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Missing Data Measurement Error

Missing Data Measurement Error. Part 13. PROJECTS ARE DUE. By midnight, Friday, May 19 th Electronic submission only to tl ouis@jhsph.edu Please name the file: [myname]-project.[filetype] or [name1_name2]-project.[filetype]. Overview. Missing data are inevitable

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Missing Data Measurement Error

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  1. Missing DataMeasurement Error Part 13 BIO656--Multilevel Models

  2. PROJECTS ARE DUE By midnight, Friday, May 19th Electronic submission only to tlouis@jhsph.edu Please name the file: [myname]-project.[filetype] or [name1_name2]-project.[filetype] BIO656--Multilevel Models

  3. Overview • Missing data are inevitable • Some missing data are “inherent” • Prevention is better than statistical “cures” • Too much missing information invalidates a study • There are many methods for accommodating missing data • Their validity depends on the missing data mechanism and the analytic approach • Issues can be subtle • A little data on the missingness process can be helpful BIO656--Multilevel Models

  4. Common types of missing data • Survey non-response • Missing dependent variables • Missing covariates • Dropouts • Censoring • administrative, due to competing events or due to loss to follow-up • Non-reporting or delayed reporting • Noncompliance • Measurement error BIO656--Multilevel Models

  5. Implications of missing data Missing data produces/induces • Unbalanced data • Loss of information and reduced efficiency • Extent of information loss depends on • Amount of missingness • Missingness pattern • Association between the missing and observed data • Parameters of interest • Method of analysis Care is needed to avoid biased inferences, inferences that target a reference population other than that intended • e.g., those who stay in the study BIO656--Multilevel Models

  6. Inherent missingness Right-censoring • We know only that the event has yet to occur • Issue: “No news is no news” versus “no news is good news” Latent disease state • Disease Free/Latent Disease/Clinical Disease • Screen and discover latent disease • Only known that transition DFLD occurred before the screening time and that LDCD has yet to occur BIO656--Multilevel Models

  7. Missing Data MechanismsLittle RJA, Rubin D. Statistical analysis with missing data. Chichester, NY: John Wiley & Sons; 2002 Missing Completely at random (MCAR) • Pr(missing) is unrelated to process under study Missing at Random (MAR) • Pr(missing) depends only on observed data Not Missing at Random (NMAR) • Pr(missing) depends on both observed and unobserved data These distinctions are important because validity of an analysis depends on the missing data mechanism BIO656--Multilevel Models

  8. Notation (for a missing dependent variable in a longitudinal study) i indexes participant (unit), i = 1,…,n j indexes measurement (sub-unit), j = 1,…,J • Potential response vector Yi = (Yi1, Yi2, …, YiJ) • Response Indicators Ri = (Ri1, Ri2, …, RiJ) Rij = 1 if Yij is observed and Rij = 0 if Yij is missing • Given Ri, Yi can be partitioned into two components: YiO observed responses YiM missing responses BIO656--Multilevel Models

  9. Schematic Representation of Response vector and Response indicators • Eg: Y2 = (Y21, Y22, Y23, … , Y2J) R2 = (1, 0, 1, … , 1) • Y2O = (Y21, Y23, …, Y2J) Y2M = (Y22) BIO656--Multilevel Models

  10. More general missing data • A similar notation can be used for missing regressors (Xij) and for missing components of an even more general data structure • Using “Y” to denote all of the potential data (regressors, dependent variable, etc.), the foregoing notation applies in general BIO656--Multilevel Models

  11. Missing Data Mechanisms • Some mechanisms are relatively benign and do not complicate or bias an analysis • Others are not benign and can induce bias Example • Goal is to predict weight from gender and height • Use information from Bio656 students • Possible reasons for missing data • Absence from class • Gender-associated, non-response • Weight-associated, non-response How would each of the above reasons affect results? BIO656--Multilevel Models

  12. Missing Completely at Random (MCAR) • Missingness is a chance mechanism that does not depend on observed or unobserved responses • Ri is independent of both YiO and YiM Pr(Ri | YiO , YiM ) = Pr(Ri) • In the weight survey example, missingness due to absence from class is unlikely to be related to the relation between weight, height and gender • The dataset can be regarded as a random sample from the target population (the full class, Bio620 over the years, ....) • A complete-case analysis is appropriate, albeit with a drop in efficiency relative to obtaining more data BIO656--Multilevel Models

