An Efficient Estimation Method for Longitudinal Surveys with Monotone Missing Data

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1 An Efficient Estimation Method for Longitudinal Surveys with Monotone Missing Data Jae-Kwang Kim 1 Iowa State University June 28, Joint work with Dr. Ming Zhou (when he was a PhD student at ISU)

2 2 / 47 Reference Zhou, M. and Kim, J.K. (2012). An Efficient Method of Estimation for Longitudinal Surveys with Monotone Missing Data, Biometrika, Accepted for publication.

3 3 / 47 Outline 1 Basic Setup 2 Propensity Score Method 3 GLS Approach with Propensity Score Method 4 Application to Longitudinal Missing 5 Simulation Study 6 Conclusion

4 4 / 47 Basic Setup X, Y: random variables from some distribution θ: parameter of interest, defined through Examples E {U(θ; X, Y)} = 0. 1 θ = E(Y): U(θ; X, Y) = Y θ 2 θ = F 1 Y (1/2): U(θ) = F Y(θ) 1/2 3 θ is regression coefficient: U(θ) = X (Y Xθ).

5 5 / 47 Basic Setup Estimator of θ: Solve Û n (θ) n U(θ; x i, y i ) = 0 i=1 to get ˆθ n, where (x i, y i ) are IID realizations of (X, Y). Under some conditions, ˆθ n converges in probability to θ and follows from a normal distribution when n. What if some of y i are missing?

6 6 / 47 Basic Setup x 1,, x n are fully observed. Some of y i are not observed. Let r i = { 1 if yi is observed 0 if y i is missing. Note that r i is also a random variable (whose probability distribution is generally unknown).

7 7 / 47 Basic Setup Complete-Case (CC) method Solve n r i U(θ; x i, y i ) = 0 i=1 Biased unless Pr (r = 1 X, Y) does not depend on (X, Y), i.e. biased unless the set of the respondents is a simple random sample from the original data.

8 8 / 47 Basic Setup Weighted Complete-Case (WCC) method Solve Û W (θ) n r i w i U(θ; x i, y i ) = 0 i=1 for some weights w i. The weight is often called the propensity scores (or propensity weights). The choice of w i = 1 Pr (r i = 1 x i, y i ) will make the resulting estimator consistent. Requires some assumption about Pr (r i = 1 x i, y i ).

9 9 / 47 Basic Setup Justification for using w i = 1/Pr (r i = 1 x i, y i ) Note that } E {ÛW (θ) x 1,, x n, y 1,, y n = Û n (θ) where the expectation is taken with respect to r. Thus, the probability limit of the solution to Û W (θ) = 0 is equal to the probability limit of the solution to Û n (θ) = 0. No distributional assumptions made about (X, Y).

10 Propensity score method Idea For simplicity, assume that Pr (r i = 1 x i, y i ) takes a parametric form. Pr (r i = 1 x i, y i ) = π(x i, y i ; φ ) for some unknown φ. The functional form of π( ) is known. For example, π(x, y; φ ) = exp(φ 0 + φ 1 x + φ 2 y) 1 + exp(φ 0 + φ 1 x + φ 2 y) Propensity score approach to missing data: obtain ˆθ PS which solves n 1 Û PS (θ) r i π(x, y; ˆφ) U(θ; x i, y i ) = 0 i=1 for some ˆφ which converges to φ in probability. 10 / 47

11 11 / 47 Propensity score method Issues Identifiability: Model parameters may not be fully identifiable from the observed sample. May assume Pr (r i = 1 x i, y i ) = Pr (r i = 1 x i ). This condition is often called MAR (Missing at random). For longitudinal data with monotone missing pattern, the MAR condition means Pr (r i,t = 1 x i, y i1,, y it ) = Pr (r i,t = 1 x i, y i1,, y i,t 1 ). That is, the response probability at time t may depend on the value of y observed up to time t.

12 12 / 47 Propensity score method Issues Estimation of φ Maximum likelihood method: Solve S(φ) n {r i π i (φ)} q i (φ) = 0 i=1 where q i = logit{π i (φ)}/ φ. Maximum likelihood method does not always lead to efficient estimation (see Example 1 next). Inference using ˆθ PS : Note that ˆθ PS = ˆθ PS ( ˆφ). We need to incorporate the sampling variability of ˆφ in making inference about θ using ˆθ PS.

