outcome research 1

Time-to-event, Time-to-event, event event

history/event history/event outcomeoutcome

Wei-Chu ChieWei-Chu ChiePreventive MedicinePreventive Medicine

outcome research 2

Long-term outcomesLong-term outcomes– Time to events /events (change of status)

• mortality/survival (final)• recurrence• onset of an expected disease/adverse

effect– ‘hard’ endpoints: more objective/most

important in primary endpoints– two components:

• time or person-time• event: binary, clear-cut:

occurred/censored

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Long-term outcomesLong-term outcomes• Strengths

– objective: ‘hard’ endpoints– relatively more accurate and precise– relatively easier to access and measure

• Weakness– time consuming– too much simplified/insensitive– too late for prevention

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Classification of EventsClassification of Events• Death• Recurrence/metastasis, ...• Occurrence

– diseases– complications– adverse effects

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DeathDeath– The last ‘end’ point: late and insensitive– Easy to access: registry available– Event per se: precise and accurate– Causes of death: less precise and

accurate• better in easy-diagnosing, highly fatal

diseases with short courses/some cancers, major injuries

• bad example: stigmatization of diabetes

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DeathDeath• Overall deaths

– overall impact of the treatment

• Specific cause(s) of death– which the treatment planned to prevent– e.g. fatal myocardial infarction, breast

cancer death

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DeathDeath• Usually used in

– (sometimes) vaccination– screening– clinical trials– prognostic factors studies

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Recurrence/metastasis/Recurrence/metastasis/occurrence of certain occurrence of certain

complicationscomplications• Closer and more sensitive to treatment• not too late for prevention • not always predictive to the last endpoint• more difficult to access: must be from

clinical records or follow-up• precision and accuracy depend on quality of

clinical records• affected by patients’ moving between

different hospitals

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Recurrence/metastasis/Recurrence/metastasis/occurrence of certain occurrence of certain

complicationscomplications

• Usually used in– diseases with different and progressive

status• e.g. cancer: recurrence, metastasis• e.g. diabetes: occurrence of retinopathy,

nephropathy, neuropathy, stroke, myocardial infarction, …

– multiple recurrence: multiplicative intensity models

– Markov process

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Occurrence of effects or Occurrence of effects or adverse effectsadverse effects

• Nature: change of rate (diseases/conditions)

– expected helpful effects (efficacy)– side or adverse effects including deaths

(safety)– unexpected helpful effects: off-label use

• Strengths and weakness– The same strengths and weakness of

recurrence/metastasis/occurrence of certain complications

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Occurrence of effects or Occurrence of effects or adverse effectsadverse effects

• Usually used in– clinical trials of vaccine, drugs or procedure

s– pharmaco-epidemiology

• post-marketing surveillance• adverse effects after applied to population

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Characteristics of eventsCharacteristics of events• Usually binary at a time

– sometimes polychotomous– sometimes multiple at different time points

• Change of status: 0 to 1• Person-time or time passed

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Time to startTime to start• Inception cohort

– from the onset of a disease

• proxy of the ‘onset’– first report– first visit/admission– first diagnosis

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Time ‘at’ the eventTime ‘at’ the event• The event already occurred

– left censoring

• The event occurred between two observations– interval censoring

• The event hasn’t occurred to the end– right censoring (usual definition)– non- susceptibility

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The onset of the event and its The onset of the event and its proxyproxy

• Exact onset• proxy

– first and each subsequent reports– first and each subsequent

visits/admissions– first diagnosis and each subsequent

visits/admissions with the same diagnosis

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Person-time: open or closed Person-time: open or closed cohortcohort

– The real denominator or ‘field’ to grow events

– Best developed in epidemiologic studies• cut personal characteristics*time into small

pieces• pool small pieces bearing same

characteristics together e.g. [40-44 years male obese smoking …]

• count events growing from different pieces

– Poisson’s regression– Rates: rate difference & rate ratio

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Person-time: open or closed Person-time: open or closed cohortcohort

• Assumptions and limitations– constant hazard assumption– no memory (independent characteristic

groups)– not considering non-susceptibility

• some subjects will NEVER develop the outcome

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Time to event: open or closed Time to event: open or closed cohortcohort

– Follow up from inception to a certain time later

– Each individual:• expected event occurred• competing event occurred• censored (no event occurred)

