EBCP. Random vs Systemic error Random error: errors in measurement that lead to measured values being inconsistent when repeated measures are taken. Ie:

Slides:



Advertisements
Similar presentations
How to assess an abstract
Advertisements

Studying a Study and Testing a Test: Sensitivity Training, “Don’t Make a Good Test Bad”, and “Analyze This” Borrowed Liberally from Riegelman and Hirsch,
Observational Studies and RCT Libby Brewin. What are the 3 types of observational studies? Cross-sectional studies Case-control Cohort.
Critically Evaluating the Evidence: diagnosis, prognosis, and screening Elizabeth Crabtree, MPH, PhD (c) Director of Evidence-Based Practice, Quality Management.
The Bahrain Branch of the UK Cochrane Centre In Collaboration with Reyada Training & Management Consultancy, Dubai-UAE Cochrane Collaboration and Systematic.
1 Case-Control Study Design Two groups are selected, one of people with the disease (cases), and the other of people with the same general characteristics.
Introduction to Critical Appraisal : Quantitative Research
CRITICAL APPRAISAL Dr. Cristina Ana Stoian Resident Journal Club
Interpreting Basic Statistics
Epidemiology in Medicine Sandra Rodriguez Internal Medicine TTUHSC.
Ghassan Fraij EBM Board Review 6/8/2009. Epidemiologic Measures ► Measures of disease occurrence:  Risk (the likelihood that and individual will contract.
Statistics By Z S Chaudry. Why do I need to know about statistics ? Tested in AKT To understand Journal articles and research papers.
Statistics for Health Care
By Dr. Ahmed Mostafa Assist. Prof. of anesthesia & I.C.U. Evidence-based medicine.
Study Designs By Az and Omar.
The Bahrain Branch of the UK Cochrane Centre In Collaboration with Reyada Training & Management Consultancy, Dubai-UAE Cochrane Collaboration and Systematic.
Are the results valid? Was the validity of the included studies appraised?
Absolute, Relative and Attributable Risks. Outcomes or differences that we are interested in:  Differences in means or proportions  Odds ratio (OR)
 Mean: true average  Median: middle number once ranked  Mode: most repetitive  Range : difference between largest and smallest.
Multiple Choice Questions for discussion
Stats Tutorial. Is My Coin Fair? Assume it is no different from others (null hypothesis) When will you no longer accept this assumption?
EBD for Dental Staff Seminar 2: Core Critical Appraisal Dominic Hurst evidenced.qm.
Copyright © 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 14 Screening and Prevention of Illnesses and Injuries: Research Methods.
DEB BYNUM, MD AUGUST 2010 Evidence Based Medicine: Review of the basics.
Acute Bacterial Rhinosinusitis. Brief Background Typically follows viral infection Dx is by clinical manifestations Streptococcus pneumoniae, Haemophilus.
Research Skills Basic understanding of P values and Confidence limits CHE Level 5 March 2014 Sian Moss.
Study design P.Olliaro Nov04. Study designs: observational vs. experimental studies What happened?  Case-control study What’s happening?  Cross-sectional.
Evidence Based Practice in Psychology – Lecture 3 May 31, 2007.
Simon Thornley Meta-analysis: pooling study results.
How to Analyze Therapy in the Medical Literature (part 2)
Tissue Plasminogen Activator for Acute Ischemic Stroke National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group.
CAT 3 Harm, Causation Maribeth Chitkara, MD Rachel Boykan, MD.
Understanding real research 4. Randomised controlled trials.
Systematic Reviews By Jonathan Tsun & Ilona Blee.
CRITICAL READING ST HELIER VTS 2008 RCGP Curriculum Core Statement Domain 3 AS.
VSM CHAPTER 6: HARM Evidence-Based Medicine How to Practice and Teach EMB.
How to read a paper D. Singh-Ranger. Academic viva 2 papers 1 hour to read both Viva on both papers Summary-what is the paper about.
Stats Facts Mark Halloran. Diagnostic Stats Disease present Disease absent TOTALS Test positive aba+b Test negative cdc+d TOTALSa+cb+da+b+c+d.
Statistics for the board September 14 th 2007 Jean-Sebastien Rachoin MD.
Making epidemiological evidence more accessible using pictures Rod Jackson Updated November 09.
Critical Appraisal Dr. Chris Hall – Facilitator Dr. Dave Dyck R3 March 20/2003.
Study designs. Kate O’Donnell General Practice & Primary Care.
Objectives  Identify the key elements of a good randomised controlled study  To clarify the process of meta analysis and developing a systematic review.
Organization of statistical research. The role of Biostatisticians Biostatisticians play essential roles in designing studies, analyzing data and.
Compliance Original Study Design Randomised Surgical care Medical care.
EVALUATING u After retrieving the literature, you have to evaluate or critically appraise the evidence for its validity and applicability to your patient.
BIOSTATISTICS Lecture 2. The role of Biostatisticians Biostatisticians play essential roles in designing studies, analyzing data and creating methods.
PTP 560 Research Methods Week 12 Thomas Ruediger, PT.
EVIDENCE BASED PRACTICE 4 Summarising the findings.
Biostatistics Board Review Parul Chaudhri, DO Family Medicine Faculty Development Fellow, UPMC St Margaret March 5, 2016.
What are the Chances Dr? Nick Pendleton. Can I have a Prostate Check? ?
2 3 انواع مطالعات توصيفي (Descriptive) تحليلي (Analytic) مداخله اي (Interventional) مشاهده اي ( Observational ) كارآزمايي باليني كارآزمايي اجتماعي كارآزمايي.
Objectives (Chapter 20) Comparing two proportions  Comparing 2 independent samples  Confidence interval for 2 proportion  Large sample method  Plus.
Measures of disease frequency Simon Thornley. Measures of Effect and Disease Frequency Aims – To define and describe the uses of common epidemiological.
GP ST2 Group, 28/9/11 Tom Gamble
EBM R1張舜凱.
HelpDesk Answers Synthesizing the Evidence
An Idiots Guide to Statistics Curriculum 3.6
Statistical Core Didactic
How to read a paper D. Singh-Ranger.
Confidence Intervals and p-values
Interventional trials
مقدمه‌ای بر طب مبتنی بر شواهد
Aiying Chen, Scott Patterson, Fabrice Bailleux and Ehab Bassily
کارگاه تکميلی کشوری تربيت مربی آموزش طب مبتنی بر شواهد
Interpreting Basic Statistics
ASPIRE CLASS 6: Interpreting Results and Writing an Abstract
EAST GRADE course 2019 Introduction to Meta-Analysis
How to assess an abstract
PICO model for developing EBM questions
Presentation transcript:

