Estimation and comparison of rates of change in longitudinal studies with informative drop‐outs

Touloumi, Giota; Pocock, S. J.; Babiker, Abdel; Darbyshire, Janet

doi:10.1002/(sici)1097-0258(19990530)18:10<1215::aid-sim118>3.3.co;2-y

Cited by 22 publications

(53 citation statements)

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“…However, given the full survival information now available, we were able to assess the performance of our method of parameter estimation for the censored data by comparing our results to those obtained when the same model was applied to the uncensored data using the conventional RIGLS algorithm for parameter estimation. The results for this model ÿtted using the piecewise linear model (shown in the last column of Table II) show close agreement to those for the censored data using the modiÿed algorithm of Touloumi et al [9], suggesting that this estimation algorithm can perform well for data analysis when full survival information on all subjects in a study is not available.…”

Section: Model Checkingsupporting

confidence: 69%

“…The multilevel model version of Schluchter's trivariate Normal model [9] treats the QL response and survival as a bivariate problem. This can be viewed as a two-level model for the QL response [3], modelling alongside a log duration model for the survival outcome [12].…”

Section: Trivariate Normal Modelmentioning

confidence: 99%

“…When some survival times are censored, this same underlying model applies but a modiÿcation is needed to the estimation algorithm [9]. In short, this algorithm treats the survival times and their corresponding residual components for censored individuals as missing data and adopts an EM approach.…”

Section: Trivariate Normal Modelmentioning

confidence: 99%

“…This procedure was implemented in standard software [13] with a little additional programming. Touloumi et al [9] give a full account of the algorithm and a discussion of the properties of the parameter estimates obtained.…”

Section: Trivariate Normal Modelmentioning

confidence: 99%

“…Parameter estimates for the QL response within the model are now weighted over the distribution of observed and expected survival times. Touloumi et al [9] applied the multilevel modelling framework using restricted iterative generalized least-squares (RIGLS) estimation [10; 11] as the M-step of the EM algorithm. It is an application of this method that we present here for the joint analysis of QL and survival data from a recent clinical trial in cancer research.…”

Section: Introductionmentioning

confidence: 99%

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A joint analysis of quality of life and survival using a random effect selection model

2000

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Section: Model Checkingsupporting

confidence: 69%

Section: Trivariate Normal Modelmentioning

confidence: 99%

Section: Trivariate Normal Modelmentioning

confidence: 99%

Section: Trivariate Normal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A joint analysis of quality of life and survival using a random effect selection model

2000

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Longitudinal analysis when the experimenter does not determine when treatment ends: what is dose–response?

Feaster

Newman

Rice

2003

Clin Psychology and Psychoth

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The most appropriate amount of psychotherapy to address a particular problem is of interest to clinicians, consumers and those responsible for funding of care. The dose-response relationship has been examined within the context of randomized clinical trials, meta-analysis as well as naturalistic studies; however, each of these approaches has limits. Many of these approaches have conceptually blurred two distinct concepts: do participants with different characteristics need different amounts of therapy and do otherwise equal participants show different outcomes when given different levels of (a particular type of) therapy? For any study design, if the experimenter does not determine the duration of therapy, then the length of therapy is said to be endogenous. This endogeneity can bias any attempt to untangle the answer to these two questions. An extension of the biasing effect of this endogeneity involves the choice of times to assess outcome; if outcome assessment depends on when therapy is terminated (rather than exogenously chosen) then estimates of the trajectory of outcome may be biased. Design considerations to minimize this effect are discussed.

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Jointly modelling the relationship between survival and pulmonary function in cystic fibrosis patients

Schluchter

Konstan

Davis

2002

Statistics in Medicine

106

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Modelling the relationship between pulmonary function and survival in cystic fibrosis (CF) is complicated by the fact that measures of pulmonary function commonly used such as the forced expiratory volume in one second (FEV(1)) are measured with error, and patients with the poorest lung function are increasingly censored by death, that is, data are available only for the patients who have survived to the current age. We assume a linear random effects model for FEV1 per cent predicted, where the random intercept and slope of FEV(1) per cent predicted, along with a specified transformation of the age at death follow a trivariate normal distribution. We illustrate how this model can be used to describe the relationship between age at death and parameters of the individual patient's regression of FEV(1) per cent predicted versus age, such as the slope and the intercept or true value of FEV(1) per cent predicted at a given age. We also illustrate how the model provides empirical Bayes estimates of these individual parameters. In particular, we explore how the predicted value of the age at death might be used as a prognostic or severity index. The model and methods are illustrated on a cohort of 188 cystic fibrosis patients with a common genotype (homozygous for the DeltaF508 mutation), born on or after 1965 and followed at the CF Center at the Rainbow Babies and Children's Hospital, Cleveland, OH, U.S.A.

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Estimation and comparison of rates of change in longitudinal studies with informative drop‐outs

Cited by 22 publications

References 0 publications

A joint analysis of quality of life and survival using a random effect selection model

A joint analysis of quality of life and survival using a random effect selection model

Longitudinal analysis when the experimenter does not determine when treatment ends: what is dose–response?

Jointly modelling the relationship between survival and pulmonary function in cystic fibrosis patients

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