13 13 / 47 Propensity score method Example 1 Response model Parameter of interest: θ = E(Y). PS estimator of θ: Solve jointly π i (φ ) = exp(φ 0 + φ 1 x i) 1 + exp(φ 0 + φ 1 x i) U(θ, φ) = S(φ) = n r i {π i (φ)} 1 (y i θ) = 0 i=1 n {r i π i (φ)}(1, x i ) = (0, 0) i=1

14 Propensity score method Example 1 (Cont d) Taylor linearization { ˆθ PS ( ˆφ) = ˆθPS (φ ) E ( )} { U E φ ( )} S 1 S(φ ) φ = ˆθ PS (φ ) {Cov (U, S)} {V(S)} 1 S(φ ), by the property of zero-mean function. (i.e. If E(U) = 0, then E( U/ φ) = Cov(U, S).) So, we have where V{ˆθ PS ( ˆφ)} = V{ˆθ PS (φ ) S } V{ˆθ PS (φ )}, V{ˆθ S } = V(ˆθ) Cov(ˆθ, S) {V(S)} 1 Cov(S, ˆθ). 14 / 47

15 15 / 47 GLS approach with propensity score method Motivation The propensity score method is used to reduce the bias, rather than to reduce the variance. In the previous example, the PS estimator for θ x = E(X) is ˆθ x,ps = n i=1 r iˆπ 1 n i=1 r iˆπ 1 i i x i where ˆπ i = π i ( ˆφ). Note that ˆθ x,ps is not necessarily equal to x n = n 1 n i=1 x i. How to incorporate the extra information of x n?

16 16 / 47 GLS approach with propensity score method GLS (or GMM) approach Let θ = (θ x, θ y ). We have three estimators for two parameters. Find θ that minimizes Q PS (θ) = x n θ x ˆθ x,ps θ x ˆθ y,ps θ y where ˆθ PS = ˆθ PS ( ˆφ). ˆV x n θ x ˆθ x,ps θ x ˆθ y,ps θ y Computation for ˆV is somewhat cumbersome. 1 x n θ x ˆθ x,ps θ x ˆθ y,ps θ y

17 17 / 47 GLS approach with propensity score method Alternative GLS (or GMM) approach Find (θ, φ) that minimizes x n θ x ˆθ x,ps (φ) θ x ˆθ y,ps (φ) θ y S(φ) ˆV x n θ x ˆθ x,ps (φ) θ x ˆθ y,ps (φ) θ y S(φ) 1 x n θ x ˆθ x,ps (φ) θ x ˆθ y,ps (φ) θ y S(φ) Computation for ˆV is easier since we can treat φ as if known. Let Q (θ, φ) be the above objective function. It can be shown that Q (θ, ˆφ) = Q PS (θ) and so minimizing Q (θ, ˆφ) is equivalent to minimizing Q PS (θ)..

18 18 / 47 GLS approach with propensity score method Justification for the equivalence May write Q (θ, φ) = ( ÛPS (θ, φ) S(φ) = Q 1 (θ φ) + Q 2 (φ) ) ( V11 V 12 V 21 V 22 ) 1 ( ÛPS (θ, φ) S(φ) ) where Q 1 (θ φ) = ) {V (ÛPS V 12 V 1 22 S ( UPS S )} ( ) 1 Û PS V 12 V 1 22 S Q 2 (φ) = S(φ) {ˆV (S)} 1 S(φ) For the MLE ˆφ, we have Q 2 ( ˆφ) = 0 and Q 1 (θ ˆφ) = Q PS (θ).

19 19 / 47 GLS approach with propensity score method Back to Example 1 Response model π i (φ ) = exp(φ 0 + φ 1 x i) 1 + exp(φ 0 + φ 1 x i) Three direct PS estimators of (1, θ x, θ y ): (ˆθ 1,PS, ˆθ x,ps, ˆθ y,ps ) = n 1 x n = n 1 n i=1 x i available. What is the optimal estimator of θ y? n i=1 r iˆπ 1 i (1, x i, y i ).

20 20 / 47 GLS approach with propensity score method Example 1 (Cont d) Minimize x n θ x ˆθ 1,PS (φ) 1 ˆθ x,ps (φ) θ x ˆV ˆθ y,ps (φ) θ y S(φ) with respect to (θ x, θ y, φ), where S(φ) = n i=1 with h i (φ) = π i (φ)(1, x i ). x n ˆθ 1,PS (φ) ˆθ x,ps (φ) ˆθ y,ps (φ) S(φ) 1 ( ) ri π i (φ) 1 h i (φ) = 0 x n θ x ˆθ 1,PS (φ) 1 ˆθ x,ps (φ) θ x ˆθ y,ps (φ) θ y S(φ)

21 21 / 47 GLS approach with propensity score method Example 1 (Cont d) Equivalently, minimize ˆθ y,ps (φ) θ y ˆθ 1,PS (φ) 1 ˆθ x,ps (φ) x n S(φ) ˆV ˆθ y,ps (φ) ˆθ 1,PS (φ) ˆθ x,ps (φ) x n S(φ) 1 ˆθ y,ps (φ) θ y ˆθ 1,PS (φ) 1 ˆθ x,ps (φ) x n S(φ) with respect to (θ y, φ), since the optimal estimator of θ x is x n.

