Oct-2007 [email protected] 1 Ferran Torres, MD PhD Unidad de Soporte en Estadística y...
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Transcript of Oct-2007 [email protected] 1 Ferran Torres, MD PhD Unidad de Soporte en Estadística y...
Oct-2007Oct-2007
[email protected]@uab.es 11
Ferran Torres, MD PhDFerran Torres, MD PhD
Unidad de Soporte en Estadística y Metodología (USEM)Servei de Farmacologia Clínica (UASP). Hospital Clínic
Profesor Bioestadística. Facultat Medicina. UAB
Herramientas básicas Herramientas básicas para un buen diseño para un buen diseño
EpidemiológicoEpidemiológico
Oct-2007Oct-2007 [email protected]@uab.es 22
2 extreme views about 2 extreme views about observational studiesobservational studies
Observational studies aren’t useful. Observational studies aren’t useful. RCTs are the gold standard and the RCTs are the gold standard and the only valid design for “truth”only valid design for “truth”
Observational study evidence trumps Observational study evidence trumps RCT evidence. RCTs are not RCT evidence. RCTs are not applicable to real-world practiceapplicable to real-world practice
Oct-2007Oct-2007 [email protected]@uab.es 33
Epidemiological studies. Epidemiological studies. TerminologyTerminology
““Observational” studies: Lack investigator Observational” studies: Lack investigator allocation to an interventionallocation to an intervention
Studies not trialsStudies not trials
Include: case series, cross-sectional, case-Include: case series, cross-sectional, case-control & cohort studies, before and after, control & cohort studies, before and after, time-series, database studies, historical time-series, database studies, historical controlscontrols
““Non-randomized studies” is broader termNon-randomized studies” is broader term
Oct-2007Oct-2007 [email protected]@uab.es 44
NRS vs RCT evidenceNRS vs RCT evidence
– Well-designed cohort or case-control & Well-designed cohort or case-control & RCTs have similar effect sizes (24 clinical RCTs have similar effect sizes (24 clinical topics). topics). Concato et al. Benson et al NEJM 2000;342:1887-92.Concato et al. Benson et al NEJM 2000;342:1887-92.
– ““Strong evidence that quasi-randomized Strong evidence that quasi-randomized trials provided biased effect size trials provided biased effect size estimates of about 30%”--at least for estimates of about 30%”--at least for medical Rx. medical Rx. Cochrane NRSG approach (Cochrane NRSG approach (http://www.cochrane.dk/nrsmghttp://www.cochrane.dk/nrsmg))
– Results of RCTs and NRS sometimes but Results of RCTs and NRS sometimes but not always differ. not always differ. Deeks. HTA report. 2003;Vol 7:no. 27.Deeks. HTA report. 2003;Vol 7:no. 27.
Oct-2007Oct-2007 [email protected]@uab.es 55
Extending findings from available Extending findings from available RCTs RCTs
Population limitations (homogeneous, limited Population limitations (homogeneous, limited co-morbidities, unstudied vulnerable groups)co-morbidities, unstudied vulnerable groups)
Small sample sizes (adverse events, rare Small sample sizes (adverse events, rare events)events)
Short follow-up (maintenance of benefits, Short follow-up (maintenance of benefits, adverse events)adverse events)
Important outcomes not available (patient Important outcomes not available (patient priorities, long-term effects, natural priorities, long-term effects, natural history/background rate)history/background rate)
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Oct-2007Oct-2007 [email protected]@uab.es 77
Esperanza de vida al nacer
años
0
20
40
60
80
1900 1996
47,3
78,1
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Key points in study designKey points in study design
Economical- BudgetEconomical- Budget
Logistic-organizationLogistic-organization
EthicalEthical
ScientificScientific
Oct-2007Oct-2007 [email protected]@uab.es 99
Errors in researchErrors in research
There are basically 2 types of error in research. There are basically 2 types of error in research.
1.1. One is One is random errorrandom error due to random variation due to random variation in subjects’ responses or measurement. in subjects’ responses or measurement. Inferential statistics (the p value and 95% Inferential statistics (the p value and 95% confidence interval) measure the amount of confidence interval) measure the amount of random error and thus allow us to draw random error and thus allow us to draw conclusion based on our research data.conclusion based on our research data.
