Missing outcome data are a very common problem in smoking cessation trials. It is common that any such missing data are assumed to correspond to smokers 1 2 3 4 . This assumption could be justified by the notion that anyone in a trial who successfully gives up smoking will report this fact. Foulds et al. 5 provide some evidence that missing data are smokers. Hajek and West 6 argue that the “missing=smoking” assumption is plausible because “successful quitters are usually keen to let the treatment providers know of their success” and that “treatment failures feel embarrassed”. The Russell standard requires that smokers lost to follow-up are classified as continuing to smoke 6 7 .

However the evidence from Foulds et al. is of limited value because it is based upon just fifty participants with missing outcome data. Furthermore this was in a hospital setting and there is no reason why this should translate to other settings, and in particular to an Internet based trial. Although some may find the “missing=smoking” assumption plausible, and this provides a simple way to handle the missing data, it is open to immediate criticism. One reason for this is because imputing missing outcome data as smokers is a single imputation based procedure, which does not take into account the uncertainty in the missing values 8, p. 45 ; if the “missing=smoking” assumption is incorrect then measures of uncertainty, such as standard errors, can be artificially and very considerably diminished. “Missing=smoking” also assumes that all quitters respond. Finally, the “missing=smoking” assumption tacitly assumes that any baseline and intermediate data have no additional value for predicting the outcome for participants whose outcome is unknown.

A further source of concern is the bias in the estimated treatment effect that may result from incorrectly assuming “missing=smoking”. Nelson et al. 9 show that this assumption is “as likely to lead to liberal estimates as to conservative estimates relative to the complete case analysis” and argue that better statistical methods are needed for handling missing data in the tobacco cessation research community. Barnes et al. 10 investigate a range of methods for handling missing data in their trial and conclude that imputing missing data as smokers “can cause a large amount of bias if imputing smoking is an incorrect assumption”.

The principal contribution of this paper is the use of empirical evidence to explore the plausibility of different missing data models in the context of a smoking cessation trial. Our aim is to determine which variables play an important role in these models. Particular interest lies in the role of the outcome itself, in order to assess the appropriateness of the “missing=smoking” assumption.

The rest of this paper is set out as follows. We begin by introducing the iQuit trial, an Internet based smoking cessation trial with a large amount of missing outcome data 11 . Here we also describe ten baseline covariates that were thought, a priori, to be potential predictors in the missing data model. We also describe the repeated attempts that were made by the trial investigators to obtain outcome data. In section “Which baseline variables are predictive of missingness? A selection model approach”, we develop our selection model, where we assess whether any of the baseline variables play an important role in the missing data model, whilst also allowing the outcome itself to influence this in a sensitivity analysis. The attempt to simultaneously estimate the baseline covariate and the outcome effects in this selection modelling framework was, as anticipated, not very successful so in section “Is the primary trial outcome predictive of missingness? Modelling the repeated attempts” we describe and use our model for the repeated attempts made to obtain outcome data. Finally we summarise our findings and draw conclusions for smoking cessation trialists.

The iQuit trial is an Internet based smoking cessation randomised controlled trial to assess the benefit of self-help smoking cessation materials tailored to individual smoker characteristics over generic self-help materials, conducted among the general population of smokers seeking help from web-based sources 11 . Participants sign up for the trial via the QUIT website (www.quit.org.uk). They fill in a questionnaire and receive an online advice report to help them quit smoking. They are randomised either to receive the tailored version or the generic version. Six months later they receive a telephone interview to find out whether they are still smoking, see if their smoking-related beliefs have changed at all, and find out what they thought of the advice they received.

The primary iQuit trial outcome is whether or not participants have abstained from smoking (self-reported three months prolonged abstinence) and the primary research question is whether or not tailored materials are more effective than generic materials in helping participants achieving this. The corresponding analysis is described in detail by Mason et al. 11 , who found a lack of evidence for a treatment effect. However, and despite the intensive follow up from the trialists to obtain outcome data, there is a large amount of missing data; smoking status is unknown for 1036 of the 1758 participants (59%). This compromises the primary analysis, as explained by Mason et al., but provides an excellent opportunity to investigate the reasons for missing data. The pattern of missing data is summarised in Table 1.

