Sensitivity analyses for partially observed recurrent event data

Akacha, Mouna; Ogundimu, Emmanuel O.

doi:10.1002/pst.1720

Cited by 15 publications

(30 citation statements)

References 23 publications

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“…Similarly, if a patient discontinues treatment and takes rescue or alternative medication, a hypothetical strategy might focus on what would have happened if the patient discontinued treatment but did not take this additional medication. In this case, the controlled imputation methods described previously may provide suitable estimators …”

Section: Discussionmentioning

confidence: 99%

“…In this case, the controlled imputation methods described previously may provide suitable estimators. 4,11 A strategy for estimands that is relevant for subjects is the "while on treatment" strategy which addresses the important issue of what effect the treatment has when it is actually taken. 12,13 An appropriate estimator corresponding to this strategy would be an analysis of the recurrent event data collected on treatment using the negative binomial model ignoring off-treatment data and including the exposure through an offset.…”

Section: Discussionmentioning

confidence: 99%

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Treatment policy estimands for recurrent event data using data collected after cessation of randomised treatment

Roger

Bratton

Mayer

et al. 2018

Pharmaceutical Statistics

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SUMMARY In the past, many clinical trials have withdrawn subjects from the study when they prematurely stopped their randomised treatment and have therefore only collected ‘on‐treatment’ data. Thus, analyses addressing a treatment policy estimand have been restricted to imputing missing data under assumptions drawn from these data only. Many confirmatory trials are now continuing to collect data from subjects in a study even after they have prematurely discontinued study treatment as this event is irrelevant for the purposes of a treatment policy estimand. However, despite efforts to keep subjects in a trial, some will still choose to withdraw. Recent publications for sensitivity analyses of recurrent event data have focused on the reference‐based imputation methods commonly applied to continuous outcomes, where imputation for the missing data for one treatment arm is based on the observed outcomes in another arm. However, the existence of data from subjects who have prematurely discontinued treatment but remained in the study has now raised the opportunity to use this ‘off‐treatment’ data to impute the missing data for subjects who withdraw, potentially allowing more plausible assumptions for the missing post‐study‐withdrawal data than reference‐based approaches. In this paper, we introduce a new imputation method for recurrent event data in which the missing post‐study‐withdrawal event rate for a particular subject is assumed to reflect that observed from subjects during the off‐treatment period. The method is illustrated in a trial in chronic obstructive pulmonary disease (COPD) where the primary endpoint was the rate of exacerbations, analysed using a negative binomial model.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Treatment policy estimands for recurrent event data using data collected after cessation of randomised treatment

Roger

Bratton

Mayer

et al. 2018

Pharmaceutical Statistics

View full text Add to dashboard Cite

show abstract

“…Specifically, the method can be easily extended to other MNAR imputation models. For instance, instead of the use of imputation model E(Y|x,S⋆<0)=β'x-σρΛν(-γ'w), which is similar to the jump to reference approach, 25,26 the imputation approach can also incorporate some form of pattern mixture-model. That is, the imputation model can be multiplied by a factor or offsets added based on subject matter knowledge.…”

Section: Discussionmentioning

confidence: 99%

A robust imputation method for missing responses and covariates in sample selection models

Ogundimu

Collins

2017

Stat Methods Med Res

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Sample selection arises when the outcome of interest is partially observed in a study. Although sophisticated statistical methods in the parametric and non-parametric framework have been proposed to solve this problem, it is yet unclear how to deal with selectively missing covariate data using simple multiple imputation techniques, especially in the absence of exclusion restrictions and deviation from normality. Motivated by the 2003-2004 NHANES data, where previous authors have studied the effect of socio-economic status on blood pressure with missing data on income variable, we proposed the use of a robust imputation technique based on the selection-t sample selection model. The imputation method, which is developed within the frequentist framework, is compared with competing alternatives in a simulation study. The results indicate that the robust alternative is not susceptible to the absence of exclusion restrictions - a property inherited from the parent selection-t model - and performs better than models based on the normal assumption even when the data is generated from the normal distribution. Applications to missing outcome and covariate data further corroborate the robustness properties of the proposed method. We implemented the proposed approach within the MICE environment in R Statistical Software.

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“…For example, in a two-arm placebo-controlled trial, participants with missing data in the active arm can be imputed to follow the distribution of the placebo arm, assuming no treatment benefit following drop-out (referred to as a jumpto-reference imputation). Delta and reference based multiple imputation methods can been implemented with continuous [7,17,18], binary [21,22], ordinal [23], count [24][25][26] and survival data [27][28][29][30].…”

Section: Analysis When Different Missing Data Assumptions Are Requirementioning

confidence: 99%

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

Cro

Morris

Kahan

et al. 2020

BMC Med Res Methodol

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Background: The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking. Methods: We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a 'pandemicfree world' and 'world including a pandemic' are of interest. Results: In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a 'pandemic-free world', participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the 'world including a pandemic', all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumptionpotentially incorporating a pandemic time-period indicator and participant infection statusor a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses. Conclusions: Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.

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Sensitivity analyses for partially observed recurrent event data

Cited by 15 publications

References 23 publications

Treatment policy estimands for recurrent event data using data collected after cessation of randomised treatment

Treatment policy estimands for recurrent event data using data collected after cessation of randomised treatment

A robust imputation method for missing responses and covariates in sample selection models

A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic

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