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Latent trajectory models: an appetizer

Latent trajectory models: an appetizer. Tim Croudace. Enuresis: repeated binary measure: data [010101]. Repeated binary outcome (night wetting Y/N) Prevalence (%) at ages 4, 6, 8, 9, 11 and 15 n=3272 (listwise deletion=complete data on all 6 occasions)

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Latent trajectory models: an appetizer

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  1. Latent trajectory models:an appetizer Tim Croudace

  2. Enuresis: repeated binary measure: data [010101]

  3. Repeated binary outcome (night wetting Y/N) Prevalence (%) at ages 4, 6, 8, 9, 11 and 15 n=3272 (listwise deletion=complete data on all 6 occasions) NIW50 0.115 Night wetting in past month at age 4 years NIW52 0.092 6 NIW54 0.059 8 NIW55 0.049 9 NIW57 0.050 11 NIW61 0.020 15 Y:Prob[wet last mnth] X:Time/Age [Yrs]

  4. NSHD Enuresis data:Frequencies of response patterns RESPONSE PATTERNS No. Pattern No. Pattern No. Pattern No. Pattern 1 000000 2 100000 3 110000 4 000001 5 011010 6 000100 7 111110 8 011000 9 011100 10 111100 11 010000 12 000010 13 011011 14 010010 15 000110 16 001000 17 011110 18 111111 19 001010 20 111010 21 001100 22 010001 23 001111 24 101000 25 010111 26 101110 27 001110 28 111000 29 010101 30 010100 31 100110 32 011111 33 100100 34 110100 35 000011 36 100101 37 110001 38 010011 39 001101 40 100001 41 010110 42 110010 43 011101 44 110110 45 100010

  5. Boys Girls

  6. Latent Class Analysis: Enuresis data – 6 binary (0/1) Age 15 NIW61 Age 6 NIW52 Age 8 NIW54 Age 9 NIW55 Age 11 NIW59 Age 4 NIW50 u2 u4 u1 u3 u3 u1 u5 u6 u2 u4 y4 Latent classes Binary indicator prevalence estimated through a logistic regression intercept

  7. Latent Class Growth Analysis: Enuresis data – 6 binary (0/1) Age 15 Age 6 Age 8 Age 9 Age 11 Binary outcome Modelled through Logistic regression- Threshold fixed across occasions Age 4 u3 u1 u5 u6 u2 u4 y4 slope quadratic intercept 1 1 1 1 1 1 1 4 6 8 9 11 15 16 36 64 121 225 Growth factors: All variances and covariances for growth factors=0

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