In addition to the primary trial outcome, a wide range of complete (no missing data) baseline variables were measured, and those thought a priori to be most likely to be good predictors of missing data are summarised in Table 2. Some of the variables in Table 2 are referred to as ‘smoking related’ variables because they are considered to more directly relate to the participant’s smoking behaviour. An aim here is to investigate which, if any, of these variables are good predictors, whilst allowing for the possibility that the primary trial outcome itself may also be predictive of its missingness.

Summary statistics are shown where means and standard deviations (in parentheses) are given for the continuous and categorical variables.

The iQuit trial provides the rich data on the number and type of attempts made to obtain outcome data shown in Table 3. It was specified in the trial design that participants would receive up to ten telephone calls to obtain outcome data and, for those of whom all calls were unsuccessful, where possible a single further attempt was made by email. The telephone attempts ceased when outcome data was obtained, the number given by the participant was found to be invalid or the participant requested that no further telephone calls were made. The decision to make telephone calls to obtain outcome data was made in order to ensure good quality data and so that the medium of follow-up was not the same as the medium of intervention. Multiple telephone calls were made in order to facilitate calling participants at different times of the day but no more than ten calls were made to avoid harassing them. The email was a ‘last ditch’ effort to obtain outcome data where the telephone calls had failed. These repeated attempts to obtain data provide the basis for our modelling in section “Is the primary trial outcome predictiveof missingness? Modelling the repeated attempts”.

The fraction and percentage of participants who successfully quit smoking (Y=1) are tabulated by the number of contact attempts (telephone calls and email). Participants received up to ten telephone calls and up to one email attempt.

Here a selection modelling approach 8, p. 30 is used in order to investigate the missing data model in smoking cessation trials. The modelling allows for an association between the trial outcome and the missing data indicator but also accommodates less extreme assumptions than “missing=smoking”. We extend this approach in the next section by using data on the repeated attempts to obtain outcome data 12 13 14 15 .

For the sake of generality, for the moment we use vectors to denote the outcomes but in our application these quantities are scalars. Let Y_i denote the ith participant’s vector of outcomes, so that participants may provide more than a single outcome, and let R_i denote the corresponding vector of missing data indicators, where R_i,j  = 1 if Y_i,j is observed, where Y _i,j and R_i,j are the jth entries of Y_i and R_i respectively. We let x_i denote the ith participant’s covariates and we posit a model for Y_i | x_i . We then posit a model R_i |( Y_i , x_i ), which is referred to as the selection model. We model the joint distribution of ( Y_i , R_i )| x_i using the factorisation provided by these two models.

A common assumption is that the data are Missing at Random (MAR). The data are said to be MAR, given the covariates x_i if, for all i, R_i is independent of the missing entries of Y_i , given those that are observed and x_i . Equivalently, the MAR assumption can be expressed as the requirement that the density of R_i |( Y_i x_i ) depends only on Y_i through the entries that are observed. However it is not clear from this definition whether MAR requires this condition for the observed pattern of missing data or for all possible patterns of missing data under repeated sampling. The definition of MAR of Lu and Copas 16 makes this requirement explicit for all possible missingness patterns and they show that, with the further assumption that separate parameters are used in the models for the outcome and the selection model, their definition of MAR implies that the model for the missing data R_i |( Y_i x _i ) is ignorable and valid inferences for the outcome parameters can be made using just the outcome model and the observed outcome data. A caveat however is that the observed, rather than the expected, information matrix should be used to obtain standard errors 17 .

If the MAR assumption is not satisfied then the data are Missing not at random (MNAR). We will allow MNAR models so that an association between the potentially missing outcome (smoking cessation) and the missing indicator is permitted. Although the model for the outcome is usually of central interest, because this contains population parameters such as a treatment effect, here the focus of interest lies in the selection model. This is because this model describes why data are missing, and so we also refer to this model as the missing data model.

We are primarily interested in determining which variables play an important role in this model. One reason why this investigation is important is because MAR analyses are made more plausible by including variables that are good predictors of missingness: if they predict missingness sufficiently well so that any role of missing Y_i is non-existent, or at least negligible, then the MAR assumption is adequate. It is however important to know what kind of additional variables smoking cessation trialists should routinely collect and incorporate into models to make MAR more plausible. These variables may be modelled as covariates if we are prepared to adjust for them 18 , or as further response variables if we are not 18 19 . Another reason why this investigation is important is to determine whether or not the outcome itself is a useful predictor of missingness, in order to assess whether MNAR modelling is required. However, since every MNAR model has a MAR counterpart with equal fit 20 , it is only by making distributional assumptions, such as those that follow, that this type of assessment can be made.

