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Design of Experiments and Sample Surveys Commons™
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- Clinical trials (2)
- Missing at random (2)
- Missing data (2)
- Sampling weights (2)
- Selection bias (2)
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- Weighting (2)
- Auxiliary variables (1)
- Balanced repeated replication (1)
- Bayesian methods; design-based inference; sampling weights (1)
- Case-cohort design (1)
- Censored linear regression (1)
- Conditional mean regression (1)
- Counting processes (1)
- Double robustness (1)
- Doubly randomized preference trials (1)
- EM Algorithm (1)
- Empirical Bayes estimation (1)
- Equivalence trial (1)
- E±cient score (1)
- Horvitz-Thompson estimator (1)
- Incomplete data (1)
- Influence function (1)
- Interval censored (1)
- Jackknife (1)
- Latent class; survey sampling; height; weight; body-mass index; community health center (1)
- Linear mixed model (1)
- Martin-gales (1)
- Multiple Imputation (1)
- Multiple imputation (1)
- Non-Ignorable Selection Bias (1)
Articles 1 - 16 of 16
Full-Text Articles in Design of Experiments and Sample Surveys
A Simulation Study Of Diagnostics For Bias In Non-Probability Samples, Philip S. Boonstra, Roderick Ja Little, Brady T. West, Rebecca R. Andridge, Fernanda Alvarado-Leiton
A Simulation Study Of Diagnostics For Bias In Non-Probability Samples, Philip S. Boonstra, Roderick Ja Little, Brady T. West, Rebecca R. Andridge, Fernanda Alvarado-Leiton
The University of Michigan Department of Biostatistics Working Paper Series
A non-probability sampling mechanism is likely to bias estimates of parameters with respect to a target population of interest. This bias poses a unique challenge when selection is 'non-ignorable', i.e. dependent upon the unobserved outcome of interest, since it is then undetectable and thus cannot be ameliorated. We extend a simulation study by Nishimura et al. [International Statistical Review, 84, 43--62 (2016)], adding a recently published statistic, the so-called 'standardized measure of unadjusted bias', which explicitly quantifies the extent of bias under the assumption that a specified amount of non-ignorable selection exists. Our findings suggest that this new …
Multiple Imputation In The Presence Of Outliers, Michael Elliott
Multiple Imputation In The Presence Of Outliers, Michael Elliott
The University of Michigan Department of Biostatistics Working Paper Series
We consider the problem of obtaining population-based inference in the presence of missing data and outliers in the context of estimating obesity prevalence and body-mass index (BMI) measures from the Healthy For Life Study. Identifying multiple outliers in a multivariate setting is problematic because of problems such as masking, in which groups of outliers inflate the covariance matrix in a fashion that prevents their identification when included, and swamping, in which outliers skew covariances in a fashion that make non-outling observations appear to be outliers. We develop a latent class model that assumes each observation belongs to one of $K$ …
Censored Linear Regression For Case-Cohort Studies, Bin Nan, Menggang Yu, Jack Kalbfleisch
Censored Linear Regression For Case-Cohort Studies, Bin Nan, Menggang Yu, Jack Kalbfleisch
The University of Michigan Department of Biostatistics Working Paper Series
Right censored data from a classical case-cohort design and a stratified case-cohort design are considered. In the classical case-cohort design, the subcohort is obtained as a simple random sample of the entire cohort, whereas in the stratified design, the subcohort is selected by independent Bernoulli sampling with arbitrary selection probabilities. For each design and under a linear regression model, methods for estimating the regression parameters are proposed and analyzed. These methods are derived by modifying the linear ranks tests and estimating equations that arise from full-cohort data using methods that are similar to the "pseudo-likelihood" estimating equation that has been …
New Estimating Methods For Surrogate Outcome Data, Bin Nan
New Estimating Methods For Surrogate Outcome Data, Bin Nan
The University of Michigan Department of Biostatistics Working Paper Series
Surrogate outcome data arise frequently in medical research. The true outcomes of interest are expensive or hard to ascertain, but measurements of surrogate outcomes (or more generally speaking, the correlates of the true outcomes) are usually available. In this paper we assume that the conditional expectation of the true outcome given covariates is known up to a finite dimensional parameter. When the true outcome is missing at random, the e±cient score function for the parameter in the conditional mean model has a simple form, which is similar to the generalized estimating functions. There is no integral equation involved as in …
Does Weighting For Nonresponse Increase The Variance Of Survey Means?, Rod Little, Sonya L. Vartivarian
Does Weighting For Nonresponse Increase The Variance Of Survey Means?, Rod Little, Sonya L. Vartivarian
The University of Michigan Department of Biostatistics Working Paper Series
Nonresponse weighting is a common method for handling unit nonresponse in surveys. A widespread view is that the weighting method is aimed at reducing nonresponse bias, at the expense of an increase in variance. Hence, the efficacy of weighting adjustments becomes a bias-variance trade-off. This note suggests that this view is an oversimplification -- nonresponse weighting can in fact lead to a reduction in variance as well as bias. A covariate for a weighting adjustment must have two characteristics to reduce nonresponse bias - it needs to be related to the probability of response, and it needs to be related …
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
Causal Inference In Hybrid Intervention Trials Involving Treatment Choice, Qi Long, Rod Little, Xihong Lin
The University of Michigan Department of Biostatistics Working Paper Series