22 22 / 47 GLS approach with propensity score method Example 1 (Cont d) The solution can be written as ˆθ y,opt = ˆθ ( y,ps + 1 ˆθ ) ( 1,PS ˆB 0 + x n ˆθ ) { ˆφ)} 1,PS ˆB S( Ĉ where ˆB 0 ˆB 1 Ĉ n = r i b i i=1 1 x i h i 1 x i h i 1 n r i b i i=1 1 x i h i y i and b i = ˆπ 2 i (1 ˆπ i ). Note that the last term {0 S( ˆφ)}Ĉ, which is equal to zero, does not contribute to the point estimation. But, it is used for variance estimation.

23 23 / 47 GLS approach with propensity score method Example 1 (Cont d) That is, for variance estimation, we simply express where ˆθ y,opt = n 1 n ˆη i i=1 ˆη = ˆB 0 + x iˆb 1 + h iĉ + r ( ) i y i ˆB 0 x iˆb 1 h ˆπ iĉ i and apply the standard variance formula to ˆη i. This idea can be extended to the survey sampling setup.

24 24 / 47 GLS approach with propensity score method Example 1 (Cont d) The optimal estimator is linear in y. That is, we can write ˆθ y,opt = 1 n n i=1 r i g i y i = w i y i ˆπ i r i =1 where g i satisfies n i=1 r i ˆπ i g i (1, x i, h i) = n (1, x i, h i). This, doubly robust under E ζ (y x) = β 0 + β 1 x in the sense that θ y,opt is consistent when either the response model or the superpopulation model holds. i=1

25 25 / 47 Application to longitudinal missing Basic Setup X i is always observed and remains unchanged for t = 0, 1,..., T. Y it is the response for subject i at time t. r it : The response indicator for subject i at time t. Assuming no missing in the baseline year, Y 0 can be absorbed into X. Monotone missing pattern r it = 0 r i,t+1 = 0, t = 1,..., T 1. L i,t = (X i, Y i1,..., Y i,t ) : Measurement up to t. Parameter of interest is µ t = E{Y it }.

26 26 / 47 Application to longitudinal missing Missing mechanism Missing completely at random (MCAR) : P(r it=1 r i,t 1 = 1, L i,t ) = P(r it=1 r i,t 1 = 1). Covariate-dependent missing (CDM) : P(r it = 1 r i,t 1 = 1, L i,t ) = P(r it = 1 r i,t 1 = 1, X i ). Missing at random (MAR) : P(r it = 1 r i,t 1 = 1, L i,t ) = P(r it = 1 r i,t 1 = 1, L i,t 1 ). Missing not at random (MNAR) : Missing at random does not hold.

27 27 / 47 Application to longitudinal missing Motivation Panel attrition is frequently encountered in panel surveys, while classical methods often assume covariate-dependent missing, which can be unrealistic. We want to develop a PS method under MAR. Want to make full use of available information.

28 28 / 47 Application to longitudinal missing Modeling Propensity Score Under MAR, in the longitudinal data case, we would consider the conditional probabilities: Then p it := P(r it = 1 r i,t 1 = 1, L i,t 1 ), t = 1,..., T. π it = t p ij. π t then can be modeled through modeling p t with p t (L t 1 ; φ t ). This kind of modeling is also adopted in Robins et al. (1995). j=1

29 29 / 47 Application to longitudinal missing Score Function for Longitudinal Data Under parametric models for p t s, the partial likelihood for φ 1,..., φ T is L(φ 1,..., φ T ) = n T i=1 t=1 [ p r i,t it (1 p it ) 1 r i,t ] r i,t 1, and the corresponding score function is (S 1 (φ 1 ),..., S T (φ T )), where S t (φ t ) = n r i,t 1 {r it p it (φ t )} q it (φ t ) = 0 i=1 where q it (φ t ) = logit{p it (φ t )}/ φ t. Under logistic regression model such that p t = 1/{1 + exp( φ tl t 1 )}, we have q it (φ t ) = L t 1.