  13. Height (cm) Missing Completely at Random (MCAR) • The probability of having a missing value for variable Y is unrelated to the value of Y or to any other variables in the data set • A complete-case analysis is appropriate BIO656--Multilevel Models

  14. Missing at random (MAR) • Missingness depends on the observed responses, but does not depend on what would have been measured, but was not collected Pr(Ri|YiO,YiM) = Pr(Ri|YiO) • The observed data are not a random sample from the full population • In the weight survey example, data are MAR if Pr(missing weight) depends on gender or height but not on weight • Even though not a random sample, the distribution of YiM conditional on YiO is the same as that in the reference population (the full class) • Therefore, YiM can be validly predicted using YiO • Of course, validity depends on having a correct model for the mean and dependency structure for the observed data • But, we don’t need to do these predictions to get a valid inferences BIO656--Multilevel Models

  15. Height (cm) Missing at random (MAR) • The probability of missing data on Y is unrelated to the value of Y, after controlling for other variables in the analysis • Analysis using the wrong model is not valid • e.g., uncorrelated regression, when correlation is needed A complete case analysis gives a valid slope, when selection is on the predictors, BUT correlation will be biased. BIO656--Multilevel Models

  16. When the mechanism is MAR • Complete-case methods and standard regression methods based on all the available data can produce biased estimates of mean response or trends • If the statistical model for the observed data is correct, likelihood-based methods using only the observed data are valid • Requires that the joint distribution of the observed Yis is correctly specified, • when the mean and covariance are correct • when using a correct GEE working model • when using correct random effects Ignorability • With a correct model for the observeds, under MAR the details of the missing data mechanism are not needed; the mechanism is ignorable • Ignorability is not an inherent property of the mechanism • It depends on the mechanism and on the analytic model BIO656--Multilevel Models

  17. Not missing at random (NMAR) • Missingness depends on the responses that could have been observed Pr(Ri|YiO,YiM)does depend on YiM • The observed data cannot be viewed as a random sample of the complete data • The distribution of YiM conditional on YiO is not the same as that in the reference population (the full class) • YiM depends on YiOand on Pr(Ri|YiO,YiM) and on Pr(Y) • In the weight survey example, data are NMAR if missingness depends on weight BIO656--Multilevel Models

  18. Height (cm) Missing Data Mechanisms:Not missing at random (NMAR) • Also known as • Non-ignorable missing • The probability of missing data on Y is related to the value of Y even if we control for other variables in the analysis. • A complete-case analysis is NOT valid • Any analysis that does not take dependence on Y into account is not valid • Inferences are highly model dependent BIO656--Multilevel Models

  19. MAR for Y vs XNMAR for cor(X,Y) BIO656--Multilevel Models

  20. When the mechanism is NMAR • Almost all standard methods of analysis are invalid • Valid inferences require joint modeling of the response and the missing data mechanism Pr(Ri|YiO,YiM) • Importantly, assumptions about Pr(Ri|YiO,YiM) cannot be empirically verified using the data at hand • Sensitivity analyses can be conducted (Dan Scharfstein’s research focus) • Obtaining values from some missing Ys can inform on the missing data mechanism BIO656--Multilevel Models

  21. Dropouts (if missing, missing thereafter) Dropout Completely at Random • Dropout at each occasion is independent of all past, current, and future outcomes • Is assumed for Kaplan-Meier estimator and Cox PHM Dropout at Random • Dropout depends on the previously observed outcomes up to, but not including, the current occasion • i.e., given the observed outcomes, dropout is independent of the current and future unobserved outcomes Dropout Not at Random, “informative dropout” • Dropout depends on current and future unobserved outcomes BIO656--Multilevel Models

  22. Probability of a follow-up lung function measurement depends on smoking status and current lung function Is the mechanism MAR? We don’t know! BIO656--Multilevel Models

  23. LUNG FUNCTION DECLINE IN ADULTS BIO656--Multilevel Models

  24. Longitudinal dropout example • Repeated measurements Yit i indexes people, i=1,…,n t indexes time, t=1,…,5 Yit = μit = 0 + 1t + eit cor = cov(eis, eit) = |s-t|;  0 • 0 = 5, 1 = 0.25,  = 1,  = 0.7 BIO656--Multilevel Models

