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edit approved Nov 20 at 3:48
Mario
edit approved Nov 20 at 3:48
Mario
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Is LMM a good alternative for Repeated Measures ANOVA with Missing Data?

YesYes, LMM is a great alternative to repeated measures ANOVArepeated measures ANOVA. LMM can handle cases where you have an uneven number of outcome measures on each patient (e.g. some have 1, 2, 3, or more outcome measures) or if the patients are measured at unequal time points (e.g. patient 1 was measured at 3 months, 6 months, 9 months, where patient 2 was measured at 6 months, 1 year, 2 years, etc). Repeated Measures ANOVA cannot handle either of these 2 cases.

LMM assumes the outcome is "missing at random" (MAR)"missing at random" (MAR) whereas GEE makes the stronger assumption that the outcome is "missing completely at random" (MCAR). What's the difference? MCAR is exactly what it sounds like, the missing values must be completely random which is often not true in practice. In contrast, MAR means the data must be missing at random after adjusting for covariates that impact missingness"missing completely at random" (MCAR). Basically, MAR at least gives you a chance to adjust for

What's the variables that caused missing outcomes (i.e. patient dropout or loss to followup).difference?

  • MCAR is exactly what it sounds like, the missing values must be completely random which is often not true in practice.
  • In contrast, MAR means the data must be missing at random after adjusting for covariates that impact missingness. Basically, MAR at least gives you a chance to adjust for the variables that caused missing outcomes (i.e. patient dropout or loss of follow-up).

TLDR: LMM is almost always a better framework than repeated measures anovaANOVA, it's much more flexible. Given outcomes that are missing on some patients (e.g. patient 2 missed their 6month followup6-month follow-up), then LMM is in theory better than GEE because you can adjust for variables that cause the missingness and still have valid inferences, whereas GEE does not have this property.

Yes, LMM is a great alternative to repeated measures ANOVA. LMM can handle cases where you have an uneven number of outcome measures on each patient (e.g. some have 1, 2, 3, or more outcome measures) or if the patients are measured at unequal time points (e.g. patient 1 was measured at 3 months, 6 months, 9 months, where patient 2 was measured at 6 months, 1 year, 2 years, etc). Repeated Measures ANOVA cannot handle either of these 2 cases.

LMM assumes the outcome is "missing at random" (MAR) whereas GEE makes the stronger assumption that the outcome is "missing completely at random" (MCAR). What's the difference? MCAR is exactly what it sounds like, the missing values must be completely random which is often not true in practice. In contrast, MAR means the data must be missing at random after adjusting for covariates that impact missingness. Basically, MAR at least gives you a chance to adjust for the variables that caused missing outcomes (i.e. patient dropout or loss to followup).

TLDR: LMM is almost always a better framework than repeated measures anova, it's much more flexible. Given outcomes that are missing on some patients (e.g. patient 2 missed their 6month followup), then LMM is in theory better than GEE because you can adjust for variables that cause the missingness and still have valid inferences, whereas GEE does not have this property.

Is LMM a good alternative for Repeated Measures ANOVA with Missing Data?

Yes, LMM is a great alternative to repeated measures ANOVA. LMM can handle cases where you have an uneven number of outcome measures on each patient (e.g. some have 1, 2, 3, or more outcome measures) or if the patients are measured at unequal time points (e.g. patient 1 was measured at 3 months, 6 months, 9 months, where patient 2 was measured at 6 months, 1 year, 2 years, etc). Repeated Measures ANOVA cannot handle either of these 2 cases.

LMM assumes the outcome is "missing at random" (MAR) whereas GEE makes the stronger assumption that the outcome is "missing completely at random" (MCAR).

What's the difference?

  • MCAR is exactly what it sounds like, the missing values must be completely random which is often not true in practice.
  • In contrast, MAR means the data must be missing at random after adjusting for covariates that impact missingness. Basically, MAR at least gives you a chance to adjust for the variables that caused missing outcomes (i.e. patient dropout or loss of follow-up).

TLDR: LMM is almost always a better framework than repeated measures ANOVA, it's much more flexible. Given outcomes that are missing on some patients (e.g. patient 2 missed their 6-month follow-up), then LMM is in theory better than GEE because you can adjust for variables that cause the missingness and still have valid inferences, whereas GEE does not have this property.

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jarbet
jarbet
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Yes, LMM is a great alternative to repeated measures ANOVA. LMM can handle cases where you have an uneven number of outcome measures on each patient (e.g. some have 1, 2, 3, or more outcome measures) or if the patients are measured at unequal time points (e.g. patient 1 was measured at 3 months, 6 months, 9 months, where patient 2 was measured at 6 months, 1 year, 2 years, etc). Repeated Measures ANOVA cannot handle either of these 2 cases.

LMM assumes the outcome is "missing at random" (MAR) whereas GEE makes the stronger assumption that the outcome is "missing completely at random" (MCAR). What's the difference? MCAR is exactly what it sounds like, the missing values must be completely random which is often not true in practice. In contrast, MAR means the data must be missing at random after adjusting for covariates that impact missingness. Basically, MAR at least gives you a chance to adjust for the variables that caused missing outcomes (i.e. patient dropout or loss to followup).

TLDR: LMM is almost always a better framework than repeated measures anova, it's much more flexible. Given outcomes that are missing on some patients (e.g. patient 2 missed their 6month followup), then LMM is in theory better than GEE because you can adjust for variables that cause the missingness and still have valid inferences, whereas GEE does not have this property.