Randomized allocation of treatments is a cornerstone of experimental design, but has drawbacks when a limited set of individuals are willing to be randomized, or the act of randomization undermines the success of the treatment. Choice-based experimental designs allow a subset of the participants to choose their treatments. We discuss here causal inferences for experimental designs where some participants are randomly allocated to treatments and others receive their treatment preference. This paper was motivated by the “Women Take Pride” (WTP) study (Janevic et al., 2001), a doubly randomized preference trail (DRPT) to assess behavioral interventions for women with heart disease. …
Multiple Imputation For Interval Censored Data With Auxiliary Variables, Chiu-Hsieh Hsu, Jeremy Taylor, Susan Murray
Multiple Imputation For Interval Censored Data With Auxiliary Variables, Chiu-Hsieh Hsu, Jeremy Taylor, Susan Murray
The University of Michigan Department of Biostatistics Working Paper Series
We propose a nonparametric multiple imputation scheme, NPMLE imputation, for the analysis of interval censored survival data. Features of the method are that it converts interval-censored data problems to complete data or right censored data problems to which many standard approaches can be used, and the measures of uncertainty are easily obtained. In addition to the event time of primary interest, there are frequently other auxiliary variables that are associated with the event time. For the goal of estimating the marginal survival distribution, these auxiliary variables may provide some additional information about the event time for the interval censored observations. …
Robust Likelihood-Based Analysis Of Multivariate Data With Missing Values, Rod Little, An Hyonggin
Robust Likelihood-Based Analysis Of Multivariate Data With Missing Values, Rod Little, An Hyonggin
The University of Michigan Department of Biostatistics Working Paper Series
The model-based approach to inference from multivariate data with missing values is reviewed. Regression prediction is most useful when the covariates are predictive of the missing values and the probability of being missing, and in these circumstances predictions are particularly sensitive to model misspecification. The use of penalized splines of the propensity score is proposed to yield robust model-based inference under the missing at random (MAR) assumption, assuming monotone missing data. Simulation comparisons with other methods suggest that the method works well in a wide range of populations, with little loss of efficiency relative to parametric models when the latter …
Weighting Adjustments For Unit Nonresponse With Multiple Outcome Variables, Sonya L. Vartivarian, Rod Little
Weighting Adjustments For Unit Nonresponse With Multiple Outcome Variables, Sonya L. Vartivarian, Rod Little
The University of Michigan Department of Biostatistics Working Paper Series
Weighting is a common form of unit nonresponse adjustment in sample surveys where entire questionnaires are missing due to noncontact or refusal to participate. Weights are inversely proportional to the probability of selection and response. A common approach computes the response weight adjustment cells based on covariate information. When the number of cells thus created is too large, a coarsening method such as response propensity stratification can be applied to reduce the number of adjustment cells. Simulations in Vartivarian and Little (2002) indicate improved efficiency and robustness of weighting adjustments based on the joint classification of the sample by two …
To Model Or Not To Model? Competing Modes Of Inference For Finite Population Sampling, Rod Little
To Model Or Not To Model? Competing Modes Of Inference For Finite Population Sampling, Rod Little
The University of Michigan Department of Biostatistics Working Paper Series
Finite population sampling is perhaps the only area of statistics where the primary mode of analysis is based on the randomization distribution, rather than on statistical models for the measured variables. This article reviews the debate between design and model-based inference. The basic features of the two approaches are illustrated using the case of inference about the mean from stratified random samples. Strengths and weakness of design-based and model-based inference for surveys are discussed. It is suggested that models that take into account the sample design and make weak parametric assumptions can produce reliable and efficient inferences in surveys settings. …
Inference For The Population Total From Probability-Proportional-To-Size Samples Based On Predictions From A Penalized Spline Nonparametric Model, Hui Zheng, Rod Little
Inference For The Population Total From Probability-Proportional-To-Size Samples Based On Predictions From A Penalized Spline Nonparametric Model, Hui Zheng, Rod Little
The University of Michigan Department of Biostatistics Working Paper Series
Inference about the finite population total from probability-proportional-to-size (PPS) samples is considered. In previous work (Zheng and Little, 2003), penalized spline (p-spline) nonparametric model-based estimators were shown to generally outperform the Horvitz-Thompson (HT) and generalized regression (GR) estimators in terms of the root mean squared error. In this article we develop model-based, jackknife and balanced repeated replicate variance estimation methods for the p-spline based estimators. Asymptotic properties of the jackknife method are discussed. Simulations show that p-spline point estimators and their jackknife standard errors lead to inferences that are superior to HT or GR based inferences. This suggests that nonparametric …
On The Formation Of Weighting Adjustment Cells For Unit Nonresponse, Sonya Vartivarian, Rod Little
On The Formation Of Weighting Adjustment Cells For Unit Nonresponse, Sonya Vartivarian, Rod Little
The University of Michigan Department of Biostatistics Working Paper Series
A method is proposed for weighting adjustments for unit nonresponse based on a crossclassification by the estimated propensity to respond and by the predicted mean of a survey outcome. Simulations to assess the performance of the method are described.