We will define Y_i  = 1 if the ith participant has abstained from smoking and Y_i  = 0 otherwise and R_i as the corresponding missing data indicator. We define x_i as the ith participant’s row vector of ten covariates, in the order they appear in Tables 2 and 4. Since Y_i and R_i are both binary, we use conventional logistic regression modelling for both variables and we assume that

logit ( P ( Y i = 1 | x i ) ) = α 0 + α 1 x i

and

logit ( P ( R i = 1 | Y i = y i , x i ) ) = β 0 + β 1 x i + β 2 y i .

Parameter

β ₂  = − 4

β ₂  = − 3

β ₂  = − 2

β ₂  = − 1

β ₂  = 0

β ₂  = 1

β ₂  = 2

β ₂  = 3

β ₂  = 4

( P _a  = 0.94 )

( P _a  = 0.85 )

( P _a  = 0.68 )

( P _a  = 0.44 )

( P _a  = 0.22 )

( P _a  = 0.10 )

( P _a  = 0.04 )

( P _a  = 0.01 )

( P _a  = 0.00 )

The coefficients β_1,1 to β_1,10 describe the effect of each of the ten baseline covariates in Table 2. The tabulated P_a  = P(Y = 1|R = 0,x) are obtained from equation (4) with logit(P(Y = 1|R = 1,x)) = logit(0.22)and the corresponding value of β₂. Statistically significant estimates, at the 5% level, are shown in bold and standard errors are in parentheses.

β_1,1 (Treatment)

-0.073(0.163)

-0.072(0.149)

-0.081(0.127)

-0.088(0.106)

-0.091(0.099)

-0.094(0.102)

-0.095(0.104)

-0.097(0.106)

-0.097(0.107)

β_1,2 (Age)

0.038(0.008)

0.035(0.007)

0.033(0.006)

0.030(0.005)

0.028(0.005)

0.027(0.005)

0.026(0.005)

0.026(0.005)

0.025(0.005)

β_1,3 (Sex)

0.005(0.172)

0.036(0.157)

0.070(0.133)

0.102(0.111)

0.131(0.104)

0.152(0.107)

0.165(0.110)

0.171(0.111)

0.174(0.112)

β_1,4 (Qualifications)

0.072(0.077)

0.073(0.070)

0.072(0.061)

0.071(0.051)

0.072(0.047)

0.073(0.049)

0.071(0.050)

0.069(0.050)

0.067(0.050)

β_1,5 (Deprivation)

-0.253(0.088)

-0.222(0.079)

-0.175(0.067)

-0.116(0.055)

-0.066 (0.051)

-0.036(0.053)

-0.024(0.054)

-0.020(0.054)

-0.018(0.054)

β_1,6 (Conscientiousness)

-0.230(0.097)

-0.208(0.089)

-0.177(0.076)

-0.139(0.064)

-0.105(0.060)

-0.084(0.062)

-0.074(0.063)

-0.069(0.064)

-0.067(0.064)

β_1,7 (Determination)

0.240(0.114)

0.206(0.103)

0.153(0.087)

0.089(0.073)

0.034(0.067)

0.001(0.069)

-0.015(0.071)

-0.022(0.071)

-0.024(0.072)

β_1,8 (Support)

0.131(0.067)

0.121(0.062)

0.107(0.052)

0.087(0.044)

0.069(0.041)

0.057(0.042)

0.051(0.043)

0.049(0.044)

0.048(0.044)

β_1,9 (Dependence)

0.059(0.059)

0.055(0.052)

0.039(0.043)

0.016(0.036)

-0.001(0.034)

-0.011(0.035)

-0.015(0.035)

-0.017(0.036)

-0.018(0.036)

β_1,10 (Previous)

-0.020(0.169)

-0.041(0.153)

-0.084(0.131)

-0.138(0.109)

-0.183(0.103)

-0.211(0.105)

-0.224(0.108)

-0.228(0.109)

-0.230(0.110)

