Quantitative Health Research Designs
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Quantitative Health Research Designs

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  • Some were given cider, others seawater, others a mixture of garlic, mustard and horseradish. Another group of two were given spoonfuls of vinegar, and the last two oranges and lemons

Quantitative Health Research Designs Quantitative Health Research Designs Presentation Transcript

  • Quantitative Research Designs Dono Widiatmoko
  • Study Types Case Reports / Case Series Survey / Cross Sectional Studies Descriptive Ecological Studies Case Control Studies Observational Cohort Studies Analytic Interventional/ Experimental Uncontrolled Trials Controlled Trials
  • Study Types Case Reports / Case Series Survey / Cross Sectional Studies Descriptive Ecological Studies Case Control Studies Observational Cohort Studies Analytic Interventional/ Experimental Uncontrolled Trials Controlled Trials
  • Descriptive Studies  The variables of interests are only observed in their original state and not manipulated; no deliberate intervention with their natural state  Descriptive studies can be used to generate hypotheses
  • Study Types Case Reports / Case Series Survey / Cross Sectional Studies Descriptive Ecological Studies Case Control Studies Observational Cohort Studies Analytic Interventional/ Experimental Uncontrolled Trials Controlled Trials
  • Case Reports / Case Series  Case reports Where the case of an individual(s) are recorded, elaborated and reported  Case series Where there are multiple reports, which were serially reported to provide a continuation of reports Case Report Case Report Case Report Case Series
  • Case Reports / Case Series  Uses:  To explore the context, characteristics of cases with condition of interest, often to determine whether they share any common features  Focus on circumstances, dynamics, and complexity of a single case  Useful to generate interest, and generate hypotheses  Quick and economical
  • Case Reports / Case Series  Issues  Extremes of cases, not particularly generalisable  Data generated mainly qualitative
  • Study Types Case Reports / Case Series Survey / Cross Sectional Studies Descriptive Ecological Studies Case Control Studies Observational Cohort Studies Analytic Interventional/ Experimental Uncontrolled Trials Controlled Trials
  • Cross Sectional Studies / Surveys  Data are collected on each study participant at a single point in time  Collect information on the frequency and distribution of a particular variable (s) in a defined population  It may be used to investigate possible association between exposure to risk factors and the outcome of interest  Normally, a selection of samples are targeted for data collection
  • Survey / Cross Sectional Studies  Data collection method:  Clinical Examination, Direct Observation  Interviews and Questionnaires (self filed/postal/internet)  Clinical records and other sources
  • Survey / Cross Sectional Studies  “Quick and easy”  Determine the occurrence of observed values of the variables in a defined population  These data can then described, analysed and association between variables can be established
  • Survey / Cross Sectional Studies  Issues:  in selecting study sample  in recruiting subjects (selection issues)  Someone will be more likely to agree joining a study if it is on their interest  Non responses, systematic bias if those responding have different characteristics from those who don’t.  when asking questions about past events (recall bias)  someone will be more likely to remember something if they aware that there may be link to a potential risk factor
  • Survey / Cross Sectional Studies  data on outcome and risk factors/determinants are collected simultaneously  capture prevalent rather than incident observations/cases
  • Study Types Case Reports / Case Series Survey / Cross Sectional Studies Descriptive Ecological Studies Case Control Studies Observational Cohort Studies Analytic Interventional/ Experimental Uncontrolled Trials Controlled Trials
  • Ecological Ecological Studies study  Unit of Analysis:  Population groups  National data, ethnic groups, administrative boundaries, etc  Data can be collected:  cross sectionally data collected at a single point of time  Longitudinally data collected over time to look for trends or changes.
