brd project

Download Brd project

Post on 21-May-2015

451 views

Category:

Documents

4 download

Embed Size (px)

TRANSCRIPT

  • 1. Multi-study Analysis Of Survival Data For Bovine Respiratory Disease Reporter: Chao Charlie Huang Project presentation

2. OUTLINE

  • 1. Introduction
    • Bovine Respiratory Disease
    • Survival analysis
    • Meta-analysis
    • Statistical models combining multi-study
    • Arends multivariate random-effects model
  • 2. Methodology
    • Data manipulation
    • Modeling
  • 3. Results and discussion
    • No covariates method
    • Covariate method
  • 4. Conclusion

3. 1. INTRODUCTION

  • 1.1 Bovine Respiratory Disease (BRD)
    • a severe cattle disease
    • coughing, fever, dehydration and death
    • accounting for approximately 75 percent of feedlot morbidity and 50 percent to 70 percent of all feedlot deaths in the United States (Stotts 2010).

BRD occurrenceClinical diagnosis ( temperature,haptoglobin, etc) Survival analysis 4. The table is modified based on Brian F. Gage, 2004

    • 1.2Survival analysis
      • models time-to-event data
        • censoring
          • incomplete observation due to death, withdrawal, etc
        • time-dependent covariates

Generalized linear model Type of predictor variableType of response variable Censor?Linear regressionCategorical or continuousNormally distributedNo Logistic regressionCategorical or continuousBinaryNo Survival analysis Categorical or continuous (maybe time-dependent) Binary Allowed 5. h(t) = P{ t < T < (t + t) | T >t} S(t) = P{T > t} 6. 7.

  • BRD Data from OSU Animal Science Department
    • Study I
      • 137 cattle; 21 days; covariates(reticular temperature, haptoglobin, etc)
    • Study II
      • 265 cattle; 42 days; covariates(rectal temperature, haptoglobin, etc)
    • Study III
      • 347 cattle; 56 days

8.

  • Using Study I and II, Li (2009) finished survival analysis with Kaplan-Meier method and Cox's proportional hazards regression.
    • Overall nearly half of the sick animals developed the disease in the first 7 days after arrival and when temperature is higher, the hazard of developing BRD is higher for both data sets.
    • when the haptoglobin level is higher, the hazard for developing BRD also increases for Study I, and the two coefficients, temperature and the interaction between temperature and time, are significant for Study II.

9.

  • Next step
    • Increased sample size more power
    • How about we combine the three studies together?

10.

  • 1.3 Meta-analysis
    • a statistical method to combine several studies results targeting the same or similar hypotheses
      • controls between-study variation
      • increases statistical power

11.

  • An example

Meta-analysis of the effects of psychosocial interventions on survival time in cancer patients 12.

  • However, our data
    • Has messy structure
      • Missing or invalid variable
      • Different duration
    • Is observational data
      • No randomization
      • No treatment vs. treatment
  • If we cannot use the traditional meta-analysis, how can we combine these three studies?

13.

  • 1.4Statistical models combining multi-study

14.

  • Iterative generalized least-squares

15.

  • 1.5 Arends multivariate random-effects model

Survival proportion estimated by survival analysis methods Parameter vector of fixed effects Parameter vector of random effects Coefficient and covariance are estimated by iterative generalized linear regression 16. 2. METHODOLOGY

    • Data transformation
      • Study I
        • Reticular temperature (RETT) rectal temperature (RECT)
          • RECT=15.88 + 0.587*RETT by Bewley et al. (2008)
    • Data cleaning
      • Study I
        • 137 animals 129
      • Study III
        • 347 animals 230
    • Data transformation
      • Study I
        • Reticular temperature (RETT) rectal temperature (RECT)
          • RECT=15.88 + 0.587*RETT by Bewley et al. (2008)
    • Data cleaning
      • Study I
        • 137 animals 129 animals
      • Study III
        • 347 animals 230 animals

17. 2. METHODOLOGYNocovariates method Covariate method 18. 3. RESULTS AND DISCUSSION

  • 3.1 No covariates method

Time 19. 3. RESULTS AND DISCUSSION

  • 3.1 No covariates method

20. 3. RESULTS AND DISCUSSION

  • 3.1 No covariates method

21. 3. RESULTS AND DISCUSSION

  • 3.1 No covariates method

22. 3. RESULTS AND DISCUSSION

  • 3.1 No covariates method

Study-specific resultCombined resultAfter the model in equation (6) 23. 3. RESULTS AND DISCUSSION

  • 3.1 No covariates method

Study-specific resultCombined resultAfter the model in equation (7) 24. 3. RESULTS AND DISCUSSION

  • 3.2 Covariate method

Time Temperature 25. 3. RESULTS AND DISCUSSION

  • 3.2 Covariate method

Study I Study II Survival proportion95% confidence interval 26. 3. RESULTS AND DISCUSSION

  • 3.2 Covariate method

27. 3. RESULTS AND DISCUSSION

  • 3.2 Covariate method

The selected fixed effecttemperature, ln(day), [ln(day)] 2 28. 3. RESULTS AND DISCUSSION

  • 3.2 Covariate method

29. 3. RESULTS AND DISCUSSION

  • 3.2 Covariate method

Study-specific resultsSurvival proportion95% confidence intervalStudy I Study II 30. 3. RESULTS AND DISCUSSION

  • 3.2 Covariate method

31. 3. RESULTS AND DISCUSSION

  • 3.2 Covariate method

Survival proportion95% confidence intervalCombined result 32. 4. CONCLUSION

  • Strength
    • Handles the observational data
    • Simple and robust
    • Easy to be programmed in SAS
  • Weakness
    • Not a real survival curve
    • Random effects have the normal distributions
    • Over-fitting may occur
    • Journal papers?

33.

  • Future Improvement
    • ln(-ln) transformation
      • Regression splines, fractional polynomials, etc.
      • Simulation test may decide the best transformation
    • Normal distribution assumption
      • A gamma distribution by Fiocco, Putter and van Houwelingen (2009)