) The Structural Model, The Summary Table, and the One- Way ANOVA Limitations of the t-test Although the t-test is commonly used, it has limitations Can only
11. Analysis of Case-control Studies Logistic Regression
Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:
Multivariate Logistic Regression
1 Multivariate Logistic Regression As in univariate logistic regression, let π(x) represent the probability of an event that depends on p covariates or independent variables. Then, using an inv.logit formulation
UNDERSTANDING THE INDEPENDENT-SAMPLES t TEST
UNDERSTANDING The independent-samples t test evaluates the difference between the means of two independent or unrelated groups. That is, we evaluate whether the means for two independent groups are significantly
Handling attrition and non-response in longitudinal data
Longitudinal and Life Course Studies 2009 Volume 1 Issue 1 Pp 63-72 Handling attrition and non-response in longitudinal data Harvey Goldstein University of Bristol Correspondence. Professor H. Goldstein
PROTOCOL SYNOPSIS Evaluation of long-term opioid efficacy for chronic pain
P a g e 1 PROTOCOL SYNOPSIS Evaluation of long-term opioid efficacy for chronic pain Clinical Phase 4 Study Centers Study Period 25 U.S. sites identified and reviewed by the Steering Committee and Contract
Online Reading Support London School of Economics Sandra McNally
Online Reading Support London School of Economics Sandra McNally Evaluation Summary Age range Primary (Year 1) Number of pupils c. 2700 Number of schools 60 Design Randomised controlled trial randomised
Regression Modeling Strategies
Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis With 141 Figures Springer Contents Preface Typographical Conventions
Overview of Non-Parametric Statistics PRESENTER: ELAINE EISENBEISZ OWNER AND PRINCIPAL, OMEGA STATISTICS
Overview of Non-Parametric Statistics PRESENTER: ELAINE EISENBEISZ OWNER AND PRINCIPAL, OMEGA STATISTICS About Omega Statistics Private practice consultancy based in Southern California, Medical and Clinical
Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012
Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts
Imputation and Analysis. Peter Fayers
Missing Data in Palliative Care Research Imputation and Analysis Peter Fayers Department of Public Health University of Aberdeen NTNU Det medisinske fakultet Missing data Missing data is a major problem
Data, Measurements, Features
Data, Measurements, Features Middle East Technical University Dep. of Computer Engineering 2009 compiled by V. Atalay What do you think of when someone says Data? We might abstract the idea that data are
How To Weigh Data From A Study
PRINCIPLES ON RESPONSIBLE SHARING OF TRUTHFUL AND NON-MISLEADING INFORMATION ABOUT MEDICINES WITH HEALTH CARE PROFESSIONALS AND PAYERS INTRODUCTION In the era of data-driven medicine, where all parties
Adverse Impact and Test Validation Book Series: Multiple Regression. Introduction. Comparison of Compensation using
Adverse Impact and Test Validation Book Series: Multiple Regression Using Multiple Regression to Examine Compensation Practices Introduction Reasons for Investigating Pay Equity: The Equal Pay Act of 1963
MISSING DATA: THE POINT OF VIEW OF ETHICAL COMMITTEES
I CONGRESSO NAZIONALE BIAS 2009 29/30 APRILE 2009 ELI LILLY SESTO FIORENTINO (FI) MISSING DATA: THE POINT OF VIEW OF ETHICAL COMMITTEES Anna Chiara Frigo Department of Environmental Medicine and Public
Programme du parcours Clinical Epidemiology 2014-2015. UMR 1. Methods in therapeutic evaluation A Dechartres/A Flahault
Programme du parcours Clinical Epidemiology 2014-2015 UR 1. ethods in therapeutic evaluation A /A Date cours Horaires 15/10/2014 14-17h General principal of therapeutic evaluation (1) 22/10/2014 14-17h
Correlational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots
Correlational Research Stephen E. Brock, Ph.D., NCSP California State University, Sacramento 1 Correlational Research A quantitative methodology used to determine whether, and to what degree, a relationship
Analyzing Research Data Using Excel
Analyzing Research Data Using Excel Fraser Health Authority, 2012 The Fraser Health Authority ( FH ) authorizes the use, reproduction and/or modification of this publication for purposes other than commercial
Working with SPSS. A Step-by-Step Guide For Prof PJ s ComS 171 students
