Advanced statistical analysis of epidemiological studies

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What does estimate In the competing risks model: 1 − exp(− ∫ t 0 h 1 (u)du) Prob(Dead from cause 1 before t) IF h 2 (t) = 0! i.e., if the competing risk does not exist. This hypothetical probability is rarely of interest. However, it is used frequently anyhow! “Relapse survival curve” in clinical cancer studies. “1-Kaplan-Meier” as risk estimator in epidemiological (and other) studies. 18
Censoring in survival studies Note that rates may be estimated by treating other events as censoring while risks require modeling of all competing events. When, in survival studies, we draw the Kaplan-Meier estimator only the death intensity is taken into account - NOT the censoring intensity. This makes sense if BOTH (I): the population without censoring makes sense, AND (II) censoring is “independent”. Example: event = death due to cancer, consider censoring due to • end of study • emigration, loss to follow-up • death due to traffic accidents • death due to cardiovascular disease Magnitude of (I) depends on magnitude of competing risk. 19
Page 1 and 2: Ph.d. course in “Advanced statist
Page 3 and 4: Survival data. The hazard, h(t) pro
Page 5 and 6: Relation between rates and risks. W
Page 7 and 8: E.g., k = 3 causes: Competing risks
Page 9 and 10: Transition intensities Illness-deat
Page 11 and 12: Illness-death model, recurrent dise
Page 13 and 14: Models are given by intensities: Co
Page 15 and 16: Probabilities (risks) In survival a
Page 17: This means that the cumulative inci
Page 21 and 22: Cox regression models for cause-spe
Page 23 and 24: Regression analysis of risks. The c
Page 25 and 26: Recurrent events. Suppose individua
Page 27 and 28: Register data. Kaplan-Meier estimat
Page 29 and 30: Random effects models. Random effec
Page 31 and 32: Study base: population followed fro
Page 33 and 34: Estimation of rate ratio θ: ( )
Page 35 and 36: θ case = exp(β 1 · exposure case
Page 37 and 38: The confounder is ascertained for t
Page 39 and 40: • Exposure, HPV-16 viral load, as
Page 41 and 42: Case cohort study t 0 t 1 S ❞ ❞
Page 43 and 44: Computations • SAS PROC PHREG, bu
Page 45 and 46: “Old” study. 1003 AD’s born 1
Page 47 and 48: “New” study. All AD’s (12301)

Advanced statistical analysis of epidemiological studies

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