# GUEST EDITORIAL. Twenty Statistical Errors Even YOU Can Find in Biomedical Research Articles. Tom Lang. Tom Lang Communications, Murphys, Ca, USA

Save this PDF as:

Size: px
Start display at page:

Download "GUEST EDITORIAL. Twenty Statistical Errors Even YOU Can Find in Biomedical Research Articles. Tom Lang. Tom Lang Communications, Murphys, Ca, USA"

## Transcription

3 Lang: Statistical Errors in Biomedical Research Articles Croat Med J ;5:-7 samples were repeatedly drawn from the same population of men, about 95% of these samples would be expected to have mean values between 7. mg and 7. mg. Error #: Reporting only P values for results P values are often misinterpreted (). Even when interpreted correctly, however, they have some limitations. For main results, report the absolute difference between groups (relative or percent differences can be misleading) and the 95% confidence interval for the difference, instead of, or in addition to, P values. The sentences below go from poor to good reporting: The effect of the drug was statistically significant. This sentence does not indicate the size of the effect, whether the effect is clinically important, or how statistically significant the effect is. Some readers would interpret statistically significant in this case to mean that the study supports the use of the drug. The effect of the drug on lowering diastolic blood pressure was statistically significant (P<.5) Here, the size of the drop is not given, so its clinical importance is not known. Also, P could be.9; statistically significant (at the.5 level) but so close to.5 that it should probably be interpreted similarly to a P value of, say,.5, which is not statistically significant. The use of an arbitrary cut point, such as.5, to distinguish between significant and non significant results is one of the problems of interpreting P values. The mean diastolic blood pressure of the treatment group dropped from to 9 mm Hg (P =.). This sentence is perhaps the most typical. The pre- and posttest values are given, but not the difference. The mean drop the -mm Hg difference is statistically significant, but it is also an estimate, and without a 95% confidence interval, the precision (and therefore the usefulness) of the estimate cannot be determined. The drug lowered diastolic blood pressure by a mean of mm Hg, from to 9 mm Hg (95% CI= to mm Hg; P =.). The confidence interval indicates that if the drug were to be tested on samples similar to the one reported, the average drop in blood pressure in 95 of those samples would probably range between and mm Hg. A drop of only mm Hg is not clinically important, but a drop of mm Hg is. So, although the mean drop in blood pressures in this study was statistically significant, the expected difference in blood pressures in other studies may not always be clinically important; that is, the study is inconclusive. When a study produces a confidence interval in which all the values are clinically important, the intervention is much more likely to be clinically effective. If none of the values in the interval are clinically important, the intervention is likely to be ineffective. If only some of the values are clinically important, the study probably did not enroll enough patients. Error #7: Not confirming that the data met the assumptions of the statistical tests used to analyze them There are hundreds of statistical tests, and several may be appropriate for a given analysis. However, tests may not give accurate results if their assumptions are not met (9). For this reason, both the name of the test and a statement that its assumptions were met should be included in reporting every statistical analysis. For example: The data were approximately normally distributed and thus did not violate the assumptions of the t test. The most common problems are: Using parametric tests when the data are not normally distributed (skewed). In particular, when comparing two groups, Student s t test is often used when the Wilcoxon rank-sum test (or another nonparametric test) is more appropriate. Using tests for independent samples on paired samples, which require tests for paired data. Again, Student s t test is often used when a paired t test is required. Error #: Using linear regression analysis without establishing that the relationship is, in fact, linear As stated in Guideline #7, every scientific article that includes a statistical analysis should contain a sentence confirming that the assumptions on which the analysis is based were met (). This confirmation is especially important in linear regression analysis, which assumes that the relationship between a response and an explanatory variable is linear. If this assumption is not met, the results of the analysis may be incorrect. The assumption of linearity may be tested by graphing the residuals : the difference between each data point and the regression line (Fig. ). If this graph is flat and close to zero (Fig. A), the relationship is linear. If the graph shows any other pattern, the relationship is not linear (Fig. B, C, and D.) Testing the assumption of linearity is important because simply looking at graphed data can be misleading (Fig. 5) Figure. A residual is the distance between an actual, observed value and the value predicted by the regression line.