The results from the sensitivity analysis

If R_i  = 0 then Y_i is missing and is ‘summed out’ of the log-likelihood in (3) below. Hence participants who do not provide outcome data contribute to the analysis. We further assume that participants are independent. The first α₁ parameter, which we denote as α_1,1 is the (adjusted) treatment effect, but here the focus of interest is on the covariates that are important in the missing data model, ie β₁ and β₂ are paramount. The parameter β₂ is the adjusted log odds ratio between Y_i and R_i . This parameter is therefore of particular interest because a positive infinite β₂ is equivalent to assuming “missing=smoking”. If β₂ = 0 then the data are MAR, otherwise the data are MNAR. We address the difficulty in estimating β₂ later. Separate parameters are used for the outcome (α parameters) and the selection model (the βparameters) so MAR implies that the missing data model is ignorable 16 . In this case the models (1) and (2) can be fitted as two separate conventional logistic regressions, where model (1) is fitted using the complete cases.

A participant for whom Y_i is observed ( R_i  = 1) contributes P( Y_i  =  y_i | x_i )P( R_i  = 1| Y_i  =  y_i , x_i ) to the likelihood, and a participant for whom Y_i is not observed provides P ( R i = 0 | x i ) = ∑ y i P ( Y i = y i | x i ) P ( R i = 0 | Y i = y i , x i ) . The log-likelihood of the data provided by all 1758 participants is L( α₀, α₁, β₀, β₁, β₂) = 

∑ i = 1 1758 R i log P ( Y i = y i | x i ) P ( R i = 1 | Y i = y i , x i ) + ∑ i = 1 1758 ( 1 − R i ) log P ( R i = 0 | x i ) = ∑ i = 1 1758 R i log P ( Y i = y i | x i ) P ( R i = 1 | Y i = y i , x i ) + ∑ i = 1 1758 ( 1 − R i ) log ∑ y i = 0 1 P ( Y i = y i | x i ) P ( R i = 0 | Y i = y i , x i )

where the probabilities necessary to compute this likelihood are evaluated in terms of the α and β parameters from equations (1) and (2). Participants who provide outcome data ( R_i  = 1) contribute to the first summation in (3) and those who do not provide outcome data ( R _i  = 0) contribute to the second summation.

In order to overcome the problems associated with the estimation of MNAR missing data models using selection models, models for the repeated attempts to obtain outcome data have been proposed 12 13 14 15 . This type of modelling is possible where a number of attempts to obtain outcome data are made, as is the case for the iQuit trial: as explained above, participants in the iQuit trial receive between one and ten telephone calls to obtain outcome data, and if these are unsuccessful they receive where possible a further attempt by email. Participants may receive less than ten telephone calls and then an email if, for example, they request that no more telephone calls are made and do not provide data, or if the telephone number they have provided is found to be invalid. The assumption that underlies the modelling is that outcome data from participants who require many attempts to obtain are more like those with missing data than those who require fewer attempts.

We now model the probability that a particular attempt at obtaining outcome data is successful, rather than the marginal probability that outcome data is obtained as in selection modelling. We continue to assume model (1) and we replace model (2) with our model for the attempts to obtain outcome data

logit ( P ( R i , m ′ = 1 | Y i = y i , x i ) = β 0 , m + β 1 x i + β 2 y i

where R i , m ′ is equal to one if the mth attempt, m = 1,2⋯11, to obtain outcome data from the i participant is successful; the email attempt is modelled as the 11th attempt regardless of the number of telephone calls made. We allow the email attempt to be more or less successful than the telephone attempts via its intercept β_0,11 but make the simplification that the probability of obtaining outcome data in this way does not depend on the number of telephone calls that preceded it. This is reasonable because the email is a very different way to obtain outcome data and and this represents a pragmatic approach to modelling because the email was not very successful (only 83 participants provided data in response to over a thousand emails). This assumption is relaxed as part of the sensitivity analysis below. Model (5) can be thought of as a discrete survival model, or a stratified logistic regression, where we also handle the unobserved outcomes.