EBCP

Random vs Systemic error Random error: errors in measurement that lead to measured values being inconsistent when repeated measures are taken. Ie: innacurate Systematic erros: predictable errors that happen all the time. Eg: forgeting to zero a scale. Ie: low accuracy

Bias: systemic error due to flawed methodology Study processSources of biasSolution Allocation of subjects to intervention and control groups Selection bias: systematic differences in comparison groups Randomise Implementation of study interventions Performance bias: systematic differences in careprovided apart from the intervention being studied or differences in the placebo effect. Blind subjects Follow up of participantsAttrition bias: systematic differences in withdrawals from the trial. Intention to treat analysis Evaluation of outcomesDetection bias: systematic differences in outcome Assessment Double blind (blind outcome assesors)

Types 1 vs 2 error Type 1 error: False +ve, generally due to bias Type 2 error: False –ve, insufficient statistical power (ie: CI is too wide because the sample size is too small) or bias

Confidence intervals Clinical significance Statistical significance

Causation 1.Exposure must precede outcome 2.Dose dependant gradient 3.Dechallence-rechallege- take away the exposure and the outcome decreases/disappears, then reappears when the exposure is returned Also: Is the association consistent with other studies and does it make biological sense?

Measuring Outcomes Relative risk (RR): the probability of an event in the active treatment group divided by the probability of an event in the control group. RR = Y/X. A relative risk of 1 is the null value or no difference. Absolute risk reduction (ARR) : The risk in the control group minus that in the invervention group: ARR = X-Y. Relative risk reduction (RRR)= 1-RR

Measuring Outcomes Odds ratios: used for case control trials as risk of developing the disease has no meaning since they already have it or don’t. Odds ratio = odds of exposure in the cases/odds of exposure in the controls OR= a/c ÷ b/d

Measuring Outcomes Number needed to treat (NNT): the number of patients you need to treat to prevent one additional bad outcome. The number needed to treat is the reciprocal of the absolute risk reduction (NNT= 1/ARR). Number needed to harm (NNH): the number of people who need to be subjected to the exposure for one person to develop a negative outcome (NNH= 1/ARR in a study measuring harm)

Diagnostic Tests A Sensitve test helps rule out a diagnosis: SeNsitive Negative rule OUT: SNOUT A Specific test helps confirm the diagnosis: SPecific Positive rule IN: SPIN Sensitivity: probability of true positives Specificity: probability of true negatives

Diagnostic Tests Pre test probability: The chance your pt has the diagnosis. Basically the incidence in similar people presenting with the same symptoms. Likelihood Ratios (+/-ve) : how much a positive or negative result modifies the probability of the disease. – Ratio of 1 doesn’t change the probability – Ratios greater than one increase the probability – Ratios less than one decrease the probability LR+ = sensitivity/(1-specificity) OR true+ve rate /false+ve rate LR- = (1-sensitivity)/specificity OR false-ve rate /true-ve rate

Nomograms

Prognostic Studies Usually done via observational studies like Case-control or more commonly Cohort studies. The cohort should all be at a similar point in the course of the disease. Results can be shown as a “x” year survival rate or survival curve.

Systematic Reviews Sometimes the method of selecting articles for the systematic review is biased. If the selection process is unbiased the funnel plot should look like an inverted funnel.

Systematic Reviews Forest plots: Combine the results of the studies into one graph.

Systematic Reviews Forest Plots: – Heterogeneity: Assess if any of the studies are significantly different from the others. If heterogeneity is too high then the results of the studies are too different to pool together in a meta-analysis.