4 Lang: Statistical Errors in Biomedical Research Articles Croat Med J ;5:-7 A B e e Patients undergoing small bowel resection for Chron's disease between Oct/ and May/9 N=7 x x Exclusions n= C e D e Patients meeting inclusion criteria and undergoing random asignment n=5 A - Scatter Plot x Figure. A. When the graphed residuals remain close to zero over the range of values, the regression line accurately represents the linear relationship of the data. Any other pattern (B, C, and D) indicates that the relationship is not linear, which means that linear regression analysis should not be used. B -ResidualPlot Figure 5. The appearance of linearity in a set of data can be deceptive. Here, a relationship that appears to be linear (A) is obviously not, as indicated by the graph of residuals (B). Error #9: Not accounting for all data and all patients Missing data is a common but irritating reporting problem made worse by the thought that the author is careless, lazy, or both (). Missing data raise issues about: the nature of the missing data. Were extreme values not included in the analysis? Were data lost in a lab accident? Were data ignored because they did not support the hypothesis? the generalizability of the presented data. Is the range of values really the range? Is the drop-out rate really that low? the quality of entire study. If the totals don t match in the published article, how careful was the author during the rest of the research? One of the most effective ways to account for all patients in a clinical trial is a flow chart or schematic summary (Fig. ) (9,,). Such a visual summary can account for all patients at each stage of the trial, efficiently summarize the study design, and indicate the probable denominators for proportions, percentages, and rates. Such a graphic is recommended by the CONSORT Statement for reporting randomized trials (9). - - x Extensive resection n=75 Limited resection n=75 Median follow-up=5 months Cancer recurrence n= (%) Cancer recurrence n=9 (5%) Figure. A flow chart of a randomized clinical trial with two treatment arms, showing the disposition of all patients at each stage of the study. Error #: Not reporting whether or how adjustments were made for multiple hypothesis tests Most studies report several P values, which increases the risk of making a type I error: such as saying that a treatment is effective when chance is a more likely explanation for the results (). For example, comparing each of six groups to all the others requires 5 pair-wise statistical tests 5 P values. Without adjusting for these multiple tests, the chance of making a type I error rises from 5 times in (the typical alpha level of.5) to 55 times in (an alpha of.55). The multiple testing problem may be encountered when (): establishing group equivalence by testing each of several baseline characteristics for differences between groups (hoping to find none); performing multiple pair-wise comparisons, which occurs when three or more groups of data are compared two at a time in separate analyses; testing multiple endpoints that are influenced by the same set of explanatory variables; performing secondary analyses of relationships observed during the study but not identified in the original study design; performing subgroup analyses not planned in the original study; performing interim analyses of accumulating data (one endpoint measured at several different times). comparing groups at multiple time points with a series of individual group comparisons.

5 Lang: Statistical Errors in Biomedical Research Articles Croat Med J ;5:-7 Multiple testing is often desirable, and exploratory analyses should be reported as exploratory. Data dredging, however undisclosed analyses involving computing many P values to find something that is statistically significant (and therefore worth reporting) is considered to be poor research. Error #: Unnecessarily reporting baseline statistical comparisons in randomized trials In a true randomized trial, each patient has a known and usually equal probability of being assigned to either the treatment or the control group. Thus, any differences between groups at baseline are, by definition, the result of chance. Therefore, significant differences in baseline data (Table ) do not indicate bias (as they might in other research designs) (9). Such comparisons may indicate statistical imbalances between the groups that may need to be taken into account later in the analysis, but the P values do not need to be reported (9). Table. Statistical baseline comparisons in a randomized trial. By chance, the groups differ in median albumin scores (P=.); the difference does not indicate selection bias. Here, P values need not be reported for this reason Variable Control (n=) Treatment (n=5) Difference P Median age (years) 5. Men (n, %) (9) (5) %.99 Median albumin (g/l)... g/l. Diabetes (n,%) () () %. Assuming that alpha is set at.5, of every baseline comparisons in randomized trials, 5 should be statistically significant, just by chance. However, one study found that among,7 baseline comparisons in 5 trials, only % were significant at the.5 level (). Error #: Not defining normal or abnormal when reporting diagnostic test results The importance of either a positive or a negative diagnostic test result depends on how normal and abnormal are defined. In fact, normal has at least six definitions in medicine (): A diagnostic definition of normal is based on the range of measurements over which the disease is absent and beyond which it is likely to be present. Such a definition of normal is desirable because it is clinically useful. A therapeutic definition of normal is based on the range of measurements over which a therapy is not indicated and beyond which it is beneficial. Again, this definition is clinically useful. Other definitions of normal are perhaps less useful for patient care, although they are unfortunately common: A risk factor definition of normal includes the range of measurements over which the risk of disease is not increased and beyond which the risk is increased. This definition assumes that altering the risk factor alters the actual risk of disease. For example, with rare exceptions, high serum cholesterol is not itself dangerous; only the associated increased risk of heart disease makes a high level abnormal. A statistical definition of normal is based on measurements taken from a disease-free population. This definition usually assumes that the test results are normally distributed ; that they form a bell-shaped curve. The normal range is the range of measurements that includes two standard deviations above and below the mean; that is, the range that includes the central 95% of all the measurements. However, the highest.5% and the lowest.5% of the scores the abnormal scores have no clinical meaning; they are simply uncommon. Unfortunately, many test results are not normally distributed. A percentile definition of normal expresses the normal range as the lower (or upper) percentage of the total range. For example, any value in the lower, say, 95% of all test results may be defined as normal, and only the upper 5% may be defined as abnormal. Again, this definition is based on the frequency of values and may have no clinical meaning. A social definition of normal is based on popular beliefs about what is normal. Desirable weight or the ability of a child to walk by a certain age, for example, often have social definitions of normal that may or may not be medically important. Error #: Not explaining how uncertain (equivocal) diagnostic test results were treated when calculating the test s characteristics (such as sensitivity and specificity) Not all diagnostic tests give clear positive or negative results. Perhaps not all of the barium dye was taken; perhaps the bronchoscopy neither ruled out nor confirmed the diagnosis; perhaps observers could not agree on the interpretation of clinical signs. Reporting the number and proportion of non-positive and non-negative results is important because such results affect the clinical usefulness of the test. Uncertain test results may be one of three types (5): Intermediate results are those that fall between a negative result and a positive result. In a tissue test based on the presence of cells that stain blue, bluish cells that are neither unstained nor the required shade of blue might be considered intermediate results. Indeterminate results are results that indicate neither a positive nor a negative finding. For example, responses on a psychological test may not determine whether the respondent is or is not alcohol-dependent. Uninterpretable results are produced when a test is not conducted according to specified performance standards. Glucose levels from patients who did not fast overnight may be uninterpretable, for example. How such results were counted when calculating sensitivity and specificity should be reported. Test characteristics will vary, depending on whether the 5