30 30 / 47 Application to longitudinal missing Example 2 Assume T = 3 Parameter of interest: µ x = E(X), µ t = E(Y t ), t = 1, 2, 3. PS estimator of µ p at year t: ˆθ p,t = n 1 n i=1 r itˆπ it 1 y ip, p t. Estimator under t = 0 (baseline year): ˆθ x,0 = n 1 n i=1 x i Estimator under t = 1: ˆθ x,1, ˆθ 1,1 Estimator under t = 2: ˆθ x,2, ˆθ 1,2, ˆθ 2,2 Estimator under t = 3: ˆθ x,3, ˆθ 1,3, ˆθ 2,3, ˆθ 3,3 (T + 1) p + T(T + 1)/2 estimators for p + T parameters. (p = dim(x))

31 Application to longitudinal missing GMM for Longitudinal Data Case Need to incorporate auxiliary information sequentially. T = 1 already covered in Example 1. For t = 2, we have auxiliary information about µ x from t = 0 sample (i.e. x n ) and another auxiliary information about µ 1 from t = 1 sample (i.e. ˆθ 1,opt ). Thus, the optimal estimator of θ 2 takes the form of ˆθ 2,opt = ˆθ ( 2,2 + x n ˆθ ) x,2 ˆB 1 + (ˆθ1,opt ˆθ ) 1,2 ˆB 2 for some ˆB 1 and ˆB / 47

32 Application to longitudinal missing The auxiliary information up to time t can be incorporated by the estimating function Ẽ(ξ t 1 ) = 0 where Ẽ( ) = n 1 n i=1 i and ξ t 1 := r 0 π 0 u 1 X ( ) r 1 X π 1 u 2 Y 1 r t 1 π t 1 u t. X Y 1. Y t 1 = r 0 π 0 u 1 L 0 r 1 π 1 u 2 L 1. r t 1 π t 1 u t L t 1, where u t = r t /p t 1. Note that E{ξ t 1 } = 0 because E{u t L t 1, r t 1 = 1} = / 47

33 33 / 47 Application to longitudinal missing The score function can be written as S t := (S 1,..., S t) = nẽ{ψ t 1 }, where (r 1 p 1 r ( 0 )X ) X (r 2 p 2 r 1 ) Y 1 r 0 u 1 p 1 L 0 ψ t 1 =. r 1 u 2 p 2 L 1 X =.. Y 1 r (r t p t r t 1 ) t 1 u t p t L t 1. Y t 1

34 34 / 47 Application to longitudinal missing GMM for Longitudinal Data Case This motivates the minimizing the following quadratic form: Ẽ{r t Y t /ˆπ t } µ t Ẽ{r t Y t /π t } Q t = Ẽ{ˆξ t 1 } E{ξ t 1 } Ẽ{ξ ˆV t 1 } Ẽ{ ˆψ t 1 } E{ψ t 1 } Ẽ{ψ t 1 } Ẽ{r t Y t /ˆπ t } µ t Ẽ{ˆξ t 1 } E{ξ t 1 }. Ẽ{ ˆψ t 1 } E{ψ t 1 } 1

35 Application to longitudinal missing Optimal PS Estimator Theorem (1) Under the logistic type response model, where the score function for (φ 1,..., φ T ) is Ẽ(ψ T 1 ) = 0. For each year t, the optimal estimator of µ t = E{Y t } among the class ˆµ t,bt,c t = Ẽ{r t Y t /ˆπ t } B tẽ{ˆξ t 1 } C tẽ{ ˆψ t 1 }, is given by ˆµ t,ˆb t,ĉ t, where ˆB t = (ˆB 1t,..., ˆB tt), Ĉ t = (Ĉ 1t,..., Ĉ tt) with { ( ) ( ) ( ) (ˆB j,t, Ĉ j,t) = Ẽ 1 r tˆπ j ˆπ 1 j 1 L 1 } j 1 ˆπ j 1 L j 1 ˆπ t ˆp j ˆp j L j 1 ˆp j L j 1 { ( ) ( ) } 1 1 Ẽ 1 ˆπ i 1 L i 1 r t Y t. ˆp j ˆp i L i 1 ˆπ t 35 / 47

36 36 / 47 Application to longitudinal missing Variance Estimation Theorem (2) The Ŷ t,opt estimator is asymptotically equivalent to Ẽ{η t }, where η t = r ty t π t t j=1 ( ) 1 D π j,tr j 1 u j 1 L j 1 j, p j L j 1 with D j,t = E 1 {r j 1 u 2 j ( ) ( ) 1 π j 1 L 1 } { ( ) j 1 π j 1 L 1 j 1 E u π i 1 L i 1 j p j L j 1 p j L j 1 p i L i 1 r t Y t π t }. Thus the variance of Ŷ t,opt can be consistently estimated by n 1 (n 1) 1 Ẽ{ˆη t Ẽ(ˆη t )} 2,

37 37 / 47 Application to longitudinal missing Properties of our Optimal Estimator Ŷ t,opt is asymptotically normal with mean µ t and variance that is equal to the lower bound of asymptotic variance corresponding to the following family Ŷ t,psa B Ẽ{ˆξ t 1 }. Computational advantage due to the fact that r i 1 u i L i and r j 1 u j L j are orthogonal (uncorrelated) for i j. Variance estimation is also very convenient, as implied by Theorem 2.

