1992
DOI: 10.1002/sim.4780111603
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Statistical handling of drop‐outs in longitudinal clinical trials

Abstract: This paper considers the statistical complexities that arise due to outcome related drop-outs in longitudinal clinical trials of the randomized parallel groups design with fixed assessment times and an explanatory aim. The shortcomings of currently popular methods of coping with the problem of drop-outs are discussed. It is proposed that progress can be made by applying the modern methodology that was primarily developed for sample surveys with non-response and for observational studies. A practical applicatio… Show more

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Cited by 227 publications
(125 citation statements)
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“…25,26 A logistic regression for the variable "assessed at trial end" (yes = 1, no = 0) was estimated over a constant term and adolescent mother status (the only variable associated with loss). The probability of being assessed was calculated and inverse probability weights computed.…”
Section: Discussionmentioning
confidence: 99%
“…25,26 A logistic regression for the variable "assessed at trial end" (yes = 1, no = 0) was estimated over a constant term and adolescent mother status (the only variable associated with loss). The probability of being assessed was calculated and inverse probability weights computed.…”
Section: Discussionmentioning
confidence: 99%
“…(Heytig et al, 1992;Wooldridge, 2002) A logistic regression for the variable "assessed at trial end" (yes 1, no 0) was estimated over a constant term and variables associated with loss (for 3 country analyses adolescent mother status; for analyses with Jamaica only, adolescent mother status and household crowding). The probability of being assessed was calculated and inverse probability weights computed as 1 divided by the estimated probability of being assessed.…”
Section: Discussionmentioning
confidence: 99%
“…To avoid this problem, missing data imputation becomes an important part of analysing economic data. Within this study, the last observation carried forward was used to impute missing data in order to calculate total costs and QALYs (Heyting et al, 1992). Figure 1 shows that 444 patients (out of 674) or 66% had a valid baseline QoL assessment; 230/338 (68%) in the IFN group Table 1 Comparison of samples analysed For the EORTC QLQ-C30 v1 function scales a higher score represents a better level of functioning.…”
Section: Discussionmentioning
confidence: 99%