  • Ecological Studies  To investigate differences between populations To find out whether some groups of people have a higher prevalence of disease than others  To study group specific effects i.e. Interventions aimed at group level  Convenience and availability of group level data Sometimes data are only available at the group level  Quick and cheap, as routine data are normally available
  • Ecological Studies  Group specific effects  exposures that act over the entire group and do not necessarily have a direct effect on the individual members of the group  Time trend  comparisons can be made over time within the same populations to show how incidence of disease changes over time and to identify patterns of change
  • Ecological Studies  Data are commonly in continuous variables (e.g. Prevalence of diseases, mortality rate, etc)  Analyses using scatterplot, correlation, or regression
  • Ecological Studies Income level and life expectancy
  • Ecological Studies Income level and life expectancy
  • Ecological Studies  Limitations:  Unable to measure information on other important risk factors that may also be associated with the disease (as data are collected already and were collected for other purposes)  Data on exposure and outcome may be collected in different ways with different definitions may bias the result  Geographical comparisons may suffer from migration of populations between groups over the period of study  Ecological studies do not enable us to answer questions about individual risks Ecological Fallacy: Association observed between variables on an aggregate level does not necessarily represent the association at an individual level
  • Relationship between variables  Association vs Causation  Statistical association does not necessarily imply causation.  Does causality relationship always show statistical association?
  • Relationships between variables Variable A Exposure to smoke Variable B Lung Cancer
  • Relationships between variables Independent Variable Exposure to smoke Dependent Variable Lung Cancer
  • Hypotheses  A statement which predicts whether there is a a relationship between variables  Directional (one-tailed)  Non directional (two-tailed)
  • There are two types of hypotheses: Null hypothesis : Ho  A statement which predicts that there is NO relationship between variables  ……that sample observations result purely from chance. Alternative hypothesis: H1 or HA  A statement which predicts a relationship between variables  ……. that sample observations are influenced by some non-random cause. This implies that our sample observations are linked with another variable.
  • Examples of Hypothesis  HA : The mean IQ scores of primary school girls is different with the boys (two tailed)  H0 : The mean IQ scores of primary school girls is NO different with the boys  HA : The mean IQ scores of primary school girls is higher than the boys (one tailed)  H0 : The mean IQ scores of primary school girls is NOT higher than the boys (one tailed)
  • ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Non attendanc e Birth year Gender 1 70 Male 0 80 Female 3 83 Female 2 75 Woman 1 1973 Female 1 Eighty two Male 0 80 Male 1 77 Female 3 79 Female 0 67 Male 2 75 Woman 2 72 Male 1 65 Male 0 1980 Female 2 77 Male 3 72 Male 4 73 Female 0 77 Woman 1 81 Female 2 74 Male 2 78 Male Prerevision Final Exam SATS score Score Score Work Status 66 68 65 Working 80 82 74 Yes 54 62 60 Part Time 38 43 50 Looking f work 45 55 60 Yes 65 62 62 No 55 70 73 Working 65 72 68 Part Time 48 55 58 No 62 68 72 Yes 39 45 55 Yes 48 59 58 FT student 67 70 66 Working 55 65 68 No 51 50 55 Part Time 50 54 57 Part Time 45 52 55 Casual Work 56 68 72 No 66 68 70 Yes 55 58 60 Yes 45 55 57 No Status Home Overseas Overseas Home Home Home Home Home Overseas Home British Overseas Overseas Home Overseas British English Home British Overseas Home Health related degree Y Yes No No Yes Yes Nursing Pharmacy N No Yes Yes No Medical No Yes Yes No No Yes Yes
  • Please work in groups to formulate some hypotheses that we can test from the above data. HA: …………………………………. H0: …………………………………. HA: …………………………………. H0: …………………………………. HA: …………………………………. H0: …………………………………. HA: …………………………………. H0: ………………………………….
