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Should we freeze the number of people coming in to the study for Kaplan-Meier test

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Should we freeze the number of people coming in to the study for Kaplan-Meier test


Basic questions about discrete time survival analysisWeighted Kaplan-Meier Curve Log Rank TestComparing survival times in small samples for two groupsWhat is the minimum sample size for kaplan meierKaplan-Meier for repeated measures in different exercise conditionsThe Kaplan-Meier weighted generalized M-estimationSurvival estimation with left-truncated dataDeriving the Kaplan-Meier estimator with right-censoringHow should I decide which test to use to look for differences in Kaplan-Meier curves?Equivalent of Kaplan Meier for an unbounded number of sets






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I'm conducting a Kaplan Meier study to determine user retention for a website. I'm planning to limit my timeline to 6 months and see how many users are dropping within that time period. And if a user does not have any action for a month I'm considering that as an event. But I keep seeing new users. What should I do in that case? Limit the users in my study to all the users I see in the first month?










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DINESHKUMAR MURUGAN is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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  • $begingroup$
    I gather your drop-out event is inaction for a month. But what if a user comes back on-line a few weeks later? Do you consider that a new user? Or do you erase the drop-out event? So does "survival" mean some kind of user activity within the last 4 weeks of the end of your trial? You'll have to be explicit about how you handle dropping out and re-upping. You could also study the distribution of dwell times between visits to the site. If you fit the low end to a common distribution, you could have a data-defined "death event dwell time" (6 weeks? 8 weeks?) to use with the survival study.
    $endgroup$
    – Peter Leopold
    8 hours ago

















1












$begingroup$


I'm conducting a Kaplan Meier study to determine user retention for a website. I'm planning to limit my timeline to 6 months and see how many users are dropping within that time period. And if a user does not have any action for a month I'm considering that as an event. But I keep seeing new users. What should I do in that case? Limit the users in my study to all the users I see in the first month?










share|cite|improve this question









New contributor



DINESHKUMAR MURUGAN is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$











  • $begingroup$
    I gather your drop-out event is inaction for a month. But what if a user comes back on-line a few weeks later? Do you consider that a new user? Or do you erase the drop-out event? So does "survival" mean some kind of user activity within the last 4 weeks of the end of your trial? You'll have to be explicit about how you handle dropping out and re-upping. You could also study the distribution of dwell times between visits to the site. If you fit the low end to a common distribution, you could have a data-defined "death event dwell time" (6 weeks? 8 weeks?) to use with the survival study.
    $endgroup$
    – Peter Leopold
    8 hours ago













1












1








1





$begingroup$


I'm conducting a Kaplan Meier study to determine user retention for a website. I'm planning to limit my timeline to 6 months and see how many users are dropping within that time period. And if a user does not have any action for a month I'm considering that as an event. But I keep seeing new users. What should I do in that case? Limit the users in my study to all the users I see in the first month?










share|cite|improve this question









New contributor



DINESHKUMAR MURUGAN is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.






$endgroup$




I'm conducting a Kaplan Meier study to determine user retention for a website. I'm planning to limit my timeline to 6 months and see how many users are dropping within that time period. And if a user does not have any action for a month I'm considering that as an event. But I keep seeing new users. What should I do in that case? Limit the users in my study to all the users I see in the first month?







kaplan-meier






share|cite|improve this question









New contributor



DINESHKUMAR MURUGAN is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.










share|cite|improve this question









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DINESHKUMAR MURUGAN is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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edited 8 hours ago









Peter Leopold

1,228118




1,228118






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asked 8 hours ago









DINESHKUMAR MURUGANDINESHKUMAR MURUGAN

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New contributor




DINESHKUMAR MURUGAN is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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  • $begingroup$
    I gather your drop-out event is inaction for a month. But what if a user comes back on-line a few weeks later? Do you consider that a new user? Or do you erase the drop-out event? So does "survival" mean some kind of user activity within the last 4 weeks of the end of your trial? You'll have to be explicit about how you handle dropping out and re-upping. You could also study the distribution of dwell times between visits to the site. If you fit the low end to a common distribution, you could have a data-defined "death event dwell time" (6 weeks? 8 weeks?) to use with the survival study.
    $endgroup$
    – Peter Leopold
    8 hours ago
















  • $begingroup$
    I gather your drop-out event is inaction for a month. But what if a user comes back on-line a few weeks later? Do you consider that a new user? Or do you erase the drop-out event? So does "survival" mean some kind of user activity within the last 4 weeks of the end of your trial? You'll have to be explicit about how you handle dropping out and re-upping. You could also study the distribution of dwell times between visits to the site. If you fit the low end to a common distribution, you could have a data-defined "death event dwell time" (6 weeks? 8 weeks?) to use with the survival study.
    $endgroup$
    – Peter Leopold
    8 hours ago















$begingroup$
I gather your drop-out event is inaction for a month. But what if a user comes back on-line a few weeks later? Do you consider that a new user? Or do you erase the drop-out event? So does "survival" mean some kind of user activity within the last 4 weeks of the end of your trial? You'll have to be explicit about how you handle dropping out and re-upping. You could also study the distribution of dwell times between visits to the site. If you fit the low end to a common distribution, you could have a data-defined "death event dwell time" (6 weeks? 8 weeks?) to use with the survival study.
$endgroup$
– Peter Leopold
8 hours ago




$begingroup$
I gather your drop-out event is inaction for a month. But what if a user comes back on-line a few weeks later? Do you consider that a new user? Or do you erase the drop-out event? So does "survival" mean some kind of user activity within the last 4 weeks of the end of your trial? You'll have to be explicit about how you handle dropping out and re-upping. You could also study the distribution of dwell times between visits to the site. If you fit the low end to a common distribution, you could have a data-defined "death event dwell time" (6 weeks? 8 weeks?) to use with the survival study.
$endgroup$
– Peter Leopold
8 hours ago










1 Answer
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Do not use calendar time to display the Kaplan Meier curve. Rather, take the time at which a user establishes an account, or first navigates to the site (or whatever data you have) as time 0.






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    $begingroup$

    Do not use calendar time to display the Kaplan Meier curve. Rather, take the time at which a user establishes an account, or first navigates to the site (or whatever data you have) as time 0.






    share|cite|improve this answer









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      $begingroup$

      Do not use calendar time to display the Kaplan Meier curve. Rather, take the time at which a user establishes an account, or first navigates to the site (or whatever data you have) as time 0.






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        $begingroup$

        Do not use calendar time to display the Kaplan Meier curve. Rather, take the time at which a user establishes an account, or first navigates to the site (or whatever data you have) as time 0.






        share|cite|improve this answer









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        Do not use calendar time to display the Kaplan Meier curve. Rather, take the time at which a user establishes an account, or first navigates to the site (or whatever data you have) as time 0.







        share|cite|improve this answer












        share|cite|improve this answer



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        answered 8 hours ago









        AdamOAdamO

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