6 Lang: Statistical Errors in Biomedical Research Articles Croat Med J ;5:-7 results are counted as positive or negative or were not counted at all, which is often the case. The standard table for computing diagnostic sensitivity and specificity does not include rows and columns for uncertain results (Table ). Even a highly sensitive or specific test may be of little value if the results are uncertain much of the time. Table. Standard table for computing diagnostic test characteristics* Disease Test result present absent Totals Positive a b a + b Negative c d c + d Total a+c b+d a+b+c+d *Sensitivity = a/a + c; specificity = d/b + d. Likelihood ratios can also be calculated from the table. The table does not consider uncertain results, which often and inappropriately are ignored. A. Prevalence, by area B. Prevalence, by area Error #: Using figures and tables only to store data, rather than to assist readers Tables and figures have great value in storing, analyzing, and interpreting data. In scientific presentations, however, they should be used to communicate information, not simply to store data (). As a result, published tables and figures may differ from those created to record data or to analyze the results. For example, a table presenting data for variables may take any of forms (Table ). Because numbers are most easily compared side-by-side, the most appropriate form in Table is the one in which the variables to be compared are side-by-side. That is, by putting the variables to be compared side-by-side, we encourage readers to make a specific comparison. The table and images in Figure 7 show the same data: the prevalence of a disease in nine areas. How- C. Prevalence, by area Area Rate (%) Area Dangerous High Moderate Low None 5 5 Rate (%) Figure 7. Tables and figures should be used to communicate information, not simply to store data. A. Tables are best for communicating or referencing precise numerical data. B. Dot charts are best for communicating general patterns and comparisons. C. Maps are best for communicating spatial relationships. Table. A table for reporting variables (nationality, sex, and age group) may take any of forms: Form Men Women Age (years) US China US China Form China US Age (years) men women men women Form US China Form US China - years -9 years 5+ years men women men women men women Men (age, years) Women (age, years) Form 5 - years -9 years 5+ years US China US China US China Men Women Form US (age, years) China (age, years) Men Women Form 7 Men: US China Women: US China Form US: men women China: men women - years -9 years 5+ years - years -9 years 5+ years

7 Lang: Statistical Errors in Biomedical Research Articles Croat Med J ;5:-7 ever, the table is best used to communicate and to reference precise data; the dot chart, to communicate how the areas compare with one another; and the map, to communicate the spatial relationships between the areas and disease prevalence. Error #5: Using a chart or graph in which the visual message does not support the message of the data on which it is based We remember the visual message of an image more than the message of the data on which it is based (7). For this reason, the image should be adjusted until its message is the same as that of the data. In the lost zero problem (Fig. A), column appears to be less than half as long as column. However, the chart is misleading because the columns do not start at zero: the zero has been lost. The more accurate chart, showing the baseline value of zero (Fig. B), shows that column is really two-thirds as long as column. To prevent this error, the Y axis should be broken to indicate that the columns do not start at zero (Fig. C). In the elastic scales problem, one of the axes is compressed or lengthened disproportionately with respect to the other, which can distort the relationship between the two axes (Fig. 9). Similarly, in the double scale problem, unless the scale on the right has A B C A B C Figure 9. Uneven scales can visually distort relationships among trends. Compressing the scale of the X axis (representing time in this example) makes changes seem more sudden. Compressing the scale of the Y axis makes the changes seem more gradual. Scales with equal intervals are preferred. Figure. A. Charts and graphs that do not begin at zero can create misleading visual comparisons. B. Here, the actual length of both columns can be compared accurately. C. When space prohibits starting with zero as a baseline, the axis should be broken to indicate that the baseline is not zero. some mathematical relationship with the scale on the left, the relationship between two lines can be distorted (Fig. ). A Figure. Charts with two scales, each for a different line of data, can imply a false relationship between the lines, depending on how the scales are presented. Lines A, B, and C represent the same data, but their visual relationships depend on how their respective scales are drawn. Here, Line B seems to increase at half the rate of Line A, whereas Line C seems to increase at a quarter of the rate. Unless the vertical scales are mathematically related, the relationship between the lines can be distorted simply by changing one of the scales. Error #: Confusing the units of observation when reporting and interpreting results The unit of observation is what is actually being studied. Problems occur when the unit is something other than the patient. For example, in a study of 5 eyes, how many patients are involved? What does a 5% success rate mean? If the unit of observation is the heart attack, a study of heart attacks among, people has a sample size of, not,. The fact that of, people had heart attacks may be important, but there are still only heart attacks to study. If the outcome of a diagnostic test is a judgment, a study of the test might require testing a sample of judges, not simply a sample of test results to be judged. If so, the number of judges involved would constitute the sample size, rather than the number of test results to be judged. Error #7: Interpreting studies with nonsignificant results and low statistical power as negative, when they are, in fact, inconclusive Statistical power is the ability to detect a difference of a given size, if such a difference really exists in the population of interest. In studies with low statistical power, results that are not statistically significant are not negative, they are inconclusive: The absence of proof is not proof of absence. Unfortunately, many studies reporting non-statistically significant findings are under-powered and are therefore of little value because they do not provide conclusive answers (). A B C B C 7