2.2. However, there is another type of error, However, there is another type of error, Bias Bias or systematic erroror systematic error..
Oct-2007Oct-2007 [email protected]@uab.es
Types Of Error: Random ErrorTypes Of Error: Random Error
– Larger sample produce less variable Larger sample produce less variable estimate and more likely to reflect the estimate and more likely to reflect the experience of the total populationexperience of the total population
p < 0.05 : 5 %, or 1 in 20, probability of p < 0.05 : 5 %, or 1 in 20, probability of observing a result as extreme as that observed observing a result as extreme as that observed solely by chance solely by chance
BUT, a composite measure affected by both the BUT, a composite measure affected by both the magnitude of the difference between groups magnitude of the difference between groups and the sample sizeand the sample size
Oct-2007Oct-2007 [email protected]@uab.es 1111
Bias Bias - Definition- Definition DDeviations of results (or inferences) from eviations of results (or inferences) from
the truth, or processes leading to such the truth, or processes leading to such deviation. Any trend in the selection of deviation. Any trend in the selection of subjects, data collection, analysis, subjects, data collection, analysis, interpretation, publication or review of interpretation, publication or review of data that can lead to conclusions that are data that can lead to conclusions that are systematically different from the truth. systematically different from the truth.
(Last Dictionary of epidemiology)(Last Dictionary of epidemiology)
Systematic deviation from the truth that Systematic deviation from the truth that distorts the results of research.distorts the results of research.
(Sitthi Lancet 1993)(Sitthi Lancet 1993)
Oct-2007Oct-2007 [email protected]@uab.es 1212
Válidas y precisas
Válidas e imprecisas
No Válidas y precisas
No Válidas e imprecisas
SesgoSesgo
Precisas pero con SESGO
Imprecisas y con SESGO
Oct-2007Oct-2007 [email protected]@uab.es 1313
1.1. Selection biasSelection bias
2.2. Confounding biasConfounding bias
3.3. Measurement bias Measurement bias
4. Information bias 4. Information bias
Bias –ClassificationBias –Classification
Oct-2007Oct-2007 [email protected]@uab.es 1414
Selection bias Selection bias (in entire study group(in entire study group))
Error due to systematic differences in Error due to systematic differences in characteristics between those who are characteristics between those who are selected for study and those who are not. selected for study and those who are not.
(Last Dictionary of Epidemiology)(Last Dictionary of Epidemiology)
The selected sample is not representative of The selected sample is not representative of the universe of which it is a part.the universe of which it is a part.
(Hill Principles of Medical Statistics 1971)(Hill Principles of Medical Statistics 1971)
The control or population experience may The control or population experience may not be representative of the counterfactual not be representative of the counterfactual of the casesof the cases
Oct-2007Oct-2007 [email protected]@uab.es 1515
Types of Selection BiasTypes of Selection Bias Berksonian biasBerksonian bias – – There may be a spurious There may be a spurious
association between diseases or between a association between diseases or between a characteristic and a disease because of the different characteristic and a disease because of the different probabilities of admission to a hospital for those with probabilities of admission to a hospital for those with the disease, without the disease and with the the disease, without the disease and with the characteristic of interestcharacteristic of interest
Berkson J. Limitations of the application of fourfold table analysis to Berkson J. Limitations of the application of fourfold table analysis to hospital data. hospital data. BiometricsBiometrics 1946;2:47-53 1946;2:47-53
Response BiasResponse Bias – – those who agree to be in a study may those who agree to be in a study may
be in some way different from those who refuse to be in some way different from those who refuse to
participateparticipate
– Volunteers may be different from those who are Volunteers may be different from those who are enlistedenlisted
Oct-2007Oct-2007 [email protected]@uab.es 1616
Confounding biasConfounding bias
1.1. Associated with the exposure being studiedAssociated with the exposure being studied– imbalance in the comparison groupsimbalance in the comparison groups
2.2. Independently associated with the diseaseIndependently associated with the disease3.3. Not an effect of the exposureNot an effect of the exposure
Confounding bias arises when the confounder is unequally distributed between the
group with the study risk factor and the control group without the study factor.
