1 Lecture 6: Descriptive follow-up studies Natural history of disease and prognosis Survival analysis: Kaplan-Meier survival curves Cox proportional hazards analysis, hazard ratio

2 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)

3 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)

4 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….

5 Measures of mortality/survival case-fatality rate survival rate (1-year, 5-year etc) median survival time relative survival survival curves (life-tables)

6 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)

7 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

8 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

9 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

10 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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12 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 2

13 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

14 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

15 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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19 Summarizing survival-type data Mean or median? Absolute or relative?

20 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

21 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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24 Mortality by delirium and dementia (adjusted) Hazard ratios and 95% confidence intervals: No delirium or dementia1.0 Delirium no dementia3.77 ( ) Dementia no delirium 1.57 ( ) Both 1.98 ( )

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27 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

28 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

29 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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