  25. Longitudinal dropout examplethe dropout mechanism • Dropout indicator, Di • Di = k if person i drops out between the (k-1)st and kth occasion • Assume that • Dropout is MCAR if q2 = q3 = 0 • Dropout is MAR if q3 = 0 • Dropout is NMAR if q3 ≠ 0 BIO656--Multilevel Models

  26. Population Regression Line vs. Observed Data Means MCAR (q1= -0.5,q2=q3 = 0) MAR (q1= -0.5, q2=0.5,q3 = 0) Y Y 6.5 6.5 6 6 5.5 5.5 5 5 T T 1 2 3 4 5 1 2 3 4 5 NMAR (q1= -0.5, q2=0,q3 = 0.5) Y 6.5 6 5.5 5 T BIO656--Multilevel Models 1 2 3 4 5

  27. Analysis resultsThe true regression parameters are intercept = 5.0 and slope = 0.25,  = 0.7 BIO656--Multilevel Models

  28. Misspecified GEE(when the truth is random intercepts and slopes) CompleteData (GEE) PartialMissing Data (GEE) Y Y Time Time BIO656--Multilevel Models

  29. Correctly specified Random Effects(when the truth is random intercepts and slopes) Complete Data (REM) Partial Missing Data (REM) Y Y Time Time BIO656--Multilevel Models

  30. The probabilityof dropping out depends on theobserved history BIO656--Multilevel Models

  31. One step at a time BIO656--Multilevel Models

  32. There are 5 different “trajectories” with relative weights 2 2 1 1 2 The OLS analysis has regressors 0, 1, 2 and dependent variables 0, ,2 The Indep. Increments analysis has a constant regressor “1” and so is just estimating the mean. The dependent variable is either + or - BIO656--Multilevel Models

  33. If the missing data process is MAR and if we use the correct model for the observed data, the missing data mechanism is “ignorable” • In the foregoing example, computing first differences (current value – previous value) and averaging them differences is an unbiased estimate (of 0) no matter how complicated the MAR missing data process • We don’t have to know the details of the dropout process (it can be very complicated), as long as the probabilities depend only on what has been observed and not on what would have been observed • Ignorability depends on using the correct model for the observed data (mean and dependency structure) • If the errors were independent (rather than the first differences), then standard OLS would be unbiased BIO656--Multilevel Models

  34. Analytic Approaches Complete Case Analysis • Global complete case analysis • Individual model complete case analysis • Augment with missing data indicators • primarily for missing Xs • Weighting • Imputation • Single • Multiple • Likelihood-based (model-based) methods BIO656--Multilevel Models

  35. Analytic Approaches Global complete-case Analysis (use only data for people with fully complete data) • Biased, unless the dropout is MCAR • Even if MCAR is true, can be immensely inefficient Analyze Available Data (use data for people with complete data on the regressors in the current model) • More efficient than complete-case methods, because uses maximal data • Biased unless the dropout is MCAR • Can produce floating datasets, producing “illogical” conclusions • R2 relations are not monotone Use Missing data indicators (e.g., create new covariates) BIO656--Multilevel Models

  36. Weighting • Stratify samples into J weighting classes • Zip codes • propensity score classes • Weight the observed data inversely according to the response rate of the stratum • Lower response rate  higher weight • Unbiased if observed data are a random sample in a weighting class (a special form of the MAR assumption) • Biased, if respondents differ from non-respondents in the class • Difficult to estimate the appropriate standard error because weights are estimated from the response rates BIO656--Multilevel Models

  37. Simple example of weighting adjustment • Estimate the average height of villagers in two villages • Surveys sent to 10% of the population in both villages • Direct, unweighted: 1.7*(2/3) + 1.4*(1/3) = 1.60m • Weighted: 100*1.7*0.005 + 50*1.4*0.01 = 1.55m (= 1.7*.5 + 1.4*.5) 2 x Weight BIO656--Multilevel Models

  38. Single Imputation Single Imputation • Fill in missing values with imputed values • Once a filled-in dataset has been constructed, standard methods for complete data can be applied Problem • Fails to account for the uncertainty inherent in the imputation of the missing data • Don’t use it! BIO656--Multilevel Models