Each attempt has its own intercept β_0,m , so that, for example, earlier attempts may be more successful than later ones but the identifying assumption is that the covariate effects are common across attempts. The appropriateness of this assumption for the baseline covariates was assessed by including an attempt by covariate interaction in the MAR model. Two of these interactions were statistically significant at the 5% level (age, p-value=0.04; qualifications, p-value=0.01). On a closer examination the apparent interaction between attempt and qualifications is largely explained by the observation that more educated participants appear to be more likely to respond to the email. Adding an interaction between the email attempt alone and the baseline covariates resulted in only one statistically significant interaction at the 5% level: the test for the presence of a qualifications by email interaction provided a p-value of 0.0004, where the log odds ratio associated with a unit increase in educational qualifications is 0.38. This may be plausible, because more educated participants could have greater access to, and command of, computing facilities. However, since a very small proportion of email attempts were successful, this finding should be cautiously interpreted. Despite this, more sophisticated modelling of the missing data model could commence by allowing this interaction.

Now that model (5) has replaced model (2), we refer to model (5) as the missing data model. If a single attempt is made to obtain outcome data from all participants then model (5) simplifies to (2), hence the model for the repeated attempts is an extension of the selection model.

The numbers and percentages of participants who successfully give up smoking ( Y_i  = 1) are shown by the number and type of attempts to obtain outcome data in Table 3. Finding patterns in the results for those who do not respond the telephone calls and hence are sent an email is difficult, because such little outcome data is obtained in this way, but the data for those who respond to a telephone call is slightly suggestive of a decreasing probability of smoking cessation as the number of attempts increases: fitting a complete case logistic regression of smoking cessation on the number of attempts for these participants gives an estimated slope of -0.03 (with a standard error of 0.04). Although not significant, the fitted model predicts that the probability of smoking cessation decreases with the number of attempts. Therefore we anticipate a positive estimated association between Y_i and R_i,m when fitting model (5), so that those who have given up smoking are more likely to provide outcome data than those who have not.

The proportion of those giving up smoking is higher in those who respond to the email rather than a telephone call in Table 3. This could be because this method for obtaining outcome data, although less likely to obtain data per se, is relatively more likely to obtain outcome data from nonsmokers than smokers. If true this would invalidate the assumption that the β₂ coefficient is the same for the email as the telephone attempts. Since estimating a separate β₂ for the email attempt would encounter the same type of estimation problems as in section “Fitting the full model”, an alternative would be to constrain the β₂ for the email attempt to a range of plausible values in a further sensitivity analysis.

The likelihood is similar in form to (3) but, now that each attempt to obtain outcome data contributes to this, its form is more complex and is shown in the Appendix. The MAR model ( β₂ = 0) was fitted as separate logistic regressions of Y_i on x_i , and R_i,j on x_i and attempt number, in the same manner as for the MAR model in the sensitivity analysis above. This MAR model was then used as a starting value for the numerical maximisation of the full log-likelihood and standard errors can be obtained from the observed information matrix as before.

The fitted MAR and MNAR repeated attempts models are shown in Table 6. The estimates of the β₁ parameters are not sensitive to the choice between MAR or MNAR and only slightly larger standard errors for these parameters are obtained when allowing the data to be MNAR. From Table 6 we see that the participant’s age is confirmed as having an important role in the missing data model and the effect of support (of family and friends) is also statistically significant, where participants who feel more supported are also more likely to provide outcome data. This analysis suggests that both smoking related (support) and non-smoking related (age) variables are good predictors of missing data so it would appear that both types of variables may play important roles in the missing data model.

The model for the repeated attempts incorporates more data, and hence makes more assumptions, but provides much more satisfactory estimation of β₂, and hence the role of the outcome Y in the missing data model, than the selection model.

The model for the repeated attempts has enabled us to identify the effect of the trial outcome in the missing data model because the standard error of β ̂ 2 is acceptably small. This is in sharp contrast with the results for the corresponding results using the selection model in section “Fitting the full model”. The estimate of β₂ is positive as expected but the analysis, which rests on the distributional assumptions described above, does not rule out the possibility that data are MAR because β₂ = 0 lies within the 95% confidence interval. We next explore what probabilities are predicted for smoking cessation in the missing data.

We have developed two statistical models and have explored the missing data model using the empirical evidence from the iQuit trial. In particular we found strong evidence that the participant’s age is a good predictor in this. The evidence that the trial outcome itself is important in this model is much weaker. This casts very considerable doubt on the “missing=smoking” assumption. This conclusion is also evident from an inspection of Table 3; one can imagine what would happen if the attempts to obtain outcome data were ceased after fewer attempts. Some nonsmokers in Table 3 would then be lost to follow up and designated as smokers in error by the “missing=smoking” assumption. Future methodological research could focus on methods for assessing the goodness of fit and other diagnostics for the repeated attempts model.