Oct-2007Oct-2007 [email protected]@uab.es 1717
Types of Information BiasTypes of Information Bias Interviewer BiasInterviewer Bias – an interviewer’s knowledge may – an interviewer’s knowledge may
influence the structure of questions and the manner of influence the structure of questions and the manner of presentation, which may influence responsespresentation, which may influence responses
Recall BiasRecall Bias – those with a particular outcome or – those with a particular outcome or exposure may remember events more clearly or amplify exposure may remember events more clearly or amplify their recollectionstheir recollections
Observer BiasObserver Bias – observers may have preconceived – observers may have preconceived expectations of what they should find in an examinationexpectations of what they should find in an examination
Loss to follow-upLoss to follow-up – those that are lost to follow-up or – those that are lost to follow-up or who withdraw from the study may be different from who withdraw from the study may be different from those who are followed for the entire studythose who are followed for the entire study
Oct-2007Oct-2007 [email protected]@uab.es 1818
Types of Information BiasTypes of Information Bias Hawthorne effectHawthorne effect – an effect first documented at a – an effect first documented at a
Hawthorne manufacturing plant; people act Hawthorne manufacturing plant; people act differently if they know they are being watcheddifferently if they know they are being watched
Surveillance biasSurveillance bias – the group with the known – the group with the known exposure or outcome may be followed more closely exposure or outcome may be followed more closely or longer than the comparison groupor longer than the comparison group
Misclassification biasMisclassification bias – errors are made in – errors are made in classifying either disease or exposure statusclassifying either disease or exposure status
Oct-2007Oct-2007 [email protected]@uab.es 1919
Misclassification Bias (cont.)Misclassification Bias (cont.)
250250100100150150
10010050505050NonexposedNonexposed1501505050100100ExposedExposed
TotalTotalControlsControlsCasesCases
OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3
True Classification
250250100100150150
909050504040NonexposedNonexposed
1601605050110110ExposedExposed
TotalTotalControlsControlsCasesCases
OR = ad/bc = 2.8; RR = a/(a+b)/c/(c+d) = 1.6
Differential misclassification - Overestimate exposure for 10 cases, inflate rates
Oct-2007Oct-2007 [email protected]@uab.es 2020
Misclassification Bias (cont.)Misclassification Bias (cont.)
CasesCases ControlsControls TotalTotalExposedExposed 100100 5050 150150NonexposeNonexpose
dd5050 5050 100100
150150 100100 250250OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3
True Classification
CasesCases ControlsControls TotalTotalExposedExposed 9090 5050 140140NonexposedNonexposed 6060 5050 110110
150150 100100 250250OR = ad/bc = 1.5; RR = a/(a+b)/c/(c+d) = 1.2
Differential misclassification - Underestimate exposure for 10 cases, deflate rates
Oct-2007Oct-2007 [email protected]@uab.es 2121
Misclassification Bias (cont.)Misclassification Bias (cont.)
CasesCases ControlsControls TotalTotalExposedExposed 100100 5050 150150NonexposeNonexpose
dd5050 5050 100100
150150 100100 250250OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3
True Classification
CasesCases ControlsControls TotalTotal
ExposedExposed 110110 6060 170170
NonexposNonexposeded
4040 4040 8080
150150 100100 250250OR = ad/bc = 1.8; RR = a/(a+b)/c/(c+d) = 1.3
Nondifferential misclassification - Overestimate exposure in 10 cases, 10 controls – bias towards null
Oct-2007Oct-2007 [email protected]@uab.es 2323
• Avoiding bias: DesignAvoiding bias: Design
• Correcting bias: AnalysisCorrecting bias: Analysis• Estimating magnitude and Estimating magnitude and
direction of bias: Sensitivity direction of bias: Sensitivity analysis of Biasanalysis of Bias
Dealing with Dealing with biasbias
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[email protected]@uab.es 2424
Study designStudy design–SamplingSampling–Sample SizeSample Size–Sources of data collectionSources of data collection–Methods of data collectionMethods of data collection–Content of informationContent of information–Statistical Analysis PlanStatistical Analysis Plan
Prevention of Bias (?)Prevention of Bias (?)