10 Lang: Statistical Errors in Biomedical Research Articles Croat Med J ;5:-7 Gardner MJ, Machin D, Campbell MJ. Use of checklists in assessing the statistical content of medical studies. BMJ. 9;9:-. 7 Mosteller F, Gilbert JP, McPeek B. Reporting Standards and Research Strategies for Controlled Trials. Control Clin Trials. 9;:7-5. Murray GD. Statistical guidelines for the British Journal of Surgery. Br J Surg. 99;7:7-. 9 Simon R, Wittes RE. Methodologic guidelines for reports of clinical trials. Cancer Treat Rep. 95;9:-. Zelen M. Guidelines for publishing papers on cancer clinical trials: responsibilities of editors and authors. J Clin Oncol. 9;:-9. Correspondence to: Tom Lang Tom Lang Communications PO Box 57 Murphys, CA 957, USA 7

### Guide to Biostatistics

MedPage Tools Guide to Biostatistics Study Designs Here is a compilation of important epidemiologic and common biostatistical terms used in medical research. You can use it as a reference guide when reading

### CHAPTER THREE COMMON DESCRIPTIVE STATISTICS COMMON DESCRIPTIVE STATISTICS / 13

COMMON DESCRIPTIVE STATISTICS / 13 CHAPTER THREE COMMON DESCRIPTIVE STATISTICS The analysis of data begins with descriptive statistics such as the mean, median, mode, range, standard deviation, variance,

### What are confidence intervals and p-values?

What is...? series Second edition Statistics Supported by sanofi-aventis What are confidence intervals and p-values? Huw TO Davies PhD Professor of Health Care Policy and Management, University of St Andrews

### What is meant by "randomization"? (Select the one best answer.)

Preview: Post-class quiz 5 - Clinical Trials Question 1 What is meant by "randomization"? (Select the one best answer.) Question 2 A. Selection of subjects at random. B. Randomization is a method of allocating

### CHAPTER TWELVE TABLES, CHARTS, AND GRAPHS

TABLES, CHARTS, AND GRAPHS / 75 CHAPTER TWELVE TABLES, CHARTS, AND GRAPHS Tables, charts, and graphs are frequently used in statistics to visually communicate data. Such illustrations are also a frequent

### 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

### X X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1)

CORRELATION AND REGRESSION / 47 CHAPTER EIGHT CORRELATION AND REGRESSION Correlation and regression are statistical methods that are commonly used in the medical literature to compare two or more variables.

### 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

### 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

### Basic research methods. Basic research methods. Question: BRM.2. Question: BRM.1

BRM.1 The proportion of individuals with a particular disease who die from that condition is called... BRM.2 This study design examines factors that may contribute to a condition by comparing subjects

### Intent-to-treat Analysis of Randomized Clinical Trials

Intent-to-treat Analysis of Randomized Clinical Trials Michael P. LaValley Boston University http://people.bu.edu/mlava/ ACR/ARHP Annual Scientific Meeting Orlando 10/27/2003 Outline Origin of Randomization

### "Statistical methods are objective methods by which group trends are abstracted from observations on many separate individuals." 1

BASIC STATISTICAL THEORY / 3 CHAPTER ONE BASIC STATISTICAL THEORY "Statistical methods are objective methods by which group trends are abstracted from observations on many separate individuals." 1 Medicine

### Biostatistics: Types of Data Analysis

Biostatistics: Types of Data Analysis Theresa A Scott, MS Vanderbilt University Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott Theresa A Scott, MS

Goals of This Course Be able to understand a study design (very basic concept) Be able to understand statistical concepts in a medical paper Be able to perform a data analysis Understanding: PECO study

