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Research Methods Test 2

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Narrative Review   -Selective review of the literature that broadly covers a specific topic -Does not follow strict systematic methods to locate and synthesize articles  
Systematic Review   -Utilizes exacting search strategies to make certain that the maximum extent of relevant research has been considered -Original articles are methodologically appraised and synthesized  
Meta-analysis   -Quantitatively combines the results of studies that are the result of a systematic literature review -Capable of performing a statistical analysis of the pooled results of relevant studies  
Systematic reviews   -Overview of primary studies which used explicit and reproducible results -Can be performed on group or single subject studies -Can include both quantitative and qualitative analyses  
Advantages of systematic reviews   -Increased sample sizes -Can control for between-study variation  
Disadvantages of systematic reviews   • Not a primary study -Limited by validity of individual studies -Subject to bias  
Meta-analysis   Statistical analysis of results of several similar studies (aka quantitative synthesis) -Type of quantitative systematic review, or included as part of systematic review  
Hierarchy of Evidence   1 Systematic review 2 Random Controlled Trials 3 Cohort 4 Case-control series 5 Case series 6 Case reports 7 Editorial and opinions 8 Animal research and lab studies  
Stages and phases involved in a systematic review   -Planning the review (ID of the need for a review, preparation of a proposal for review, and development of a review protocol) -Conducting the review (ID of research, selection of studies, study quality assessment, data extraction and synthesis)  
Forest plots   -Compares different studies -Statistically different when 95% confidence intervals cross effect line  
Narrative reviews advantages   -Present a general overview covering a specific topic that provides primary information or an update, or both -Fairly easy for novice authors to prepare  
Narrative reviews disadvantages   -May not provide the best available answers -Findings are less reliable  
Systematic reviews advantages   -Present a comprehensive review of the literature based on all available research with regard to a focused research question -Provide an estimate of the "true" answer to the research question  
Systematic reviews disadvantages   -Specialized expertise of reviewers is required -Involve a formal research protocol -Findings are only relevant to a single question  
Publication bias   -File Drawer Problem = only positive data presented, the rest are put in the file drawer -In situ = some parts of the studies published  
Study homogeneity   Similarity between studies, increases their ability to be compared  
Study heterogeneity   Differences between studies -Hinders comparison of subjects -Study design -Observed treatment effects more dissimilar than due to chance -Statistical test can estimate and account for this -Treatments that work across, elevate confidence  
Study heterogeneity   -Subgroup analysis (older v younger)= may be more valid, reduce stat power -Meta regression analysis = analysis of hetero. between subgroups -Sensitivity analysis = considers variation between factors other than subjects -Cumulative meta-analysis  
IMRaD   Introduction (What was asked) Methods (How was it studied) Results (What was found) and Discussion  
Introduction   Objective = the exact questions asked  
Methods   Design Setting Patients Interventions Main outcome  
Results   Key findings  
Discussion   Conclusion = key conclusions including direct clinical application  
Incidence   # of new cases in time period/population x 100,000  
Risk   estimate of proportion of unaffected person who will develop the disease of interest over a specified period of time  
Odds Ratio (OR)   odds of developing disease in exposed group / odds of developing disease in unexposed group  
Prevalence   # of existing cases in a time period / population x 100,000  
Point prevalence   proportion of population with disease at a given time (can miss episodic conditions)  
Period prevalence   proportion of population that has disease within a defined period of time  
Causation in epidemiology   3 key criteria: Temporality (temporal precedence) Consistency Dose-response  
Temporality   A causes B or B causes A or X causes A + B A not related to B; occurrence is a mere chance  
Consistency   Reproduction of study results in different populations  
Dose response   Greater exposure to risk factor leads to greater effect on health  
Bradford Hill's Criteria of Causation   Strength of Association Consistency Specificity in the case Temporality Dose-response relationship = increase dose -> increase occurrence Plausibility Coherence Experimental evidence Analogy  
Cohort studies   Longitudinal Prospective = know patient's exposure, observing ahead for disease Retrospective = know patient's exposure, looking back in time for disease  
Case-control study   Similar to retrospective cohort study Disease or condition is known, looking back in time for risk factors 2 Types: Prevalent case (includes all persons) and cumulative incidense (only new cases)  
For case-control studies you must calculate ________ and not __________   Odds Ratio (OR), risk  
OR formula   ad/bc a=exposed cases b=exposed controls c=nonexposed cases d=nonexposed controls  
Case-control studies advantages   -Good for investigating rare diseases -Can be performed quickly and inexpensively -Useful for studing disease with long latency periods -Facilitate study of multiple potential cases at once -Existing records can often be used  
Case-control studies disadvantages   -Typically rely on patients recall of past exposure -Do not permit calulation of true disease rates in the population -Difficult to validate information on exposure -Other variables that may be associated with disease are not controlled  
Recall bias   systematic differences between cases and controls in ability to recall past exposures  
Berkson's bias   (Admission rate bias) type of selection bias where hospitalized cases are different than hospitalized controls  
Stratified analysis   Cnsiders confounding variable (e.g. alcohol consumption and lung cancer) -Looks at effect each independent variable has on outcome separately  
Cohort studies   -Follow a disease free group of subjects forward in time -Some subjects are exposed to a risk factor and some are not -Purpose is to see if there is a greater proportion of disease among those who are exposed to the risk factor  
Discreet vs Continuous variables   Discreet--> smokers vs. nonsmokers Continuous--> cholesterol levels  
Inception cohort study   Tracking cohort with early stage of chronic condition  
Cohort studies   -Less subject to bias than case-control studies bc exposure levels evaluated before disease develops -Best design to determine risk level -Better for studying relatively common diseases -Most expensive type of epidemiological study, but cheaper than RCTs  
When an outcome in a research study is common (occurs in more than 10% of the unexposed group)...   The odds ratio will tend to OVERESTIMATE the risk ratio  
Relative Risk (RR) in cohort studies   RR = (a/(a+b)) // (c/(c+d))..........................RR > 1 = increase in incidence of disease in exposed group...................RR < 1 = protective effect of exposure (can be used to calculate placebo effect)  
Attributable risk (AR) in cohort studies AND Absolute risk reduction (ARR) in cohort studies   AR or ARR= a/(a+b) - c/(c+d)  
Relative risk reduction (RRR) in cohort studies   Comparative reduction in rates of bad outcomes between experimental and control groups............RRR = ARR // c/(c+d)  
Number needed to treat (NNT) in cohort studies   # of patients would would need to be treated in order to prevent one additional bad outcome......NNT = 1/ARR........related term = NNH (number needed to harm  
Cohort studies advantages   -Portray the natural history of disease -Don not rely on patient recall -Better for establishing a cause and effect relationship than case-control studies -Less vulnerability to bias or chance -Permit calculation of true disease rates in the population  
Cohort studies disadvantages   -Typically very expensive -Many people must be followed to obtain enough with the disease -Very time-consuming -Subjects frequently drop out of study over time -Difficult to generate a control group to study very common conditions  
Case studies   Usually retrospective, can be prospective, low validity but high clinical relevance, similar to IMRaD format but with case description rather than M & R (storied case report and evidence based case report)  
4 types of case studies   1) Unique case 2) Unexpected association 3) Unexpected development 4) Unusual presentation  
Is a case study the same thing as a case report?   NO, a case study is more in depth  
Purposes of case reports   -Detect rare conditions -Educational value -Learn how other doctors manage certain cases  
Limitations of case reports   -Susceptible to many biases -Unable to test hypotheses -Does not determine the effectiveness of an intervention - Unable to generalize results to other patients or practices  
Case series   Are especially prone to: Selection, observation, and publication bias (can be subjected to meta analysis)  
SSTSDs   Single subject time series design (aka N=1 design)............AB, ABA (withdrawal design), ABAB variants, ABAC (comparing 2 treatments  
Suitable candidates for SSTSDs   -Condition is chronic -Condition is stable -Spontaneous remission is not likely -Previous treatment has had limited success -No concurrent treatment is involved  
Trendline examples   Stable downward and accelerating downward trend  
Multiple baseline design   Variant: Simultaneous replication design........-Patients begin studies at same time -Treatment administered sequentially to patients only after clear treatment effect is observed for prior patient  
Descriptive statistics   Usually retrospective, can be prospective, low validity but high clinical relevance  
4 types of descriptive statistics   1) Unique case 2) Unexpected association 3) Unexpected development 4) Unusual presentation  
Frequency distribution   Usually in a histogram  
Measure of central tendency in descriptive statistics   Mean of a sample or mean of a population  
Levels of measurement   Nominal, ordinal, interval, ratio  
Nominal   Counting, central tendency=MODE, example=NUMBERS  
Ordinal   Greater or less than operations, central tendency=MEDIAN, example=MILITARY RANK  
Interval   Addition or subtraction, central tendency=(symmetrical) MEAN / (skewed) MEDIAN, example=Fahrenheit  
Ratio   Addition, subtraction, multiplication, and division, central tendency=(symmetrical) MEAN / (skewed) MEDIAN, example=Kelvin, R.O.M.  
Normal distribution   Symmetrical, unimodal histogram where the Mean=Median=Mode (they are in the same position, in the center of the Bell curve)  
Modal Division   Unimodal = 1 peak, Bimodal = 2 peaks, Multimodal = < 2 peaks  
Standard error of the mean   SE(n) = S/(square root of n)  
1 standard deviation from the mean   68.3% confidence interval  
2 standard deviations from the mean   95.5% confidence interval  
3 standard deviations from the mean   99.7% confidence interval  
The wider the bars on the histogram are distributed...   the higher the standard deviation (larger spread)  
The narrower the bars on the histogram are distributed...   the lower the standard deviation (smaller spread)  
Skewed distributions   -Positive skews have tails extending to the right, where negative skews have tails to the left -Mean is drawn towards the tail -Mode is at the peak -Median is between  
Know how to calculate z-score   Z-score is the percentage to the left of the point in question, to find the other side, subject that percentage from 100%  
True H(o) ---> Reject H(o)   Type 1 error, the odds of saying the hypothesis is true when it is actually false  
False H(o) ---> Reject H(o)   CORRECT DECISION, the odds of saying the hypothesis is false when it actually is false  
True H(o) ---> Fail to reject H(o)   CORRECT DECISION, the odds of saying the hypothesis is true when it cannot be proven to be false  
False H(o) ---> Fail to reject H(o)   Type 2 error, the odds of saying the hypothesis is false, when it cannot be proven to be false  


   




 
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Created by: reeseochoa on 2010-11-18



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