  • State the hypotheses H0 HA Tails y y 2 y y 1 y y 1 Statement about how the population mean, , is related to a specific value, say, y
  • Decision test  Normally, a statistical test then performed to test the null hypothesis  Either reject the null hypothesis (hence the accepting the alternative hypothesis) or not rejecting the null hypothesis.  The choice of statistical test will depend on the types and nature of variables to be tested
  • Causal Criterias: Bradford Hill Criterias • Plausibility – biological plausibility should be confirmed / expected • Temporality – Cause/exposure should precede the effect! – Contacts to HIV sufferers should precede the diagnosis of HIV in a person • Biological gradients (dose-response) – The more contacts made, more intense the exposure  more response • Consistency – The same relationship can be observed in another populations
  • • Coherence – That a similar observation can be made in similar relationships • Specificity – A cause leads to a single effect, not multiple effect • Strength – A strong association may indicate causality • Analogy – Analogous situations may support hypothesis • Experimental evidence
  • Study Types Case Reports / Case Series Survey / Cross Sectional Studies Descriptive Ecological Studies Case Control Studies Observational Cohort Studies Analytic Interventional/ Experimental Uncontrolled Trials Controlled Trials
  • Case Control Studies  Aim to assess whether exposure to risk factor(s) in people who have a disease is comparable to people who do not have it  Allows examination of inter-relationships and indication of possible causation  Assemble number of cases, who have disease  …and controls, who do not have disease but are similar in other respects
  • Case Control Studies  Generally easier and quicker to carry out than cohort studies  Case-control studies are retrospective (Looking back in time)
  • Case Control Studies Exposed CASES (disease/outcome present) Not Exposed Exposed CONTROLS (disease/outcome not present) Not Exposed Look back in time
  • Case Control Studies Example  Suppose that you wish to examine whether eating fresh fruit and vegetables protects against colorectal cancer  Study investigates a protective effect, rather than a causal one  The basic principles are nevertheless the same  Firstly, a number of patients who had developed colorectal cancer would be selected, as well as a group of subjects who did not have colorectal cancer, but were similar in other respects
  • Case Control Studies  Both groups would be investigated, to discover whether their diets had included regular amounts of fresh fruit and vegetables, and for how long  If an association was found between cases and controls in the proportion eating fruit and vegetables, it may be possible to establish that fresh fruit and vegetables have a protective effect against colorectal cancer
  • Case Control Studies  Agree explicit definition of what a ‘case’ is. Decide criteria for case definition  Controls should be comparable to cases (...in every respect, other than having the disease). Number depends on ease of getting controls, sample size requirements and matching needs  Sometimes desirable to use more than one control group (e.g. different exposures in different hospitals)
  • Case Control Studies Matching cases to controls  Helps minimise bias & confounding  large differences in structure between cases/controls can affect accuracy of any comparisons made  Match on age, sex & other factors  If age & sex are matched, sometimes possible to match other factors at analysis stage  Don’t match on too many factors  may artificially alter characteristics of subjects
  • Case Control Studies Data collection & analysis  Sources of data:  Medical records  Surveys on subjects or relatives (usually by structured interview)  or both  Beware biases (e.g. recall bias, more detailed medical records on cases)
  • Case Control Studies  In case-control studies, we retrospectively find people who have already developed a disease and find controls who do not have the disease, but are otherwise similar  This means that we don’t know how many people were exposed to a risk factor for the disease but did not develop it  So cannot assume our sample is representative of the true population  In these circumstances, ODDS RATIO (or OR) is used
  • Case Control Studies  Odds Ratio  Odds of exposure in case divided by odds of exposure in controls Cases Controls Total Exposed a b a+b Not exposed c d c+d a+c b+d a+b+c+d Total a OR b c d a d c b a d b c .......... ....
  • Case Control Studies Interpreting Odds Ratio If OR=1 no association between the exposure and disease If OR>1 there is a positive association between exposure and disease there is a negative association between exposure and disease. If OR<1
  • Case Control Studies Advantages  Quicker & cheaper than cohort studies  Suitable for diseases with long latent periods  Suitable for rare diseases  Allow investigation of more than one risk factor