  39. Multiple ImputationRubin 1987, Little & Rubin 2002 • Multiply impute “m” pseudo-complete data sets • Typically, a small number of imputations (e.g., 5 ≤ m ≤10) is sufficient • Combine the inferences from each of the m data sets • Acknowledges the uncertainty inherent in the imputation process • Equivalently, the uncertainty induced by the missing data mechanism • Rubin DB. Multiple Imputation for Nonresponse in Surveys, Wiley, New York, 1987 • Little RJA, Rubin D. Statistical analysis with missing data. Chichester, NY: John Wiley & Sons; 2002 BIO656--Multilevel Models

  40. Multiple Imputation BIO656--Multilevel Models

  41. Multiple Imputation: Combining Inferences • Combine m sets of parameter estimates to provide a single estimate of the parameter of interest • Combine uncertainties to obtain valid SEs • In the following, “k” indexes imputation Within-imputation variance Between-imputation variance BIO656--Multilevel Models

  42. Multiple Imputation: Combining Inferences • Combine m sets of parameter estimates to provide a single estimate of the parameter of interest • Combine uncertainties to obtain valid SEs • In the following, “k” indexes imputation Within-imputation covariance Between-imputation covariance BIO656--Multilevel Models

  43. Producing the Imputed Values Last value carried forward (LVCF) • Single Imputation (never changes) • Assumes the responses following dropout remain constant at the last observed value prior to dropout • Unrealistic unless, say, due to recovery or cure • Underestimates SEs Hot deck • Randomly choose a fill-in from outcomes of “similar” units • Distorts distribution less than imputing the mean or LVCF • Underestimates SEs BIO656--Multilevel Models

  44. Valid Imputation Build a model relating observed outcomes • Means and covariances and random effects, ... • Goal is prediction, so be liberal in including predictors • Don’t use P-values; don’t use step-wise • Do use multiple R2, predictions sums of squares, cross-validation, ... BIO656--Multilevel Models

  45. Producing Imputed Values Sample values of YiM from pr(YiM|YiO, Xi) • Can be straightforward or difficult • Monotone case: draw values of YiM from pr(YiM|YiO,Xi) in a sequential manner • Valid when dropouts are MAR or MCAR Propensity Score Method • Imputed values are obtained from observations on people who are equally likely to drop out as those lost to follow up at a given occasion • Requires a model for the propensity (probability) of dropping out, e.g., BIO656--Multilevel Models

  46. Producing Imputed ValuesRecall that “Y” is all of the data, not just the dependent variable Predictive Mean Matching (build a regression model!) • A series of regression models for Yik, given Yi1, …,Yik-1, are fit using the observed data on those who have not dropped out by the kth occasion. For example, E(Yik) = 1 + 2Yi1 +…+ kYi(k-1) V(Yik) = Yields and • Parameters * and 2* are then drawn from the distribution of the estimated parameters (to account for the uncertainty in the estimated regression) • Missing values can then be predicted from 1*+ 2*Yi1+…+ k*Yik-1+ *ei, where ei is simulated from a standard normal distribution • Repeat 1 and 2 BIO656--Multilevel Models

  47. Missing, presumed at random Cost-analysis with incomplete data* • Estimate the difference in cost between transurethral resection (TURP) and contact-laser vaporization of the prostate (Laser) • 100 patients were randomized to one of the two treatments • TURP: n = 53; Laser: n = 47 • 12 categories of medical resource usage were measured • e.g., GP visit, transfusion, outpatient consultation, etc. * Briggs A et al. Health Economics. 2003; 12, 377-392 BIO656--Multilevel Models

  48. Missing data Complete-case analysis uses only half of the patients in the study even though 90% of resource usage data were available BIO656--Multilevel Models

  49. Comparison of inferences Note that mean imputation understates uncertainty. BIO656--Multilevel Models

  50. Multiple Imputation versus likelihood analysis when data are MAR • Both multiple imputation or used of a valid statistical model for the observed data (likelihood analysis) are valid • The model-based analysis will be more efficient, but more complicated • Validity of each depends on correct modeling to produce/induce ignorability BIO656--Multilevel Models

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