Mixtures Of Varying Coefficient Models For Longitudinal Data With Discrete Or Continuous Non-Ignorable Dropout, Joseph W. Hogan, Xihong Lin, Benjamin A. Herman
Mixtures Of Varying Coefficient Models For Longitudinal Data With Discrete Or Continuous Non-Ignorable Dropout, Joseph W. Hogan, Xihong Lin, Benjamin A. Herman
The University of Michigan Department of Biostatistics Working Paper Series
The analysis of longitudinal repeated measures data is frequently complicated by missing data due to informative dropout. We describe a mixture model for joint distribution for longitudinal repeated measures, where the dropout distribution may be continuous and the dependence between response and dropout is semiparametric. Specifically, we assume that responses follow a varying coefficient random effects model conditional on dropout time, where the regression coefficients depend on dropout time through unspecified nonparametric functions that are estimated using step functions when dropout time is discrete (e.g., for panel data) and using smoothing splines when dropout time is continuous. Inference under the …
Semiparametric Regression Models With Missing Data: The Mathematics In The Work Of Robins Et Al., Menggang Yu, Bin Nan
Semiparametric Regression Models With Missing Data: The Mathematics In The Work Of Robins Et Al., Menggang Yu, Bin Nan
The University of Michigan Department of Biostatistics Working Paper Series
This review is an attempt to understand the landmark papers of Robins, Rotnitzky, and Zhao (1994) and Robins and Rotnitzky (1992). We revisit their main results and corresponding proofs using the theory outlined in the monograph by Bickel, Klaassen, Ritov, and Wellner (1993). We also discuss an illustrative example to show the details of applying these theoretical results.
Penalized Spline Nonparametric Mixed Models For Inference About A Finite Population Mean From Two-Stage Samples, Hui Zheng, Rod Little
Penalized Spline Nonparametric Mixed Models For Inference About A Finite Population Mean From Two-Stage Samples, Hui Zheng, Rod Little
The University of Michigan Department of Biostatistics Working Paper Series
Samplers often distrust model-based approaches to survey inference due to concerns about model misspecification when applied to large samples from complex populations. We suggest that the model-based paradigm can work very successfully in survey settings, provided models are chosen that take into account the sample design and avoid strong parametric assumptions. The Horvitz-Thompson (HT) estimator is a simple design-unbiased estimator of the finite population total in probability sampling designs. From a modeling perspective, the HT estimator performs well when the ratios of the outcome values and the inclusion probabilities are exchangeable. When this assumption is not met, the HT estimator …
Selective Multiple Imputation Of Keys For Statistical Disclosure Control In Microdata, Rod Little, Fang Liu
Selective Multiple Imputation Of Keys For Statistical Disclosure Control In Microdata, Rod Little, Fang Liu
The University of Michigan Department of Biostatistics Working Paper Series
The fundamental tension in statistical disclosure control (SDC) of microdata is the trade-off between the protection of individual respondents and the release of enough information for statistical inferences. We consider microdata that include key variables that contain identifying information and target variables that include sensitive information. Releasing the original data may expose some individuals in the sample to high risk of disclosure; deleting key variables is a common approach, but this loses information for some statistical analysis. This paper proposes selective multiple imputation of key variables (SMIKe) as an alternative SDC technique between those two extremes, and applies SMIKe to …