### Sample size estimation is an important concern

Sample size and power calculations made simple Evie McCrum-Gardner Background: Sample size estimation is an important concern for researchers as guidelines must be adhered to for ethics committees, grant

### Outline of Topics. Statistical Methods I. Types of Data. Descriptive Statistics

Statistical Methods I Tamekia L. Jones, Ph.D. (tjones@cog.ufl.edu) Research Assistant Professor Children s Oncology Group Statistics & Data Center Department of Biostatistics Colleges of Medicine and Public

### Critical appraisal skills are essential to informed decision-making

Resident s Page Critical appraisal skills are essential to informed decision-making Rahul Mhaskar 1,3, Patricia Emmanuel 3,5, Shobha Mishra 4, Sangita Patel 4, Eknath Naik 5, Ambuj Kumar 1,2,3,5 1 Center

### 2. Simple Linear Regression

Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according

### When Does it Make Sense to Perform a Meta-Analysis?

CHAPTER 40 When Does it Make Sense to Perform a Meta-Analysis? Introduction Are the studies similar enough to combine? Can I combine studies with different designs? How many studies are enough to carry

### MINITAB ASSISTANT WHITE PAPER

MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way

### Measuring reliability and agreement

Measuring reliability and agreement Madhukar Pai, MD, PhD Assistant Professor of Epidemiology, McGill University Montreal, Canada Professor Extraordinary, Stellenbosch University, S Africa Email: madhukar.pai@mcgill.ca

### DATA INTERPRETATION AND STATISTICS

PholC60 September 001 DATA INTERPRETATION AND STATISTICS Books A easy and systematic introductory text is Essentials of Medical Statistics by Betty Kirkwood, published by Blackwell at about 14. DESCRIPTIVE

### F. Farrokhyar, MPhil, PhD, PDoc

Learning objectives Descriptive Statistics F. Farrokhyar, MPhil, PhD, PDoc To recognize different types of variables To learn how to appropriately explore your data How to display data using graphs How

### PEER REVIEW HISTORY ARTICLE DETAILS TITLE (PROVISIONAL)

PEER REVIEW HISTORY BMJ Open publishes all reviews undertaken for accepted manuscripts. Reviewers are asked to complete a checklist review form (http://bmjopen.bmj.com/site/about/resources/checklist.pdf)

### Intervention and clinical epidemiological studies

Intervention and clinical epidemiological studies Including slides from: Barrie M. Margetts Ian L. Rouse Mathew J. Reeves,PhD Dona Schneider Tage S. Kristensen Victor J. Schoenbach Experimental / intervention

### 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,

### How to interpret scientific & statistical graphs

How to interpret scientific & statistical graphs Theresa A Scott, MS Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott 1 A brief introduction Graphics:

### 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

### Describing Data and Descriptive Statistics

Describing Data and Descriptive Statistics Peter Moffett MD May 17, 2012 Introduction Data can be categorized into nominal, ordinal, or continuous data. This data then must summarized using a measure of

### Mortality Assessment Technology: A New Tool for Life Insurance Underwriting

Mortality Assessment Technology: A New Tool for Life Insurance Underwriting Guizhou Hu, MD, PhD BioSignia, Inc, Durham, North Carolina Abstract The ability to more accurately predict chronic disease morbidity

### Form B-1. Inclusion form for the effectiveness of different methods of toilet training for bowel and bladder control

Form B-1. Inclusion form for the effectiveness of different methods of toilet training for bowel and bladder control Form B-2. Assessment of methodology for non-randomized controlled trials for the effectiveness

### Guidance for Peer Reviewers. The Journal of the American Osteopathic Association (JAOA)

Guidance for Peer Reviewers The Journal of the American Osteopathic Association (JAOA) JAOA Editorial Staff This module is available online at http://jaoa.org/documentlibrary/prmodule.pdf Guidance for

### Principal, Tom Lang Communications and Training International b. Director, Centre for Statistics in Medicine, Oxford University

Basic Statistical Reporting for Articles Published in Biomedical Journals: The Statistical Analyses and Methods in the Published Literature or The SAMPL Guidelines Thomas A. Lang a and Douglas G. Altman

### 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

### Tips for surviving the analysis of survival data. Philip Twumasi-Ankrah, PhD

Tips for surviving the analysis of survival data Philip Twumasi-Ankrah, PhD Big picture In medical research and many other areas of research, we often confront continuous, ordinal or dichotomous outcomes

### Guidance for Industry Diabetes Mellitus Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes

Guidance for Industry Diabetes Mellitus Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes U.S. Department of Health and Human Services Food and Drug Administration Center

Version 5.0 Statistics Guide Harvey Motulsky President, GraphPad Software Inc. All rights reserved. This Statistics Guide is a companion to GraphPad Prism 5. Available for both Mac and Windows, Prism makes

### Subject: No. Page PROTOCOL AND CASE REPORT FORM DEVELOPMENT AND REVIEW Standard Operating Procedure

703 1 of 11 POLICY The Beaumont Research Coordinating Center (BRCC) will provide advice to clinical trial investigators on protocol development, content and format. Upon request, the BRCC will review a

### Current reporting in published research

Current reporting in published research Doug Altman Centre for Statistics in Medicine, Oxford, UK and EQUATOR Network Research article A published research article is a permanent record that will be used

### Brief Communications. Awad Mohamed Ahmed, MD. Abstract

Awad Mohamed Ahmed, MD Professor of Medicine, University of Bahr Elghazal, Khartoum, Sudan For correspondence Professor Awad Mohamed Ahmed E-mail: awad.sd@gmail.com Tel.: +249 912344936 Abstract Randomized

### 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

### Content DESCRIPTIVE STATISTICS. Data & Statistic. Statistics. Example: DATA VS. STATISTIC VS. STATISTICS

Content DESCRIPTIVE STATISTICS Dr Najib Majdi bin Yaacob MD, MPH, DrPH (Epidemiology) USM Unit of Biostatistics & Research Methodology School of Medical Sciences Universiti Sains Malaysia. Introduction

### How To Report Statistical Analysis Results. Lehana Thabane For TIPPS

How To Report Statistical Analysis Results Lehana Thabane For TIPPS Outline: Session Objectives To highlight Common Errors To learn how to report results of descriptive analyses of baseline/demographic

### 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

### A list of FDA-approved testosterone products can be found by searching for testosterone at http://www.accessdata.fda.gov/scripts/cder/drugsatfda/.

FDA Drug Safety Communication: FDA cautions about using testosterone products for low testosterone due to aging; requires labeling change to inform of possible increased risk of heart attack and stroke

### Antipsychotic drugs are the cornerstone of treatment

Article Effectiveness of Olanzapine, Quetiapine, Risperidone, and Ziprasidone in Patients With Chronic Schizophrenia Following Discontinuation of a Previous Atypical Antipsychotic T. Scott Stroup, M.D.,

### EBM Cheat Sheet- Measurements Card

EBM Cheat Sheet- Measurements Card Basic terms: Prevalence = Number of existing cases of disease at a point in time / Total population. Notes: Numerator includes old and new cases Prevalence is cross-sectional

### Clinical Trials in Palliative Care: Challenges and Opportunities

Clinical Trials in Palliative Care: Challenges and Opportunities Russell K. Portenoy, MD Chairman and Gerald J. and Dorothy R. Friedman Chair in Pain Medicine and Palliative Care Department of Pain Medicine

### What is a P-value? Ronald A. Thisted, PhD Departments of Statistics and Health Studies The University of Chicago

What is a P-value? Ronald A. Thisted, PhD Departments of Statistics and Health Studies The University of Chicago 8 June 1998, Corrections 14 February 2010 Abstract Results favoring one treatment over another

### Exploratory data analysis (Chapter 2) Fall 2011

Exploratory data analysis (Chapter 2) Fall 2011 Data Examples Example 1: Survey Data 1 Data collected from a Stat 371 class in Fall 2005 2 They answered questions about their: gender, major, year in school,

### Critical Appraisal of the Medical Literature

Critical Appraisal of the Medical Literature James A. Hokanson, Ph.D. Department of Preventive Medicine and Community Health The University of Texas Medical Branch 2001 United States Crude Death Rates

### Chi Squared and Fisher's Exact Tests. Observed vs Expected Distributions

BMS 617 Statistical Techniques for the Biomedical Sciences Lecture 11: Chi-Squared and Fisher's Exact Tests Chi Squared and Fisher's Exact Tests This lecture presents two similarly structured tests, Chi-squared

### UNDERSTANDING CLINICAL TRIAL STATISTICS. Prepared by Urania Dafni, Xanthi Pedeli, Zoi Tsourti

UNDERSTANDING CLINICAL TRIAL STATISTICS Prepared by Urania Dafni, Xanthi Pedeli, Zoi Tsourti DISCLOSURES Urania Dafni has reported no conflict of interest Xanthi Pedeli has reported no conflict of interest

### 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

### GIM Grand Rounds Evidence Based Medicine Putting Principles into Action

GIM Grand Rounds Evidence Based Medicine Putting Principles into Action Brandon P. Combs, MD Division of General Internal Medicine University of Colorado School of Medicine May 2014 TRUE OR FALSE? 1. If

### Introduction to. Hypothesis Testing CHAPTER LEARNING OBJECTIVES. 1 Identify the four steps of hypothesis testing.

Introduction to Hypothesis Testing CHAPTER 8 LEARNING OBJECTIVES After reading this chapter, you should be able to: 1 Identify the four steps of hypothesis testing. 2 Define null hypothesis, alternative

### Primary Prevention of Cardiovascular Disease with a Mediterranean diet

Primary Prevention of Cardiovascular Disease with a Mediterranean diet Alejandro Vicente Carrillo, Brynja Ingadottir, Anne Fältström, Evelyn Lundin, Micaela Tjäderborn GROUP 2 Background The traditional

### Expert Commentary. A response to the recent JAMA meta analysis on omega 3 supplementation and cardiovascular events

Expert Commentary A response to the recent JAMA meta analysis on omega 3 supplementation and cardiovascular events Dr. Bruce J. Holub, Ph.D., Professor Emeritus, University of Guelph Expert Commentary:

### 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:

### GUIDELINES ADJUVANT SYSTEMIC BREAST CANCER

GUIDELINES ADJUVANT SYSTEMIC BREAST CANCER Author: Dr Susan O Reilly On behalf of the Breast CNG Written: December 2008 Agreed at CNG: June 2009 & June 2010 Review due: June 2011 Guidelines Adjuvant Systemic

### Statistics: revision

NST 1B Experimental Psychology Statistics practical 5 Statistics: revision Rudolf Cardinal & Mike Aitken 3 / 4 May 2005 Department of Experimental Psychology University of Cambridge Slides at pobox.com/~rudolf/psychology

### An analysis method for a quantitative outcome and two categorical explanatory variables.

Chapter 11 Two-Way ANOVA An analysis method for a quantitative outcome and two categorical explanatory variables. If an experiment has a quantitative outcome and two categorical explanatory variables that

### Statistical Analysis I

CTSI BERD Research Methods Seminar Series Statistical Analysis I Lan Kong, PhD Associate Professor Department of Public Health Sciences December 22, 2014 Biostatistics, Epidemiology, Research Design(BERD)

### Variables. Exploratory Data Analysis

Exploratory Data Analysis Exploratory Data Analysis involves both graphical displays of data and numerical summaries of data. A common situation is for a data set to be represented as a matrix. There is

### SOLUTIONS TO BIOSTATISTICS PRACTICE PROBLEMS

SOLUTIONS TO BIOSTATISTICS PRACTICE PROBLEMS BIOSTATISTICS DESCRIBING DATA, THE NORMAL DISTRIBUTION SOLUTIONS 1. a. To calculate the mean, we just add up all 7 values, and divide by 7. In Xi i= 1 fancy

### REPORTING ON PARTICIPANT DISPOSITION IN CLINICAL TRIALS. Dennis C. Turk, Ph.D. 1 University of Washington School of Medicine

1 REPORTING ON PARTICIPANT DISPOSITION IN CLINICAL TRIALS Dennis C. Turk, Ph.D. 1 University of Washington School of Medicine Running head: Patient Disposition 1 Preparation of this manuscript was supported

### Journal Club: Niacin in Patients with Low HDL Cholesterol Levels Receiving Intensive Statin Therapy by the AIM-HIGH Investigators

Journal Club: Niacin in Patients with Low HDL Cholesterol Levels Receiving Intensive Statin Therapy by the AIM-HIGH Investigators Shaikha Al Naimi Doctor of Pharmacy Student College of Pharmacy Qatar University

### Guidance for Industry

Guidance for Industry Clinical Studies Section of Labeling for Human Prescription Drug and Biological Products Content and Format U.S. Department of Health and Human Services Food and Drug Administration

### 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

### Week 4: Standard Error and Confidence Intervals

Health Sciences M.Sc. Programme Applied Biostatistics Week 4: Standard Error and Confidence Intervals Sampling Most research data come from subjects we think of as samples drawn from a larger population.

### 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

### Module 5 Hypotheses Tests: Comparing Two Groups

Module 5 Hypotheses Tests: Comparing Two Groups Objective: In medical research, we often compare the outcomes between two groups of patients, namely exposed and unexposed groups. At the completion of this

### 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

### 8 General discussion. Chapter 8: general discussion 95

8 General discussion The major pathologic events characterizing temporomandibular joint osteoarthritis include synovitis and internal derangements, giving rise to pain and restricted mobility of the temporomandibular

### Presenting survey results Report writing

Presenting survey results Report writing Introduction Report writing is one of the most important components in the survey research cycle. Survey findings need to be presented in a way that is readable

### PSA Testing 101. Stanley H. Weiss, MD. Professor, UMDNJ-New Jersey Medical School. Director & PI, Essex County Cancer Coalition. weiss@umdnj.

PSA Testing 101 Stanley H. Weiss, MD Professor, UMDNJ-New Jersey Medical School Director & PI, Essex County Cancer Coalition weiss@umdnj.edu September 23, 2010 Screening: 3 tests for PCa A good screening

### Clinical Trial Design Issues and Options for the Study of Rare Diseases

Clinical Trial Design Issues and Options for the Study of Rare Diseases Jeffrey Krischer, Ph.D. Data Management and Coordination Center Rare Diseases Clinical Research Network October 2, 2012 None Financial

### Assessing MOHLTC Research Protocols Secondary Data Studies By: Tommy Tam, MSc

Assessing MOHLTC Research Protocols Secondary Data Studies By: Tommy Tam, MSc Definition of secondary data study: Secondary data studies use data source(s) collected by someone other than the user for

### THE RISK DISTRIBUTION CURVE AND ITS DERIVATIVES. Ralph Stern Cardiovascular Medicine University of Michigan Ann Arbor, Michigan. stern@umich.

