Biostat / epi 537




Yüklə 18.15 Kb.
tarix11.04.2016
ölçüsü18.15 Kb.

BIOSTAT / EPI 537


06 Jan 2009

OUTLINE



  1. Introductions




    1. Instructor




    1. Teaching Assistant(s)




    1. Students




  1. Intro to Longitudinal Studies




    1. Event times (Biostat 537)




    1. Repeated measures (Biostat 540)




  1. Biostat 537 – Course Overview




  1. Introduction to Survival Data




    1. Examples




    1. Functions used to summarize distribution of times

My Introduction:

      • My statistical research does focus primarily on longitudinal studies – both repeated measures issues and survival.

      • Change is interesting!

      • Research and Teaching

      • Interested in B537/B540 in order to “bring together” and perhaps write a book.

Longitudinal Study – VPS



      • Prospective study with interest in both time-to-event and longitudinal health status measures.

      • Event = time-until-AIDS

        1. discrete time

        2. some drop-out

      • Longitudinal = knowledge

Longitudinal Study – Mayo PBC Data



      • outcome = time-until-death

      • Different research questions:

        1. Compare two treatment groups

        2. Predictive model?

        3. Accuracy of predictive model/marker?

Longitudinal Study – MACS data



      • Prospective cohort study

      • First Event: Incident infection (time scale?)

      • Second Event: AIDS or death given infected = t0

      • Longitudinal analysis of changes in immune status as part of natural history of infection and/or in response to treatment.

      • Idea of time-dependent covariates – can the current value of CD4 or viral load be used to predict risk of death?

Longitudinal Study – Breast Cancer Data



      • Prospective study subsequent to BC diagnosis

      • Event = time-until-death, but this is relatively rare (censoring!)

      • Some delay to enrollment (left truncation)

      • Compare the predictive ability of different cytometry measures.

Longitudinal Study – Cystic Fibrosis Registry



      • National registry of CF patients

      • Event = death

      • Goal = predict mortality, perhaps to guide selection of patients for lung transplantation.

      • Logistic regression with 5-year status (0=alive; 1=dead)

      • Survival analysis using time-dependent covariates to model the hazard of death.

      • Longitudinal analysis looks at performance status, FEV1, and changes over time – are some groups at great risk of decline in pulmonary function?

Summary


      • CHANGE

        1. In a monotone discrete status such as vital status, or disease (first occurrence).

        2. In continuous or discrete characteristics that are measured at regular time periods (e.g. yearly).

      • Survival methods are concerned with

        1. Summarizing risk of death using hazard regression methods

        2. Summarizing cumulative fraction that have died using survival curve methods (Kaplan-Meier)

        3. Missing data from censoring!

        4. Covariates may change with time (be careful!)

      • Longitudinal methods are concerned with

        1. Summarizing changes in the mean over time and group.

        2. Characterizing the sources of variation: between groups represented by covariate values (systematic); among individuals (random effects); and within-individuals over time (error, or drift).

Overview of Biostat 537



      • Contact hours: (2) lectures; (1) discussion section starting 13 Jan 2008.

      • Evaluation: (2) Quizes (45 min); midterm (take-home) and final (in-class).

      • Weekly exercises

      • Discussion section = review key content, discuss exercise details and/or issues.

      • Web page

Introduction to Censored Survival Data – Overview



      • Survival time, observation time, and censoring indicator.

      • Cause-specific and issues…

      • Hazard

      • Censoring and truncation

      • Recurrent events

Regression Methods


Mathematical Summaries of a Population of Event Times


SUMMARY for LECTURE 1



      • Longitudinal studies generally offer two analyses that focus on time: survival; and repeated measures.

      • Survival data have a number of features that require some special methods:

        1. censoring

        2. cause-specific events

        3. models for time (or avoid it!)

      • Mathematically we can focus on any one of (5) key summaries – but for survival data we focus on the survival curve and the hazard function – much more about both of these


Verilənlər bazası müəlliflik hüququ ilə müdafiə olunur ©azrefs.org 2016
rəhbərliyinə müraciət

    Ana səhifə