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Robust estimation for longitudinal data under outcome‐dependent visit processes
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| Zeitschriftentitel: | Australian & New Zealand Journal of Statistics |
|---|---|
| Personen und Körperschaften: | , |
| In: | Australian & New Zealand Journal of Statistics, 62, 2020, 2, S. 212-231 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
|
| Schlagwörter: |
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Neuhaus, John M. McCulloch, Charles E. Neuhaus, John M. McCulloch, Charles E. |
|---|---|
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Neuhaus, John M. McCulloch, Charles E. |
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Neuhaus, John M. McCulloch, Charles E. Australian & New Zealand Journal of Statistics Robust estimation for longitudinal data under outcome‐dependent visit processes Statistics, Probability and Uncertainty Statistics and Probability |
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neuhaus, john m. |
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Neuhaus, John M. McCulloch, Charles E. 1369-1473 1467-842X Wiley Statistics, Probability and Uncertainty Statistics and Probability http://dx.doi.org/10.1111/anzs.12290 <jats:title>Summary</jats:title><jats:p>In longitudinal data where the timing and frequency of the measurement of outcomes may be associated with the value of the outcome, significant bias can occur. Previous results depended on correct specification of the outcome process and a somewhat unrealistic visit process model. In practice, this will never exactly be the case, so it is important to understand to what degree the results hold when those assumptions are violated in order to guide practical use of the methods. This paper presents theory and the results of simulation studies to extend our previous work to more realistic visit process models, as well as Poisson outcomes. We also assess the effects of several types of model misspecification. The estimated bias in these new settings generally mirrors the theoretical and simulation results of our previous work and provides confidence in using maximum likelihood methods in practice. Even when the assumptions about the outcome process did not hold, mixed effects models fit by maximum likelihood produced at most small bias in estimated regression coefficients, illustrating the robustness of these methods. This contrasts with generalised estimating equations approaches where bias increased in the settings of this paper. The analysis of data from a study of change in neurological outcomes following microsurgery for a brain arteriovenous malformation further illustrate the results.</jats:p> Robust estimation for longitudinal data under outcome‐dependent visit processes Australian & New Zealand Journal of Statistics |
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Robust estimation for longitudinal data under outcome‐dependent visit processes |
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Robust estimation for longitudinal data under outcome‐dependent visit processes |
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Robust estimation for longitudinal data under outcome‐dependent visit processes |
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Robust estimation for longitudinal data under outcome‐dependent visit processes |
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Robust estimation for longitudinal data under outcome‐dependent visit processes |
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Robust estimation for longitudinal data under outcome‐dependent visit processes |
| title_sort |
robust estimation for longitudinal data under outcome‐dependent visit processes |
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Statistics, Probability and Uncertainty Statistics and Probability |
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http://dx.doi.org/10.1111/anzs.12290 |
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2020 |
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212-231 |
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<jats:title>Summary</jats:title><jats:p>In longitudinal data where the timing and frequency of the measurement of outcomes may be associated with the value of the outcome, significant bias can occur. Previous results depended on correct specification of the outcome process and a somewhat unrealistic visit process model. In practice, this will never exactly be the case, so it is important to understand to what degree the results hold when those assumptions are violated in order to guide practical use of the methods. This paper presents theory and the results of simulation studies to extend our previous work to more realistic visit process models, as well as Poisson outcomes. We also assess the effects of several types of model misspecification. The estimated bias in these new settings generally mirrors the theoretical and simulation results of our previous work and provides confidence in using maximum likelihood methods in practice. Even when the assumptions about the outcome process did not hold, mixed effects models fit by maximum likelihood produced at most small bias in estimated regression coefficients, illustrating the robustness of these methods. This contrasts with generalised estimating equations approaches where bias increased in the settings of this paper. The analysis of data from a study of change in neurological outcomes following microsurgery for a brain arteriovenous malformation further illustrate the results.</jats:p> |
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| description | <jats:title>Summary</jats:title><jats:p>In longitudinal data where the timing and frequency of the measurement of outcomes may be associated with the value of the outcome, significant bias can occur. Previous results depended on correct specification of the outcome process and a somewhat unrealistic visit process model. In practice, this will never exactly be the case, so it is important to understand to what degree the results hold when those assumptions are violated in order to guide practical use of the methods. This paper presents theory and the results of simulation studies to extend our previous work to more realistic visit process models, as well as Poisson outcomes. We also assess the effects of several types of model misspecification. The estimated bias in these new settings generally mirrors the theoretical and simulation results of our previous work and provides confidence in using maximum likelihood methods in practice. Even when the assumptions about the outcome process did not hold, mixed effects models fit by maximum likelihood produced at most small bias in estimated regression coefficients, illustrating the robustness of these methods. This contrasts with generalised estimating equations approaches where bias increased in the settings of this paper. The analysis of data from a study of change in neurological outcomes following microsurgery for a brain arteriovenous malformation further illustrate the results.</jats:p> |
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| spelling | Neuhaus, John M. McCulloch, Charles E. 1369-1473 1467-842X Wiley Statistics, Probability and Uncertainty Statistics and Probability http://dx.doi.org/10.1111/anzs.12290 <jats:title>Summary</jats:title><jats:p>In longitudinal data where the timing and frequency of the measurement of outcomes may be associated with the value of the outcome, significant bias can occur. Previous results depended on correct specification of the outcome process and a somewhat unrealistic visit process model. In practice, this will never exactly be the case, so it is important to understand to what degree the results hold when those assumptions are violated in order to guide practical use of the methods. This paper presents theory and the results of simulation studies to extend our previous work to more realistic visit process models, as well as Poisson outcomes. We also assess the effects of several types of model misspecification. The estimated bias in these new settings generally mirrors the theoretical and simulation results of our previous work and provides confidence in using maximum likelihood methods in practice. Even when the assumptions about the outcome process did not hold, mixed effects models fit by maximum likelihood produced at most small bias in estimated regression coefficients, illustrating the robustness of these methods. This contrasts with generalised estimating equations approaches where bias increased in the settings of this paper. The analysis of data from a study of change in neurological outcomes following microsurgery for a brain arteriovenous malformation further illustrate the results.</jats:p> Robust estimation for longitudinal data under outcome‐dependent visit processes Australian & New Zealand Journal of Statistics |
| spellingShingle | Neuhaus, John M., McCulloch, Charles E., Australian & New Zealand Journal of Statistics, Robust estimation for longitudinal data under outcome‐dependent visit processes, Statistics, Probability and Uncertainty, Statistics and Probability |
| title | Robust estimation for longitudinal data under outcome‐dependent visit processes |
| title_full | Robust estimation for longitudinal data under outcome‐dependent visit processes |
| title_fullStr | Robust estimation for longitudinal data under outcome‐dependent visit processes |
| title_full_unstemmed | Robust estimation for longitudinal data under outcome‐dependent visit processes |
| title_short | Robust estimation for longitudinal data under outcome‐dependent visit processes |
| title_sort | robust estimation for longitudinal data under outcome‐dependent visit processes |
| title_unstemmed | Robust estimation for longitudinal data under outcome‐dependent visit processes |
| topic | Statistics, Probability and Uncertainty, Statistics and Probability |
| url | http://dx.doi.org/10.1111/anzs.12290 |