  • Case Control Studies Disadvantages  Prone to selection and recall bias  Care needed interpreting results – an OR of ≠1 does not necessarily indicate causality  Hard to find temporal relationship between exposure/disease  Subjects do not usually represent population as whole - so incidence rates cannot be established  Cannot examine relationship between one possible cause and several diseases
  • Case Reports / Case Series Survey / Cross Sectional Studies Descriptive Ecological Studies Case Control Studies Observational Cohort Studies Analytic Interventional/ Experimental Uncontrolled Trials Controlled Trials
  • Cohort Studies Cohort studies are also called: prospective studies or longitudinal studies
  • Cohort Studies  Usually carried out prospectively  …and over a longer period  Note: Retrospective cohort studies are possible
  • Cohort Studies Information collected  Important to collect similar data for both groups - like-for-like comparisons  Exposure data is analysed to see how many exposed/non-exposed subjects developed disease  Association/evidence may indicate causality
  • Cohort Studies Prospective Cohort Study Start Point End Point Disease/outcome Exposed No Disease/outcome Study Cohort Disease/outcome Not Exposed No Disease/outcome Follow the cohort over time
  • Cohort Studies Retrospective Cohort Study End point Start Point Disease/outcome Exposed No Disease/outcome Study Cohort Disease/outcome Not Exposed No Disease/outcome Follow the cohort over time
  • Cohort Studies Prospective vs Retrospective  Both kinds of cohort study start with exposure, and end with outcome (disease)  Prospective looks forward in time  Retrospective looks back in time  Retrospective design looks similar to case-control study BUT remember:  Cohort studies always begin with exposure  Case-control studies always begin with outcome
  • Cohort Studies Subjects and Data Collection  Choose subjects with care  Those in both groups should be similar in every respect - other than exposure  Ensure no population groups are systematically missed  Consider appropriate data-collection method  Before beginning study - consider what data on primary outcomes and other factors should be collected
  • Cohort Studies Follow up and Data Analysis  Agree how often follow-up needed  Beware losses to follow-up - have strategy for tracing subjects  Agree how information will be handed over if investigators leave
  • Cohort Studies Analysis of Cohort Studies  Use Risk Ratio or Relative Risk also:  Attributable Risk  Attributable Risk Percent  Population Attributable Risk  Population Attributable Risk %
  • Cohort Studies Relative Risk (RR)  RR: how many more times a disease occurs in a group of people who have been exposed to a risk factor, compared to a group who have not been exposed Calculated as follows: = PROPORTION OF DISEASE IN EXPOSED GROUP PROPORTION OF DISEASE IN NON-EXPOSED GROUP
  • Cohort Studies Disease Present? Exposure to risk factor Yes No Total Yes a b a+b No c d c+d Total a+c b+d a+b+c+d RR = PROPORTION OF DISEASE IN EXPOSED GROUP PROPORTION OF DISEASE IN NON-EXPOSED GROUP = a / (a + b) c / (c + d)
  • Cohort Studies Interpreting RR  If RR=1 no association between the risk factor and disease  If RR>1 increased risk of developing disease, if exposed to risk factor (e.g. disease=lung cancer; risk factor=smoking) It suggests that exposure to risk factor may cause the disease  If RR<1 decreased risk of developing disease, if exposed to risk factor. (e.g. disease=colon cancer; risk factor=eating fresh fruit & veg.)
  • Cohort Studies Advantage of Cohort Studies  Allow measurement of disease incidence, in both exposed/non-exposed groups  Useful if exposure is rare  Allow assessment of temporal relationship  Can examine multiple effects of exposure  Can eliminate some sources of bias
  • Cohort Studies Disadvantage of Cohort Studies  Need to identify carefully what data to collect at outset  Not so useful if disease is rare  Can be very time-consuming  Can be very expensive  Careful follow-up of subjects vital
  • Case Reports / Case Series Survey / Cross Sectional Studies Descriptive Ecological Studies Case Control Studies Observational Cohort Studies Analytic Interventional/ Experimental Uncontrolled Trials Controlled Trials
  • Intervention/Experimental studies
  • “On the 20th of May 1747, I took twelve patients in the scurvy, on board the Salisbury at sea. Their cases were as similar as I could have them. They all in general had putrid gums, the spots and lassitude, with weakness of their knees. They lay together in one place, being a proper apartment for the sick in the fore-hold; and had one diet common to all. … Two of these were ordered each a quart of cider a day. Two others took twenty-five guts of elixir vitriol three times a day, … and so on. They continued but six days under this course. … The consequence was that the most sudden and visible good effects were perceived from the use of oranges and lemons; one of those who had taken them, being at the end of six days fit for duty.” —James Lind, 1747