Working with SPSS A Step-by-Step Guide For Prof PJ s ComS 171 students Contents Prep the Excel file for SPSS... 2 Prep the Excel file for the online survey:... 2 Make a master file... 2 Clean the data
Lecture 2: Types of Variables
2typesofvariables.pdf Michael Hallstone, Ph.D. hallston@hawaii.edu Lecture 2: Types of Variables Recap what we talked about last time Recall how we study social world using populations and samples. Recall
SPSS ADVANCED ANALYSIS WENDIANN SETHI SPRING 2011
SPSS ADVANCED ANALYSIS WENDIANN SETHI SPRING 2011 Statistical techniques to be covered Explore relationships among variables Correlation Regression/Multiple regression Logistic regression Factor analysis
Design and Analysis of Phase III Clinical Trials
Cancer Biostatistics Center, Biostatistics Shared Resource, Vanderbilt University School of Medicine June 19, 2008 Outline 1 Phases of Clinical Trials 2 3 4 5 6 Phase I Trials: Safety, Dosage Range, and
Data Mining Introduction
Data Mining Introduction Bob Stine Dept of Statistics, School University of Pennsylvania www-stat.wharton.upenn.edu/~stine What is data mining? An insult? Predictive modeling Large, wide data sets, often
Study Designs. Simon Day, PhD Johns Hopkins University
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
ASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS
DATABASE MARKETING Fall 2015, max 24 credits Dead line 15.10. ASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS PART A Gains chart with excel Prepare a gains chart from the data in \\work\courses\e\27\e20100\ass4b.xls.
MULTIPLE REGRESSION WITH CATEGORICAL DATA
DEPARTMENT OF POLITICAL SCIENCE AND INTERNATIONAL RELATIONS Posc/Uapp 86 MULTIPLE REGRESSION WITH CATEGORICAL DATA I. AGENDA: A. Multiple regression with categorical variables. Coding schemes. Interpreting
Methods for Meta-analysis in Medical Research
Methods for Meta-analysis in Medical Research Alex J. Sutton University of Leicester, UK Keith R. Abrams University of Leicester, UK David R. Jones University of Leicester, UK Trevor A. Sheldon University
Descriptive Analysis
Research Methods William G. Zikmund Basic Data Analysis: Descriptive Statistics Descriptive Analysis The transformation of raw data into a form that will make them easy to understand and interpret; rearranging,
SPSS Explore procedure
SPSS Explore procedure One useful function in SPSS is the Explore procedure, which will produce histograms, boxplots, stem-and-leaf plots and extensive descriptive statistics. To run the Explore procedure,
Introduction to Regression and Data Analysis
Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it
Section Format Day Begin End Building Rm# Instructor. 001 Lecture Tue 6:45 PM 8:40 PM Silver 401 Ballerini
NEW YORK UNIVERSITY ROBERT F. WAGNER GRADUATE SCHOOL OF PUBLIC SERVICE Course Syllabus Spring 2016 Statistical Methods for Public, Nonprofit, and Health Management Section Format Day Begin End Building
Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study)
Cairo University Faculty of Economics and Political Science Statistics Department English Section Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study) Prepared
Critical Appraisal of Article on Therapy
Critical Appraisal of Article on Therapy What question did the study ask? Guide Are the results Valid 1. Was the assignment of patients to treatments randomized? And was the randomization list concealed?
Independent t- Test (Comparing Two Means)
Independent t- Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent t-test when to use the independent t-test the use of SPSS to complete an independent
Multiple Imputation for Missing Data: A Cautionary Tale
Multiple Imputation for Missing Data: A Cautionary Tale Paul D. Allison University of Pennsylvania Address correspondence to Paul D. Allison, Sociology Department, University of Pennsylvania, 3718 Locust
Sample Size and Power in Clinical Trials
Sample Size and Power in Clinical Trials Version 1.0 May 011 1. Power of a Test. Factors affecting Power 3. Required Sample Size RELATED ISSUES 1. Effect Size. Test Statistics 3. Variation 4. Significance
C2 Training: June 8 9, 2010
C2 Training: June 8 9, 2010 Data Evaluation: Initial screening and Coding Adapted from David B. Wilson and Mark W. Lipsey The Campbell Collaboration www.campbellcollaboration.org Overview Coding protocol: