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    Coursebooklet Coursebooklet Presentation Transcript

    • Essentials of Manuscript Review Gary G. Poehling, MD – Editor-in-Chief Wake Forest University School of Medicine Winston-Salem, North Carolina, USA
    • Essentials of Manuscript Review Arthroscopy The Journal of Arthroscopic and Related Surgery
    • Essentials of Manuscript Review
      • How to optimize scientific communication
      • How to organize a manuscript
      • How to use the essentials of statistics
      • How to review a submitted manuscript
      Learning objectives of this course
    • How to Organize a Manuscript
      • Text (of an Original Article)
      • Introduction
      • Methods
      • Results
      • Discussion
      • Conclusion
    • How to Organize a Manuscript
      • Supporting Structure
      • Title
      • Abstract
      • References
      • Figures
      • Tables
    • Introduction
      • Present succinct referenced review
      • Create reader interest
      • Identify controversy
      • State purpose
      • State hypothesis
    • Introduction
      • Controversy stimulates questions.
      • The purpose of any study is to answer a question.
      • The hypothesis is a tentative theory
      • in which you state
      • what you believed you
      • would find *before*
      • you started the study.
    • Introduction
      • Example
      • Controversy: Is arthroscopy of the knee beneficial to patients with osteoarthritis (OA)?
      • Purpose: To determine whether arthroscopy of the knee benefits patients with OA.
      • Hypothesis: Arthroscopy of the knee is of moderate benefit to patients with OA.
    • Methods
      • Should include:
      • Technical description
      • of the study design
        • Make it complete
        • Make it reproducible
      • Rationale for experimental design
      • Statistical methods
    • Methods
      • Study Design
      • Focus on the purpose.
      • Select the type of study that fits your purpose.
      • Use valid measurement tools.
      • Apply appropriate statistical methods.
    • Methods
      • Study Design
      • Flaws in study design can be fatal.
    • Levels of Evidence
      • Therapeutic Studies
      • Prognostic Studies
      • Diagnostic Studies
      • Economic and Decision Analyses
      Types of Studies—Clinical Only
    • Levels of Evidence (pg 5 of the Journal’s Instructions for Authors)
    • Levels of Evidence
      • Randomized controlled trial = Level I or II
      • Comparative study = Level II or III
      • Case-control study = Level III
      • Case series study = Level IV
      Therapeutic Studies
    • Types of Studies
      • RETROSPECTIVE - Looks Back
      • Easier to do: collect data & see what you have
      • Higher risk of bias
      • PROSPECTIVE - Looks Ahead
      • Better type of study
      • Long time to complete
      • Considerable effort & resources
      • Straightforward conclusions
    • Types of Studies
      • Observational Studies
      • Nature is allowed to take its course.
      • Investigator does *not* intervene.
      • Retrospective design
        • Case report - single subject - no controls
        • Case series - multiple subjects - no controls
      Level of Evidence = IV
    • Types of Studies
      • Observational Studies
      • Retrospective design: case-control study
        • Starts with subjects who have a disease
        • Requires suitable control group without the disease
          • Look for suspected risk factor in both groups
          • May help determine causal relationships
          • Use it to study conditions with low incidence
      Level of Evidence = III
    • Types of Studies
      • Observational Studies
      • Prospectively designed comparative study = patients treated one way compared with patients treated another way at the same institution
      Level of Evidence = II
      • Retrospectively designed comparative study :
      • Level of Evidence = III
    • Types of Studies
      • Experimental Studies - Prospective
      • Investigator has total control of patient allocation.
      • Independent variables (manipulated by the investigator) are systematically changed.
        • Example: Knee Arthroscopy vs. a Placebo in Patients with Osteoarthritis
      • Investigator makes observations: How do these changes affect the dependent variables?
        • Examples: Pain (more or less?); Function (better or worse?)
    • Types of Studies
      • Experimental Studies - Prospective
      • Randomized Controlled Trials
        • Population is randomly allocated.
        • Study group: All receive intervention.
        • Control group: All lack intervention or standard treatment.
        • Example: Knee arthroscopy vs. placebo
        • in VA patients with osteoarthritis
      Level of Evidence = I or II
    • Levels of Evidence (pg 5 of the Journal’s Instructions for Authors)
    • Data Collection Instruments
      • Requirements
      • Reliable
      • Valid
      • Responsive
      • Universal
      • Unbiased
    • Data Collection Instrument
      • Is it reliable?
      • Will the instrument measure consistently across:
      • Different testing situations?
        • Test-retest reliability
      • Different judges?
        • Inter-rater reliability
    • Data Collection Instrument
      • Is it valid?
      • Is the instrument being used to measure the kind of data for which it was intended?
    • Data Collection Instrument
      • Is it responsive?
      • The instrument should be equally sensitive, whether a characteristic is present or absent.
      • For example, MRI vs. physical examination for isolated tears of the ACL . . .
      • Must measure both as compared to arthroscopy.
      • False-negatives:
      • You thought it was intact, but it was torn.
      • False-positives:
      • You thought it was torn, but it was intact.
    • Data Collection Instrument
      • Is it universal?
      • The investigator should employ a widely used data collection instrument, which helps minimize reporting bias because the data can then be compared with other published literature.
    • Data Collection Instrument
      • There should be no difference between the true value and the value that an investigator actually obtains– other than a difference caused by sampling variability.
      Is it unbiased?
    • Bias in Clinical Trials
      • Areas in which bias can occur
      • Systematic error in . . .
      • Allocation
      • Response
      • Assessment
    • Bias in Clinical Trials
      • Allocation or Susceptibility Bias
      • Can occur when patient assignments to a trial group are influenced by an investigator’s knowledge of the treatment to be received.
      • Can result in treatment groups that have different prognoses.
    • Bias in Clinical Trials
      • Allocation or Susceptibility Bias
      • Treatment groups must have similar prognoses, which is achieved by:
        • Randomization of patients
        • Prospective evaluation of patients
        • Well-defined inclusion and exclusion criteria
    • Randomization in Clinical Trials
      • Occurs when patients are assigned to treatments by means of a mechanism that prevents both the patients and the investigator from knowing which treatment is being assigned.
    • Benefits of Randomization
      • Prevents the systematic introduction of bias.
      • Minimizes the possibility of allocation bias.
      • Balances prognostic factors for treatment groups.
      • Improves the validity of statistical tests used to compare treatments.
    • Bias in Clinical Trials
      • Response & Assessment/Recording Bias
      • Can occur when a patient reports a treatment response or when an investigator assesses that response—either person can be influenced by knowing the treatment.
      • A patient or an investigator may have a preconceived idea of which treatment is better. The patient may also want to please the investigator.
    • Bias in Clinical Trials
      • Blinding
      • To minimize Response & Assessment/Recording Bias
      • Single Blind (patient blinded): protects against response bias.
      • Double Blind (patient and investigator blinded): protects against assessment/recording bias as well as response bias.
    • Bias in Clinical Trials
      • Transfer bias
        • Occurs when patients are lost to follow-up.
        • Must be minimized.
      • Performance bias
        • Can occur with a single surgeon or with multiple surgeons.
    • Rationale for Experimental Design
      • Here the investigator explains how the methods address the purpose of the study.
      • The rationale for experimental design also is used to clarify basic science for lay readers.
    • Statistics Standard Distribution Curve
    • Statistics Standard Deviation Curve
    • Statistics Standard Deviation Curve
    • Statistics Standard Deviation Curve
    • Statistics Standard Deviation Curve
    • Statistics
      • Z-Score
      • Another way to view standard deviation:
        • Number of Standard Deviations Needed
    • Statistics
      • Z-score
      • Another way to view standard deviation:
        • Number of Standard Deviations Needed
        • 95% = 1.96 SD (use Z-Score 2)
    • Statistics
      • Z-score
      • Another way to view standard deviation:
        • Number of Standard Deviations Needed
        • 95% = 1.96 SD (use Z-Score 2)
        • 99% = 2.58 SD (use Z-Score 2.5)
    • Statistics
      • Standard Error
      • Standard Error (SEM, SDM) = Sample Quality
        • Standard deviation of the sample divided by the square root of the sample size
    • Statistics
      • Confidence Interval
      • (Z-score x Standard Error)
    • Statistics
      • Confidence Interval
      • (Z-score x Standard Error)
      • Z-Score: Number of Standard Deviations
    • Statistics
      • Confidence Interval
      • (Z-score x Standard Error)
      • Z-Score: Number of Standard Deviations
      • Standard Error: Sample Quality
    • Hypothesis
      • What the investigator believes—before the study begins—that the study can prove.
    • Null Hypothesis
      • A statement of no effect
      • A “null hypothesis” is the converse of what the investigator believes can be proved.
    • Standard Deviation Curve Null Hypothesis Hypothesis
    • Null Hypothesis
      • State of the World (the population)
      Your Decision Based on Data
    • Null Hypothesis
      • State of the World (the population)
      Do Not Reject Null Hypothesis Your Decision Based on Data Reject Null Hypothesis
    • Null Hypothesis
      • State of the World (the population)
      Your Decision Based on Data Null Hypothesis True Reject Null Hypothesis Do Not Reject Null Hypothesis
    • Null Hypothesis
      • State of the World (the population)
      Your Decision Based on Data Null Hypothesis True Reject Null Hypothesis Do Not Reject Null Hypothesis Correct Decision
    • Null Hypothesis
      • State of the World (the population)
      Your Decision Based on Data Null Hypothesis True Null Hypothesis False Reject Null Hypothesis Do Not Reject Null Hypothesis Correct Decision
    • Null Hypothesis
      • State of the World (the population)
      Your Decision Based on Data Null Hypothesis True Null Hypothesis False Reject Null Hypothesis Correct Decision Do Not Reject Null Hypothesis Correct Decision
    • Null Hypothesis
      • State of the World (the population)
      Your Decision Based on Data Null Hypothesis True Null Hypothesis False Reject Null Hypothesis Type I Error Correct Decision Do Not Reject Null Hypothesis Correct Decision
    • Null Hypothesis
      • State of the World (the population)
      Your Decision Based on Data Null Hypothesis True Null Hypothesis False Reject Null Hypothesis Type I Error Correct Decision Do Not Reject Null Hypothesis Correct Decision Type ll Error
    • Null Hypothesis
      • Type I error α (alpha error)
    • Null Hypothesis
      • Type I error α (alpha error)
      • Occurs when rejecting the null hypothesis, although the null hypothesis actually is true (sampling error – bias).
    • Null Hypothesis
      • Type I error α (alpha error)
      • Occurs when rejecting the null hypothesis, although the null hypothesis actually is true (sampling error – bias).
      • Type II error β (beta error)
    • Null Hypothesis
      • Type I error α (alpha error)
      • Occurs when rejecting the null hypothesis, although the null hypothesis actually is true (sampling error – bias).
      • Type II error β (beta error)
      • Occurs when accepting the null hypothesis, although the null hypothesis actually is false (too-small sample).
    • Statistical Power
      • Probability that the null hypothesis will be rejected if it is indeed false
    • Statistical Power
      • Probability that the null hypothesis will be rejected if it is indeed false
      • The capacity to detect a difference, if one exists
    • Statistical Power
      • Probability that the null hypothesis will be rejected if it is indeed false
      • The capacity to detect a difference, if one exists
      • Power = 1 -  (type II error)
    • Statistical Power
      • N = 2 σ 2 (Z 1- α + Z 1- β ) 2 / δ 2
      •  = Standard deviation of outcome (variability)
        • Assumed to be known.
        • Estimated from pilot data.
        • Obtained from the literature.
      • Z 1-   = Allowable type I error
      • Z 1-   = Allowable type II error
      •  = Difference the investigator wants to detect
        • Between (or among) groups
      www.mc.vanderbilt.edu/prevmed/ps.htm
    • Statistical Power
      • Problems with Inadequate Power
      • Chances of false-negative findings increase.
        • Note that failing to show a difference is not the same as showing that no difference exists.
    • Statistical Power
      • Problems with Inadequate Power
      • Chances of false-negative findings increase.
        • Note that failing to show a difference is not the same as showing that no difference exists.
      • Wastes the time of patients & investigators.
    • Statistical Power
      • Problems with Inadequate Power
      • Chances of false-negative findings increase.
        • Note that failing to show a difference is not the same as showing that no difference exists.
      • Wastes the time of patients & investigators.
      • Wastes money.
    • Evidence in Clinical Research
      • A p-value indicates how unlikely it is that a test statistic as extreme as—or more extreme than—the one derived from this study’s data will be found for this patient population if the null hypothesis is true.
    • Evidence in Clinical Research
      • P-values do not provide simple Yes or No answers.
    • Evidence in Clinical Research
      • P-values do not provide simple Yes or No answers.
      • Instead, p-values provide general ideas about the strength of evidence against null hypotheses.
    • Evidence in Clinical Research
      • P-values do not provide simple Yes or No answers.
      • Instead, p-values provide general ideas about the strength of evidence against null hypotheses.
      • The lower the p-value, the stronger the evidence.
    • Evidence in Clinical Research
      • The Confidence Interval (CI) indicates a range of likely differences.
    • Evidence in Clinical Research
      • The Confidence Interval (CI) indicates a range of likely differences.
      • Less confusion exists in the literature about Confidence Intervals because:
        • The range of possible true values is more clearly stated than with p-values.
    • Evidence in Clinical Research
      • The Confidence Interval (CI) indicates a range of likely differences.
      • Less confusion exists in the literature about Confidence Intervals because:
        • The range of possible true values is more clearly stated than with p-values.
        • Apparently contradictory research can be found to have overlapping Confidence Intervals.
    • Results
      • Clear Presentation of Data
      • Organize it like the materials and methods.
      • The numbers must add up.
      • All results must be proposed in the methods.
      • Everything in the methods must be reported.
      • Text must be consistent with tables & figures.
    • Results
      • Evidence in Clinical Research
      • Confidence Interval = range of differences between (or among) treatment groups. Confidence Interval data are extremely useful and their use needs to be encouraged.
      • p < .05 = statistical significance
      • 95% sure that the difference is true – anything else assumes that it is not different or that the null hypothesis is true.
    • Misinterpretation of Results
      • Comparison of 2 Groups
      • Failure to show a difference is not the same as showing that there is no difference – lack of power.
    • Discussion
      • Compare Your Results to Previous Studies.
      • Discuss similarities and differences.
      • Clarify the meaning of your results.
    • Discussion
      • Speculate – if reasonable and feasible.
      • Clearly distinguish your theories or opinions (from your conclusions, which are based on your results).
      Consider alternative explanations.
    • Discussion: Include Limitations.
      • Point out study weaknesses.
      • Specifically consider bias:
        • Allocation (Susceptibility)
        • Response
        • Assessment (Recording)
        • Transfer
        • Performance
    • Conclusion
      • Here you must address the hypothesis: was it proved?
      • What did your data support?
      • What did your results show?
      • Your conclusion must *not* include statements that lie outside the study’s scope.
      • Expressed another way . . .
      • Make no statements in the conclusion that the results do not support.
    • Abstract
      • Original articles require a structured abstract (a maximum of 300 words).
      • In the structured abstract, present the essential details.
        • Purpose
        • Methods
        • Results
        • Conclusions
        • Level of Evidence (or Clinical Relevance)
        • Keywords (a maximum of 6)
    • Abstract
      • Technical notes and case reports require . . .
      • An unstructured abstract (200-word maximum)
    • Abstract
      • Technical notes and case reports require . . .
      • An unstructured abstract (200-word maximum)
      • Great majority of these articles -> “hybrids”
    • Abstract
      • Technical notes and case reports require . . .
      • An unstructured abstract (200-word maximum)
      • Great majority of these articles -> “hybrids”
      • Hybrid = unstructured abstract & 1 figure/2 parts
    • Abstract
      • Technical notes and case reports require . . .
      • An unstructured abstract (200-word maximum)
      • Great majority of these articles -> “hybrids”
      • Hybrid = unstructured abstract & 1 figure/2 parts
      • Abstract must give core message of article!
    • Abstract
      • Technical notes and case reports require . . .
      • An unstructured abstract (200-word maximum)
      • Great majority of these articles -> “hybrids”
      • Hybrid = unstructured abstract & 1 figure/2 parts
      • Abstract must give core message of article!
      • Online -> entire article including all figures
    • Title
      • Describe the Topic that was Studied.
      • Accurate and representative of the study’s content and scope
      • Clear
      • Informative
      • Brief
    • References
      • Catalog Previously Published Information.
      • Choose references directly related to the study.
      • Read the complete referenced article.
      • Avoid 2nd hand or abstract reference sources.
      • Check and then doublecheck the final draft. Hint: Beware the word processor shuffle!
    • Figures
      • “ A Picture is Worth a Thousand Words.”
      • Use figures to clarify your essential point.
      • Label arthroscopic views.
      • Include a self-explanatory legend for *each* figure part.
      • Take care not to mislead.
    • Tables
      • Provide a Concise Summary of Data.
      • Do not repeat material found in the text.
      • Label columns clearly.
      • Group data logically.
      • Check that each table can stand on its own.
        • N, Mean, SD
        • Define all abbreviations, table by table.
    • Reviewer Objectives
      • Faulty Grammar, Syntax, Typos?
      • Yes, a reviewer can mention them.
      • Remedy is the job of the copy editor.
      • Be sure that errors like these do not cause scientific misunderstanding that the copy editor may not know
      • should be corrected.
    • Reviewer Objectives
      • Find the Pearl of Knowledge.
      • Indicate the strengths of the manuscript.
      • Provide constructive comments.
      • Review critically: uncover flaws in thinking.
      • Check for clarity of presentation.
    • Manuscript Assessment
      • Writing a Review
      • Number your comments for the author’s response by referencing . . .
        • Page Numbers
        • Line Numbers
    • Manuscript Assessment
      • Writing a Review
      • Be sure that the author makes statements only once . If it’s in the introduction, it should not be in the discussion. If it’s in a table, it should not be in the text.
      • Sole exception: If it first appears in the abstract, one repetition elsewhere is OK.
    • Manuscript Assessment
      • Designate time to read it *twice*.
      • First – General. Brief. Let it sink in.
      • Second – Comprehensively mark up, then dictate/write/type review (not a linear
      • critique . . . iterative instead).
        • Address the hypothesis first. Then the conclusions.
        • Methods as key – adequate to answer the question?
        • Results – do these data lead to the conclusions?
        • Discussion – are alternative methods considered?
        • Introduction – is the study properly positioned?
        • Abstract – are all key points included?
    • Writing a Review
      • Suggested Order
      • 1. Introduction
      • 2. Methods
      • 3. Results
      • 4. Discussion
      • 5. Conclusion
      6. Abstract 7. Title 8. References 9. Figures 10. Tables
    • Manuscript Assessment
      • Does the Introduction include:
      • Purpose?
      • Hypothesis?
      • Methods
      • Are they reproducible?
      • Do they minimize bias?
      • Do they address the purpose?
      • Is there a rationale for the experimental design: Is the basic science clarified for the lay reader?
    • Manuscript Assessment
      • Results
      • Are they clearly presented and unambiguous?
      • Are they relevant to the study or research problem?
      • Do the tables and figures clarify or confuse?
      • Is there duplication among the text, figures, or tables?
    • Manuscript Assessment
      • Results
      • When results are unbelievably good, they probably are unbelievable!
      • However, the subjective beliefs of a reviewer should not override the objective results of a sound study.
    • Manuscript Assessment
      • Discussion
      • Does it assess the relevant published literature?
      • Does it distinguish author opinion from the conclusions?
      • Does it examine the study’s limitations, including bias?
    • Manuscript Assessment
      • Conclusion
      • Is it based on the data described in the results?
      • Does it address the hypothesis?
      • Does it stray beyond the boundaries of the study?
    • Manuscript Assessment
      • Abstract, Title, References, Figures, Tables
      • Do they follow the guidelines discussed earlier in this online presentation?
    • Manuscript Assessment
      • Establishes a baseline threshold for acceptance.
      • It’s biased and imperfect.
      • Erroneous decisions are inevitable.