  • Intervention/Experimental studies Evaluating Interventions Comparing new interventions with control E.g.:  New drugs and new treatment of diseases  New medical and health care technology  New ways of organizing and delivering health services  New community health programs  New behavioral intervention programs
  • Randomised Controlled Trials • Examine the effectiveness of an intervention • Also called experimental studies • Groups of subjects are formed, • Each receives a different sort of intervention or treatment • RCTs usually carried out to compare effectiveness of a specific treatment against one or more others • May also be used for preventional (prophylactic) interventions
  • Randomised Controlled Trials • Two groups of subjects studied – those who receive the treatment of interest and those who do not • Groups are called treatment arms • Very often, a new treatment will be compared with an old one, or even with a non-treatment called ‘placebo’ • A placebo is effectively a dummy treatment, which appears to be real
  • Randomised Controlled Trials
  • Randomised Controlled Trials Comparing interventions with control groups:  Intervention vs No Therapy  Intervention vs Placebo  Intervention vs Therapy B Types of comparison/control  Historical controls  Simultaneous controls  Simultaneous non-randomized controls  Simultaneous randomized controls
  • Randomised Controlled Trials Randomisation Subjects randomised to a particular treatment arm “Randomization is the process by which allocation of subjects to treatment groups is done by chance, without the ability to predict who is in what group” Methods: • Random number tables • Sealed envelopes containing details of the treatment category • Can use use stratified allocation, if: • Small numbers of patients • Many prognostic factors - involves randomising separately for each prognostic factor
  • Randomised Controlled Trials • Randomisation aims to remove bias from patients’ individual characteristics • So … more likely that only treatment effect will influence the results • Also helps to reduce allocation bias in the selection of subjects (e.g. prevent a clinician from selecting only healthier patients to receive a new treatment) • Randomised trials are gold standard of study designs because the potential for bias (selection into treatment groups) is avoided
  • Randomised Controlled Trials Blinding • Patients do not know which treatment they are receiving • Double blind RCT - neither patients nor medical staff knows which treatment has been allocated. • Blinding reduces likelihood of patients’ outcomes being influenced by their expectation that a new treatment is better or worse • Excludes possibility of medical staff consciously/ unconsciously managing patients differently, if they know which treatment given
  • Randomised Controlled Trials Blinding  Single blind patient doesn’t know  Double blind neither patient nor investigator knows  Triple blind none of the patients, investigators or analysts know  If blinding not possible use objective measurements or independent assessors
  • Randomised Controlled Trials Crossover trials / Matching • Subjects receive one intervention for a period, then change over to another • Can be useful when treatment is subjective rather than curative (e.g. short-term symptom relief) • Also possible to match pairs of patients on certain characteristics (e.g. age, sex, tumour staging, disease on left or right hand side)
  • Randomised Controlled Trials Before commencing an RCT: • Agree explicit eligibility criteria (based on characteristics of the disease and subjects to be studied) • Decide what will constitute the end of the trial (e.g. changes in subjects’ condition, death or other physical status) • Draw up a strict and detailed protocol, describing the exact features of the trial, outcomes to be recorded, the treatments and controls to be used, how these will be used, and what records will be kept • When trial starts, follow-up subjects in both treatment & control groups until end-point is reached
  • Randomised Controlled Trials Ethical issues • Consider in planning stage • Subjects should not be exposed to known or potential hazards • May be unacceptable to withhold treatment from subjects in control arm • Definitely need approval from Local Research Ethics Committee (LREC) before commencing • Potential subjects should be told about desire to enter them into trial, and given full details • Subjects should be informed and their written consent obtained, before being randomised into treatment groups
  • Randomised Controlled Trials Stopping a trial • If one treatment proves to be significantly superior before the agreed endpoint is reached, the trial is sometimes stopped • But... decision of whether to stop is complex and best taken by experts.