Oct-2007Oct-2007 [email protected]@uab.es 2525
Avoiding bias: Avoiding bias: DesignDesign Standard source of informationStandard source of information
– More than one source: Multiple standard sources to confirm More than one source: Multiple standard sources to confirm informationinformation
Methods to assure participation and compliance and Methods to assure participation and compliance and ffollow-upollow-up
Strategy to maximise participation rate (response, Strategy to maximise participation rate (response, consent), and to maximise complete follow upconsent), and to maximise complete follow up
Defining study population: Defining study population: – population based study less vulnerablepopulation based study less vulnerable
– Define, a priori, who is a case or what constitutes exposure so that there Define, a priori, who is a case or what constitutes exposure so that there is no overlapis no overlap
Define categories within groups clearly (age groups, aggregates of Define categories within groups clearly (age groups, aggregates of person years)person years)
“ Prevention is
better than cure”
Oct-2007Oct-2007 [email protected]@uab.es 2626
Avoiding bias: Avoiding bias: DesignDesign
Well defined populationWell defined population• In Cohort studies, the population should be chosen In Cohort studies, the population should be chosen
independent of the risk of disease in question.independent of the risk of disease in question.• In Case control studies, the selection of the controls In Case control studies, the selection of the controls
should be independent of the exposure in questionshould be independent of the exposure in question
Set up strict guidelines for data collectionSet up strict guidelines for data collection TrainTrain observers or interviewers to obtain data in the same fashion observers or interviewers to obtain data in the same fashion It is It is preferablepreferable to use to use more than one observer or interviewermore than one observer or interviewer, but , but
not so many that they cannot be trained in an identical mannernot so many that they cannot be trained in an identical manner
Use of a good control groupUse of a good control group
Oct-2007Oct-2007 [email protected]@uab.es 2727
Be purposeful in the study design to minimize the Be purposeful in the study design to minimize the chance for bias, ex. chance for bias, ex. more than one control groupmore than one control group
Selection of control in case control study : to equalise incentive Selection of control in case control study : to equalise incentive or motivation to recall, use a third control arm that has similar or motivation to recall, use a third control arm that has similar disease but not disease under study. Example, congenital disease but not disease under study. Example, congenital abnormality study, case mothers, normal control mothers, a abnormality study, case mothers, normal control mothers, a third group of other abnormalitythird group of other abnormality
Sampling : probability sampling required to ensure Sampling : probability sampling required to ensure representative sample representative sample external validity external validity
Experimental design for RCT : parallel groups design Experimental design for RCT : parallel groups design best. Others for example self-controlled design, best. Others for example self-controlled design, historical control etc prone to biaseshistorical control etc prone to biases
Oct-2007Oct-2007 [email protected]@uab.es 2828
Avoiding bias - DesignAvoiding bias - Design
Randomisation: Randomisation: – random allocation to comparison groups to random allocation to comparison groups to
avoid selection bias by investigators as well as avoid selection bias by investigators as well as to minimise confounding bias. to minimise confounding bias.
Randomly allocate observers/interviewer data collection Randomly allocate observers/interviewer data collection assignmentsassignments
Matching on important confoundersMatching on important confounders
Blinding of subjects, investigators and / or Blinding of subjects, investigators and / or statisticianstatistician
Oct-2007Oct-2007 [email protected]@uab.es 2929
Restriction of subjects to obtain Restriction of subjects to obtain homogenous group. homogenous group.
Quality control procedures in data Quality control procedures in data collectioncollection
……all detailed in advance in a all detailed in advance in a written protocolwritten protocol
Avoiding bias - DesignAvoiding bias - Design
Oct-2007Oct-2007 [email protected]@uab.es 3030
Current GuidelineCurrent GuidelineInitiatives…Initiatives…
Observational & Non-randomized Observational & Non-randomized StudiesStudies– STROBE: STROBE: www.strobe-www.strobe-
statement.orgstatement.org– TREND: TREND: www.trend-statement.orgwww.trend-statement.org
Randomized Clinical TrialsRandomized Clinical Trials – CONSORT: CONSORT: www.consort-statement.orgwww.consort-statement.org
Oct-2007Oct-2007 [email protected]@uab.es 3131
Med Clin (Barc) Dic-2005. Vol 125, Med Clin (Barc) Dic-2005. Vol 125, Supl.1Supl.1
Estudios epidemiológicos (Estudios epidemiológicos (STROBESTROBE). ).