THE RISK DISTRIBUTION CURVE AND ITS DERIVATIVES Ralph Stern Cardiovascular Medicine University of Michigan Ann Arbor, Michigan stern@umich.edu ABSTRACT Risk stratification is most directly and informatively

### Improving reporting in randomised trials: CONSORT statement and extensions Doug Altman

Improving reporting in randomised trials: CONSORT statement and extensions Doug Altman Centre for Statistics in Medicine University of Oxford Whatever the outcome of a study, it is really hard for the

### CHILDHOOD CANCER SURVIVOR STUDY Analysis Concept Proposal

CHILDHOOD CANCER SURVIVOR STUDY Analysis Concept Proposal 1. STUDY TITLE: Longitudinal Assessment of Chronic Health Conditions: The Aging of Childhood Cancer Survivors 2. WORKING GROUP AND INVESTIGATORS:

### The Prevention and Treatment of Missing Data in Clinical Trials: An FDA Perspective on the Importance of Dealing With It

nature publishing group The Prevention and Treatment of Missing Data in Clinical Trials: An FDA Perspective on the Importance of Dealing With It RT O Neill 1 and R Temple 2 At the request of the Food and

### Effective Use of Tables and Figures in Abstracts, Presentations, and Papers

Effective Use of Tables and Figures in Abstracts, Presentations, and Papers Charles G Durbin Jr MD FAARC Introduction Patient Flow Chart Graphs to Represent Data Summary In some situations, tables, graphs,

### 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.

I CSI INSTITUTE FOR CLINICAL SYSTEMS IMPROVEMENT EVIDENCE GRADING SYSTEM The evidence grading system used in ICSI guidelines and technology assessment reports is periodically reviewed and modified. The

### Effect estimation versus hypothesis testing

Department of Epidemiology and Public Health Unit of Biostatistics and Computational Sciences Effect estimation versus hypothesis testing PD Dr. C. Schindler Swiss Tropical and Public Health Institute

### A practical approach to Bland-Altman plots and variation coefficients for log transformed variables

5 A practical approach to Bland-Altman plots and variation coefficients for log transformed variables A.M. Euser F.W. Dekker S. le Cessie J Clin Epidemiol 2008; 61: 978-982 Chapter 5 Abstract Objective

### Eliminating Bias in Randomized Controlled Trials: Importance of Allocation Concealment and Masking

132 February 2007 Family Medicine Research Series Eliminating Bias in Randomized Controlled Trials: Importance of Allocation Concealment and Masking Anthony J. Viera, MD, MPH; Shrikant I. Bangdiwala, PhD

### Outline. Publication and other reporting biases; funnel plots and asymmetry tests. The dissemination of evidence...

Cochrane Methodology Annual Training Assessing Risk Of Bias In Cochrane Systematic Reviews Loughborough UK, March 0 Publication and other reporting biases; funnel plots and asymmetry tests Outline Sources

### PEER REVIEW HISTORY ARTICLE DETAILS VERSION 1 - REVIEW. Elizabeth Comino Centre fo Primary Health Care and Equity 12-Aug-2015

PEER REVIEW HISTORY BMJ Open publishes all reviews undertaken for accepted manuscripts. Reviewers are asked to complete a checklist review form (http://bmjopen.bmj.com/site/about/resources/checklist.pdf)

### GUIDE TO RANDOMISED CONTROLLED TRIAL PROTOCOL CONTENT AND FORMAT (FOR NON CTIMP S)

GUIDE TO RANDOMISED CONTROLLED TRIAL PROTOCOL CONTENT AND FORMAT (FOR NON CTIMP S) The aim of this guide is to help researchers with the content and structure of protocols for randomised controlled trials

### Inferential Statistics

Inferential Statistics Sampling and the normal distribution Z-scores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are

### 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

### Articles Presented. Journal Presentation. Dr Albert Lo. Dr Albert Lo

* This presentation is prepared by the author in one s personal capacity for the purpose of academic exchange and does not represent the views of his/her organisations on the topic discussed. Journal Presentation

### What is critical appraisal?

...? series Second edition Evidence-based medicine Supported by sanofi-aventis What is critical appraisal? Amanda Burls MBBS BA MSc FFPH Director of the Critical Appraisal Skills Programme, Director of

### Comparative Tolerability and Harms of Individual Statins

Comparative Tolerability and Harms of Individual Statins J.J.Brugts MD PhD MSc(1); H. Naci PhD MHS(2); T. Ades PhD(2) (1) Department of Cardiology, Erasmus MC, Rotterdam, the Netherlands (2) London School

### Session 5: How to critically appraise a paper an example

Session 5: How to critically appraise a paper an example Appraising research broad questions l What is the research question? l Is the study ethical? l Is the study design valid and appropriate? l What