Perhaps our most important finding is that we estimate the probability not smoking in those failing to provide outcome data to be between 0.14 and 0.19. This excludes both the “missing=smoking” assumption and the MAR analysis that makes makes no use of the baseline covariates (22% of participants who provide outcome data abstained from smoking). This finding, in conjunction with the arguments of Nelson et al. 9 and Barnes et al. 10 , provide a case for “missing=smoking” analyses to be abandoned altogether. However the MAR assumption seems to be a good option, provided that suitable covariates are collected and included in the model.

We do not show parameter estimates of the outcome model (1) because we do not wish to distract the reader from the investigation of the missing data mechanism, which provides our focus. However when fitting this outcome model using maximum likelihood, in conjunction with either (2) or (5), all parameter estimates are obtained simultaneously. Hence parameter estimates of model (1) could be also presented such as the treatment effect, which is usually the parameter of primary interest.

The suspicion that participants with more educational qualifications may be more likely to respond to an email reminds us that the variables that are important in the missing data model are likely to be context specific, and can be anticipated to depend on the nature of the trial and how data are collected. For example, if email was the primary method for obtaining response data then, if correct, this suspicion suggests that qualifications would be a crucially important variable to consider when modelling the missing data. Trialists therefore should not take our investigation as a definitive statement of which variables are important in smoking cessation trials in full generality, but our results suggest that both smoking and non-smoking related variables can play a role in this. We therefore recommend that, if additional variables are to be incorporated into the analysis to make the MAR assumption more plausible, trialists should consider both kinds of variables, and also any other variables that they think may explain why their data are missing. A rich set of baseline, and possibly auxiliary post randomisation, variables should be collected for this purpose.

Even if the many such variables are collected and incorporated in the analysis then the possibility that the outcome itself may play a role persists, as epitomised by the “missing=smoking” assumption. However this requires MNAR modelling and the approaches used here, although suitable for our special investigations, are perhaps too computationally intensive for more routine use. We are therefore developing a simpler MNAR modelling approach, where “missing=smoking”, MAR and Last Observation Carried Forward analyses (LOCF 8, p. 45 ) are embedded into a much wider class of models. Hence the implications of many possibilities for the treatment effect can be quickly and easily assessed. Despite the computational power that is now available, the trade-off between sophisticated methodology and computationally straightforward methods remains, so we hope that this will make MNAR modelling more accessible to applied researchers and that they will be inspired to attempt this.

We continue to let R_i  = 1 denote that the ith participant provides outcome data and we let the binary variable E_i denote whether or not an attempt by email was made, where E_i  = 1 if this was made and E_i  = 0 otherwise. We let t_i equal the number of telephone call attempts to obtain outcome data. The log-likelihood of the data is given by L( α₀, α₁, β₀, β₁, β₂)=

∑ i = 1 1758 E i R i log P ( Y i = y i | x i ) P ( R i , 11 ′ = 1 | Y i = y i , x i ) × ∏ j = 1 t i P ( R i , j ′ = 0 | Y i = y i , x i ) + ∑ i = 1 1758 ( 1 − E i ) R i log P ( Y i = y i | x i ) P ( R i , t i ′ = 1 | Y i = y i , x i ) × ∏ j = 1 t i − 1 P ( R i , j ′ = 0 | Y i = y i , x i ) + ∑ i = 1 1758 E i ( 1 − R i ) log ∑ y i = 0 1 P ( Y i = y i | x i ) P ( R i , 11 ′ = 0 | Y i = y i , x i ) × ∏ j = 1 t i P ( R i , j ′ = 0 | Y i = y i , x i ) + ∑ i = 1 1758 ( 1 − E i ) ( 1 − R i ) log ∑ y i = 0 1 P ( Y i = y i | x i ) × ∏ j = 1 t i P ( R i , j ′ = 0 | Y i = y i , x i )

where the probabilities necessary to compute this likelihood are evaluated in terms of the α and β parameters from equations (1) and (5). Empty products in this likelihood are defined to be one.

The authors declare that they have no competing interest.

DJ performed the statistical analyses and all authors contributed to the writing of the paper. All authors read and approved the final manuscript.