Metaanálisis (Metaanálisis (QUOROM, MOOSEQUOROM, MOOSE). ). Estudios de intervención no aleatorizados Estudios de intervención no aleatorizados
((TRENDTREND). ). Estudios de precisión diagnóstica (Estudios de precisión diagnóstica (STARDSTARD) y ) y
pronóstica (pronóstica (REMARKREMARK). ). Otros:Otros:
– Recomendaciones metodológicas de las Recomendaciones metodológicas de las agencias agencias reguladorasreguladoras. .
– Instrumentos de medida de Instrumentos de medida de calidad de vida calidad de vida relacionada con la saludrelacionada con la salud y de otros resultados y de otros resultados percibidos por los pacientes. percibidos por los pacientes.
– Estudios de Estudios de evaluación económicaevaluación económica en salud. en salud. – Ensayos clínicos aleatorizados (Ensayos clínicos aleatorizados (CONSORTCONSORT).).– Ensayos clínicos aleatorizados comunitarios Ensayos clínicos aleatorizados comunitarios
((CONSORTCONSORT CLUSTERCLUSTER). ).
Oct-2007Oct-2007 [email protected]@uab.es 3232
ICHE9ICHE9 Statistical PrinciplesStatistical Principles for Clinical Trials for Clinical Trials CPMP/EWP/908/99CPMP/EWP/908/99 CPMP Points to Consider on CPMP Points to Consider on MultiplicityMultiplicity issues issues
in Clinical Trials in Clinical Trials (Apr 2003)(Apr 2003)
CPMP/EWP/2863/99CPMP/EWP/2863/99 Points to Consider on Adjustment for Points to Consider on Adjustment for BaselineBaseline CovariatesCovariates (Nov 2003)(Nov 2003)
CPMP/2330/99CPMP/2330/99 Points to Consider on Application with 1.) Points to Consider on Application with 1.) Meta-Meta-analysesanalyses and 2.) One Pivotal study and 2.) One Pivotal study (May 2001)(May 2001)
CPMP/EWP/2158/99CPMP/EWP/2158/99 Guideline on the Choice of a Guideline on the Choice of a Non-Inferiority Non-Inferiority MarginMargin (Jan2006)(Jan2006)
CPMP/EWP/482/99CPMP/EWP/482/99 Points to Consider on Points to Consider on SwitchingSwitching between between Superiority and Non-inferiority Superiority and Non-inferiority (Feb 2001)(Feb 2001)
CPMP/EWP/1776/99CPMP/EWP/1776/99 Points to Consider on Points to Consider on Missing DataMissing Data (Jan 2002)(Jan 2002)
CHMP/EWP/83561/05CHMP/EWP/83561/05 Guideline on Clinical Trials in Guideline on Clinical Trials in Small Small PopulationsPopulations (Feb2007)(Feb2007)
CHMP/EWP/2459/02CHMP/EWP/2459/02 Reflection Paper on Methodological Issues Reflection Paper on Methodological Issues in Confirmatory Clinical Trials with in Confirmatory Clinical Trials with Flexible DesignFlexible Design and and Analysis Plan Analysis Plan (Draft)(Draft)
NormativasNormativas
Oct-2007Oct-2007 [email protected]@uab.es 3333
Population of Oldenburg, Germany, 1930-1936(Ornithologische Monatsberichte 44, Jahrgang, 1936, Berlin)
Storks (1000s)
Humans(1000s)
Oct-2007Oct-2007 [email protected]@uab.es 3434
http://ferran.torres.name/docencia/hcphttp://ferran.torres.name/docencia/hcp
Do Storks Bring Do Storks Bring Babies?Babies?