– Two components• time: from inception to event or censoring• event: yes/no

– Rate ratio (hazard ratio), mean survival time

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Time to event: open or closed Time to event: open or closed cohortcohort

– Descriptive and simple comparison:• Kaplan-Meier product-limit• other methods• Log-rank test

– Cox’s proportional hazard regression• When constant hazard does not exist while

constant proportion of hazards exist– parametric part: hazard ratio exp (beta)– non-parametric part

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Time to event: open or closed Time to event: open or closed cohortcohort

• Assumptions and limitations– constant proportion of hazards

assumption• not always exists in clinical conditions• time-dependent hazard ratios

– not considering non-susceptibility• some subjects will NEVER develop the

outcome

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Event at the end of follow-up: Event at the end of follow-up: closed cohortclosed cohort

– Count the proportion of events in different exposure levels or groups of predictors

– Statistical methods• Chi-square/Multiple logistic regression

– binary outcomes (responses)– polychotomous outcomes (responses)

– Problems• inadequate time of follow-up• non-susceptibility

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Access to data/instrument Access to data/instrument selectionselection

• Event/time-to-event– primary data: follow-up– secondary data: electronic or paper

• mortality: national mortality registry• cancer: cancer registry• other special registries• NHI database• hospital records

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Primary data: follow-upPrimary data: follow-up• Methods

– telephone/mail/visit/interview/informants– “How are you?” (Are you still alive? What

happened to you since last follow-up?)– validated by clinical

records/informants/registry

• Limitations– moving/reluctance/dishonest/unable to

validate– not easy to follow up all subjects

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Secondary data: national Secondary data: national registerregister

– Major registries and steps of use• mortality/cancer/other (local or time-limited)• application/consent• personal electronic data protection law• key variable: citizen’s ID linkage and

removal• checking variables: gender/date of birth/ ...

– Limitations• only death and limited important events• inaccuracy and incompleteness

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Secondary data: NHI claimsSecondary data: NHI claims• Major strength: wide coverage• Major weakness: personal electronic

data protection law/scrambled ID– inaccessible to original record/individual,

unable to examine validity of data or improve quality

– data bases linkage impossible: poverty of contents

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Secondary data: NHI claimsSecondary data: NHI claims• currently available data bases (CD

by month/year)– basic information files– utilization records:

• one utilization record as unit• outpatients/inpatients/pharmacies• inpatient records have better quality

– individual-based files• all utilization records of sampled insured

individuals

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Secondary data: NHI claimsSecondary data: NHI claims• Making the best use for event outcomes

– external linkage impossible• preparation: informed consents of study

subjects

– internal linkage• identify a certain inception cohort (all

newly-diagnosed inpatients with a certain disease or undergoing a certain procedure)

• linkage to all later utilization records with the citizen’s ID

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Secondary data: NHI claimsSecondary data: NHI claims– Internal linkage: incidence/utilization/cost

• Incidence of certain illness (new, recurrent) if tracing long enough by internal linkage of utilization

• effects/adverse effects of certain procedures• health care cost

– Count existing registers: prevalence/incidence• prevalence of certain serious illness重大傷病• incidence … if tracing long enough

– Not good for death: die at home

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Secondary data: hospital Secondary data: hospital recordsrecords

– computerized• hospital cancer registry• hospital NHI claims data• other computerized records

– not computerized• written form on medical records• special tests/examinations/studies

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Secondary data: hospital Secondary data: hospital recordsrecords

• Major strengths– validation possible– more detailed data available beyond events

• Major weakness– kept by separate hospitals/patients moving– quality unsure/information needed not always

existing– inappropriate for death ascertainment

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Secondary data: hospital Secondary data: hospital recordsrecords

• Making best use– from external data or studies

• informed consent and linkage by citizen’s ID

• patients’ authorization and records check– internal linkage by chart ID: usually limited

by patients’ moving– linkage to death registry/NHI data base

(later) by citizen’s ID– cross -hospital cooperation

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Multiple sourcesMultiple sources– Concept of multiple triangulation– use more than one data source– mutually check to improve validity and

ascertainment of the event• e.g. registry/records + follow-up• e.g. records + laboratory data• linkage with ID/check with date of birth/gender• check and correction of incompatible information• set up guidelines/inter or intra-observer reliability