      • Reviewer agreement does not assure that the study is accurate or valid.
      • However, the process is indispensable – better than any current alternative.
    • Online Submission & Review
      • http://ees.elsevier.com/arth/
    • Online Submission & Review
      • http://ees.elsevier.com/arth/
      • Journal Website ( www.arthroscopyjournal.org ) home page also has a link to the online system.
      • Many, many benefits with the online system:
      • * Faster turnaround and no lost-in-mail manuscripts
      • * No postage to pay
      • * Manuscript tracking online for corresponding author
      • * Electronic image files instead of photographic prints
    • A Request for New Reviewers
      • http://ees.elsevier.com/arth/
      • Use the online system to register as an Author, being sure to specify personal classifications.
      • Email the Editorial Office that you have registered as an Author, have attended the Journal Review Course, and want to be a Reviewer.
      • The Editorial Office will make you a Reviewer & soon send you an Original Article to review online.
      • Thanks to the online system, it’s just that easy!
    • Additional Statistical Material
      • Confidence Interval
      • Statistical Power
      • Sample Size
      • Data Types
      • Statistical Tests
    • Confidence Interval (CI)
      • Example: 49 women; mean weight, 140 lbs. Standard deviation = 3.5 lbs.
      • Standard error = 3.5/7 =.5
        • (Standard deviation/square root of the sample size)
      • Z-Score 95% = 2 Z-Score 99% = 2.5
      • CI of 95% = 2 x .5 = 140 ± 1 CI of 99% = 2.5 x .5 = 140 ± 1.25
    • Confidence Interval (CI)
      • CI of 95% = 2 x .5 = 140 ± 1
        • 95% confidence that the population’s true mean weight is between 139 lbs. and 141 lbs.
      • CI of 99% = 2.5 x .5 = 140 ± 1.25
        • 99% confidence that the population’s true mean weight is between 138.75 lbs. and 141.25 lbs.
      • Two groups can be significantly different and yet have an overlapping Confidence Interval.
    • Statistical Power
      • The Capacity to Detect a True Difference
      • A Test Has Greater Power When:
      • The sample size is larger.
      • Variability decreases.
      • The effect size is larger.
      • The chance of Type I error is greater . . .
        • Which may lead the investigator to reject the null hypothesis although it is true.
    • Sample Size
      • N = 2 σ 2 (Z 1- α + Z 1- β ) 2 / δ 2
      • N increases as:
      • Variability increases.
      • Type I & Type II errors decrease.
      • The difference that the investigator wants to detect decreases.
    • What are the Data Types?
      • The Type of Scale used to express the Outcome is the Key.
      • Discrete
        • Nominal - Put in boxes (Male vs. Female) 0-0
        • Ordinal - Rank order (Intervals Unequal) ± 0
          • Stages of disease
    • What are the Data Types?
      • The Type of Scale used to express the Outcome is the Key.
      • Continuous
        • Interval - numerical (intervals equal; e.g.,+-+ temperature: 80 ° to 40 ° F., 26.6 ° to 6.1 ° C. ) +/-
        • Ratio - has an absolute zero (e.g., height and +-+ weight) +/- but also x or ÷
    • What Statistical Test To Run?
      • If Interval Data Only
      • Pearson Correlation - ? Linear Correlation
      • Regression - Nature of relationship
    • What Statistical Test To Run?
      • If Nominal Data Only
      • Chi-Square Test – use with samples > 25
      • Fisher’s Exact Test – use with samples ≤ 25
    • What Statistical Test To Run?
      • If Ordinal Data Only
      • Spearman Rank Order Correlation
    • What Statistical Test To Run?
      • If Interval and Nominal Data Are Combined
      • One-Way Analysis of Variance (ANOVA)
        • 1 interval and 1 nominal variable with > 2 groups
      • Two-Way Analysis of Variance (ANOVA)
        • 1 interval and 2 nominal variables
    • What Statistical Test To Run?
      • If Interval and Nominal Data Are Combined
      • t-test: 1 Interval and 1 Nominal Variable with 2 groups
        • If nondirectional: Two-tailed test
        • If directional: One-tailed test
      Tail Tail
    • Thank You