Dataset Information

Estimation and inference on correlations between biomarkers with repeated measures and left-censoring due to minimum detection levels.

ABSTRACT: Statistical approaches for estimating and drawing inference on the correlation between two biomarkers that are repeatedly assessed over time and subject to left-censoring because minimum detection levels are lacking. We propose a linear mixed-effects model and estimate the parameters with the Monte Carlo expectation maximization (MCEM) method. Inferences regarding the model parameters and the correlation between the biomarkers are performed by applying Louis's method and the delta method. Simulation studies were conducted to compare the proposed MCEM method with existing methods including the maximum likelihood estimation method, the multiple imputation method, and two widely used ad hoc approaches: replacing the censored values with the detection limit or with half of the detection limit. The results show that the performance of the MCEM with respect to relative bias and coverage probability for the 95% confidence interval is superior to the detection limit and half of the detection limit approaches and exceeds that of the multiple imputation method at medium to high levels of censoring, and the standard error estimates from the MCEM method are close to ideal. The maximum likelihood estimation method can estimate the parameters accurately; however, a nonpositive definite information matrix can occur so that the variances are not estimable. These five methods are illustrated with data from a longitudinal human immunodeficiency virus study to estimate and draw inference on the correlation between human immunodeficiency virus RNA levels measured in plasma and in cervical secretions at multiple time points.

SUBMITTER: Xie X

PROVIDER: S-EPMC3875381 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Estimation and inference on correlations between biomarkers with repeated measures and left-censoring due to minimum detection levels.

Xie Xianhong X Xue Xiaonan X Gange Stephen J SJ Strickler Howard D HD Kim Mimi Y MY

Statistics in medicine 20120619 21

Statistical approaches for estimating and drawing inference on the correlation between two biomarkers that are repeatedly assessed over time and subject to left-censoring because minimum detection levels are lacking. We propose a linear mixed-effects model and estimate the parameters with the Monte Carlo expectation maximization (MCEM) method. Inferences regarding the model parameters and the correlation between the biomarkers are performed by applying Louis's method and the delta method. Simula ...[more]

PMID: 22714546

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Project description:BackgroundMeasures of patient satisfaction are increasingly used to measure patient experience. Most satisfaction measures have notable ceiling effects, which limits our ability to learn from variation among relatively satisfied patients. This study tested a variety of single-question satisfaction measures for their mean overall score, ceiling and floor effect, and data distribution. In addition, we assessed the correlation between satisfaction and psychological factors and assessed how the various methods for measuring satisfaction affected net promoter scores (NPSs).MethodologyA total of 212 patients visiting orthopedic offices were enrolled in this randomized controlled trial. Patients were randomized to 1 of 5 newly designed, single-question satisfaction scales: (a) a helpfulness 11-point ordinal scale from 0 to 10, (b) a helpfulness ordinal 11-point scale from 0 to 5 (ie, with 1.5, 2.5, etc), (c) a helpfulness 100-point slider, (d) a satisfaction 11-point ordinal scale from 0 to 10, and (e) a willingness to recommend 11-point ordinal scale from 0 to 10. Additionally, patients completed the 2-item Pain Self-Efficacy Questionnaire (PSEQ-2), 5-item Short Health Anxiety Inventory (SHAI-5) Scale, and Patient-Reported Outcomes Measurement Information System (PROMIS) Depression. We assessed mean and median score, ceiling and floor effect, and skewness and kurtosis for each scale. Spearman's correlation tests were used to test correlations between satisfaction and psychological status. Finally, we assessed the NPS for the various scales.ResultsCeiling effects ranged from 29% to 68%. The 11-point ordinal helpfulness scale from 0 to 10 had the least ceiling effect (29%). All of the scales were asymmetrically distributed, with the 11-point ordinal scale from 0 to 5 having the most Gaussian distribution (skew = 0.64 and kurtosis = 2.3). Satisfaction scores did not correlate with psychological factors: PSEQ-2 (r = 0.04; P = .57), SHAI-5 (r = 0.01; P = .93), and PROMIS Depression (r = -0.04; P = .61). Net promoter scores varied substantially by scale design, with higher scores corresponding with greater ceiling effects.ConclusionsVariations in scale types, text anchors, and lead-in statements do not eliminate the ceiling effect of single-question measures of satisfaction with a visit to an orthopedic specialist. Further studies might test other scale designs and labels.Level of evidenceDiagnostic; Level II.

Dataset Information

Estimation and inference on correlations between biomarkers with repeated measures and left-censoring due to minimum detection levels.

Publications

Estimation and inference on correlations between biomarkers with repeated measures and left-censoring due to minimum detection levels.

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