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Lecture 6: Descriptive follow-up studies

• Natural history of disease and prognosis

• Survival analysis: Kaplan-Meier survival curves

• Cox proportional hazards analysis, hazard ratio

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Natural history (clinical course) and prognosis of a disease

• Why?– Patient/family counseling– Development and evaluation of interventions

• Types of study– Descriptive (persons with the disease only)– Analytical (comparison group) – Prognostic factors (risk factors for poor

prognosis)

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Natural history/prognosis studies: Aspects of interest

• stages of the disease (subclinical, clinical)

• outcomes – death– disease (cure, progression)– disability (physical, mental)– distress (pain, other symptoms)

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International Classification of Impairments, Disabilities, and Handicaps (ICIDH)

• IMPAIRMENT:

– ...loss or abnormality of psychological, physiological, or anatomical structure or function.

• DISABILITY:

– ...restriction or lack (resulting from an impairment) of ability to perform an activity …

• HANDICAP:

– ...disadvantage... resulting from an impairment or disability, that limits or prevents the fulfillment of a role ….that is normal for that individual….

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Measures of mortality/survival

• case-fatality rate

• survival rate (1-year, 5-year etc)

• median survival time

• relative survival

• survival curves (life-tables)

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Measures of disease

• Disease definition– diagnostic criteria– clinical measures, pathology etc

• Time to key events:– Progression to another stage

• Prevalence of disease at specified follow-up time(s)

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Measures of disability

• Activities of daily living (ADL)– independencein:

• basic ADL (e.g., feeding, washing)

• instrumental ADL (e.g., telephone, money management)

• Sources of information– observation (performance)– self-report– proxy report

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Measures of distress

• Subjective experience of disease– e.g., pain, discomfort, psychological distress,

depressive symptoms

• Sources of information – primarily self-report– for subjects unable to self-report, observational

methods may be needed

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What is time zero?

• Date of first symptoms?• Date of detection?• Date of diagnosis?• Beware of differences in “time zero”between

study groups:– screening/early detection intervention shifts time zero

– intervention appears to lengthen time to outcome without real change in prognosis

– “lead time” bias

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Example: evaluation of the effectiveness of breast cancer

screening (HIP study)

• Possible outcomes:– survival rate (1 year, 5 year)– case-fatality rate– mortality rate

• Which is most appropriate?

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Computation of lead-time of breast cancer screening (HIP study)

• using relationship between incidence, prevalence and mean duration

• data available:– incidence rate of clinical breast cancer = 1.84/1,000 per year

– prevalence of pre-clinical breast cancer (from screening) = 2.73 per 1,000

– average duration of pre-clinical breast cancer = 2.73/1.84 = 1.48 years

– assumption: on average, patients are detected halfway through the pre-clinical stage

– lead-time = duration of pre-clinical stage = 1.48/2 = 0.74 years

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Life-table methods: why are they needed?

• Not needed if all members of a cohort have complete follow-up to death

• Patients drop out of follow-up studies:– how should they be treated?

• At any point in time in a study, patients have been followed for different periods of time

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Censoring of follow-up data

• Censoring: loss of subjects from follow-up at time when outcome of interest has not occurred:

– Death

– Enrolled too recently

– Did not complete follow-up interview:

• moved away

• refused

• could not contact

• did not attend follow-up appointment

• Assumption: Reason for censoring is independent of the outcome of interest

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Types of life-tables

• Kaplan-Meier (clinical) life tables:– exact time to outcome is known

• Actuarial (population) life tables:– exact time to outcome unknown– outcome occurs in interval– estimation of average time to outcome within

interval

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Summarizing survival-type data

• Mean or median?

• Absolute or relative?

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Cox proportional hazards analysis

• Multivariate technique, allowing adjustment for covariates (confounding variables)

• Similar to multiple logistic regression, except that dependent variable is time to outcome

• Hazard ratio (HR) interpretation similar to risk ratio

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Example: Prognosis of delirium

• Study population: hospitalized patients aged 65+

• Time zero: hospital admission

• Outcomes:– survival (over 1 year)– cognitive impairment and disability (at 2, 6, 12

months)

Selection of cohorts

• Delirium cohort (n=243): patients meeting CAM criteria (DSM-IIIR) for delirium either at enrolment (prevalent cases) or during next week (incident cases)

• Control cohort (n=118): selected from patients without delirium, with weighted sampling to reduce confounding by dementia.

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Mortality by delirium and dementia (adjusted)

Hazard ratios and 95% confidence intervals:

• No delirium or dementia 1.0

• Delirium no dementia 3.77 (1.30-10.20)

• Dementia no delirium 1.57 (0.52 - 4.71)

• Both 1.98 (0.76 - 5.05)

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Results: worse cognitive status

Mea

n M

MS

E S

core

Figure 1: Mean (95% Confidence Intervals) of MMSE scores at enrolment and follow-upby delirium and dementia at enrolment

05

1015

2025

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Enrolment 2 6 12

Neither (n=42,40,35,33)Dementia only (n=52,46,39,37)Delirium only (n=56,44,33,24)Delirium & Dementia (n=164,139,112,93)

Time (months)

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Results: worse physical function

Mea

n B

arth

el In

dex

Sco

re

Figure 2: Mean (95% Confidence Intervals) of Barthel Index scores at enrolment and follow-upby delirium and dementia at enrolment

020

4060

8010

0

Enrolment 2 6 12

Neither (n=42,39,36,35)Dementia only (n=53,48,42,41)Delirium only (n=56,43,34,27)Delirium & Dementia (n=164,136,114,95)

Time (months)

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Example: effect of drug abuse rehabilitation programs on time

to first drug use

• 2 concurrent randomized controlled trials of residential drug abuse treatment programs of different planned duration:

– traditional therapeutic community (TC)

• abstinence-oriented

• 6 vs 12 months

– modified TC with relapse prevention approach

• relapse prevention/health education orientation

• 3 vs 6 months

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Example: effect of drug abuse rehabilitation programs on time

to first drug use

• PRIMARY OUTCOME: time to first drug use (measured at follow-up interviews)

• PROBLEM:– high rates of attrition from treatment – patients assumed drug-free during treatment

• TIME ZERO?– Date of admission?– Date of discharge/exit

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Methodological Questions• Censoring:

• loss to follow-up:

– outcome or censored data?

• Decision on time zero:

– primary analyses using admission, secondary analyses using exit

• Decision on censoring:

– primary analyses: censoring of loss to follow-up

– secondary analyses: loss to follow-up considered to have used drugs on day after exit from program

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