38 38 / 47 Numerical Study Robins et al. (1995) Estimator Robins et al. (1995) proposed a class of estimators for estimating µ t in the longitudinal data case with monotone missing, by incorporating a regression model of E(Y it X i ) = m(x i ; β t ). When m(x i ; β) = X i β t, the weighted estimating equation method in Robins et al. (1995) would give an estimator ˆµ t (out of that family) that is a solution to Ẽ which gives [ rt ˆπ t {Y t µ t β 1,t(X Ẽ[X])} ( )] 1 X Ẽ(X) ˆµ t = Ẽ{r (Ẽ{rt ) ty t /ˆπ t } Ẽ{r t /ˆπ t } ˆβ 1,t Y t /ˆπ t } Ẽ{r t /ˆπ t } X n.,

39 39 / 47 Numerical Study Estimators under Study The estimator using the full sample, i.e. Ẽ{Y t }, for reference. The naive estimator, i.e. the simple average of the complete sample, which is ˆµ t,naive = Ẽ{r t Y t }/Ẽ{r t }. The direct propensity score adjusted estimator, that is, ˆµ t,psa = Ẽ{r t Y t /ˆπ t }. The estimator using weighted estimating equations by Robins et al. (1995), denoted by ˆµ t,rrz. Our estimator given in theorem 1, denoted by ˆµ t,opt.

40 40 / 47 Numerical Study Simulation Study I Y 0 = 2(X 1) + e 0, Y t = 2(X 1) + 2Y t 1 + e t, for t > 1, where X N(1, 1), e t = 0.5e t 1 + v t, e 0 N(0, 1) and v t N(0, 1) independently for different t. The missing indicator r t follows the following distribution: P(r t = 1 X, Y t 1, r t 1 = 1) = expit(1 + 2X Y t 1 /(t + 1)), and there is no missing in the baseline year.

41 41 / 47 Numerical Study Simulation Study I We used B = Monte Carlo samples of size n = 300 for this simulation. The response rates for t = 1, 2, 3 are 0.93, 0.87, 0.75 respectively. We also computed variance estimator of the optimal estimator using the formula in Theorem 2. The relative biases of the variance estimator, for t = 1, 2, 3 are , , respectively.

42 42 / 47 Numerical Study Results from Simulation Study 1 Table: Comparison for different methods when n = 300, T = 3 with Monte Carlo sample size for simulation study 1, using the full data as baseline. 100*RMSE/RMSE.Full t = 1 t = 2 t = 3 Full Naive PS RRZ Opt

43 43 / 47 Numerical Study Simulation Study II Y 0 = 2(X 1) 1/3 + e 0, Y t = 2(X 1) 1/3 + 2Y t 1 + e t, for t > 1, where X N(1, 1), e t = 0.5e t 1 + v t, e 0 N(0, 1) and v t N(0, 1) independently for different t. The missing indicator r t follows the following distribution: P(r t = 1 X, Y t 1, r t 1 = 1) = expit(1 + 2X Y t 1 /(t + 1)).

44 44 / 47 Numerical Study Simulation Study II B = 10000, n = 300, response rates for t = 1, 2, 3 are 0.92, 0.85, 0.74 respectively. Using the same formula for variance estimation, in this simulation study, for the optimal estimator, the relative biases of the variance estimator, for t = 1, 2, 3 are , , respectively.

45 45 / 47 Numerical Study Results from Simulation Study II 100*RMSE/RMSE.Full t = 1 t = 2 t = 3 Full Naive PS RRZ Opt

46 46 / 47 Concluding Remarks We adopted GLS (GMM) technique and constructed an optimal estimator among a class of unbiased estimators. Under monotone missing pattern, applying GLS (GMM) method to estimate µ X, µ 1,..., µ T simultaneously is exactly the same as what we proposed (estimate µ X, µ 1,..., µ T one by one). This method is directly applicable to the case when the baseline year sample is selected with a complex probability sample. Extension to non-monotone missing pattern, time-dependent covariate can be important topics for further investigation.

47 47 / 47 Thank You! Questions? : jkim@iastate.edu

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