  • Randomised Controlled Trials Intention to Treat analysis • Once allocated to a treatment arm, patients should be analysed as part of that arm – regardless of whether they comply with treatment or leave trial • If subjects who refuse to accept experimental treatment are given (and analysed on) conventional alternative, this will result in bias, and reduce the power of the trial’s results • So ... data is analysed according to how subjects were originally intended to be treated
  • Randomised Controlled Trials Data Collection • Collected at agreed points throughout the trial • Advisable to check patient compliance with any treatments given, including placebos • Information needed on many factors, including any side effects of treatment
  • Randomised Controlled Trials Advantages of RCTs • Allows potential effectiveness of new treatment to be evaluated • Provides strong evidence of cause & effect • Less prone to confounding - know who is & is not exposed
  • Randomised Controlled Trials Disadvantages of RCTs • • • • • • Expensive to carry out Need large sample size Careful attention to ethical issues Must have informed patient consent Patients may refuse experimental treatment Patient non-compliance can affect results
  • Study Validity External Validity  The extent to which the results of an investigation can be generalised to other samples or situations (Polgar, 2008)  Population validity – ecological validity
  • Study Validity Internal Validity  The extent to which the design and characteristics of the study is robustly designed and executed (The degree to which studies meet basic logical criteria for absence of bias and confounders)  So that the evidence found by the research can be confidently accepted
  • Error vs Bias Are they different?
  • Error  Error: Deviation/divergence of a measurement from the true value  Types of error:  Random (e.g. problems with small sample)  Systematic
  • Types of error • Type I: When our study suggest association when in fact there is none • Type II: When our study suggest no association when in fact there is one
  • Types of error • Type I: When a study suggest association when in fact there is none • Type II: When a study suggest no association when in fact there is one • Which type or error would you want to avoid?
  • Types of Error Test Results Truth No Association Association No Association Association Correct Type-I error (probability=α) Correct Type-II error (probability=β)
  • Bias  A systematic error that results in an incorrect estimate of the association between exposure and risk of disease  Normally caused by faults in study design and implementation/execution  Difficult to eliminate entirely - needs to be taken into account in analysis and interpretation  Particular problem in retrospective studies  Can make your results completely unreliable
  • Type of Bias  Selection Bias  Errors in the process of identifying the study population  Non-comparable groups are enrolled in a study  Inclusion in the study because of exposure or disease status is somehow dependent on the other axis…  Sampling bias:  Where the choice of sampling lead to biased selection of sample  E.g. the use of volunteer, phone interviews, convenient sampling  Responder bias:  Where the responder characteristics lead to the bias  Recall bias
  • Type of Bias • Information/observation bias Systematic differences in the way data on exposure or outcome are obtained from the various study groups • • • • Recording/interviewer/questionnaire bias Social acceptability bias Loss to follow-up Misclassification
  • Controlling bias • • • • Choice of study population (eligibility criteria) Methods of data collection Sources of exposure and disease information Assess the extent and direction of the bias
  • Exercises: Type of bias  A study of patients with pancreatic cancer: cases are interviewed in hospital and controls at home.  A questionnaire is sent to all men over 65. It includes questions about diet, health and physical activity. The intention is to estimate the amount of physical disability in the over 65s.  In a study of a new treatment, the first 20 patients arriving in clinic were allocated to treatment and the next twenty to the old treatment.  1000 smokers and 1000 non-smokers are followed up for 10 years to see if they develop lung cancer. At the end of the study, 2% of the 800 smokers they are able to trace have lung cancer, and 1% of the 900 non-smokers traced have lung cancer.
  • Questions • What are the possible biasses from those four studies?
  • Confounding  A situation in which a measure of effect of an exposure on risk is distorted because of the association of exposure with other factor(s) that influence the outcome under study  Confounders may be under or over estimate the observed association  A confounder is a variable that is an independent determinant of the outcome of interest and is not equally distributed among the exposed and nonexposed
  • Confounding Effects of a third factor associated with the risk factor under study AND independently with the outcome Confounder Exposure Outcome
  • Control of confounding • • • • • Study design Randomisation Restriction (eligibility) Matching subjects Stratification
  • References  Bowling, A (2009) Research methods in health – Investigating health and health services. 3rd edition. Maidenhead: Open University Press.  Bruce, N. Pope, D. and Stanistreet, D. (2008) Quantitative methods for health research. Chichester: John Wiley & Sons  David, M. and Sutton, C. (2011) Social research – An introduction. 2nd edition. London: Sage  Neale, J. (2009) Research methods for health and social care. London: Palgrave Macmillan  Polgar, S. and Thomas, S. (2008) Introduction to Research in the Health Sciences. 5th edition. Philadelphia: Elsevier.  Sim, J. and Wright, C. (2000) Research in health care – Concepts, designs, and methods. Cheltenham: Nelson Thomas Ltd.